diff --git a/BigGAN_PyTorch/BigGAN.py b/BigGAN_PyTorch/BigGAN.py new file mode 100644 index 0000000000000000000000000000000000000000..1a7ab459c05c61694d5e46fd8a11b9ecfbbeb445 --- /dev/null +++ b/BigGAN_PyTorch/BigGAN.py @@ -0,0 +1,711 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License + +import numpy as np +import math +import functools +import os + +import torch +import torch.nn as nn +from torch.nn import init +import torch.optim as optim +import torch.nn.functional as F + +# from torch.nn import Parameter as P +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +import BigGAN_PyTorch.layers as layers + +# from sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d +from BigGAN_PyTorch.diffaugment_utils import DiffAugment + +# Architectures for G +# Attention is passed in in the format '32_64' to mean applying an attention +# block at both resolution 32x32 and 64x64. Just '64' will apply at 64x64. +def G_arch(ch=64, attention="64", ksize="333333", dilation="111111"): + arch = {} + arch[512] = { + "in_channels": [ch * item for item in [16, 16, 8, 8, 4, 2, 1]], + "out_channels": [ch * item for item in [16, 8, 8, 4, 2, 1, 1]], + "upsample": [True] * 7, + "resolution": [8, 16, 32, 64, 128, 256, 512], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 10) + }, + } + arch[256] = { + "in_channels": [ch * item for item in [16, 16, 8, 8, 4, 2]], + "out_channels": [ch * item for item in [16, 8, 8, 4, 2, 1]], + "upsample": [True] * 6, + "resolution": [8, 16, 32, 64, 128, 256], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 9) + }, + } + arch[128] = { + "in_channels": [ch * item for item in [16, 16, 8, 4, 2]], + "out_channels": [ch * item for item in [16, 8, 4, 2, 1]], + "upsample": [True] * 5, + "resolution": [8, 16, 32, 64, 128], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 8) + }, + } + arch[64] = { + "in_channels": [ch * item for item in [16, 16, 8, 4]], + "out_channels": [ch * item for item in [16, 8, 4, 2]], + "upsample": [True] * 4, + "resolution": [8, 16, 32, 64], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 7) + }, + } + arch[32] = { + "in_channels": [ch * item for item in [4, 4, 4]], + "out_channels": [ch * item for item in [4, 4, 4]], + "upsample": [True] * 3, + "resolution": [8, 16, 32], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 6) + }, + } + + return arch + + +class Generator(nn.Module): + def __init__( + self, + G_ch=64, + dim_z=128, + bottom_width=4, + resolution=128, + G_kernel_size=3, + G_attn="64", + n_classes=1000, + num_G_SVs=1, + num_G_SV_itrs=1, + G_shared=True, + shared_dim=0, + hier=False, + cross_replica=False, + mybn=False, + G_activation=nn.ReLU(inplace=False), + G_lr=5e-5, + G_B1=0.0, + G_B2=0.999, + adam_eps=1e-8, + BN_eps=1e-5, + SN_eps=1e-12, + G_mixed_precision=False, + G_fp16=False, + G_init="ortho", + skip_init=False, + no_optim=False, + G_param="SN", + norm_style="bn", + class_cond=True, + embedded_optimizer=True, + instance_cond=False, + G_shared_feat=True, + shared_dim_feat=2048, + **kwargs + ): + super(Generator, self).__init__() + # Channel width mulitplier + self.ch = G_ch + # Dimensionality of the latent space + self.dim_z = dim_z + # The initial spatial dimensions + self.bottom_width = bottom_width + # Resolution of the output + self.resolution = resolution + # Kernel size? + self.kernel_size = G_kernel_size + # Attention? + self.attention = G_attn + # number of classes, for use in categorical conditional generation + self.n_classes = n_classes + # Use shared embeddings? + self.G_shared = G_shared + # Dimensionality of the shared embedding? Unused if not using G_shared + self.shared_dim = shared_dim if shared_dim > 0 else dim_z + # Hierarchical latent space? + self.hier = hier + # Cross replica batchnorm? + self.cross_replica = cross_replica + # Use my batchnorm? + self.mybn = mybn + # nonlinearity for residual blocks + self.activation = G_activation + # Initialization style + self.init = G_init + # Parameterization style + self.G_param = G_param + # Normalization style + self.norm_style = norm_style + # Epsilon for BatchNorm? + self.BN_eps = BN_eps + # Epsilon for Spectral Norm? + self.SN_eps = SN_eps + # fp16? + self.fp16 = G_fp16 + # Use embeddings for instance features? + self.G_shared_feat = G_shared_feat + self.shared_dim_feat = shared_dim_feat + # Architecture dict + self.arch = G_arch(self.ch, self.attention)[resolution] + + # If using hierarchical latents, adjust z + if self.hier: + # Number of places z slots into + self.num_slots = len(self.arch["in_channels"]) + 1 + self.z_chunk_size = self.dim_z // self.num_slots + # Recalculate latent dimensionality for even splitting into chunks + self.dim_z = self.z_chunk_size * self.num_slots + else: + self.num_slots = 1 + self.z_chunk_size = 0 + + # Which convs, batchnorms, and linear layers to use + if self.G_param == "SN": + self.which_conv = functools.partial( + layers.SNConv2d, + kernel_size=3, + padding=1, + num_svs=num_G_SVs, + num_itrs=num_G_SV_itrs, + eps=self.SN_eps, + ) + self.which_linear = functools.partial( + layers.SNLinear, + num_svs=num_G_SVs, + num_itrs=num_G_SV_itrs, + eps=self.SN_eps, + ) + else: + self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1) + self.which_linear = nn.Linear + + # We use a non-spectral-normed embedding here regardless; + # For some reason applying SN to G's embedding seems to randomly cripple G + self.which_embedding = nn.Embedding + bn_linear = ( + functools.partial(self.which_linear, bias=False) + if self.G_shared + else self.which_embedding + ) + if not class_cond and not instance_cond: + input_sz_bn = self.n_classes + else: + input_sz_bn = self.z_chunk_size + if class_cond: + input_sz_bn += self.shared_dim + if instance_cond: + input_sz_bn += self.shared_dim_feat + self.which_bn = functools.partial( + layers.ccbn, + which_linear=bn_linear, + cross_replica=self.cross_replica, + mybn=self.mybn, + input_size=input_sz_bn, + norm_style=self.norm_style, + eps=self.BN_eps, + ) + + # Prepare model + # If not using shared embeddings, self.shared is just a passthrough + self.shared = ( + self.which_embedding(n_classes, self.shared_dim) + if G_shared + else layers.identity() + ) + self.shared_feat = ( + self.which_linear(2048, self.shared_dim_feat) + if G_shared_feat + else layers.identity() + ) + # First linear layer + self.linear = self.which_linear( + self.dim_z // self.num_slots, + self.arch["in_channels"][0] * (self.bottom_width ** 2), + ) + + # self.blocks is a doubly-nested list of modules, the outer loop intended + # to be over blocks at a given resolution (resblocks and/or self-attention) + # while the inner loop is over a given block + self.blocks = [] + for index in range(len(self.arch["out_channels"])): + self.blocks += [ + [ + layers.GBlock( + in_channels=self.arch["in_channels"][index], + out_channels=self.arch["out_channels"][index], + which_conv=self.which_conv, + which_bn=self.which_bn, + activation=self.activation, + upsample=( + functools.partial(F.interpolate, scale_factor=2) + if self.arch["upsample"][index] + else None + ), + ) + ] + ] + + # If attention on this block, attach it to the end + if self.arch["attention"][self.arch["resolution"][index]]: + print( + "Adding attention layer in G at resolution %d" + % self.arch["resolution"][index] + ) + self.blocks[-1] += [ + layers.Attention(self.arch["out_channels"][index], self.which_conv) + ] + + # Turn self.blocks into a ModuleList so that it's all properly registered. + self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks]) + + # output layer: batchnorm-relu-conv. + # Consider using a non-spectral conv here + self.output_layer = nn.Sequential( + layers.bn( + self.arch["out_channels"][-1], + cross_replica=self.cross_replica, + mybn=self.mybn, + ), + self.activation, + self.which_conv(self.arch["out_channels"][-1], 3), + ) + + # Initialize weights. Optionally skip init for testing. + if not skip_init: + self.init_weights() + + # Set up optimizer + # If this is an EMA copy, no need for an optim, so just return now + if no_optim or not embedded_optimizer: + return + self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps + if G_mixed_precision: + print("Using fp16 adam in G...") + import utils + + self.optim = utils.Adam16( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + else: + self.optim = optim.Adam( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + + # LR scheduling, left here for forward compatibility + # self.lr_sched = {'itr' : 0}# if self.progressive else {} + # self.j = 0 + + # Initialize + def init_weights(self): + self.param_count = 0 + for module in self.modules(): + if ( + isinstance(module, nn.Conv2d) + or isinstance(module, nn.Linear) + or isinstance(module, nn.Embedding) + ): + if self.init == "ortho": + init.orthogonal_(module.weight) + elif self.init == "N02": + init.normal_(module.weight, 0, 0.02) + elif self.init in ["glorot", "xavier"]: + init.xavier_uniform_(module.weight) + else: + print("Init style not recognized...") + self.param_count += sum( + [p.data.nelement() for p in module.parameters()] + ) + print("Param count for G" "s initialized parameters: %d" % self.param_count) + + # Get conditionings + + def get_condition_embeddings(self, cl=None, feat=None): + c_embed = [] + if cl is not None: + c_embed.append(self.shared(cl)) + if feat is not None: + c_embed.append(self.shared_feat(feat)) + if len(c_embed) > 0: + c_embed = torch.cat(c_embed, dim=-1) + return c_embed + + # Note on this forward function: we pass in a y vector which has + # already been passed through G.shared to enable easy class-wise + # interpolation later. If we passed in the one-hot and then ran it through + # G.shared in this forward function, it would be harder to handle. + def forward(self, z, label=None, feats=None): + y = self.get_condition_embeddings(label, feats) + # If hierarchical, concatenate zs and ys + if self.hier: + zs = torch.split(z, self.z_chunk_size, 1) + z = zs[0] + ys = [torch.cat([y, item], 1) for item in zs[1:]] + else: + ys = [y] * len(self.blocks) + + # First linear layer + h = self.linear(z) + # Reshape + h = h.view(h.size(0), -1, self.bottom_width, self.bottom_width) + + # Loop over blocks + for index, blocklist in enumerate(self.blocks): + # Second inner loop in case block has multiple layers + for block in blocklist: + h = block(h, ys[index]) + + # Apply batchnorm-relu-conv-tanh at output + return torch.tanh(self.output_layer(h)) + + +# Discriminator architecture, same paradigm as G's above +def D_arch(ch=64, attention="64", ksize="333333", dilation="111111"): + arch = {} + arch[256] = { + "in_channels": [3] + [ch * item for item in [1, 2, 4, 8, 8, 16]], + "out_channels": [item * ch for item in [1, 2, 4, 8, 8, 16, 16]], + "downsample": [True] * 6 + [False], + "resolution": [128, 64, 32, 16, 8, 4, 4], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 8) + }, + } + arch[128] = { + "in_channels": [3] + [ch * item for item in [1, 2, 4, 8, 16]], + "out_channels": [item * ch for item in [1, 2, 4, 8, 16, 16]], + "downsample": [True] * 5 + [False], + "resolution": [64, 32, 16, 8, 4, 4], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 8) + }, + } + arch[64] = { + "in_channels": [3] + [ch * item for item in [1, 2, 4, 8]], + "out_channels": [item * ch for item in [1, 2, 4, 8, 16]], + "downsample": [True] * 4 + [False], + "resolution": [32, 16, 8, 4, 4], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 7) + }, + } + arch[32] = { + "in_channels": [3] + [item * ch for item in [4, 4, 4]], + "out_channels": [item * ch for item in [4, 4, 4, 4]], + "downsample": [True, True, False, False], + "resolution": [16, 16, 16, 16], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 6) + }, + } + return arch + + +class Discriminator(nn.Module): + def __init__( + self, + D_ch=64, + D_wide=True, + resolution=128, + D_kernel_size=3, + D_attn="64", + n_classes=1000, + num_D_SVs=1, + num_D_SV_itrs=1, + D_activation=nn.ReLU(inplace=False), + D_lr=2e-4, + D_B1=0.0, + D_B2=0.999, + adam_eps=1e-8, + SN_eps=1e-12, + output_dim=1, + D_mixed_precision=False, + D_fp16=False, + D_init="ortho", + skip_init=False, + D_param="SN", + class_cond=True, + embedded_optimizer=True, + instance_cond=False, + instance_sz=2048, + **kwargs + ): + super(Discriminator, self).__init__() + # Width multiplier + self.ch = D_ch + # Use Wide D as in BigGAN and SA-GAN or skinny D as in SN-GAN? + self.D_wide = D_wide + # Resolution + self.resolution = resolution + # Kernel size + self.kernel_size = D_kernel_size + # Attention? + self.attention = D_attn + # Number of classes + self.n_classes = n_classes + # Activation + self.activation = D_activation + # Initialization style + self.init = D_init + # Parameterization style + self.D_param = D_param + # Epsilon for Spectral Norm? + self.SN_eps = SN_eps + # Fp16? + self.fp16 = D_fp16 + # Architecture + self.arch = D_arch(self.ch, self.attention)[resolution] + + # Which convs, batchnorms, and linear layers to use + # No option to turn off SN in D right now + if self.D_param == "SN": + self.which_conv = functools.partial( + layers.SNConv2d, + kernel_size=3, + padding=1, + num_svs=num_D_SVs, + num_itrs=num_D_SV_itrs, + eps=self.SN_eps, + ) + self.which_linear = functools.partial( + layers.SNLinear, + num_svs=num_D_SVs, + num_itrs=num_D_SV_itrs, + eps=self.SN_eps, + ) + self.which_embedding = functools.partial( + layers.SNEmbedding, + num_svs=num_D_SVs, + num_itrs=num_D_SV_itrs, + eps=self.SN_eps, + ) + # Prepare model + # self.blocks is a doubly-nested list of modules, the outer loop intended + # to be over blocks at a given resolution (resblocks and/or self-attention) + self.blocks = [] + for index in range(len(self.arch["out_channels"])): + self.blocks += [ + [ + layers.DBlock( + in_channels=self.arch["in_channels"][index], + out_channels=self.arch["out_channels"][index], + which_conv=self.which_conv, + wide=self.D_wide, + activation=self.activation, + preactivation=(index > 0), + downsample=( + nn.AvgPool2d(2) if self.arch["downsample"][index] else None + ), + ) + ] + ] + # If attention on this block, attach it to the end + if self.arch["attention"][self.arch["resolution"][index]]: + print( + "Adding attention layer in D at resolution %d" + % self.arch["resolution"][index] + ) + self.blocks[-1] += [ + layers.Attention(self.arch["out_channels"][index], self.which_conv) + ] + # Turn self.blocks into a ModuleList so that it's all properly registered. + self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks]) + # Linear output layer. The output dimension is typically 1, but may be + # larger if we're e.g. turning this into a VAE with an inference output + self.linear = self.which_linear(self.arch["out_channels"][-1], output_dim) + # Embedding for projection discrimination + if class_cond and instance_cond: + self.linear_feat = self.which_linear( + instance_sz, self.arch["out_channels"][-1] // 2 + ) + self.embed = self.which_embedding( + self.n_classes, self.arch["out_channels"][-1] // 2 + ) + elif class_cond: + # Embedding for projection discrimination + self.embed = self.which_embedding( + self.n_classes, self.arch["out_channels"][-1] + ) + elif instance_cond: + self.linear_feat = self.which_linear( + instance_sz, self.arch["out_channels"][-1] + ) + + # Initialize weights + if not skip_init: + self.init_weights() + + # Set up optimizer + if embedded_optimizer: + self.lr, self.B1, self.B2, self.adam_eps = D_lr, D_B1, D_B2, adam_eps + if D_mixed_precision: + print("Using fp16 adam in D...") + import utils + + self.optim = utils.Adam16( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + else: + self.optim = optim.Adam( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + # LR scheduling, left here for forward compatibility + # self.lr_sched = {'itr' : 0}# if self.progressive else {} + # self.j = 0 + + # Initialize + def init_weights(self): + self.param_count = 0 + for module in self.modules(): + if ( + isinstance(module, nn.Conv2d) + or isinstance(module, nn.Linear) + or isinstance(module, nn.Embedding) + ): + if self.init == "ortho": + init.orthogonal_(module.weight) + elif self.init == "N02": + init.normal_(module.weight, 0, 0.02) + elif self.init in ["glorot", "xavier"]: + init.xavier_uniform_(module.weight) + else: + print("Init style not recognized...") + self.param_count += sum( + [p.data.nelement() for p in module.parameters()] + ) + print("Param count for D" "s initialized parameters: %d" % self.param_count) + + def forward(self, x, y=None, feat=None): + # Stick x into h for cleaner for loops without flow control + h = x + # Loop over blocks + for index, blocklist in enumerate(self.blocks): + for block in blocklist: + h = block(h) + # Apply global sum pooling as in SN-GAN + h = torch.sum(self.activation(h), [2, 3]) + # Get initial class-unconditional output + out = self.linear(h) + # Condition on both class and instance features + if y is not None and feat is not None: + out = out + torch.sum( + torch.cat([self.embed(y), self.linear_feat(feat)], dim=-1) * h, + 1, + keepdim=True, + ) + # Condition on class only + elif y is not None: + # Get projection of final featureset onto class vectors and add to evidence + out = out + torch.sum(self.embed(y) * h, 1, keepdim=True) + # Condition on instance features only + elif feat is not None: + out = out + torch.sum(self.linear_feat(feat) * h, 1, keepdim=True) + return out + + +# Parallelized G_D to minimize cross-gpu communication +# Without this, Generator outputs would get all-gathered and then rebroadcast. +class G_D(nn.Module): + def __init__(self, G, D, optimizer_G=None, optimizer_D=None): + super(G_D, self).__init__() + self.G = G + self.D = D + self.optimizer_G = optimizer_G + self.optimizer_D = optimizer_D + + def forward( + self, + z, + gy, + feats_g=None, + x=None, + dy=None, + feats=None, + train_G=False, + return_G_z=False, + split_D=False, + policy=False, + DA=False, + ): + # If training G, enable grad tape + with torch.set_grad_enabled(train_G): + # Get Generator output given noise + G_z = self.G(z, gy, feats_g) + # Cast as necessary + # if self.G.fp16 and not self.D.fp16: + # G_z = G_z.float() + # if self.D.fp16 and not self.G.fp16: + # G_z = G_z.half() + # Split_D means to run D once with real data and once with fake, + # rather than concatenating along the batch dimension. + if split_D: + D_fake = self.D(G_z, gy, feats_g) + if x is not None: + D_real = self.D(x, dy, feats) + return D_fake, D_real + else: + if return_G_z: + return D_fake, G_z + else: + return D_fake + # If real data is provided, concatenate it with the Generator's output + # along the batch dimension for improved efficiency. + else: + D_input = torch.cat([G_z, x], 0) if x is not None else G_z + D_class = torch.cat([gy, dy], 0) if dy is not None else gy + if feats_g is not None: + D_feats = ( + torch.cat([feats_g, feats], 0) if feats is not None else feats_g + ) + else: + D_feats = None + if DA: + D_input = DiffAugment(D_input, policy=policy) + # Get Discriminator output + D_out = self.D(D_input, D_class, D_feats) + if x is not None: + return torch.split(D_out, [G_z.shape[0], x.shape[0]]) # D_fake, D_real + else: + if return_G_z: + return D_out, G_z + else: + return D_out diff --git a/BigGAN_PyTorch/BigGANdeep.py b/BigGAN_PyTorch/BigGANdeep.py new file mode 100644 index 0000000000000000000000000000000000000000..208ff060b50ca3ebafba0e50cfb4a403d4214f2c --- /dev/null +++ b/BigGAN_PyTorch/BigGANdeep.py @@ -0,0 +1,734 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +import numpy as np +import math +import functools + +import torch +import torch.nn as nn +from torch.nn import init +import torch.optim as optim +import torch.nn.functional as F +from torch.nn import Parameter as P + +import layers +from sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d + +# BigGAN-deep: uses a different resblock and pattern + + +# Architectures for G +# Attention is passed in in the format '32_64' to mean applying an attention +# block at both resolution 32x32 and 64x64. Just '64' will apply at 64x64. + +# Channel ratio is the ratio of +class GBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + which_conv=nn.Conv2d, + which_bn=layers.bn, + activation=None, + upsample=None, + channel_ratio=4, + ): + super(GBlock, self).__init__() + + self.in_channels, self.out_channels = in_channels, out_channels + self.hidden_channels = self.in_channels // channel_ratio + self.which_conv, self.which_bn = which_conv, which_bn + self.activation = activation + # Conv layers + self.conv1 = self.which_conv( + self.in_channels, self.hidden_channels, kernel_size=1, padding=0 + ) + self.conv2 = self.which_conv(self.hidden_channels, self.hidden_channels) + self.conv3 = self.which_conv(self.hidden_channels, self.hidden_channels) + self.conv4 = self.which_conv( + self.hidden_channels, self.out_channels, kernel_size=1, padding=0 + ) + # Batchnorm layers + self.bn1 = self.which_bn(self.in_channels) + self.bn2 = self.which_bn(self.hidden_channels) + self.bn3 = self.which_bn(self.hidden_channels) + self.bn4 = self.which_bn(self.hidden_channels) + # upsample layers + self.upsample = upsample + + def forward(self, x, y): + # Project down to channel ratio + h = self.conv1(self.activation(self.bn1(x, y))) + # Apply next BN-ReLU + h = self.activation(self.bn2(h, y)) + # Drop channels in x if necessary + if self.in_channels != self.out_channels: + x = x[:, : self.out_channels] + # Upsample both h and x at this point + if self.upsample: + h = self.upsample(h) + x = self.upsample(x) + # 3x3 convs + h = self.conv2(h) + h = self.conv3(self.activation(self.bn3(h, y))) + # Final 1x1 conv + h = self.conv4(self.activation(self.bn4(h, y))) + return h + x + + +def G_arch(ch=64, attention="64", ksize="333333", dilation="111111"): + arch = {} + arch[256] = { + "in_channels": [ch * item for item in [16, 16, 8, 8, 4, 2]], + "out_channels": [ch * item for item in [16, 8, 8, 4, 2, 1]], + "upsample": [True] * 6, + "resolution": [8, 16, 32, 64, 128, 256], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 9) + }, + } + arch[128] = { + "in_channels": [ch * item for item in [16, 16, 8, 4, 2]], + "out_channels": [ch * item for item in [16, 8, 4, 2, 1]], + "upsample": [True] * 5, + "resolution": [8, 16, 32, 64, 128], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 8) + }, + } + arch[64] = { + "in_channels": [ch * item for item in [16, 16, 8, 4]], + "out_channels": [ch * item for item in [16, 8, 4, 2]], + "upsample": [True] * 4, + "resolution": [8, 16, 32, 64], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 7) + }, + } + arch[32] = { + "in_channels": [ch * item for item in [4, 4, 4]], + "out_channels": [ch * item for item in [4, 4, 4]], + "upsample": [True] * 3, + "resolution": [8, 16, 32], + "attention": { + 2 ** i: (2 ** i in [int(item) for item in attention.split("_")]) + for i in range(3, 6) + }, + } + + return arch + + +class Generator(nn.Module): + def __init__( + self, + G_ch=64, + G_depth=2, + dim_z=128, + bottom_width=4, + resolution=128, + G_kernel_size=3, + G_attn="64", + n_classes=1000, + num_G_SVs=1, + num_G_SV_itrs=1, + G_shared=True, + shared_dim=0, + hier=False, + cross_replica=False, + mybn=False, + G_activation=nn.ReLU(inplace=False), + G_lr=5e-5, + G_B1=0.0, + G_B2=0.999, + adam_eps=1e-8, + BN_eps=1e-5, + SN_eps=1e-12, + G_mixed_precision=False, + G_fp16=False, + G_init="ortho", + skip_init=False, + no_optim=False, + G_param="SN", + norm_style="bn", + **kwargs + ): + super(Generator, self).__init__() + # Channel width mulitplier + self.ch = G_ch + # Number of resblocks per stage + self.G_depth = G_depth + # Dimensionality of the latent space + self.dim_z = dim_z + # The initial spatial dimensions + self.bottom_width = bottom_width + # Resolution of the output + self.resolution = resolution + # Kernel size? + self.kernel_size = G_kernel_size + # Attention? + self.attention = G_attn + # number of classes, for use in categorical conditional generation + self.n_classes = n_classes + # Use shared embeddings? + self.G_shared = G_shared + # Dimensionality of the shared embedding? Unused if not using G_shared + self.shared_dim = shared_dim if shared_dim > 0 else dim_z + # Hierarchical latent space? + self.hier = hier + # Cross replica batchnorm? + self.cross_replica = cross_replica + # Use my batchnorm? + self.mybn = mybn + # nonlinearity for residual blocks + self.activation = G_activation + # Initialization style + self.init = G_init + # Parameterization style + self.G_param = G_param + # Normalization style + self.norm_style = norm_style + # Epsilon for BatchNorm? + self.BN_eps = BN_eps + # Epsilon for Spectral Norm? + self.SN_eps = SN_eps + # fp16? + self.fp16 = G_fp16 + # Architecture dict + self.arch = G_arch(self.ch, self.attention)[resolution] + + # Which convs, batchnorms, and linear layers to use + if self.G_param == "SN": + self.which_conv = functools.partial( + layers.SNConv2d, + kernel_size=3, + padding=1, + num_svs=num_G_SVs, + num_itrs=num_G_SV_itrs, + eps=self.SN_eps, + ) + self.which_linear = functools.partial( + layers.SNLinear, + num_svs=num_G_SVs, + num_itrs=num_G_SV_itrs, + eps=self.SN_eps, + ) + else: + self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1) + self.which_linear = nn.Linear + + # We use a non-spectral-normed embedding here regardless; + # For some reason applying SN to G's embedding seems to randomly cripple G + self.which_embedding = nn.Embedding + bn_linear = ( + functools.partial(self.which_linear, bias=False) + if self.G_shared + else self.which_embedding + ) + self.which_bn = functools.partial( + layers.ccbn, + which_linear=bn_linear, + cross_replica=self.cross_replica, + mybn=self.mybn, + input_size=( + self.shared_dim + self.dim_z if self.G_shared else self.n_classes + ), + norm_style=self.norm_style, + eps=self.BN_eps, + ) + + # Prepare model + # If not using shared embeddings, self.shared is just a passthrough + self.shared = ( + self.which_embedding(n_classes, self.shared_dim) + if G_shared + else layers.identity() + ) + # First linear layer + self.linear = self.which_linear( + self.dim_z + self.shared_dim, + self.arch["in_channels"][0] * (self.bottom_width ** 2), + ) + + # self.blocks is a doubly-nested list of modules, the outer loop intended + # to be over blocks at a given resolution (resblocks and/or self-attention) + # while the inner loop is over a given block + self.blocks = [] + for index in range(len(self.arch["out_channels"])): + self.blocks += [ + [ + GBlock( + in_channels=self.arch["in_channels"][index], + out_channels=self.arch["in_channels"][index] + if g_index == 0 + else self.arch["out_channels"][index], + which_conv=self.which_conv, + which_bn=self.which_bn, + activation=self.activation, + upsample=( + functools.partial(F.interpolate, scale_factor=2) + if self.arch["upsample"][index] + and g_index == (self.G_depth - 1) + else None + ), + ) + ] + for g_index in range(self.G_depth) + ] + + # If attention on this block, attach it to the end + if self.arch["attention"][self.arch["resolution"][index]]: + print( + "Adding attention layer in G at resolution %d" + % self.arch["resolution"][index] + ) + self.blocks[-1] += [ + layers.Attention(self.arch["out_channels"][index], self.which_conv) + ] + + # Turn self.blocks into a ModuleList so that it's all properly registered. + self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks]) + + # output layer: batchnorm-relu-conv. + # Consider using a non-spectral conv here + self.output_layer = nn.Sequential( + layers.bn( + self.arch["out_channels"][-1], + cross_replica=self.cross_replica, + mybn=self.mybn, + ), + self.activation, + self.which_conv(self.arch["out_channels"][-1], 3), + ) + + # Initialize weights. Optionally skip init for testing. + if not skip_init: + self.init_weights() + + # Set up optimizer + # If this is an EMA copy, no need for an optim, so just return now + if no_optim: + return + self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps + if G_mixed_precision: + print("Using fp16 adam in G...") + import utils + + self.optim = utils.Adam16( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + else: + self.optim = optim.Adam( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + + # LR scheduling, left here for forward compatibility + # self.lr_sched = {'itr' : 0}# if self.progressive else {} + # self.j = 0 + + # Initialize + def init_weights(self): + self.param_count = 0 + for module in self.modules(): + if ( + isinstance(module, nn.Conv2d) + or isinstance(module, nn.Linear) + or isinstance(module, nn.Embedding) + ): + if self.init == "ortho": + init.orthogonal_(module.weight) + elif self.init == "N02": + init.normal_(module.weight, 0, 0.02) + elif self.init in ["glorot", "xavier"]: + init.xavier_uniform_(module.weight) + else: + print("Init style not recognized...") + self.param_count += sum( + [p.data.nelement() for p in module.parameters()] + ) + print("Param count for G" "s initialized parameters: %d" % self.param_count) + + # Note on this forward function: we pass in a y vector which has + # already been passed through G.shared to enable easy class-wise + # interpolation later. If we passed in the one-hot and then ran it through + # G.shared in this forward function, it would be harder to handle. + # NOTE: The z vs y dichotomy here is for compatibility with not-y + def forward(self, z, y): + # If hierarchical, concatenate zs and ys + if self.hier: + z = torch.cat([y, z], 1) + y = z + # First linear layer + h = self.linear(z) + # Reshape + h = h.view(h.size(0), -1, self.bottom_width, self.bottom_width) + # Loop over blocks + for index, blocklist in enumerate(self.blocks): + # Second inner loop in case block has multiple layers + for block in blocklist: + h = block(h, y) + + # Apply batchnorm-relu-conv-tanh at output + return torch.tanh(self.output_layer(h)) + + +class DBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + which_conv=layers.SNConv2d, + wide=True, + preactivation=True, + activation=None, + downsample=None, + channel_ratio=4, + ): + super(DBlock, self).__init__() + self.in_channels, self.out_channels = in_channels, out_channels + # If using wide D (as in SA-GAN and BigGAN), change the channel pattern + self.hidden_channels = self.out_channels // channel_ratio + self.which_conv = which_conv + self.preactivation = preactivation + self.activation = activation + self.downsample = downsample + + # Conv layers + self.conv1 = self.which_conv( + self.in_channels, self.hidden_channels, kernel_size=1, padding=0 + ) + self.conv2 = self.which_conv(self.hidden_channels, self.hidden_channels) + self.conv3 = self.which_conv(self.hidden_channels, self.hidden_channels) + self.conv4 = self.which_conv( + self.hidden_channels, self.out_channels, kernel_size=1, padding=0 + ) + + self.learnable_sc = True if (in_channels != out_channels) else False + if self.learnable_sc: + self.conv_sc = self.which_conv( + in_channels, out_channels - in_channels, kernel_size=1, padding=0 + ) + + def shortcut(self, x): + if self.downsample: + x = self.downsample(x) + if self.learnable_sc: + x = torch.cat([x, self.conv_sc(x)], 1) + return x + + def forward(self, x): + # 1x1 bottleneck conv + h = self.conv1(F.relu(x)) + # 3x3 convs + h = self.conv2(self.activation(h)) + h = self.conv3(self.activation(h)) + # relu before downsample + h = self.activation(h) + # downsample + if self.downsample: + h = self.downsample(h) + # final 1x1 conv + h = self.conv4(h) + return h + self.shortcut(x) + + +# Discriminator architecture, same paradigm as G's above +def D_arch(ch=64, attention="64", ksize="333333", dilation="111111"): + arch = {} + arch[256] = { + "in_channels": [item * ch for item in [1, 2, 4, 8, 8, 16]], + "out_channels": [item * ch for item in [2, 4, 8, 8, 16, 16]], + "downsample": [True] * 6 + [False], + "resolution": [128, 64, 32, 16, 8, 4, 4], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 8) + }, + } + arch[128] = { + "in_channels": [item * ch for item in [1, 2, 4, 8, 16]], + "out_channels": [item * ch for item in [2, 4, 8, 16, 16]], + "downsample": [True] * 5 + [False], + "resolution": [64, 32, 16, 8, 4, 4], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 8) + }, + } + arch[64] = { + "in_channels": [item * ch for item in [1, 2, 4, 8]], + "out_channels": [item * ch for item in [2, 4, 8, 16]], + "downsample": [True] * 4 + [False], + "resolution": [32, 16, 8, 4, 4], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 7) + }, + } + arch[32] = { + "in_channels": [item * ch for item in [4, 4, 4]], + "out_channels": [item * ch for item in [4, 4, 4]], + "downsample": [True, True, False, False], + "resolution": [16, 16, 16, 16], + "attention": { + 2 ** i: 2 ** i in [int(item) for item in attention.split("_")] + for i in range(2, 6) + }, + } + return arch + + +class Discriminator(nn.Module): + def __init__( + self, + D_ch=64, + D_wide=True, + D_depth=2, + resolution=128, + D_kernel_size=3, + D_attn="64", + n_classes=1000, + num_D_SVs=1, + num_D_SV_itrs=1, + D_activation=nn.ReLU(inplace=False), + D_lr=2e-4, + D_B1=0.0, + D_B2=0.999, + adam_eps=1e-8, + SN_eps=1e-12, + output_dim=1, + D_mixed_precision=False, + D_fp16=False, + D_init="ortho", + skip_init=False, + D_param="SN", + **kwargs + ): + super(Discriminator, self).__init__() + # Width multiplier + self.ch = D_ch + # Use Wide D as in BigGAN and SA-GAN or skinny D as in SN-GAN? + self.D_wide = D_wide + # How many resblocks per stage? + self.D_depth = D_depth + # Resolution + self.resolution = resolution + # Kernel size + self.kernel_size = D_kernel_size + # Attention? + self.attention = D_attn + # Number of classes + self.n_classes = n_classes + # Activation + self.activation = D_activation + # Initialization style + self.init = D_init + # Parameterization style + self.D_param = D_param + # Epsilon for Spectral Norm? + self.SN_eps = SN_eps + # Fp16? + self.fp16 = D_fp16 + # Architecture + self.arch = D_arch(self.ch, self.attention)[resolution] + + # Which convs, batchnorms, and linear layers to use + # No option to turn off SN in D right now + if self.D_param == "SN": + self.which_conv = functools.partial( + layers.SNConv2d, + kernel_size=3, + padding=1, + num_svs=num_D_SVs, + num_itrs=num_D_SV_itrs, + eps=self.SN_eps, + ) + self.which_linear = functools.partial( + layers.SNLinear, + num_svs=num_D_SVs, + num_itrs=num_D_SV_itrs, + eps=self.SN_eps, + ) + self.which_embedding = functools.partial( + layers.SNEmbedding, + num_svs=num_D_SVs, + num_itrs=num_D_SV_itrs, + eps=self.SN_eps, + ) + + # Prepare model + # Stem convolution + self.input_conv = self.which_conv(3, self.arch["in_channels"][0]) + # self.blocks is a doubly-nested list of modules, the outer loop intended + # to be over blocks at a given resolution (resblocks and/or self-attention) + self.blocks = [] + for index in range(len(self.arch["out_channels"])): + self.blocks += [ + [ + DBlock( + in_channels=self.arch["in_channels"][index] + if d_index == 0 + else self.arch["out_channels"][index], + out_channels=self.arch["out_channels"][index], + which_conv=self.which_conv, + wide=self.D_wide, + activation=self.activation, + preactivation=True, + downsample=( + nn.AvgPool2d(2) + if self.arch["downsample"][index] and d_index == 0 + else None + ), + ) + for d_index in range(self.D_depth) + ] + ] + # If attention on this block, attach it to the end + if self.arch["attention"][self.arch["resolution"][index]]: + print( + "Adding attention layer in D at resolution %d" + % self.arch["resolution"][index] + ) + self.blocks[-1] += [ + layers.Attention(self.arch["out_channels"][index], self.which_conv) + ] + # Turn self.blocks into a ModuleList so that it's all properly registered. + self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks]) + # Linear output layer. The output dimension is typically 1, but may be + # larger if we're e.g. turning this into a VAE with an inference output + self.linear = self.which_linear(self.arch["out_channels"][-1], output_dim) + # Embedding for projection discrimination + self.embed = self.which_embedding(self.n_classes, self.arch["out_channels"][-1]) + + # Initialize weights + if not skip_init: + self.init_weights() + + # Set up optimizer + self.lr, self.B1, self.B2, self.adam_eps = D_lr, D_B1, D_B2, adam_eps + if D_mixed_precision: + print("Using fp16 adam in D...") + import utils + + self.optim = utils.Adam16( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + else: + self.optim = optim.Adam( + params=self.parameters(), + lr=self.lr, + betas=(self.B1, self.B2), + weight_decay=0, + eps=self.adam_eps, + ) + # LR scheduling, left here for forward compatibility + # self.lr_sched = {'itr' : 0}# if self.progressive else {} + # self.j = 0 + + # Initialize + def init_weights(self): + self.param_count = 0 + for module in self.modules(): + if ( + isinstance(module, nn.Conv2d) + or isinstance(module, nn.Linear) + or isinstance(module, nn.Embedding) + ): + if self.init == "ortho": + init.orthogonal_(module.weight) + elif self.init == "N02": + init.normal_(module.weight, 0, 0.02) + elif self.init in ["glorot", "xavier"]: + init.xavier_uniform_(module.weight) + else: + print("Init style not recognized...") + self.param_count += sum( + [p.data.nelement() for p in module.parameters()] + ) + print("Param count for D" "s initialized parameters: %d" % self.param_count) + + def forward(self, x, y=None): + # Run input conv + h = self.input_conv(x) + # Loop over blocks + for index, blocklist in enumerate(self.blocks): + for block in blocklist: + h = block(h) + # Apply global sum pooling as in SN-GAN + h = torch.sum(self.activation(h), [2, 3]) + # Get initial class-unconditional output + out = self.linear(h) + # Get projection of final featureset onto class vectors and add to evidence + out = out + torch.sum(self.embed(y) * h, 1, keepdim=True) + return out + + +# Parallelized G_D to minimize cross-gpu communication +# Without this, Generator outputs would get all-gathered and then rebroadcast. +class G_D(nn.Module): + def __init__(self, G, D): + super(G_D, self).__init__() + self.G = G + self.D = D + + def forward( + self, z, gy, x=None, dy=None, train_G=False, return_G_z=False, split_D=False + ): + # If training G, enable grad tape + with torch.set_grad_enabled(train_G): + # Get Generator output given noise + G_z = self.G(z, self.G.shared(gy)) + # Cast as necessary + if self.G.fp16 and not self.D.fp16: + G_z = G_z.float() + if self.D.fp16 and not self.G.fp16: + G_z = G_z.half() + # Split_D means to run D once with real data and once with fake, + # rather than concatenating along the batch dimension. + if split_D: + D_fake = self.D(G_z, gy) + if x is not None: + D_real = self.D(x, dy) + return D_fake, D_real + else: + if return_G_z: + return D_fake, G_z + else: + return D_fake + # If real data is provided, concatenate it with the Generator's output + # along the batch dimension for improved efficiency. + else: + D_input = torch.cat([G_z, x], 0) if x is not None else G_z + D_class = torch.cat([gy, dy], 0) if dy is not None else gy + # Get Discriminator output + D_out = self.D(D_input, D_class) + if x is not None: + return torch.split(D_out, [G_z.shape[0], x.shape[0]]) # D_fake, D_real + else: + if return_G_z: + return D_out, G_z + else: + return D_out diff --git a/BigGAN_PyTorch/LICENSE b/BigGAN_PyTorch/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..c2dd72189bed14d6fa56dc22b8796798c45ae5f5 --- /dev/null +++ b/BigGAN_PyTorch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 Andy Brock + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/BigGAN_PyTorch/README.md b/BigGAN_PyTorch/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e4ea44d3f62bbe14b9ab9235fe9e74d4f6ca4eed --- /dev/null +++ b/BigGAN_PyTorch/README.md @@ -0,0 +1,144 @@ +# BigGAN-PyTorch +The author's officially unofficial PyTorch BigGAN implementation. + +![Dogball? Dogball!](imgs/header_image.jpg?raw=true "Dogball? Dogball!") + + +This repo contains code for 4-8 GPU training of BigGANs from [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://arxiv.org/abs/1809.11096) by Andrew Brock, Jeff Donahue, and Karen Simonyan. + +This code is by Andy Brock and Alex Andonian. + +## How To Use This Code +You will need: + +- [PyTorch](https://PyTorch.org/), version 1.0.1 +- tqdm, numpy, scipy, and h5py +- The ImageNet training set + +First, you may optionally prepare a pre-processed HDF5 version of your target dataset for faster I/O. Following this (or not), you'll need the Inception moments needed to calculate FID. These can both be done by modifying and running + +```sh +sh scripts/utils/prepare_data.sh +``` + +Which by default assumes your ImageNet training set is downloaded into the root folder `data` in this directory, and will prepare the cached HDF5 at 128x128 pixel resolution. + +In the scripts folder, there are multiple bash scripts which will train BigGANs with different batch sizes. This code assumes you do not have access to a full TPU pod, and accordingly +spoofs mega-batches by using gradient accumulation (averaging grads over multiple minibatches, and only taking an optimizer step after N accumulations). By default, the `launch_BigGAN_bs256x8.sh` script trains a +full-sized BigGAN model with a batch size of 256 and 8 gradient accumulations, for a total batch size of 2048. On 8xV100 with full-precision training (no Tensor cores), this script takes 15 days to train to 150k iterations. + +You will first need to figure out the maximum batch size your setup can support. The pre-trained models provided here were trained on 8xV100 (16GB VRAM each) which can support slightly more than the BS256 used by default. +Once you've determined this, you should modify the script so that the batch size times the number of gradient accumulations is equal to your desired total batch size (BigGAN defaults to 2048). + +Note also that this script uses the `--load_in_mem` arg, which loads the entire (~64GB) I128.hdf5 file into RAM for faster data loading. If you don't have enough RAM to support this (probably 96GB+), remove this argument. + + +## Metrics and Sampling +![I believe I can fly!](imgs/interp_sample.jpg?raw=true "I believe I can fly!") + +During training, this script will output logs with training metrics and test metrics, will save multiple copies (2 most recent and 5 highest-scoring) of the model weights/optimizer params, and will produce samples and interpolations every time it saves weights. +The logs folder contains scripts to process these logs and plot the results using MATLAB (sorry not sorry). + +After training, one can use `sample.py` to produce additional samples and interpolations, test with different truncation values, batch sizes, number of standing stat accumulations, etc. See the `sample_BigGAN_bs256x8.sh` script for an example. + +By default, everything is saved to weights/samples/logs/data folders which are assumed to be in the same folder as this repo. +You can point all of these to a different base folder using the `--base_root` argument, or pick specific locations for each of these with their respective arguments (e.g. `--logs_root`). + +We include scripts to run BigGAN-deep, but we have not fully trained a model using them, so consider them untested. Additionally, we include scripts to run a model on CIFAR, and to run SA-GAN (with EMA) and SN-GAN on ImageNet. The SA-GAN code assumes you have 4xTitanX (or equivalent in terms of GPU RAM) and will run with a batch size of 128 and 2 gradient accumulations. + +## An Important Note on Inception Metrics +This repo uses the PyTorch in-built inception network to calculate IS and FID. +These scores are different from the scores you would get using the official TF inception code, and are only for monitoring purposes! +Run sample.py on your model, with the `--sample_npz` argument, then run inception_tf13 to calculate the actual TensorFlow IS. Note that you will need to have TensorFlow 1.3 or earlier installed, as TF1.4+ breaks the original IS code. + +## Pretrained models +![PyTorch Inception Score and FID](imgs/IS_FID.png) +We include two pretrained model checkpoints (with G, D, the EMA copy of G, the optimizers, and the state dict): +- The main checkpoint is for a BigGAN trained on ImageNet at 128x128, using BS256 and 8 gradient accumulations, taken just before collapse, with a TF Inception Score of 97.35 +/- 1.79: [LINK](https://drive.google.com/open?id=1nAle7FCVFZdix2--ks0r5JBkFnKw8ctW) +- An earlier checkpoint of the first model (100k G iters), at high performance but well before collapse, which may be easier to fine-tune: [LINK](https://drive.google.com/open?id=1dmZrcVJUAWkPBGza_XgswSuT-UODXZcO) + + + +Pretrained models for Places-365 coming soon. + +This repo also contains scripts for porting the original TFHub BigGAN Generator weights to PyTorch. See the scripts in the TFHub folder for more details. + +## Fine-tuning, Using Your Own Dataset, or Making New Training Functions +![That's deep, man](imgs/DeepSamples.png?raw=true "Deep Samples") + +If you wish to resume interrupted training or fine-tune a pre-trained model, run the same launch script but with the `--resume` argument added. +Experiment names are automatically generated from the configuration, but can be overridden using the `--experiment_name` arg (for example, if you wish to fine-tune a model using modified optimizer settings). + +To prep your own dataset, you will need to add it to datasets.py and modify the convenience dicts in utils.py (dset_dict, imsize_dict, root_dict, nclass_dict, classes_per_sheet_dict) to have the appropriate metadata for your dataset. +Repeat the process in prepare_data.sh (optionally produce an HDF5 preprocessed copy, and calculate the Inception Moments for FID). + +By default, the training script will save the top 5 best checkpoints as measured by Inception Score. +For datasets other than ImageNet, Inception Score can be a very poor measure of quality, so you will likely want to use `--which_best FID` instead. + +To use your own training function (e.g. train a BigVAE): either modify train_fns.GAN_training_function or add a new train fn and add it after the `if config['which_train_fn'] == 'GAN':` line in `train.py`. + + +## Neat Stuff +- We include the full training and metrics logs [here](https://drive.google.com/open?id=1ZhY9Mg2b_S4QwxNmt57aXJ9FOC3ZN1qb) for reference. I've found that one of the hardest things about re-implementing a paper can be checking if the logs line up early in training, +especially if training takes multiple weeks. Hopefully these will be helpful for future work. +- We include an accelerated FID calculation--the original scipy version can require upwards of 10 minutes to calculate the matrix sqrt, this version uses an accelerated PyTorch version to calculate it in under a second. +- We include an accelerated, low-memory consumption ortho reg implementation. +- By default, we only compute the top singular value (the spectral norm), but this code supports computing more SVs through the `--num_G_SVs` argument. + +## Key Differences Between This Code And The Original BigGAN +- We use the optimizer settings from SA-GAN (G_lr=1e-4, D_lr=4e-4, num_D_steps=1, as opposed to BigGAN's G_lr=5e-5, D_lr=2e-4, num_D_steps=2). +While slightly less performant, this was the first corner we cut to bring training times down. +- By default, we do not use Cross-Replica BatchNorm (AKA Synced BatchNorm). +The two variants we tried (a custom, naive one and the one included in this repo) have slightly different gradients (albeit identical forward passes) from the built-in BatchNorm, which appear to be sufficient to cripple training. +- Gradient accumulation means that we update the SV estimates and the BN statistics 8 times more frequently. This means that the BN stats are much closer to standing stats, and that the singular value estimates tend to be more accurate. +Because of this, we measure metrics by default with G in test mode (using the BatchNorm running stat estimates instead of computing standing stats as in the paper). We do still support standing stats (see the sample.sh scripts). +This could also conceivably result in gradients from the earlier accumulations being stale, but in practice this does not appear to be a problem. +- The currently provided pretrained models were not trained with orthogonal regularization. Training without ortho reg seems to increase the probability that models will not be amenable to truncation, +but it looks like this particular model got a winning ticket. Regardless, we provide two highly optimized (fast and minimal memory consumption) ortho reg implementations which directly compute the ortho reg. gradients. + +## A Note On The Design Of This Repo +This code is designed from the ground up to serve as an extensible, hackable base for further research code. +We've put a lot of thought into making sure the abstractions are the *right* thickness for research--not so thick as to be impenetrable, but not so thin as to be useless. +The key idea is that if you want to experiment with a SOTA setup and make some modification (try out your own new loss function, architecture, self-attention block, etc) you should be able to easily do so just by dropping your code in one or two places, without having to worry about the rest of the codebase. +Things like the use of self.which_conv and functools.partial in the BigGAN.py model definition were put together with this in mind, as was the design of the Spectral Norm class inheritance. + +With that said, this is a somewhat large codebase for a single project. While we tried to be thorough with the comments, if there's something you think could be more clear, better written, or better refactored, please feel free to raise an issue or a pull request. + +## Feature Requests +Want to work on or improve this code? There are a couple things this repo would benefit from, but which don't yet work. + +- Synchronized BatchNorm (AKA Cross-Replica BatchNorm). We tried out two variants of this, but for some unknown reason it crippled training each time. + We have not tried the [apex](https://github.com/NVIDIA/apex) SyncBN as my school's servers are on ancient NVIDIA drivers that don't support it--apex would probably be a good place to start. +- Mixed precision training and making use of Tensor cores. This repo includes a naive mixed-precision Adam implementation which works early in training but leads to early collapse, and doesn't do anything to activate Tensor cores (it just reduces memory consumption). + As above, integrating [apex](https://github.com/NVIDIA/apex) into this code and employing its mixed-precision training techniques to take advantage of Tensor cores and reduce memory consumption could yield substantial speed gains. + +## Misc Notes +See [This directory](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) for ImageNet labels. + +If you use this code, please cite +```text +@inproceedings{ +brock2018large, +title={Large Scale {GAN} Training for High Fidelity Natural Image Synthesis}, +author={Andrew Brock and Jeff Donahue and Karen Simonyan}, +booktitle={International Conference on Learning Representations}, +year={2019}, +url={https://openreview.net/forum?id=B1xsqj09Fm}, +} +``` + +## Acknowledgments +Thanks to Google for the generous cloud credit donations. + +[SyncBN](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch) by Jiayuan Mao and Tete Xiao. + +[Progress bar](https://github.com/Lasagne/Recipes/tree/master/papers/densenet) originally from Jan Schlüter. + +Test metrics logger from [VoxNet.](https://github.com/dimatura/voxnet) + +PyTorch [implementation of cov](https://discuss.PyTorch.org/t/covariance-and-gradient-support/16217/2) from Modar M. Alfadly. + +PyTorch [fast Matrix Sqrt](https://github.com/msubhransu/matrix-sqrt) for FID from Tsung-Yu Lin and Subhransu Maji. + +TensorFlow Inception Score code from [OpenAI's Improved-GAN.](https://github.com/openai/improved-gan) + diff --git a/BigGAN_PyTorch/TFHub/README.md b/BigGAN_PyTorch/TFHub/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fe29751eed338c81e8999ea0f871390a131bd485 --- /dev/null +++ b/BigGAN_PyTorch/TFHub/README.md @@ -0,0 +1,14 @@ +# BigGAN-PyTorch TFHub converter +This dir contains scripts for taking the [pre-trained generator weights from TFHub](https://tfhub.dev/s?q=biggan) and porting them to BigGAN-Pytorch. + +In addition to the base libraries for BigGAN-PyTorch, to run this code you will need: + +TensorFlow +TFHub +parse + +Note that this code is only presently set up to run the ported models without truncation--you'll need to accumulate standing stats at each truncation level yourself if you wish to employ it. + +To port the 128x128 model from tfhub, produce a pretrained weights .pth file, and generate samples using all your GPUs, run + +`python converter.py -r 128 --generate_samples --parallel` \ No newline at end of file diff --git a/BigGAN_PyTorch/TFHub/biggan_v1.py b/BigGAN_PyTorch/TFHub/biggan_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..548988711e14d3ec2c6a4c91ec1effdddf7e5cc1 --- /dev/null +++ b/BigGAN_PyTorch/TFHub/biggan_v1.py @@ -0,0 +1,441 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +# BigGAN V1: +# This is now deprecated code used for porting the TFHub modules to pytorch, +# included here for reference only. +import numpy as np +import torch +from scipy.stats import truncnorm +from torch import nn +from torch.nn import Parameter +from torch.nn import functional as F + + +def l2normalize(v, eps=1e-4): + return v / (v.norm() + eps) + + +def truncated_z_sample(batch_size, z_dim, truncation=0.5, seed=None): + state = None if seed is None else np.random.RandomState(seed) + values = truncnorm.rvs(-2, 2, size=(batch_size, z_dim), random_state=state) + return truncation * values + + +def denorm(x): + out = (x + 1) / 2 + return out.clamp_(0, 1) + + +class SpectralNorm(nn.Module): + def __init__(self, module, name="weight", power_iterations=1): + super(SpectralNorm, self).__init__() + self.module = module + self.name = name + self.power_iterations = power_iterations + if not self._made_params(): + self._make_params() + + def _update_u_v(self): + u = getattr(self.module, self.name + "_u") + v = getattr(self.module, self.name + "_v") + w = getattr(self.module, self.name + "_bar") + + height = w.data.shape[0] + _w = w.view(height, -1) + for _ in range(self.power_iterations): + v = l2normalize(torch.matmul(_w.t(), u)) + u = l2normalize(torch.matmul(_w, v)) + + sigma = u.dot((_w).mv(v)) + setattr(self.module, self.name, w / sigma.expand_as(w)) + + def _made_params(self): + try: + getattr(self.module, self.name + "_u") + getattr(self.module, self.name + "_v") + getattr(self.module, self.name + "_bar") + return True + except AttributeError: + return False + + def _make_params(self): + w = getattr(self.module, self.name) + + height = w.data.shape[0] + width = w.view(height, -1).data.shape[1] + + u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False) + v = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False) + u.data = l2normalize(u.data) + v.data = l2normalize(v.data) + w_bar = Parameter(w.data) + + del self.module._parameters[self.name] + self.module.register_parameter(self.name + "_u", u) + self.module.register_parameter(self.name + "_v", v) + self.module.register_parameter(self.name + "_bar", w_bar) + + def forward(self, *args): + self._update_u_v() + return self.module.forward(*args) + + +class SelfAttention(nn.Module): + """ Self Attention Layer""" + + def __init__(self, in_dim, activation=F.relu): + super().__init__() + self.chanel_in = in_dim + self.activation = activation + + self.theta = SpectralNorm( + nn.Conv2d( + in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1, bias=False + ) + ) + self.phi = SpectralNorm( + nn.Conv2d( + in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1, bias=False + ) + ) + self.pool = nn.MaxPool2d(2, 2) + self.g = SpectralNorm( + nn.Conv2d( + in_channels=in_dim, out_channels=in_dim // 2, kernel_size=1, bias=False + ) + ) + self.o_conv = SpectralNorm( + nn.Conv2d( + in_channels=in_dim // 2, out_channels=in_dim, kernel_size=1, bias=False + ) + ) + self.gamma = nn.Parameter(torch.zeros(1)) + + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x): + m_batchsize, C, width, height = x.size() + N = height * width + + theta = self.theta(x) + phi = self.phi(x) + phi = self.pool(phi) + phi = phi.view(m_batchsize, -1, N // 4) + theta = theta.view(m_batchsize, -1, N) + theta = theta.permute(0, 2, 1) + attention = self.softmax(torch.bmm(theta, phi)) + g = self.pool(self.g(x)).view(m_batchsize, -1, N // 4) + attn_g = torch.bmm(g, attention.permute(0, 2, 1)).view( + m_batchsize, -1, width, height + ) + out = self.o_conv(attn_g) + return self.gamma * out + x + + +class ConditionalBatchNorm2d(nn.Module): + def __init__(self, num_features, num_classes, eps=1e-4, momentum=0.1): + super().__init__() + self.num_features = num_features + self.bn = nn.BatchNorm2d(num_features, affine=False, eps=eps, momentum=momentum) + self.gamma_embed = SpectralNorm( + nn.Linear(num_classes, num_features, bias=False) + ) + self.beta_embed = SpectralNorm(nn.Linear(num_classes, num_features, bias=False)) + + def forward(self, x, y): + out = self.bn(x) + gamma = self.gamma_embed(y) + 1 + beta = self.beta_embed(y) + out = gamma.view(-1, self.num_features, 1, 1) * out + beta.view( + -1, self.num_features, 1, 1 + ) + return out + + +class GBlock(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size=[3, 3], + padding=1, + stride=1, + n_class=None, + bn=True, + activation=F.relu, + upsample=True, + downsample=False, + z_dim=148, + ): + super().__init__() + + self.conv0 = SpectralNorm( + nn.Conv2d( + in_channel, + out_channel, + kernel_size, + stride, + padding, + bias=True if bn else True, + ) + ) + self.conv1 = SpectralNorm( + nn.Conv2d( + out_channel, + out_channel, + kernel_size, + stride, + padding, + bias=True if bn else True, + ) + ) + + self.skip_proj = False + if in_channel != out_channel or upsample or downsample: + self.conv_sc = SpectralNorm(nn.Conv2d(in_channel, out_channel, 1, 1, 0)) + self.skip_proj = True + + self.upsample = upsample + self.downsample = downsample + self.activation = activation + self.bn = bn + if bn: + self.HyperBN = ConditionalBatchNorm2d(in_channel, z_dim) + self.HyperBN_1 = ConditionalBatchNorm2d(out_channel, z_dim) + + def forward(self, input, condition=None): + out = input + + if self.bn: + out = self.HyperBN(out, condition) + out = self.activation(out) + if self.upsample: + out = F.interpolate(out, scale_factor=2) + out = self.conv0(out) + if self.bn: + out = self.HyperBN_1(out, condition) + out = self.activation(out) + out = self.conv1(out) + + if self.downsample: + out = F.avg_pool2d(out, 2) + + if self.skip_proj: + skip = input + if self.upsample: + skip = F.interpolate(skip, scale_factor=2) + skip = self.conv_sc(skip) + if self.downsample: + skip = F.avg_pool2d(skip, 2) + else: + skip = input + return out + skip + + +class Generator128(nn.Module): + def __init__(self, code_dim=120, n_class=1000, chn=96, debug=False): + super().__init__() + + self.linear = nn.Linear(n_class, 128, bias=False) + + if debug: + chn = 8 + + self.first_view = 16 * chn + + self.G_linear = SpectralNorm(nn.Linear(20, 4 * 4 * 16 * chn)) + + z_dim = code_dim + 28 + + self.GBlock = nn.ModuleList( + [ + GBlock(16 * chn, 16 * chn, n_class=n_class, z_dim=z_dim), + GBlock(16 * chn, 8 * chn, n_class=n_class, z_dim=z_dim), + GBlock(8 * chn, 4 * chn, n_class=n_class, z_dim=z_dim), + GBlock(4 * chn, 2 * chn, n_class=n_class, z_dim=z_dim), + GBlock(2 * chn, 1 * chn, n_class=n_class, z_dim=z_dim), + ] + ) + + self.sa_id = 4 + self.num_split = len(self.GBlock) + 1 + self.attention = SelfAttention(2 * chn) + self.ScaledCrossReplicaBN = nn.BatchNorm2d(1 * chn, eps=1e-4) + self.colorize = SpectralNorm(nn.Conv2d(1 * chn, 3, [3, 3], padding=1)) + + def forward(self, input, class_id): + codes = torch.chunk(input, self.num_split, 1) + class_emb = self.linear(class_id) # 128 + + out = self.G_linear(codes[0]) + out = out.view(-1, 4, 4, self.first_view).permute(0, 3, 1, 2) + for i, (code, GBlock) in enumerate(zip(codes[1:], self.GBlock)): + if i == self.sa_id: + out = self.attention(out) + condition = torch.cat([code, class_emb], 1) + out = GBlock(out, condition) + + out = self.ScaledCrossReplicaBN(out) + out = F.relu(out) + out = self.colorize(out) + return torch.tanh(out) + + +class Generator256(nn.Module): + def __init__(self, code_dim=140, n_class=1000, chn=96, debug=False): + super().__init__() + + self.linear = nn.Linear(n_class, 128, bias=False) + + if debug: + chn = 8 + + self.first_view = 16 * chn + + self.G_linear = SpectralNorm(nn.Linear(20, 4 * 4 * 16 * chn)) + + self.GBlock = nn.ModuleList( + [ + GBlock(16 * chn, 16 * chn, n_class=n_class), + GBlock(16 * chn, 8 * chn, n_class=n_class), + GBlock(8 * chn, 8 * chn, n_class=n_class), + GBlock(8 * chn, 4 * chn, n_class=n_class), + GBlock(4 * chn, 2 * chn, n_class=n_class), + GBlock(2 * chn, 1 * chn, n_class=n_class), + ] + ) + + self.sa_id = 5 + self.num_split = len(self.GBlock) + 1 + self.attention = SelfAttention(2 * chn) + self.ScaledCrossReplicaBN = nn.BatchNorm2d(1 * chn, eps=1e-4) + self.colorize = SpectralNorm(nn.Conv2d(1 * chn, 3, [3, 3], padding=1)) + + def forward(self, input, class_id): + codes = torch.chunk(input, self.num_split, 1) + class_emb = self.linear(class_id) # 128 + + out = self.G_linear(codes[0]) + out = out.view(-1, 4, 4, self.first_view).permute(0, 3, 1, 2) + for i, (code, GBlock) in enumerate(zip(codes[1:], self.GBlock)): + if i == self.sa_id: + out = self.attention(out) + condition = torch.cat([code, class_emb], 1) + out = GBlock(out, condition) + + out = self.ScaledCrossReplicaBN(out) + out = F.relu(out) + out = self.colorize(out) + return torch.tanh(out) + + +class Generator512(nn.Module): + def __init__(self, code_dim=128, n_class=1000, chn=96, debug=False): + super().__init__() + + self.linear = nn.Linear(n_class, 128, bias=False) + + if debug: + chn = 8 + + self.first_view = 16 * chn + + self.G_linear = SpectralNorm(nn.Linear(16, 4 * 4 * 16 * chn)) + + z_dim = code_dim + 16 + + self.GBlock = nn.ModuleList( + [ + GBlock(16 * chn, 16 * chn, n_class=n_class, z_dim=z_dim), + GBlock(16 * chn, 8 * chn, n_class=n_class, z_dim=z_dim), + GBlock(8 * chn, 8 * chn, n_class=n_class, z_dim=z_dim), + GBlock(8 * chn, 4 * chn, n_class=n_class, z_dim=z_dim), + GBlock(4 * chn, 2 * chn, n_class=n_class, z_dim=z_dim), + GBlock(2 * chn, 1 * chn, n_class=n_class, z_dim=z_dim), + GBlock(1 * chn, 1 * chn, n_class=n_class, z_dim=z_dim), + ] + ) + + self.sa_id = 4 + self.num_split = len(self.GBlock) + 1 + self.attention = SelfAttention(4 * chn) + self.ScaledCrossReplicaBN = nn.BatchNorm2d(1 * chn) + self.colorize = SpectralNorm(nn.Conv2d(1 * chn, 3, [3, 3], padding=1)) + + def forward(self, input, class_id): + codes = torch.chunk(input, self.num_split, 1) + class_emb = self.linear(class_id) # 128 + + out = self.G_linear(codes[0]) + out = out.view(-1, 4, 4, self.first_view).permute(0, 3, 1, 2) + for i, (code, GBlock) in enumerate(zip(codes[1:], self.GBlock)): + if i == self.sa_id: + out = self.attention(out) + condition = torch.cat([code, class_emb], 1) + out = GBlock(out, condition) + + out = self.ScaledCrossReplicaBN(out) + out = F.relu(out) + out = self.colorize(out) + return torch.tanh(out) + + +class Discriminator(nn.Module): + def __init__(self, n_class=1000, chn=96, debug=False): + super().__init__() + + def conv(in_channel, out_channel, downsample=True): + return GBlock( + in_channel, out_channel, bn=False, upsample=False, downsample=downsample + ) + + if debug: + chn = 8 + self.debug = debug + + self.pre_conv = nn.Sequential( + SpectralNorm(nn.Conv2d(3, 1 * chn, 3, padding=1)), + nn.ReLU(), + SpectralNorm(nn.Conv2d(1 * chn, 1 * chn, 3, padding=1)), + nn.AvgPool2d(2), + ) + self.pre_skip = SpectralNorm(nn.Conv2d(3, 1 * chn, 1)) + + self.conv = nn.Sequential( + conv(1 * chn, 1 * chn, downsample=True), + conv(1 * chn, 2 * chn, downsample=True), + SelfAttention(2 * chn), + conv(2 * chn, 2 * chn, downsample=True), + conv(2 * chn, 4 * chn, downsample=True), + conv(4 * chn, 8 * chn, downsample=True), + conv(8 * chn, 8 * chn, downsample=True), + conv(8 * chn, 16 * chn, downsample=True), + conv(16 * chn, 16 * chn, downsample=False), + ) + + self.linear = SpectralNorm(nn.Linear(16 * chn, 1)) + + self.embed = nn.Embedding(n_class, 16 * chn) + self.embed.weight.data.uniform_(-0.1, 0.1) + self.embed = SpectralNorm(self.embed) + + def forward(self, input, class_id): + + out = self.pre_conv(input) + out += self.pre_skip(F.avg_pool2d(input, 2)) + out = self.conv(out) + out = F.relu(out) + out = out.view(out.size(0), out.size(1), -1) + out = out.sum(2) + out_linear = self.linear(out).squeeze(1) + embed = self.embed(class_id) + + prod = (out * embed).sum(1) + + return out_linear + prod diff --git a/BigGAN_PyTorch/TFHub/converter.py b/BigGAN_PyTorch/TFHub/converter.py new file mode 100644 index 0000000000000000000000000000000000000000..e31100b009968610e1e7b395c4ca7bfb496f00a7 --- /dev/null +++ b/BigGAN_PyTorch/TFHub/converter.py @@ -0,0 +1,558 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License + +"""Utilities for converting TFHub BigGAN generator weights to PyTorch. + +Recommended usage: + +To convert all BigGAN variants and generate test samples, use: + +```bash +CUDA_VISIBLE_DEVICES=0 python converter.py --generate_samples +``` + +See `parse_args` for additional options. +""" + +import argparse +import os +import sys + +import h5py +import torch +import torch.nn as nn +from torchvision.utils import save_image +import tensorflow as tf +import tensorflow_hub as hub +import parse + +# import reference biggan from this folder +import biggan_v1 as biggan_for_conversion + +# Import model from main folder +sys.path.append("..") +import BigGAN + + +DEVICE = "cuda" +HDF5_TMPL = "biggan-{}.h5" +PTH_TMPL = "biggan-{}.pth" +MODULE_PATH_TMPL = "https://tfhub.dev/deepmind/biggan-{}/2" +Z_DIMS = {128: 120, 256: 140, 512: 128} +RESOLUTIONS = list(Z_DIMS) + + +def dump_tfhub_to_hdf5(module_path, hdf5_path, redownload=False): + """Loads TFHub weights and saves them to intermediate HDF5 file. + + Args: + module_path ([Path-like]): Path to TFHub module. + hdf5_path ([Path-like]): Path to output HDF5 file. + + Returns: + [h5py.File]: Loaded hdf5 file containing module weights. + """ + if os.path.exists(hdf5_path) and (not redownload): + print("Loading BigGAN hdf5 file from:", hdf5_path) + return h5py.File(hdf5_path, "r") + + print("Loading BigGAN module from:", module_path) + tf.reset_default_graph() + hub.Module(module_path) + print("Loaded BigGAN module from:", module_path) + + initializer = tf.global_variables_initializer() + sess = tf.Session() + sess.run(initializer) + + print("Saving BigGAN weights to :", hdf5_path) + h5f = h5py.File(hdf5_path, "w") + for var in tf.global_variables(): + val = sess.run(var) + h5f.create_dataset(var.name, data=val) + print(f"Saving {var.name} with shape {val.shape}") + h5f.close() + return h5py.File(hdf5_path, "r") + + +class TFHub2Pytorch(object): + + TF_ROOT = "module" + + NUM_GBLOCK = {128: 5, 256: 6, 512: 7} + + w = "w" + b = "b" + u = "u0" + v = "u1" + gamma = "gamma" + beta = "beta" + + def __init__( + self, state_dict, tf_weights, resolution=256, load_ema=True, verbose=False + ): + self.state_dict = state_dict + self.tf_weights = tf_weights + self.resolution = resolution + self.verbose = verbose + if load_ema: + for name in ["w", "b", "gamma", "beta"]: + setattr(self, name, getattr(self, name) + "/ema_b999900") + + def load(self): + self.load_generator() + return self.state_dict + + def load_generator(self): + GENERATOR_ROOT = os.path.join(self.TF_ROOT, "Generator") + + for i in range(self.NUM_GBLOCK[self.resolution]): + name_tf = os.path.join(GENERATOR_ROOT, "GBlock") + name_tf += f"_{i}" if i != 0 else "" + self.load_GBlock(f"GBlock.{i}.", name_tf) + + self.load_attention("attention.", os.path.join(GENERATOR_ROOT, "attention")) + self.load_linear("linear", os.path.join(self.TF_ROOT, "linear"), bias=False) + self.load_snlinear("G_linear", os.path.join(GENERATOR_ROOT, "G_Z", "G_linear")) + self.load_colorize("colorize", os.path.join(GENERATOR_ROOT, "conv_2d")) + self.load_ScaledCrossReplicaBNs( + "ScaledCrossReplicaBN", os.path.join(GENERATOR_ROOT, "ScaledCrossReplicaBN") + ) + + def load_linear(self, name_pth, name_tf, bias=True): + self.state_dict[name_pth + ".weight"] = self.load_tf_tensor( + name_tf, self.w + ).permute(1, 0) + if bias: + self.state_dict[name_pth + ".bias"] = self.load_tf_tensor(name_tf, self.b) + + def load_snlinear(self, name_pth, name_tf, bias=True): + self.state_dict[name_pth + ".module.weight_u"] = self.load_tf_tensor( + name_tf, self.u + ).squeeze() + self.state_dict[name_pth + ".module.weight_v"] = self.load_tf_tensor( + name_tf, self.v + ).squeeze() + self.state_dict[name_pth + ".module.weight_bar"] = self.load_tf_tensor( + name_tf, self.w + ).permute(1, 0) + if bias: + self.state_dict[name_pth + ".module.bias"] = self.load_tf_tensor( + name_tf, self.b + ) + + def load_colorize(self, name_pth, name_tf): + self.load_snconv(name_pth, name_tf) + + def load_GBlock(self, name_pth, name_tf): + self.load_convs(name_pth, name_tf) + self.load_HyperBNs(name_pth, name_tf) + + def load_convs(self, name_pth, name_tf): + self.load_snconv(name_pth + "conv0", os.path.join(name_tf, "conv0")) + self.load_snconv(name_pth + "conv1", os.path.join(name_tf, "conv1")) + self.load_snconv(name_pth + "conv_sc", os.path.join(name_tf, "conv_sc")) + + def load_snconv(self, name_pth, name_tf, bias=True): + if self.verbose: + print(f"loading: {name_pth} from {name_tf}") + self.state_dict[name_pth + ".module.weight_u"] = self.load_tf_tensor( + name_tf, self.u + ).squeeze() + self.state_dict[name_pth + ".module.weight_v"] = self.load_tf_tensor( + name_tf, self.v + ).squeeze() + self.state_dict[name_pth + ".module.weight_bar"] = self.load_tf_tensor( + name_tf, self.w + ).permute(3, 2, 0, 1) + if bias: + self.state_dict[name_pth + ".module.bias"] = self.load_tf_tensor( + name_tf, self.b + ).squeeze() + + def load_conv(self, name_pth, name_tf, bias=True): + + self.state_dict[name_pth + ".weight_u"] = self.load_tf_tensor( + name_tf, self.u + ).squeeze() + self.state_dict[name_pth + ".weight_v"] = self.load_tf_tensor( + name_tf, self.v + ).squeeze() + self.state_dict[name_pth + ".weight_bar"] = self.load_tf_tensor( + name_tf, self.w + ).permute(3, 2, 0, 1) + if bias: + self.state_dict[name_pth + ".bias"] = self.load_tf_tensor(name_tf, self.b) + + def load_HyperBNs(self, name_pth, name_tf): + self.load_HyperBN(name_pth + "HyperBN", os.path.join(name_tf, "HyperBN")) + self.load_HyperBN(name_pth + "HyperBN_1", os.path.join(name_tf, "HyperBN_1")) + + def load_ScaledCrossReplicaBNs(self, name_pth, name_tf): + self.state_dict[name_pth + ".bias"] = self.load_tf_tensor( + name_tf, self.beta + ).squeeze() + self.state_dict[name_pth + ".weight"] = self.load_tf_tensor( + name_tf, self.gamma + ).squeeze() + self.state_dict[name_pth + ".running_mean"] = self.load_tf_tensor( + name_tf + "bn", "accumulated_mean" + ) + self.state_dict[name_pth + ".running_var"] = self.load_tf_tensor( + name_tf + "bn", "accumulated_var" + ) + self.state_dict[name_pth + ".num_batches_tracked"] = torch.tensor( + self.tf_weights[os.path.join(name_tf + "bn", "accumulation_counter:0")][()], + dtype=torch.float32, + ) + + def load_HyperBN(self, name_pth, name_tf): + if self.verbose: + print(f"loading: {name_pth} from {name_tf}") + beta = name_pth + ".beta_embed.module" + gamma = name_pth + ".gamma_embed.module" + self.state_dict[beta + ".weight_u"] = self.load_tf_tensor( + os.path.join(name_tf, "beta"), self.u + ).squeeze() + self.state_dict[gamma + ".weight_u"] = self.load_tf_tensor( + os.path.join(name_tf, "gamma"), self.u + ).squeeze() + self.state_dict[beta + ".weight_v"] = self.load_tf_tensor( + os.path.join(name_tf, "beta"), self.v + ).squeeze() + self.state_dict[gamma + ".weight_v"] = self.load_tf_tensor( + os.path.join(name_tf, "gamma"), self.v + ).squeeze() + self.state_dict[beta + ".weight_bar"] = self.load_tf_tensor( + os.path.join(name_tf, "beta"), self.w + ).permute(1, 0) + self.state_dict[gamma + ".weight_bar"] = self.load_tf_tensor( + os.path.join(name_tf, "gamma"), self.w + ).permute(1, 0) + + cr_bn_name = name_tf.replace("HyperBN", "CrossReplicaBN") + self.state_dict[name_pth + ".bn.running_mean"] = self.load_tf_tensor( + cr_bn_name, "accumulated_mean" + ) + self.state_dict[name_pth + ".bn.running_var"] = self.load_tf_tensor( + cr_bn_name, "accumulated_var" + ) + self.state_dict[name_pth + ".bn.num_batches_tracked"] = torch.tensor( + self.tf_weights[os.path.join(cr_bn_name, "accumulation_counter:0")][()], + dtype=torch.float32, + ) + + def load_attention(self, name_pth, name_tf): + + self.load_snconv(name_pth + "theta", os.path.join(name_tf, "theta"), bias=False) + self.load_snconv(name_pth + "phi", os.path.join(name_tf, "phi"), bias=False) + self.load_snconv(name_pth + "g", os.path.join(name_tf, "g"), bias=False) + self.load_snconv( + name_pth + "o_conv", os.path.join(name_tf, "o_conv"), bias=False + ) + self.state_dict[name_pth + "gamma"] = self.load_tf_tensor(name_tf, self.gamma) + + def load_tf_tensor(self, prefix, var, device="0"): + name = os.path.join(prefix, var) + f":{device}" + return torch.from_numpy(self.tf_weights[name][:]) + + +# Convert from v1: This function maps +def convert_from_v1(hub_dict, resolution=128): + weightname_dict = {"weight_u": "u0", "weight_bar": "weight", "bias": "bias"} + convnum_dict = {"conv0": "conv1", "conv1": "conv2", "conv_sc": "conv_sc"} + attention_blocknum = {128: 3, 256: 4, 512: 3}[resolution] + hub2me = { + "linear.weight": "shared.weight", # This is actually the shared weight + # Linear stuff + "G_linear.module.weight_bar": "linear.weight", + "G_linear.module.bias": "linear.bias", + "G_linear.module.weight_u": "linear.u0", + # output layer stuff + "ScaledCrossReplicaBN.weight": "output_layer.0.gain", + "ScaledCrossReplicaBN.bias": "output_layer.0.bias", + "ScaledCrossReplicaBN.running_mean": "output_layer.0.stored_mean", + "ScaledCrossReplicaBN.running_var": "output_layer.0.stored_var", + "colorize.module.weight_bar": "output_layer.2.weight", + "colorize.module.bias": "output_layer.2.bias", + "colorize.module.weight_u": "output_layer.2.u0", + # Attention stuff + "attention.gamma": "blocks.%d.1.gamma" % attention_blocknum, + "attention.theta.module.weight_u": "blocks.%d.1.theta.u0" % attention_blocknum, + "attention.theta.module.weight_bar": "blocks.%d.1.theta.weight" + % attention_blocknum, + "attention.phi.module.weight_u": "blocks.%d.1.phi.u0" % attention_blocknum, + "attention.phi.module.weight_bar": "blocks.%d.1.phi.weight" + % attention_blocknum, + "attention.g.module.weight_u": "blocks.%d.1.g.u0" % attention_blocknum, + "attention.g.module.weight_bar": "blocks.%d.1.g.weight" % attention_blocknum, + "attention.o_conv.module.weight_u": "blocks.%d.1.o.u0" % attention_blocknum, + "attention.o_conv.module.weight_bar": "blocks.%d.1.o.weight" + % attention_blocknum, + } + + # Loop over the hub dict and build the hub2me map + for name in hub_dict.keys(): + if "GBlock" in name: + if "HyperBN" not in name: # it's a conv + out = parse.parse("GBlock.{:d}.{}.module.{}", name) + blocknum, convnum, weightname = out + if weightname not in weightname_dict: + continue # else hyperBN in + out_name = "blocks.%d.0.%s.%s" % ( + blocknum, + convnum_dict[convnum], + weightname_dict[weightname], + ) # Increment conv number by 1 + else: # hyperbn not conv + BNnum = 2 if "HyperBN_1" in name else 1 + if "embed" in name: + out = parse.parse("GBlock.{:d}.{}.module.{}", name) + blocknum, gamma_or_beta, weightname = out + if weightname not in weightname_dict: # Ignore weight_v + continue + out_name = "blocks.%d.0.bn%d.%s.%s" % ( + blocknum, + BNnum, + "gain" if "gamma" in gamma_or_beta else "bias", + weightname_dict[weightname], + ) + else: + out = parse.parse("GBlock.{:d}.{}.bn.{}", name) + blocknum, dummy, mean_or_var = out + if "num_batches_tracked" in mean_or_var: + continue + out_name = "blocks.%d.0.bn%d.%s" % ( + blocknum, + BNnum, + "stored_mean" if "mean" in mean_or_var else "stored_var", + ) + hub2me[name] = out_name + + # Invert the hub2me map + me2hub = {hub2me[item]: item for item in hub2me} + new_dict = {} + dimz_dict = {128: 20, 256: 20, 512: 16} + for item in me2hub: + # Swap input dim ordering on batchnorm bois to account for my arbitrary change of ordering when concatenating Ys and Zs + if ( + ("bn" in item and "weight" in item) + and ("gain" in item or "bias" in item) + and ("output_layer" not in item) + ): + new_dict[item] = torch.cat( + [ + hub_dict[me2hub[item]][:, -128:], + hub_dict[me2hub[item]][:, : dimz_dict[resolution]], + ], + 1, + ) + # Reshape the first linear weight, bias, and u0 + elif item == "linear.weight": + new_dict[item] = ( + hub_dict[me2hub[item]] + .contiguous() + .view(4, 4, 96 * 16, -1) + .permute(2, 0, 1, 3) + .contiguous() + .view(-1, dimz_dict[resolution]) + ) + elif item == "linear.bias": + new_dict[item] = ( + hub_dict[me2hub[item]] + .view(4, 4, 96 * 16) + .permute(2, 0, 1) + .contiguous() + .view(-1) + ) + elif item == "linear.u0": + new_dict[item] = ( + hub_dict[me2hub[item]] + .view(4, 4, 96 * 16) + .permute(2, 0, 1) + .contiguous() + .view(1, -1) + ) + elif ( + me2hub[item] == "linear.weight" + ): # THIS IS THE SHARED WEIGHT NOT THE FIRST LINEAR LAYER + # Transpose shared weight so that it's an embedding + new_dict[item] = hub_dict[me2hub[item]].t() + elif "weight_u" in me2hub[item]: # Unsqueeze u0s + new_dict[item] = hub_dict[me2hub[item]].unsqueeze(0) + else: + new_dict[item] = hub_dict[me2hub[item]] + return new_dict + + +def get_config(resolution): + attn_dict = {128: "64", 256: "128", 512: "64"} + dim_z_dict = {128: 120, 256: 140, 512: 128} + config = { + "G_param": "SN", + "D_param": "SN", + "G_ch": 96, + "D_ch": 96, + "D_wide": True, + "G_shared": True, + "shared_dim": 128, + "dim_z": dim_z_dict[resolution], + "hier": True, + "cross_replica": False, + "mybn": False, + "G_activation": nn.ReLU(inplace=True), + "G_attn": attn_dict[resolution], + "norm_style": "bn", + "G_init": "ortho", + "skip_init": True, + "no_optim": True, + "G_fp16": False, + "G_mixed_precision": False, + "accumulate_stats": False, + "num_standing_accumulations": 16, + "G_eval_mode": True, + "BN_eps": 1e-04, + "SN_eps": 1e-04, + "num_G_SVs": 1, + "num_G_SV_itrs": 1, + "resolution": resolution, + "n_classes": 1000, + } + return config + + +def convert_biggan( + resolution, weight_dir, redownload=False, no_ema=False, verbose=False +): + module_path = MODULE_PATH_TMPL.format(resolution) + hdf5_path = os.path.join(weight_dir, HDF5_TMPL.format(resolution)) + pth_path = os.path.join(weight_dir, PTH_TMPL.format(resolution)) + + tf_weights = dump_tfhub_to_hdf5(module_path, hdf5_path, redownload=redownload) + G_temp = getattr(biggan_for_conversion, f"Generator{resolution}")() + state_dict_temp = G_temp.state_dict() + + converter = TFHub2Pytorch( + state_dict_temp, + tf_weights, + resolution=resolution, + load_ema=(not no_ema), + verbose=verbose, + ) + state_dict_v1 = converter.load() + state_dict = convert_from_v1(state_dict_v1, resolution) + # Get the config, build the model + config = get_config(resolution) + G = BigGAN.Generator(**config) + G.load_state_dict(state_dict, strict=False) # Ignore missing sv0 entries + torch.save(state_dict, pth_path) + + # output_location ='pretrained_weights/TFHub-PyTorch-128.pth' + + return G + + +def generate_sample(G, z_dim, batch_size, filename, parallel=False): + + G.eval() + G.to(DEVICE) + with torch.no_grad(): + z = torch.randn(batch_size, G.dim_z).to(DEVICE) + y = torch.randint( + low=0, + high=1000, + size=(batch_size,), + device=DEVICE, + dtype=torch.int64, + requires_grad=False, + ) + if parallel: + images = nn.parallel.data_parallel(G, (z, G.shared(y))) + else: + images = G(z, G.shared(y)) + save_image(images, filename, scale_each=True, normalize=True) + + +def parse_args(): + usage = "Parser for conversion script." + parser = argparse.ArgumentParser(description=usage) + parser.add_argument( + "--resolution", + "-r", + type=int, + default=None, + choices=[128, 256, 512], + help="Resolution of TFHub module to convert. Converts all resolutions if None.", + ) + parser.add_argument( + "--redownload", + action="store_true", + default=False, + help="Redownload weights and overwrite current hdf5 file, if present.", + ) + parser.add_argument("--weights_dir", type=str, default="pretrained_weights") + parser.add_argument("--samples_dir", type=str, default="pretrained_samples") + parser.add_argument( + "--no_ema", action="store_true", default=False, help="Do not load ema weights." + ) + parser.add_argument( + "--verbose", action="store_true", default=False, help="Additionally logging." + ) + parser.add_argument( + "--generate_samples", + action="store_true", + default=False, + help="Generate test sample with pretrained model.", + ) + parser.add_argument( + "--batch_size", type=int, default=64, help="Batch size used for test sample." + ) + parser.add_argument( + "--parallel", action="store_true", default=False, help="Parallelize G?" + ) + args = parser.parse_args() + return args + + +if __name__ == "__main__": + + args = parse_args() + os.makedirs(args.weights_dir, exist_ok=True) + os.makedirs(args.samples_dir, exist_ok=True) + + if args.resolution is not None: + G = convert_biggan( + args.resolution, + args.weights_dir, + redownload=args.redownload, + no_ema=args.no_ema, + verbose=args.verbose, + ) + if args.generate_samples: + filename = os.path.join( + args.samples_dir, f"biggan{args.resolution}_samples.jpg" + ) + print("Generating samples...") + generate_sample( + G, Z_DIMS[args.resolution], args.batch_size, filename, args.parallel + ) + else: + for res in RESOLUTIONS: + G = convert_biggan( + res, + args.weights_dir, + redownload=args.redownload, + no_ema=args.no_ema, + verbose=args.verbose, + ) + if args.generate_samples: + filename = os.path.join(args.samples_dir, f"biggan{res}_samples.jpg") + print("Generating samples...") + generate_sample( + G, Z_DIMS[res], args.batch_size, filename, args.parallel + ) diff --git a/BigGAN_PyTorch/animal_hash.py b/BigGAN_PyTorch/animal_hash.py new file mode 100644 index 0000000000000000000000000000000000000000..12a1dea124f6c32d78f7d49f170c42df4890ff3c --- /dev/null +++ b/BigGAN_PyTorch/animal_hash.py @@ -0,0 +1,2652 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License + +c = [ + "Aardvark", + "Abyssinian", + "Affenpinscher", + "Akbash", + "Akita", + "Albatross", + "Alligator", + "Alpaca", + "Angelfish", + "Ant", + "Anteater", + "Antelope", + "Ape", + "Armadillo", + "Ass", + "Avocet", + "Axolotl", + "Baboon", + "Badger", + "Balinese", + "Bandicoot", + "Barb", + "Barnacle", + "Barracuda", + "Bat", + "Beagle", + "Bear", + "Beaver", + "Bee", + "Beetle", + "Binturong", + "Bird", + "Birman", + "Bison", + "Bloodhound", + "Boar", + "Bobcat", + "Bombay", + "Bongo", + "Bonobo", + "Booby", + "Budgerigar", + "Buffalo", + "Bulldog", + "Bullfrog", + "Burmese", + "Butterfly", + "Caiman", + "Camel", + "Capybara", + "Caracal", + "Caribou", + "Cassowary", + "Cat", + "Caterpillar", + "Catfish", + "Cattle", + "Centipede", + "Chameleon", + "Chamois", + "Cheetah", + "Chicken", + "Chihuahua", + "Chimpanzee", + "Chinchilla", + "Chinook", + "Chipmunk", + "Chough", + "Cichlid", + "Clam", + "Coati", + "Cobra", + "Cockroach", + "Cod", + "Collie", + "Coral", + "Cormorant", + "Cougar", + "Cow", + "Coyote", + "Crab", + "Crane", + "Crocodile", + "Crow", + "Curlew", + "Cuscus", + "Cuttlefish", + "Dachshund", + "Dalmatian", + "Deer", + "Dhole", + "Dingo", + "Dinosaur", + "Discus", + "Dodo", + "Dog", + "Dogball", + "Dogfish", + "Dolphin", + "Donkey", + "Dormouse", + "Dove", + "Dragonfly", + "Drever", + "Duck", + "Dugong", + "Dunker", + "Dunlin", + "Eagle", + "Earwig", + "Echidna", + "Eel", + "Eland", + "Elephant", + "ElephantSeal", + "Elk", + "Emu", + "Falcon", + "Ferret", + "Finch", + "Fish", + "Flamingo", + "Flounder", + "Fly", + "Fossa", + "Fox", + "Frigatebird", + "Frog", + "Galago", + "Gar", + "Gaur", + "Gazelle", + "Gecko", + "Gerbil", + "Gharial", + "GiantPanda", + "Gibbon", + "Giraffe", + "Gnat", + "Gnu", + "Goat", + "Goldfinch", + "Goldfish", + "Goose", + "Gopher", + "Gorilla", + "Goshawk", + "Grasshopper", + "Greyhound", + "Grouse", + "Guanaco", + "GuineaFowl", + "GuineaPig", + "Gull", + "Guppy", + "Hamster", + "Hare", + "Harrier", + "Havanese", + "Hawk", + "Hedgehog", + "Heron", + "Herring", + "Himalayan", + "Hippopotamus", + "Hornet", + "Horse", + "Human", + "Hummingbird", + "Hyena", + "Ibis", + "Iguana", + "Impala", + "Indri", + "Insect", + "Jackal", + "Jaguar", + "Javanese", + "Jay", + "Jellyfish", + "Kakapo", + "Kangaroo", + "Kingfisher", + "Kiwi", + "Koala", + "KomodoDragon", + "Kouprey", + "Kudu", + "Labradoodle", + "Ladybird", + "Lapwing", + "Lark", + "Lemming", + "Lemur", + "Leopard", + "Liger", + "Lion", + "Lionfish", + "Lizard", + "Llama", + "Lobster", + "Locust", + "Loris", + "Louse", + "Lynx", + "Lyrebird", + "Macaw", + "Magpie", + "Mallard", + "Maltese", + "Manatee", + "Mandrill", + "Markhor", + "Marten", + "Mastiff", + "Mayfly", + "Meerkat", + "Millipede", + "Mink", + "Mole", + "Molly", + "Mongoose", + "Mongrel", + "Monkey", + "Moorhen", + "Moose", + "Mosquito", + "Moth", + "Mouse", + "Mule", + "Narwhal", + "Neanderthal", + "Newfoundland", + "Newt", + "Nightingale", + "Numbat", + "Ocelot", + "Octopus", + "Okapi", + "Olm", + "Opossum", + "Orang-utan", + "Oryx", + "Ostrich", + "Otter", + "Owl", + "Ox", + "Oyster", + "Pademelon", + "Panther", + "Parrot", + "Partridge", + "Peacock", + "Peafowl", + "Pekingese", + "Pelican", + "Penguin", + "Persian", + "Pheasant", + "Pig", + "Pigeon", + "Pika", + "Pike", + "Piranha", + "Platypus", + "Pointer", + "Pony", + "Poodle", + "Porcupine", + "Porpoise", + "Possum", + "PrairieDog", + "Prawn", + "Puffin", + "Pug", + "Puma", + "Quail", + "Quelea", + "Quetzal", + "Quokka", + "Quoll", + "Rabbit", + "Raccoon", + "Ragdoll", + "Rail", + "Ram", + "Rat", + "Rattlesnake", + "Raven", + "RedDeer", + "RedPanda", + "Reindeer", + "Rhinoceros", + "Robin", + "Rook", + "Rottweiler", + "Ruff", + "Salamander", + "Salmon", + "SandDollar", + "Sandpiper", + "Saola", + "Sardine", + "Scorpion", + "SeaLion", + "SeaUrchin", + "Seahorse", + "Seal", + "Serval", + "Shark", + "Sheep", + "Shrew", + "Shrimp", + "Siamese", + "Siberian", + "Skunk", + "Sloth", + "Snail", + "Snake", + "Snowshoe", + "Somali", + "Sparrow", + "Spider", + "Sponge", + "Squid", + "Squirrel", + "Starfish", + "Starling", + "Stingray", + "Stinkbug", + "Stoat", + "Stork", + "Swallow", + "Swan", + "Tang", + "Tapir", + "Tarsier", + "Termite", + "Tetra", + "Tiffany", + "Tiger", + "Toad", + "Tortoise", + "Toucan", + "Tropicbird", + "Trout", + "Tuatara", + "Turkey", + "Turtle", + "Uakari", + "Uguisu", + "Umbrellabird", + "Viper", + "Vulture", + "Wallaby", + "Walrus", + "Warthog", + "Wasp", + "WaterBuffalo", + "Weasel", + "Whale", + "Whippet", + "Wildebeest", + "Wolf", + "Wolverine", + "Wombat", + "Woodcock", + "Woodlouse", + "Woodpecker", + "Worm", + "Wrasse", + "Wren", + "Yak", + "Zebra", + "Zebu", + "Zonkey", +] +a = [ + "able", + "above", + "absent", + "absolute", + "abstract", + "abundant", + "academic", + "acceptable", + "accepted", + "accessible", + "accurate", + "accused", + "active", + "actual", + "acute", + "added", + "additional", + "adequate", + "adjacent", + "administrative", + "adorable", + "advanced", + "adverse", + "advisory", + "aesthetic", + "afraid", + "african", + "aggregate", + "aggressive", + "agreeable", + "agreed", + "agricultural", + "alert", + "alive", + "alleged", + "allied", + "alone", + "alright", + "alternative", + "amateur", + "amazing", + "ambitious", + "american", + "amused", + "ancient", + "angry", + "annoyed", + "annual", + "anonymous", + "anxious", + "appalling", + "apparent", + "applicable", + "appropriate", + "arab", + "arbitrary", + "architectural", + "armed", + "arrogant", + "artificial", + "artistic", + "ashamed", + "asian", + "asleep", + "assistant", + "associated", + "atomic", + "attractive", + "australian", + "automatic", + "autonomous", + "available", + "average", + "awake", + "aware", + "awful", + "awkward", + "back", + "bad", + "balanced", + "bare", + "basic", + "beautiful", + "beneficial", + "better", + "bewildered", + "big", + "binding", + "biological", + "bitter", + "bizarre", + "black", + "blank", + "blind", + "blonde", + "bloody", + "blue", + "blushing", + "boiling", + "bold", + "bored", + "boring", + "bottom", + "brainy", + "brave", + "breakable", + "breezy", + "brief", + "bright", + "brilliant", + "british", + "broad", + "broken", + "brown", + "bumpy", + "burning", + "busy", + "calm", + "canadian", + "capable", + "capitalist", + "careful", + "casual", + "catholic", + "causal", + "cautious", + "central", + "certain", + "changing", + "characteristic", + "charming", + "cheap", + "cheerful", + "chemical", + "chief", + "chilly", + "chinese", + "chosen", + "christian", + "chronic", + "chubby", + "circular", + "civic", + "civil", + "civilian", + "classic", + "classical", + "clean", + "clear", + "clever", + "clinical", + "close", + "closed", + "cloudy", + "clumsy", + "coastal", + "cognitive", + "coherent", + "cold", + "collective", + "colonial", + "colorful", + "colossal", + "coloured", + "colourful", + "combative", + "combined", + "comfortable", + "coming", + "commercial", + "common", + "communist", + "compact", + "comparable", + "comparative", + "compatible", + "competent", + "competitive", + "complete", + "complex", + "complicated", + "comprehensive", + "compulsory", + "conceptual", + "concerned", + "concrete", + "condemned", + "confident", + "confidential", + "confused", + "conscious", + "conservation", + "conservative", + "considerable", + "consistent", + "constant", + "constitutional", + "contemporary", + "content", + "continental", + "continued", + "continuing", + "continuous", + "controlled", + "controversial", + "convenient", + "conventional", + "convinced", + "convincing", + "cooing", + "cool", + "cooperative", + "corporate", + "correct", + "corresponding", + "costly", + "courageous", + "crazy", + "creative", + "creepy", + "criminal", + "critical", + "crooked", + "crowded", + "crucial", + "crude", + "cruel", + "cuddly", + "cultural", + "curious", + "curly", + "current", + "curved", + "cute", + "daily", + "damaged", + "damp", + "dangerous", + "dark", + "dead", + "deaf", + "deafening", + "dear", + "decent", + "decisive", + "deep", + "defeated", + "defensive", + "defiant", + "definite", + "deliberate", + "delicate", + "delicious", + "delighted", + "delightful", + "democratic", + "dependent", + "depressed", + "desirable", + "desperate", + "detailed", + "determined", + "developed", + "developing", + "devoted", + "different", + "difficult", + "digital", + "diplomatic", + "direct", + "dirty", + "disabled", + "disappointed", + "disastrous", + "disciplinary", + "disgusted", + "distant", + "distinct", + "distinctive", + "distinguished", + "disturbed", + "disturbing", + "diverse", + "divine", + "dizzy", + "domestic", + "dominant", + "double", + "doubtful", + "drab", + "dramatic", + "dreadful", + "driving", + "drunk", + "dry", + "dual", + "due", + "dull", + "dusty", + "dutch", + "dying", + "dynamic", + "eager", + "early", + "eastern", + "easy", + "economic", + "educational", + "eerie", + "effective", + "efficient", + "elaborate", + "elated", + "elderly", + "eldest", + "electoral", + "electric", + "electrical", + "electronic", + "elegant", + "eligible", + "embarrassed", + "embarrassing", + "emotional", + "empirical", + "empty", + "enchanting", + "encouraging", + "endless", + "energetic", + "english", + "enormous", + "enthusiastic", + "entire", + "entitled", + "envious", + "environmental", + "equal", + "equivalent", + "essential", + "established", + "estimated", + "ethical", + "ethnic", + "european", + "eventual", + "everyday", + "evident", + "evil", + "evolutionary", + "exact", + "excellent", + "exceptional", + "excess", + "excessive", + "excited", + "exciting", + "exclusive", + "existing", + "exotic", + "expected", + "expensive", + "experienced", + "experimental", + "explicit", + "extended", + "extensive", + "external", + "extra", + "extraordinary", + "extreme", + "exuberant", + "faint", + "fair", + "faithful", + "familiar", + "famous", + "fancy", + "fantastic", + "far", + "fascinating", + "fashionable", + "fast", + "fat", + "fatal", + "favourable", + "favourite", + "federal", + "fellow", + "female", + "feminist", + "few", + "fierce", + "filthy", + "final", + "financial", + "fine", + "firm", + "fiscal", + "fit", + "fixed", + "flaky", + "flat", + "flexible", + "fluffy", + "fluttering", + "flying", + "following", + "fond", + "foolish", + "foreign", + "formal", + "formidable", + "forthcoming", + "fortunate", + "forward", + "fragile", + "frail", + "frantic", + "free", + "french", + "frequent", + "fresh", + "friendly", + "frightened", + "front", + "frozen", + "fucking", + "full", + "full-time", + "fun", + "functional", + "fundamental", + "funny", + "furious", + "future", + "fuzzy", + "gastric", + "gay", + "general", + "generous", + "genetic", + "gentle", + "genuine", + "geographical", + "german", + "giant", + "gigantic", + "given", + "glad", + "glamorous", + "gleaming", + "global", + "glorious", + "golden", + "good", + "gorgeous", + "gothic", + "governing", + "graceful", + "gradual", + "grand", + "grateful", + "greasy", + "great", + "greek", + "green", + "grey", + "grieving", + "grim", + "gross", + "grotesque", + "growing", + "grubby", + "grumpy", + "guilty", + "handicapped", + "handsome", + "happy", + "hard", + "harsh", + "head", + "healthy", + "heavy", + "helpful", + "helpless", + "hidden", + "high", + "high-pitched", + "hilarious", + "hissing", + "historic", + "historical", + "hollow", + "holy", + "homeless", + "homely", + "hon", + "honest", + "horizontal", + "horrible", + "hostile", + "hot", + "huge", + "human", + "hungry", + "hurt", + "hushed", + "husky", + "icy", + "ideal", + "identical", + "ideological", + "ill", + "illegal", + "imaginative", + "immediate", + "immense", + "imperial", + "implicit", + "important", + "impossible", + "impressed", + "impressive", + "improved", + "inadequate", + "inappropriate", + "inc", + "inclined", + "increased", + "increasing", + "incredible", + "independent", + "indian", + "indirect", + "individual", + "industrial", + "inevitable", + "influential", + "informal", + "inherent", + "initial", + "injured", + "inland", + "inner", + "innocent", + "innovative", + "inquisitive", + "instant", + "institutional", + "insufficient", + "intact", + "integral", + "integrated", + "intellectual", + "intelligent", + "intense", + "intensive", + "interested", + "interesting", + "interim", + "interior", + "intermediate", + "internal", + "international", + "intimate", + "invisible", + "involved", + "iraqi", + "irish", + "irrelevant", + "islamic", + "isolated", + "israeli", + "italian", + "itchy", + "japanese", + "jealous", + "jewish", + "jittery", + "joint", + "jolly", + "joyous", + "judicial", + "juicy", + "junior", + "just", + "keen", + "key", + "kind", + "known", + "korean", + "labour", + "large", + "large-scale", + "late", + "latin", + "lazy", + "leading", + "left", + "legal", + "legislative", + "legitimate", + "lengthy", + "lesser", + "level", + "lexical", + "liable", + "liberal", + "light", + "like", + "likely", + "limited", + "linear", + "linguistic", + "liquid", + "literary", + "little", + "live", + "lively", + "living", + "local", + "logical", + "lonely", + "long", + "long-term", + "loose", + "lost", + "loud", + "lovely", + "low", + "loyal", + "ltd", + "lucky", + "mad", + "magenta", + "magic", + "magnetic", + "magnificent", + "main", + "major", + "male", + "mammoth", + "managerial", + "managing", + "manual", + "many", + "marginal", + "marine", + "marked", + "married", + "marvellous", + "marxist", + "mass", + "massive", + "mathematical", + "mature", + "maximum", + "mean", + "meaningful", + "mechanical", + "medical", + "medieval", + "melodic", + "melted", + "mental", + "mere", + "metropolitan", + "mid", + "middle", + "middle-class", + "mighty", + "mild", + "military", + "miniature", + "minimal", + "minimum", + "ministerial", + "minor", + "miserable", + "misleading", + "missing", + "misty", + "mixed", + "moaning", + "mobile", + "moderate", + "modern", + "modest", + "molecular", + "monetary", + "monthly", + "moral", + "motionless", + "muddy", + "multiple", + "mushy", + "musical", + "mute", + "mutual", + "mysterious", + "naked", + "narrow", + "nasty", + "national", + "native", + "natural", + "naughty", + "naval", + "near", + "nearby", + "neat", + "necessary", + "negative", + "neighbouring", + "nervous", + "net", + "neutral", + "new", + "nice", + "nineteenth-century", + "noble", + "noisy", + "normal", + "northern", + "nosy", + "notable", + "novel", + "nuclear", + "numerous", + "nursing", + "nutritious", + "nutty", + "obedient", + "objective", + "obliged", + "obnoxious", + "obvious", + "occasional", + "occupational", + "odd", + "official", + "ok", + "okay", + "old", + "old-fashioned", + "olympic", + "only", + "open", + "operational", + "opposite", + "optimistic", + "oral", + "orange", + "ordinary", + "organic", + "organisational", + "original", + "orthodox", + "other", + "outdoor", + "outer", + "outrageous", + "outside", + "outstanding", + "overall", + "overseas", + "overwhelming", + "painful", + "pale", + "palestinian", + "panicky", + "parallel", + "parental", + "parliamentary", + "part-time", + "partial", + "particular", + "passing", + "passive", + "past", + "patient", + "payable", + "peaceful", + "peculiar", + "perfect", + "permanent", + "persistent", + "personal", + "petite", + "philosophical", + "physical", + "pink", + "plain", + "planned", + "plastic", + "pleasant", + "pleased", + "poised", + "polish", + "polite", + "political", + "poor", + "popular", + "positive", + "possible", + "post-war", + "potential", + "powerful", + "practical", + "precious", + "precise", + "preferred", + "pregnant", + "preliminary", + "premier", + "prepared", + "present", + "presidential", + "pretty", + "previous", + "prickly", + "primary", + "prime", + "primitive", + "principal", + "printed", + "prior", + "private", + "probable", + "productive", + "professional", + "profitable", + "profound", + "progressive", + "prominent", + "promising", + "proper", + "proposed", + "prospective", + "protective", + "protestant", + "proud", + "provincial", + "psychiatric", + "psychological", + "public", + "puny", + "pure", + "purple", + "purring", + "puzzled", + "quaint", + "qualified", + "quick", + "quickest", + "quiet", + "racial", + "radical", + "rainy", + "random", + "rapid", + "rare", + "raspy", + "rational", + "ratty", + "raw", + "ready", + "real", + "realistic", + "rear", + "reasonable", + "recent", + "red", + "reduced", + "redundant", + "regional", + "registered", + "regular", + "regulatory", + "related", + "relative", + "relaxed", + "relevant", + "reliable", + "relieved", + "religious", + "reluctant", + "remaining", + "remarkable", + "remote", + "renewed", + "representative", + "repulsive", + "required", + "resident", + "residential", + "resonant", + "respectable", + "respective", + "responsible", + "resulting", + "retail", + "retired", + "revolutionary", + "rich", + "ridiculous", + "right", + "rigid", + "ripe", + "rising", + "rival", + "roasted", + "robust", + "rolling", + "roman", + "romantic", + "rotten", + "rough", + "round", + "royal", + "rubber", + "rude", + "ruling", + "running", + "rural", + "russian", + "sacred", + "sad", + "safe", + "salty", + "satisfactory", + "satisfied", + "scared", + "scary", + "scattered", + "scientific", + "scornful", + "scottish", + "scrawny", + "screeching", + "secondary", + "secret", + "secure", + "select", + "selected", + "selective", + "selfish", + "semantic", + "senior", + "sensible", + "sensitive", + "separate", + "serious", + "severe", + "sexual", + "shaggy", + "shaky", + "shallow", + "shared", + "sharp", + "sheer", + "shiny", + "shivering", + "shocked", + "short", + "short-term", + "shrill", + "shy", + "sick", + "significant", + "silent", + "silky", + "silly", + "similar", + "simple", + "single", + "skilled", + "skinny", + "sleepy", + "slight", + "slim", + "slimy", + "slippery", + "slow", + "small", + "smart", + "smiling", + "smoggy", + "smooth", + "so-called", + "social", + "socialist", + "soft", + "solar", + "sole", + "solid", + "sophisticated", + "sore", + "sorry", + "sound", + "sour", + "southern", + "soviet", + "spanish", + "spare", + "sparkling", + "spatial", + "special", + "specific", + "specified", + "spectacular", + "spicy", + "spiritual", + "splendid", + "spontaneous", + "sporting", + "spotless", + "spotty", + "square", + "squealing", + "stable", + "stale", + "standard", + "static", + "statistical", + "statutory", + "steady", + "steep", + "sticky", + "stiff", + "still", + "stingy", + "stormy", + "straight", + "straightforward", + "strange", + "strategic", + "strict", + "striking", + "striped", + "strong", + "structural", + "stuck", + "stupid", + "subjective", + "subsequent", + "substantial", + "subtle", + "successful", + "successive", + "sudden", + "sufficient", + "suitable", + "sunny", + "super", + "superb", + "superior", + "supporting", + "supposed", + "supreme", + "sure", + "surprised", + "surprising", + "surrounding", + "surviving", + "suspicious", + "sweet", + "swift", + "swiss", + "symbolic", + "sympathetic", + "systematic", + "tall", + "tame", + "tan", + "tart", + "tasteless", + "tasty", + "technical", + "technological", + "teenage", + "temporary", + "tender", + "tense", + "terrible", + "territorial", + "testy", + "then", + "theoretical", + "thick", + "thin", + "thirsty", + "thorough", + "thoughtful", + "thoughtless", + "thundering", + "tight", + "tiny", + "tired", + "top", + "tory", + "total", + "tough", + "toxic", + "traditional", + "tragic", + "tremendous", + "tricky", + "tropical", + "troubled", + "turkish", + "typical", + "ugliest", + "ugly", + "ultimate", + "unable", + "unacceptable", + "unaware", + "uncertain", + "unchanged", + "uncomfortable", + "unconscious", + "underground", + "underlying", + "unemployed", + "uneven", + "unexpected", + "unfair", + "unfortunate", + "unhappy", + "uniform", + "uninterested", + "unique", + "united", + "universal", + "unknown", + "unlikely", + "unnecessary", + "unpleasant", + "unsightly", + "unusual", + "unwilling", + "upper", + "upset", + "uptight", + "urban", + "urgent", + "used", + "useful", + "useless", + "usual", + "vague", + "valid", + "valuable", + "variable", + "varied", + "various", + "varying", + "vast", + "verbal", + "vertical", + "very", + "victorian", + "victorious", + "video-taped", + "violent", + "visible", + "visiting", + "visual", + "vital", + "vivacious", + "vivid", + "vocational", + "voiceless", + "voluntary", + "vulnerable", + "wandering", + "warm", + "wasteful", + "watery", + "weak", + "wealthy", + "weary", + "wee", + "weekly", + "weird", + "welcome", + "well", + "well-known", + "welsh", + "western", + "wet", + "whispering", + "white", + "whole", + "wicked", + "wide", + "wide-eyed", + "widespread", + "wild", + "willing", + "wise", + "witty", + "wonderful", + "wooden", + "working", + "working-class", + "worldwide", + "worried", + "worrying", + "worthwhile", + "worthy", + "written", + "wrong", + "yellow", + "young", + "yummy", + "zany", + "zealous", +] +b = [ + "abiding", + "accelerating", + "accepting", + "accomplishing", + "achieving", + "acquiring", + "acteding", + "activating", + "adapting", + "adding", + "addressing", + "administering", + "admiring", + "admiting", + "adopting", + "advising", + "affording", + "agreeing", + "alerting", + "alighting", + "allowing", + "altereding", + "amusing", + "analyzing", + "announcing", + "annoying", + "answering", + "anticipating", + "apologizing", + "appearing", + "applauding", + "applieding", + "appointing", + "appraising", + "appreciating", + "approving", + "arbitrating", + "arguing", + "arising", + "arranging", + "arresting", + "arriving", + "ascertaining", + "asking", + "assembling", + "assessing", + "assisting", + "assuring", + "attaching", + "attacking", + "attaining", + "attempting", + "attending", + "attracting", + "auditeding", + "avoiding", + "awaking", + "backing", + "baking", + "balancing", + "baning", + "banging", + "baring", + "bating", + "bathing", + "battling", + "bing", + "beaming", + "bearing", + "beating", + "becoming", + "beging", + "begining", + "behaving", + "beholding", + "belonging", + "bending", + "beseting", + "beting", + "biding", + "binding", + "biting", + "bleaching", + "bleeding", + "blessing", + "blinding", + "blinking", + "bloting", + "blowing", + "blushing", + "boasting", + "boiling", + "bolting", + "bombing", + "booking", + "boring", + "borrowing", + "bouncing", + "bowing", + "boxing", + "braking", + "branching", + "breaking", + "breathing", + "breeding", + "briefing", + "bringing", + "broadcasting", + "bruising", + "brushing", + "bubbling", + "budgeting", + "building", + "bumping", + "burning", + "bursting", + "burying", + "busting", + "buying", + "buzing", + "calculating", + "calling", + "camping", + "caring", + "carrying", + "carving", + "casting", + "cataloging", + "catching", + "causing", + "challenging", + "changing", + "charging", + "charting", + "chasing", + "cheating", + "checking", + "cheering", + "chewing", + "choking", + "choosing", + "choping", + "claiming", + "claping", + "clarifying", + "classifying", + "cleaning", + "clearing", + "clinging", + "cliping", + "closing", + "clothing", + "coaching", + "coiling", + "collecting", + "coloring", + "combing", + "coming", + "commanding", + "communicating", + "comparing", + "competing", + "compiling", + "complaining", + "completing", + "composing", + "computing", + "conceiving", + "concentrating", + "conceptualizing", + "concerning", + "concluding", + "conducting", + "confessing", + "confronting", + "confusing", + "connecting", + "conserving", + "considering", + "consisting", + "consolidating", + "constructing", + "consulting", + "containing", + "continuing", + "contracting", + "controling", + "converting", + "coordinating", + "copying", + "correcting", + "correlating", + "costing", + "coughing", + "counseling", + "counting", + "covering", + "cracking", + "crashing", + "crawling", + "creating", + "creeping", + "critiquing", + "crossing", + "crushing", + "crying", + "curing", + "curling", + "curving", + "cuting", + "cycling", + "daming", + "damaging", + "dancing", + "daring", + "dealing", + "decaying", + "deceiving", + "deciding", + "decorating", + "defining", + "delaying", + "delegating", + "delighting", + "delivering", + "demonstrating", + "depending", + "describing", + "deserting", + "deserving", + "designing", + "destroying", + "detailing", + "detecting", + "determining", + "developing", + "devising", + "diagnosing", + "diging", + "directing", + "disagreing", + "disappearing", + "disapproving", + "disarming", + "discovering", + "disliking", + "dispensing", + "displaying", + "disproving", + "dissecting", + "distributing", + "diving", + "diverting", + "dividing", + "doing", + "doubling", + "doubting", + "drafting", + "draging", + "draining", + "dramatizing", + "drawing", + "dreaming", + "dressing", + "drinking", + "driping", + "driving", + "dropping", + "drowning", + "druming", + "drying", + "dusting", + "dwelling", + "earning", + "eating", + "editeding", + "educating", + "eliminating", + "embarrassing", + "employing", + "emptying", + "enacteding", + "encouraging", + "ending", + "enduring", + "enforcing", + "engineering", + "enhancing", + "enjoying", + "enlisting", + "ensuring", + "entering", + "entertaining", + "escaping", + "establishing", + "estimating", + "evaluating", + "examining", + "exceeding", + "exciting", + "excusing", + "executing", + "exercising", + "exhibiting", + "existing", + "expanding", + "expecting", + "expediting", + "experimenting", + "explaining", + "exploding", + "expressing", + "extending", + "extracting", + "facing", + "facilitating", + "fading", + "failing", + "fancying", + "fastening", + "faxing", + "fearing", + "feeding", + "feeling", + "fencing", + "fetching", + "fighting", + "filing", + "filling", + "filming", + "finalizing", + "financing", + "finding", + "firing", + "fiting", + "fixing", + "flaping", + "flashing", + "fleing", + "flinging", + "floating", + "flooding", + "flowing", + "flowering", + "flying", + "folding", + "following", + "fooling", + "forbiding", + "forcing", + "forecasting", + "foregoing", + "foreseing", + "foretelling", + "forgeting", + "forgiving", + "forming", + "formulating", + "forsaking", + "framing", + "freezing", + "frightening", + "frying", + "gathering", + "gazing", + "generating", + "geting", + "giving", + "glowing", + "gluing", + "going", + "governing", + "grabing", + "graduating", + "grating", + "greasing", + "greeting", + "grinning", + "grinding", + "griping", + "groaning", + "growing", + "guaranteeing", + "guarding", + "guessing", + "guiding", + "hammering", + "handing", + "handling", + "handwriting", + "hanging", + "happening", + "harassing", + "harming", + "hating", + "haunting", + "heading", + "healing", + "heaping", + "hearing", + "heating", + "helping", + "hiding", + "hitting", + "holding", + "hooking", + "hoping", + "hopping", + "hovering", + "hugging", + "hmuming", + "hunting", + "hurrying", + "hurting", + "hypothesizing", + "identifying", + "ignoring", + "illustrating", + "imagining", + "implementing", + "impressing", + "improving", + "improvising", + "including", + "increasing", + "inducing", + "influencing", + "informing", + "initiating", + "injecting", + "injuring", + "inlaying", + "innovating", + "inputing", + "inspecting", + "inspiring", + "installing", + "instituting", + "instructing", + "insuring", + "integrating", + "intending", + "intensifying", + "interesting", + "interfering", + "interlaying", + "interpreting", + "interrupting", + "interviewing", + "introducing", + "inventing", + "inventorying", + "investigating", + "inviting", + "irritating", + "itching", + "jailing", + "jamming", + "jogging", + "joining", + "joking", + "judging", + "juggling", + "jumping", + "justifying", + "keeping", + "kepting", + "kicking", + "killing", + "kissing", + "kneeling", + "kniting", + "knocking", + "knotting", + "knowing", + "labeling", + "landing", + "lasting", + "laughing", + "launching", + "laying", + "leading", + "leaning", + "leaping", + "learning", + "leaving", + "lecturing", + "leding", + "lending", + "leting", + "leveling", + "licensing", + "licking", + "lying", + "lifteding", + "lighting", + "lightening", + "liking", + "listing", + "listening", + "living", + "loading", + "locating", + "locking", + "loging", + "longing", + "looking", + "losing", + "loving", + "maintaining", + "making", + "maning", + "managing", + "manipulating", + "manufacturing", + "mapping", + "marching", + "marking", + "marketing", + "marrying", + "matching", + "mating", + "mattering", + "meaning", + "measuring", + "meddling", + "mediating", + "meeting", + "melting", + "melting", + "memorizing", + "mending", + "mentoring", + "milking", + "mining", + "misleading", + "missing", + "misspelling", + "mistaking", + "misunderstanding", + "mixing", + "moaning", + "modeling", + "modifying", + "monitoring", + "mooring", + "motivating", + "mourning", + "moving", + "mowing", + "muddling", + "muging", + "multiplying", + "murdering", + "nailing", + "naming", + "navigating", + "needing", + "negotiating", + "nesting", + "noding", + "nominating", + "normalizing", + "noting", + "noticing", + "numbering", + "obeying", + "objecting", + "observing", + "obtaining", + "occuring", + "offending", + "offering", + "officiating", + "opening", + "operating", + "ordering", + "organizing", + "orienteding", + "originating", + "overcoming", + "overdoing", + "overdrawing", + "overflowing", + "overhearing", + "overtaking", + "overthrowing", + "owing", + "owning", + "packing", + "paddling", + "painting", + "parking", + "parting", + "participating", + "passing", + "pasting", + "pating", + "pausing", + "paying", + "pecking", + "pedaling", + "peeling", + "peeping", + "perceiving", + "perfecting", + "performing", + "permiting", + "persuading", + "phoning", + "photographing", + "picking", + "piloting", + "pinching", + "pining", + "pinpointing", + "pioneering", + "placing", + "planing", + "planting", + "playing", + "pleading", + "pleasing", + "plugging", + "pointing", + "poking", + "polishing", + "poping", + "possessing", + "posting", + "pouring", + "practicing", + "praiseding", + "praying", + "preaching", + "preceding", + "predicting", + "prefering", + "preparing", + "prescribing", + "presenting", + "preserving", + "preseting", + "presiding", + "pressing", + "pretending", + "preventing", + "pricking", + "printing", + "processing", + "procuring", + "producing", + "professing", + "programing", + "progressing", + "projecting", + "promising", + "promoting", + "proofreading", + "proposing", + "protecting", + "proving", + "providing", + "publicizing", + "pulling", + "pumping", + "punching", + "puncturing", + "punishing", + "purchasing", + "pushing", + "puting", + "qualifying", + "questioning", + "queuing", + "quiting", + "racing", + "radiating", + "raining", + "raising", + "ranking", + "rating", + "reaching", + "reading", + "realigning", + "realizing", + "reasoning", + "receiving", + "recognizing", + "recommending", + "reconciling", + "recording", + "recruiting", + "reducing", + "referring", + "reflecting", + "refusing", + "regreting", + "regulating", + "rehabilitating", + "reigning", + "reinforcing", + "rejecting", + "rejoicing", + "relating", + "relaxing", + "releasing", + "relying", + "remaining", + "remembering", + "reminding", + "removing", + "rendering", + "reorganizing", + "repairing", + "repeating", + "replacing", + "replying", + "reporting", + "representing", + "reproducing", + "requesting", + "rescuing", + "researching", + "resolving", + "responding", + "restoreding", + "restructuring", + "retiring", + "retrieving", + "returning", + "reviewing", + "revising", + "rhyming", + "riding", + "riding", + "ringing", + "rinsing", + "rising", + "risking", + "robing", + "rocking", + "rolling", + "roting", + "rubing", + "ruining", + "ruling", + "runing", + "rushing", + "sacking", + "sailing", + "satisfying", + "saving", + "sawing", + "saying", + "scaring", + "scattering", + "scheduling", + "scolding", + "scorching", + "scraping", + "scratching", + "screaming", + "screwing", + "scribbling", + "scrubing", + "sealing", + "searching", + "securing", + "seing", + "seeking", + "selecting", + "selling", + "sending", + "sensing", + "separating", + "serving", + "servicing", + "seting", + "settling", + "sewing", + "shading", + "shaking", + "shaping", + "sharing", + "shaving", + "shearing", + "sheding", + "sheltering", + "shining", + "shivering", + "shocking", + "shoing", + "shooting", + "shoping", + "showing", + "shrinking", + "shruging", + "shuting", + "sighing", + "signing", + "signaling", + "simplifying", + "sining", + "singing", + "sinking", + "siping", + "siting", + "sketching", + "skiing", + "skiping", + "slaping", + "slaying", + "sleeping", + "sliding", + "slinging", + "slinking", + "sliping", + "sliting", + "slowing", + "smashing", + "smelling", + "smiling", + "smiting", + "smoking", + "snatching", + "sneaking", + "sneezing", + "sniffing", + "snoring", + "snowing", + "soaking", + "solving", + "soothing", + "soothsaying", + "sorting", + "sounding", + "sowing", + "sparing", + "sparking", + "sparkling", + "speaking", + "specifying", + "speeding", + "spelling", + "spending", + "spilling", + "spining", + "spiting", + "spliting", + "spoiling", + "spoting", + "spraying", + "spreading", + "springing", + "sprouting", + "squashing", + "squeaking", + "squealing", + "squeezing", + "staining", + "stamping", + "standing", + "staring", + "starting", + "staying", + "stealing", + "steering", + "stepping", + "sticking", + "stimulating", + "stinging", + "stinking", + "stirring", + "stitching", + "stoping", + "storing", + "straping", + "streamlining", + "strengthening", + "stretching", + "striding", + "striking", + "stringing", + "stripping", + "striving", + "stroking", + "structuring", + "studying", + "stuffing", + "subleting", + "subtracting", + "succeeding", + "sucking", + "suffering", + "suggesting", + "suiting", + "summarizing", + "supervising", + "supplying", + "supporting", + "supposing", + "surprising", + "surrounding", + "suspecting", + "suspending", + "swearing", + "sweating", + "sweeping", + "swelling", + "swimming", + "swinging", + "switching", + "symbolizing", + "synthesizing", + "systemizing", + "tabulating", + "taking", + "talking", + "taming", + "taping", + "targeting", + "tasting", + "teaching", + "tearing", + "teasing", + "telephoning", + "telling", + "tempting", + "terrifying", + "testing", + "thanking", + "thawing", + "thinking", + "thriving", + "throwing", + "thrusting", + "ticking", + "tickling", + "tying", + "timing", + "tiping", + "tiring", + "touching", + "touring", + "towing", + "tracing", + "trading", + "training", + "transcribing", + "transfering", + "transforming", + "translating", + "transporting", + "traping", + "traveling", + "treading", + "treating", + "trembling", + "tricking", + "triping", + "troting", + "troubling", + "troubleshooting", + "trusting", + "trying", + "tuging", + "tumbling", + "turning", + "tutoring", + "twisting", + "typing", + "undergoing", + "understanding", + "undertaking", + "undressing", + "unfastening", + "unifying", + "uniting", + "unlocking", + "unpacking", + "untidying", + "updating", + "upgrading", + "upholding", + "upseting", + "using", + "utilizing", + "vanishing", + "verbalizing", + "verifying", + "vexing", + "visiting", + "wailing", + "waiting", + "waking", + "walking", + "wandering", + "wanting", + "warming", + "warning", + "washing", + "wasting", + "watching", + "watering", + "waving", + "wearing", + "weaving", + "wedding", + "weeping", + "weighing", + "welcoming", + "wending", + "weting", + "whining", + "whiping", + "whirling", + "whispering", + "whistling", + "wining", + "winding", + "winking", + "wiping", + "wishing", + "withdrawing", + "withholding", + "withstanding", + "wobbling", + "wondering", + "working", + "worrying", + "wrapping", + "wrecking", + "wrestling", + "wriggling", + "wringing", + "writing", + "x-raying", + "yawning", + "yelling", + "zipping", + "zooming", +] diff --git a/BigGAN_PyTorch/config_files/COCO_Stuff/BigGAN/unconditional_biggan_res128.json b/BigGAN_PyTorch/config_files/COCO_Stuff/BigGAN/unconditional_biggan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..c7fe8c7ce0cff6d3eaf84cf882bffb8b403a5c81 --- /dev/null +++ b/BigGAN_PyTorch/config_files/COCO_Stuff/BigGAN/unconditional_biggan_res128.json @@ -0,0 +1,44 @@ +{ + "experiment_name": "unconditional_biggan_class_cond_res128_COCO", + "which_dataset": "coco", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 4, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 1, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 2, + + "constant_conditioning": true, + "class_cond": true, + "hier": true, + "resolution": 128, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "G_shared": true, + "batch_size": 64, + "D_lr": 4e-4, + "G_lr": 1e-4, + "G_ch": 48, + "D_ch": 48, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/COCO_Stuff/BigGAN/unconditional_biggan_res256.json b/BigGAN_PyTorch/config_files/COCO_Stuff/BigGAN/unconditional_biggan_res256.json new file mode 100644 index 0000000000000000000000000000000000000000..f5df1649a4151412a6e0d5645f2db9b0bbdc475d --- /dev/null +++ b/BigGAN_PyTorch/config_files/COCO_Stuff/BigGAN/unconditional_biggan_res256.json @@ -0,0 +1,44 @@ +{ + "experiment_name": "unconditional_biggan_class_cond_res256_COCO", + "which_dataset": "coco", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 2, + "n_gpus_per_node": 8, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 1, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 2, + + "constant_conditioning": true, + "class_cond": true, + "hier": true, + "resolution": 256, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "G_shared": true, + "batch_size": 16, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 48, + "D_ch": 48, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/COCO_Stuff/IC-GAN/icgan_res128_ddp.json b/BigGAN_PyTorch/config_files/COCO_Stuff/IC-GAN/icgan_res128_ddp.json new file mode 100644 index 0000000000000000000000000000000000000000..5537e5475a9587ce24505d44fc8d25aaf7d00f29 --- /dev/null +++ b/BigGAN_PyTorch/config_files/COCO_Stuff/IC-GAN/icgan_res128_ddp.json @@ -0,0 +1,51 @@ +{ + "experiment_name": "icgan_res128_COCO", + "which_dataset": "coco", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 4, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + "feature_augmentation": true, + + "test_every": 5, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": false, + "instance_cond": true, + "hier": true, + "resolution": 128, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 5, + "feature_extractor": "selfsupervised", + + "batch_size": 64, + "D_lr": 4e-4, + "G_lr": 1e-4, + "G_ch": 48, + "D_ch": 48, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/COCO_Stuff/IC-GAN/icgan_res256_ddp.json b/BigGAN_PyTorch/config_files/COCO_Stuff/IC-GAN/icgan_res256_ddp.json new file mode 100644 index 0000000000000000000000000000000000000000..304e2e9fb9e81c9e19a475d3304e0cad776cc2b3 --- /dev/null +++ b/BigGAN_PyTorch/config_files/COCO_Stuff/IC-GAN/icgan_res256_ddp.json @@ -0,0 +1,51 @@ +{ + "experiment_name": "icgan_res256_COCO", + "which_dataset": "coco", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 2, + "n_gpus_per_node": 8, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + "feature_augmentation": true, + + "test_every": 5, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": false, + "instance_cond": true, + "hier": true, + "resolution": 256, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 5, + "feature_extractor": "selfsupervised", + + "batch_size": 16, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 48, + "D_ch": 48, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res128.json b/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..22fdaad0837861e80b79e6761148e1b1687b370f --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res128.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "biggan_imagenet_lt_class_cond_res128", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 2, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 10, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 2, + + "class_cond": true, + "hier": true, + "resolution": 128, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "G_shared": true, + "batch_size": 64, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "longtail": true, + "longtail_gen": true, + "use_balanced_sampler": false, + "custom_distrib_gen": false, + "longtail_temperature": 1, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res256.json b/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res256.json new file mode 100644 index 0000000000000000000000000000000000000000..7672be294089d0fb3fe37b1e2a027a39b94acc33 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res256.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "biggan_imagenet_lt_class_cond_res256", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 8, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 10, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 2, + + "class_cond": true, + "hier": true, + "resolution": 256, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "G_shared": true, + "batch_size": 16, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "longtail": true, + "longtail_gen": true, + "use_balanced_sampler": false, + "custom_distrib_gen": false, + "longtail_temperature": 1, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res64.json b/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res64.json new file mode 100644 index 0000000000000000000000000000000000000000..c94f12bd42b18fcf199b78a056a7820bcb0588b7 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet-LT/BigGAN/biggan_res64.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "biggan_imagenet_lt_class_cond_res64", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 1, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 1, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": true, + "hier": true, + "resolution": 64, + "G_attn": "32", + "D_attn": "32", + "shared_dim": 128, + "G_shared": true, + "batch_size": 128, + "D_lr": 1e-3, + "G_lr": 1e-5, + "G_ch": 64, + "D_ch": 64, + + "longtail": true, + "longtail_gen": true, + "use_balanced_sampler": false, + "custom_distrib_gen": false, + "longtail_temperature": 1, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res128.json b/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..74783e5c1cd97bb01988cfd6137f64257cc64694 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res128.json @@ -0,0 +1,56 @@ +{ + "experiment_name": "cc_icgan_biggan_imagenet_res128", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 2, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 10, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 2, + + "class_cond": true, + "instance_cond": true, + "which_knn_balance": "instance_balance", + "hier": true, + "resolution": 128, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 5, + "feature_extractor": "classification", + + "batch_size": 64, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "longtail": true, + "longtail_gen": true, + "use_balanced_sampler": false, + "custom_distrib_gen": false, + "longtail_temperature": 1, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res256.json b/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res256.json new file mode 100644 index 0000000000000000000000000000000000000000..56fe164f987804e5516d049133cdd3520e662d81 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res256.json @@ -0,0 +1,56 @@ +{ + "experiment_name": "cc_icgan_biggan_imagenet_res256", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 8, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 10, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 2, + + "class_cond": true, + "instance_cond": true, + "which_knn_balance": "instance_balance", + "hier": true, + "resolution": 256, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 5, + "feature_extractor": "classification", + + "batch_size": 16, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "longtail": true, + "longtail_gen": true, + "use_balanced_sampler": false, + "custom_distrib_gen": false, + "longtail_temperature": 1, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res64.json b/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res64.json new file mode 100644 index 0000000000000000000000000000000000000000..eaa3de10c8103723ffc0982ced04a77cbea3ae14 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet-LT/cc_IC-GAN/cc_icgan_res64.json @@ -0,0 +1,56 @@ +{ + "experiment_name": "cc_icgan_biggan_imagenet_res64", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 1, + "hflips": true, + "DA": true, + "DiffAugment": "translation", + + "test_every": 1, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": true, + "instance_cond": true, + "which_knn_balance": "instance_balance", + "hier": true, + "resolution": 64, + "G_attn": "32", + "D_attn": "32", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 5, + "feature_extractor": "classification", + + "batch_size": 128, + "D_lr": 1e-3, + "G_lr": 1e-5, + "G_ch": 64, + "D_ch": 64, + + "longtail": true, + "longtail_gen": true, + "use_balanced_sampler": false, + "custom_distrib_gen": false, + "longtail_temperature": 1, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res128.json b/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..b41a04d505ea33a8b875baf114ab7253c8036a9e --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res128.json @@ -0,0 +1,40 @@ +{ + "experiment_name": "biggan_imagenet_res128", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 4, + "n_gpus_per_node": 8, + "hflips": true, + + "test_every": 5, + "save_every": 2, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": true, + "hier": true, + "resolution": 128, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "G_shared": true, + "batch_size": 64, + "D_lr": 4e-4, + "G_lr": 1e-4, + "G_ch": 96, + "D_ch": 96, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res256_half_cap.json b/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res256_half_cap.json new file mode 100644 index 0000000000000000000000000000000000000000..b7abbb5abaac6393e74c0d74027f4a5f3bafe515 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res256_half_cap.json @@ -0,0 +1,40 @@ +{ + "experiment_name": "biggan_class_cond_res256_half_cap_noflips", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 4, + "n_gpus_per_node": 8, + "hflips": false, + + "test_every": 5, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 4, + "num_D_accumulations": 4, + "num_D_steps": 1, + + "class_cond": true, + "hier": true, + "resolution": 256, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "G_shared": true, + "batch_size": 16, + "D_lr": 4e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res64.json b/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res64.json new file mode 100644 index 0000000000000000000000000000000000000000..e9b61526be488d3e95440847afdba7faa9d436dd --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/BigGAN/biggan_res64.json @@ -0,0 +1,40 @@ +{ + "experiment_name": "biggan_imagenet_res64", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 1, + "hflips": true, + + "test_every": 1, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": true, + "hier": true, + "resolution": 64, + "G_attn": "32", + "D_attn": "32", + "shared_dim": 128, + "G_shared": true, + "batch_size": 256, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet/IC-GAN/icgan_res128.json b/BigGAN_PyTorch/config_files/ImageNet/IC-GAN/icgan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..f1f289cb4d9c5ec9dea9a73e77c4cbe39812be67 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/IC-GAN/icgan_res128.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "icgan_biggan_imagenet_res128", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 4, + "n_gpus_per_node": 8, + "hflips": true, + "feature_augmentation": true, + + "test_every": 5, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + 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b/BigGAN_PyTorch/config_files/ImageNet/IC-GAN/icgan_res256_halfcap.json @@ -0,0 +1,47 @@ +{ + "experiment_name": "icgan_biggan_imagenet_res256_halfcap", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 4, + "n_gpus_per_node": 8, + "hflips": true, + "feature_augmentation": true, + + "test_every": 5, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 4, + "num_D_accumulations": 4, + "num_D_steps": 2, + + "class_cond": false, + "instance_cond": true, + "hier": true, + "resolution": 256, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 50, + "feature_extractor": "selfsupervised", + + "batch_size": 16, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "load_weights": "" +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet/IC-GAN/icgan_res64.json b/BigGAN_PyTorch/config_files/ImageNet/IC-GAN/icgan_res64.json new file mode 100644 index 0000000000000000000000000000000000000000..79079efa3bc902c8e87fcc74fe5511d3bc64078b --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/IC-GAN/icgan_res64.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "icgan_biggan_imagenet_res64", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 1, + "hflips": true, + "feature_augmentation": true, + + "test_every": 1, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": false, + "instance_cond": true, + "hier": true, + "resolution": 64, + "G_attn": "32", + "D_attn": "32", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 50, + "feature_extractor": "selfsupervised", + + "batch_size": 256, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet/cc_IC-GAN/cc_icgan_res128.json b/BigGAN_PyTorch/config_files/ImageNet/cc_IC-GAN/cc_icgan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..b1b04467e7c4ce40bf84e60cbf33b5acf2cb3971 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/cc_IC-GAN/cc_icgan_res128.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "cc_icgan_biggan_imagenet_res128", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 4, + "n_gpus_per_node": 8, + "hflips": true, + "feature_augmentation": true, + + "test_every": 5, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + 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0000000000000000000000000000000000000000..912f93c0ca44f822575c421b338dad9cb55bf8bd --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/cc_IC-GAN/cc_icgan_res256_halfcap.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "cc_icgan_biggan_imagenet_res256_halfcap", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 4, + "n_gpus_per_node": 8, + "hflips": true, + "feature_augmentation": true, + + "test_every": 5, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 4, + "num_D_accumulations": 4, + "num_D_steps": 2, + + "class_cond": true, + "instance_cond": true, + "hier": true, + "resolution": 256, + "G_attn": "64", + "D_attn": "64", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 50, + "feature_extractor": "classification", + + "batch_size": 16, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/config_files/ImageNet/cc_IC-GAN/cc_icgan_res64.json b/BigGAN_PyTorch/config_files/ImageNet/cc_IC-GAN/cc_icgan_res64.json new file mode 100644 index 0000000000000000000000000000000000000000..878383c4543361e62a3ffe5dec76e2b19d8db678 --- /dev/null +++ b/BigGAN_PyTorch/config_files/ImageNet/cc_IC-GAN/cc_icgan_res64.json @@ -0,0 +1,48 @@ +{ + "experiment_name": "cc_icgan_biggan_imagenet_res64", + "run_setup": "local_debug", + "deterministic_run": true, + "num_workers": 10, + + "ddp_train": true, + "n_nodes": 1, + "n_gpus_per_node": 1, + "hflips": true, + "feature_augmentation": true, + + "test_every": 1, + "save_every": 1, + "num_epochs": 3000, + "es_patience": 50, + "shuffle": true, + + "G_eval_mode": true, + "ema": true, + "use_ema": true, + "num_G_accumulations": 1, + "num_D_accumulations": 1, + "num_D_steps": 1, + + "class_cond": true, + "instance_cond": true, + "hier": true, + "resolution": 64, + "G_attn": "32", + "D_attn": "32", + "shared_dim": 128, + "shared_dim_feat": 512, + "G_shared": true, + "G_shared_feat": true, + + "k_nn": 50, + "feature_extractor": "classification", + + "batch_size": 256, + "D_lr": 1e-4, + "G_lr": 1e-4, + "G_ch": 64, + "D_ch": 64, + + "load_weights": "" + +} \ No newline at end of file diff --git a/BigGAN_PyTorch/diffaugment_utils.py b/BigGAN_PyTorch/diffaugment_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d24075cc3959bb5dcf9dac0e185af346f97c0c54 --- /dev/null +++ b/BigGAN_PyTorch/diffaugment_utils.py @@ -0,0 +1,119 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2020, Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# * Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import torch +import torch.nn.functional as F + + +def DiffAugment(x, policy="", channels_first=True): + if policy: + if not channels_first: + x = x.permute(0, 3, 1, 2) + for p in policy.split(","): + for f in AUGMENT_FNS[p]: + x = f(x) + if not channels_first: + x = x.permute(0, 2, 3, 1) + x = x.contiguous() + return x + + +def rand_brightness(x): + x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5) + return x + + +def rand_saturation(x): + x_mean = x.mean(dim=1, keepdim=True) + x = (x - x_mean) * ( + torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2 + ) + x_mean + return x + + +def rand_contrast(x): + x_mean = x.mean(dim=[1, 2, 3], keepdim=True) + x = (x - x_mean) * ( + torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5 + ) + x_mean + return x + + +def rand_translation(x, ratio=0.125): + shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) + translation_x = torch.randint( + -shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device + ) + translation_y = torch.randint( + -shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device + ) + grid_batch, grid_x, grid_y = torch.meshgrid( + torch.arange(x.size(0), dtype=torch.long, device=x.device), + torch.arange(x.size(2), dtype=torch.long, device=x.device), + torch.arange(x.size(3), dtype=torch.long, device=x.device), + ) + grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1) + grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1) + x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0]) + x = ( + x_pad.permute(0, 2, 3, 1) + .contiguous()[grid_batch, grid_x, grid_y] + .permute(0, 3, 1, 2) + ) + return x + + +def rand_cutout(x, ratio=0.5): + cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) + offset_x = torch.randint( + 0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device + ) + offset_y = torch.randint( + 0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device + ) + grid_batch, grid_x, grid_y = torch.meshgrid( + torch.arange(x.size(0), dtype=torch.long, device=x.device), + torch.arange(cutout_size[0], dtype=torch.long, device=x.device), + torch.arange(cutout_size[1], dtype=torch.long, device=x.device), + ) + grid_x = torch.clamp( + grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1 + ) + grid_y = torch.clamp( + grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1 + ) + mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device) + mask[grid_batch, grid_x, grid_y] = 0 + x = x * mask.unsqueeze(1) + return x + + +AUGMENT_FNS = { + "color": [rand_brightness, rand_saturation, rand_contrast], + "translation": [rand_translation], + "cutout": [rand_cutout], +} diff --git a/BigGAN_PyTorch/imagenet_lt/ImageNet_LT_train.txt b/BigGAN_PyTorch/imagenet_lt/ImageNet_LT_train.txt new file mode 100644 index 0000000000000000000000000000000000000000..a063f095fcd475db4cf8b0658c4b73955535bbf9 --- /dev/null +++ b/BigGAN_PyTorch/imagenet_lt/ImageNet_LT_train.txt @@ -0,0 +1,115846 @@ +train/n01440764/n01440764_190.JPEG 0 +train/n01440764/n01440764_13797.JPEG 0 +train/n01440764/n01440764_5879.JPEG 0 +train/n01440764/n01440764_11570.JPEG 0 +train/n01440764/n01440764_1514.JPEG 0 +train/n01440764/n01440764_12523.JPEG 0 +train/n01440764/n01440764_3011.JPEG 0 +train/n01440764/n01440764_1869.JPEG 0 +train/n01440764/n01440764_752.JPEG 0 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reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +""" Layers + This file contains various layers for the BigGAN models. +""" +import os +import numpy as np +import torch +import torch.nn as nn +from torch.nn import init +import torch.optim as optim +import torch.nn.functional as F +from torch.nn import Parameter as P + +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +from BigGAN_PyTorch.sync_batchnorm import SynchronizedBatchNorm2d as SyncBN2d + + +# Projection of x onto y +def proj(x, y): + return torch.mm(y, x.t()) * y / torch.mm(y, y.t()) + + +# Orthogonalize x wrt list of vectors ys +def gram_schmidt(x, ys): + for y in ys: + x = x - proj(x, y) + return x + + +# Apply num_itrs steps of the power method to estimate top N singular values. +def power_iteration(W, u_, update=True, eps=1e-12): + # Lists holding singular vectors and values + us, vs, svs = [], [], [] + for i, u in enumerate(u_): + # Run one step of the power iteration + with torch.no_grad(): + v = torch.matmul(u, W) + # Run Gram-Schmidt to subtract components of all other singular vectors + v = F.normalize(gram_schmidt(v, vs), eps=eps) + # Add to the list + vs += [v] + # Update the other singular vector + u = torch.matmul(v, W.t()) + # Run Gram-Schmidt to subtract components of all other singular vectors + u = F.normalize(gram_schmidt(u, us), eps=eps) + # Add to the list + us += [u] + if update: + u_[i][:] = u + # Compute this singular value and add it to the list + svs += [torch.squeeze(torch.matmul(torch.matmul(v, W.t()), u.t()))] + # svs += [torch.sum(F.linear(u, W.transpose(0, 1)) * v)] + return svs, us, vs + + +# Convenience passthrough function +class identity(nn.Module): + def forward(self, input): + return input + + +# Spectral normalization base class +class SN(object): + def __init__(self, num_svs, num_itrs, num_outputs, transpose=False, eps=1e-12): + # Number of power iterations per step + self.num_itrs = num_itrs + # Number of singular values + self.num_svs = num_svs + # Transposed? + self.transpose = transpose + # Epsilon value for avoiding divide-by-0 + self.eps = eps + # Register a singular vector for each sv + for i in range(self.num_svs): + self.register_buffer("u%d" % i, torch.randn(1, num_outputs)) + self.register_buffer("sv%d" % i, torch.ones(1)) + + # Singular vectors (u side) + @property + def u(self): + return [getattr(self, "u%d" % i) for i in range(self.num_svs)] + + # Singular values; + # note that these buffers are just for logging and are not used in training. + @property + def sv(self): + return [getattr(self, "sv%d" % i) for i in range(self.num_svs)] + + # Compute the spectrally-normalized weight + def W_(self): + W_mat = self.weight.view(self.weight.size(0), -1) + if self.transpose: + W_mat = W_mat.t() + # Apply num_itrs power iterations + for _ in range(self.num_itrs): + svs, us, vs = power_iteration( + W_mat, self.u, update=self.training, eps=self.eps + ) + # Update the svs + if self.training: + with torch.no_grad(): # Make sure to do this in a no_grad() context or you'll get memory leaks! + for i, sv in enumerate(svs): + self.sv[i][:] = sv + return self.weight / svs[0] + + +# 2D Conv layer with spectral norm +class SNConv2d(nn.Conv2d, SN): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + num_svs=1, + num_itrs=1, + eps=1e-12, + ): + nn.Conv2d.__init__( + self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + ) + SN.__init__(self, num_svs, num_itrs, out_channels, eps=eps) + + def forward(self, x): + return F.conv2d( + x, + self.W_(), + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + ) + + +# Linear layer with spectral norm +class SNLinear(nn.Linear, SN): + def __init__( + self, in_features, out_features, bias=True, num_svs=1, num_itrs=1, eps=1e-12 + ): + nn.Linear.__init__(self, in_features, out_features, bias) + SN.__init__(self, num_svs, num_itrs, out_features, eps=eps) + + def forward(self, x): + return F.linear(x, self.W_(), self.bias) + + +# Embedding layer with spectral norm +# We use num_embeddings as the dim instead of embedding_dim here +# for convenience sake +class SNEmbedding(nn.Embedding, SN): + def __init__( + self, + num_embeddings, + embedding_dim, + padding_idx=None, + max_norm=None, + norm_type=2, + scale_grad_by_freq=False, + sparse=False, + _weight=None, + num_svs=1, + num_itrs=1, + eps=1e-12, + ): + nn.Embedding.__init__( + self, + num_embeddings, + embedding_dim, + padding_idx, + max_norm, + norm_type, + scale_grad_by_freq, + sparse, + _weight, + ) + SN.__init__(self, num_svs, num_itrs, num_embeddings, eps=eps) + + def forward(self, x): + return F.embedding(x, self.W_()) + + +# A non-local block as used in SA-GAN +# Note that the implementation as described in the paper is largely incorrect; +# refer to the released code for the actual implementation. +class Attention(nn.Module): + def __init__(self, ch, which_conv=SNConv2d, name="attention"): + super(Attention, self).__init__() + # Channel multiplier + self.ch = ch + self.which_conv = which_conv + self.theta = self.which_conv( + self.ch, self.ch // 8, kernel_size=1, padding=0, bias=False + ) + self.phi = self.which_conv( + self.ch, self.ch // 8, kernel_size=1, padding=0, bias=False + ) + self.g = self.which_conv( + self.ch, self.ch // 2, kernel_size=1, padding=0, bias=False + ) + self.o = self.which_conv( + self.ch // 2, self.ch, kernel_size=1, padding=0, bias=False + ) + # Learnable gain parameter + self.gamma = P(torch.tensor(0.0), requires_grad=True) + + def forward(self, x, y=None): + # Apply convs + theta = self.theta(x) + phi = F.max_pool2d(self.phi(x), [2, 2]) + g = F.max_pool2d(self.g(x), [2, 2]) + # Perform reshapes + theta = theta.view(-1, self.ch // 8, x.shape[2] * x.shape[3]) + phi = phi.view(-1, self.ch // 8, x.shape[2] * x.shape[3] // 4) + g = g.view(-1, self.ch // 2, x.shape[2] * x.shape[3] // 4) + # Matmul and softmax to get attention maps + beta = F.softmax(torch.bmm(theta.transpose(1, 2), phi), -1) + # Attention map times g path + o = self.o( + torch.bmm(g, beta.transpose(1, 2)).view( + -1, self.ch // 2, x.shape[2], x.shape[3] + ) + ) + return self.gamma * o + x + + +# Fused batchnorm op +def fused_bn(x, mean, var, gain=None, bias=None, eps=1e-5): + # Apply scale and shift--if gain and bias are provided, fuse them here + # Prepare scale + scale = torch.rsqrt(var + eps) + # If a gain is provided, use it + if gain is not None: + scale = scale * gain + # Prepare shift + shift = mean * scale + # If bias is provided, use it + if bias is not None: + shift = shift - bias + return x * scale - shift + # return ((x - mean) / ((var + eps) ** 0.5)) * gain + bias # The unfused way. + + +# Manual BN +# Calculate means and variances using mean-of-squares minus mean-squared +def manual_bn(x, gain=None, bias=None, return_mean_var=False, eps=1e-5): + # Cast x to float32 if necessary + float_x = x.float() + # Calculate expected value of x (m) and expected value of x**2 (m2) + # Mean of x + m = torch.mean(float_x, [0, 2, 3], keepdim=True) + # Mean of x squared + m2 = torch.mean(float_x ** 2, [0, 2, 3], keepdim=True) + # Calculate variance as mean of squared minus mean squared. + var = m2 - m ** 2 + # Cast back to float 16 if necessary + var = var.type(x.type()) + m = m.type(x.type()) + # Return mean and variance for updating stored mean/var if requested + if return_mean_var: + return fused_bn(x, m, var, gain, bias, eps), m.squeeze(), var.squeeze() + else: + return fused_bn(x, m, var, gain, bias, eps) + + +# My batchnorm, supports standing stats +class myBN(nn.Module): + def __init__(self, num_channels, eps=1e-5, momentum=0.1): + super(myBN, self).__init__() + # momentum for updating running stats + self.momentum = momentum + # epsilon to avoid dividing by 0 + self.eps = eps + # Momentum + self.momentum = momentum + # Register buffers + self.register_buffer("stored_mean", torch.zeros(num_channels)) + self.register_buffer("stored_var", torch.ones(num_channels)) + self.register_buffer("accumulation_counter", torch.zeros(1)) + # Accumulate running means and vars + self.accumulate_standing = False + + # reset standing stats + def reset_stats(self): + self.stored_mean[:] = 0 + self.stored_var[:] = 0 + self.accumulation_counter[:] = 0 + + def forward(self, x, gain, bias): + if self.training: + out, mean, var = manual_bn( + x, gain, bias, return_mean_var=True, eps=self.eps + ) + # If accumulating standing stats, increment them + if self.accumulate_standing: + self.stored_mean[:] = self.stored_mean + mean.data + self.stored_var[:] = self.stored_var + var.data + self.accumulation_counter += 1.0 + # If not accumulating standing stats, take running averages + else: + self.stored_mean[:] = ( + self.stored_mean * (1 - self.momentum) + mean * self.momentum + ) + self.stored_var[:] = ( + self.stored_var * (1 - self.momentum) + var * self.momentum + ) + return out + # If not in training mode, use the stored statistics + else: + mean = self.stored_mean.view(1, -1, 1, 1) + var = self.stored_var.view(1, -1, 1, 1) + # If using standing stats, divide them by the accumulation counter + if self.accumulate_standing: + mean = mean / self.accumulation_counter + var = var / self.accumulation_counter + return fused_bn(x, mean, var, gain, bias, self.eps) + + +# Simple function to handle groupnorm norm stylization +def groupnorm(x, norm_style): + # If number of channels specified in norm_style: + if "ch" in norm_style: + ch = int(norm_style.split("_")[-1]) + groups = max(int(x.shape[1]) // ch, 1) + # If number of groups specified in norm style + elif "grp" in norm_style: + groups = int(norm_style.split("_")[-1]) + # If neither, default to groups = 16 + else: + groups = 16 + return F.group_norm(x, groups) + + +# Class-conditional bn +# output size is the number of channels, input size is for the linear layers +# Andy's Note: this class feels messy but I'm not really sure how to clean it up +# Suggestions welcome! (By which I mean, refactor this and make a pull request +# if you want to make this more readable/usable). +class ccbn(nn.Module): + def __init__( + self, + output_size, + input_size, + which_linear, + eps=1e-5, + momentum=0.1, + cross_replica=False, + mybn=False, + norm_style="bn", + ): + super(ccbn, self).__init__() + self.output_size, self.input_size = output_size, input_size + # Prepare gain and bias layers + self.gain = which_linear(input_size, output_size) + self.bias = which_linear(input_size, output_size) + # epsilon to avoid dividing by 0 + self.eps = eps + # Momentum + self.momentum = momentum + # Use cross-replica batchnorm? + self.cross_replica = cross_replica + # Use my batchnorm? + self.mybn = mybn + # Norm style? + self.norm_style = norm_style + + if self.cross_replica: + # self.bn = SyncBN2d(output_size, eps=self.eps, momentum=self.momentum, affine=False) + self.bn = nn.BatchNorm2d( + output_size, eps=self.eps, momentum=self.momentum, affine=False + ) + elif self.mybn: + self.bn = myBN(output_size, self.eps, self.momentum) + elif self.norm_style in ["bn", "in"]: + self.register_buffer("stored_mean", torch.zeros(output_size)) + self.register_buffer("stored_var", torch.ones(output_size)) + + def forward(self, x, y): + # Calculate class-conditional gains and biases + gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1) + bias = self.bias(y).view(y.size(0), -1, 1, 1) + + # If using my batchnorm + if self.cross_replica: + out = self.bn(x) + out = out * gain + bias + return out + elif self.mybn: + return self.bn(x, gain=gain, bias=bias) + else: + if self.norm_style == "bn": + out = F.batch_norm( + x, + self.stored_mean, + self.stored_var, + None, + None, + self.training, + 0.1, + self.eps, + ) + elif self.norm_style == "in": + out = F.instance_norm( + x, + self.stored_mean, + self.stored_var, + None, + None, + self.training, + 0.1, + self.eps, + ) + elif self.norm_style == "gn": + out = groupnorm(x, self.normstyle) + elif self.norm_style == "nonorm": + out = x + return out * gain + bias + + def extra_repr(self): + s = "out: {output_size}, in: {input_size}," + s += " cross_replica={cross_replica}" + return s.format(**self.__dict__) + + +# Normal, non-class-conditional BN +class bn(nn.Module): + def __init__( + self, + output_size, + eps=1e-5, + momentum=0.1, + cross_replica=False, + mybn=False, + **kwargs + ): + super(bn, self).__init__() + self.output_size = output_size + + # epsilon to avoid dividing by 0 + self.eps = eps + # Momentum + self.momentum = momentum + # Use cross-replica batchnorm? + self.cross_replica = cross_replica + # Use my batchnorm? + self.mybn = mybn + + if self.cross_replica: + # self.bn = SyncBN2d(output_size, eps=self.eps, momentum=self.momentum, affine=False) + self.bn = nn.BatchNorm2d( + output_size, eps=self.eps, momentum=self.momentum, affine=True + ) + elif mybn: + # Prepare gain and bias layers + self.bn = myBN(output_size, self.eps, self.momentum) + # Register buffers if neither of the above + else: + self.register_buffer("stored_mean", torch.zeros(output_size)) + self.register_buffer("stored_var", torch.ones(output_size)) + + if not self.cross_replica: + self.gain = P(torch.ones(output_size), requires_grad=True) + self.bias = P(torch.zeros(output_size), requires_grad=True) + + def forward(self, x, y=None): + if self.cross_replica: + out = self.bn(x) + return out + elif self.mybn: + gain = self.gain.view(1, -1, 1, 1) + bias = self.bias.view(1, -1, 1, 1) + return self.bn(x, gain=gain, bias=bias) + else: + return F.batch_norm( + x, + self.stored_mean, + self.stored_var, + self.gain, + self.bias, + self.training, + self.momentum, + self.eps, + ) + + +# Generator blocks +# Note that this class assumes the kernel size and padding (and any other +# settings) have been selected in the main generator module and passed in +# through the which_conv arg. Similar rules apply with which_bn (the input +# size [which is actually the number of channels of the conditional info] must +# be preselected) +class GBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + which_conv=nn.Conv2d, + which_bn=bn, + activation=None, + upsample=None, + ): + super(GBlock, self).__init__() + + self.in_channels, self.out_channels = in_channels, out_channels + self.which_conv, self.which_bn = which_conv, which_bn + self.activation = activation + self.upsample = upsample + # Conv layers + self.conv1 = self.which_conv(self.in_channels, self.out_channels) + self.conv2 = self.which_conv(self.out_channels, self.out_channels) + self.learnable_sc = in_channels != out_channels or upsample + if self.learnable_sc: + self.conv_sc = self.which_conv( + in_channels, out_channels, kernel_size=1, padding=0 + ) + # Batchnorm layers + self.bn1 = self.which_bn(in_channels) + self.bn2 = self.which_bn(out_channels) + # upsample layers + self.upsample = upsample + + def forward(self, x, y): + h = self.activation(self.bn1(x, y)) + if self.upsample: + h = self.upsample(h) + x = self.upsample(x) + h = self.conv1(h) + h = self.activation(self.bn2(h, y)) + h = self.conv2(h) + if self.learnable_sc: + x = self.conv_sc(x) + return h + x + + +# Residual block for the discriminator +class DBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + which_conv=SNConv2d, + wide=True, + preactivation=False, + activation=None, + downsample=None, + ): + super(DBlock, self).__init__() + self.in_channels, self.out_channels = in_channels, out_channels + # If using wide D (as in SA-GAN and BigGAN), change the channel pattern + self.hidden_channels = self.out_channels if wide else self.in_channels + self.which_conv = which_conv + self.preactivation = preactivation + self.activation = activation + self.downsample = downsample + + # Conv layers + self.conv1 = self.which_conv(self.in_channels, self.hidden_channels) + self.conv2 = self.which_conv(self.hidden_channels, self.out_channels) + self.learnable_sc = ( + True if (in_channels != out_channels) or downsample else False + ) + if self.learnable_sc: + self.conv_sc = self.which_conv( + in_channels, out_channels, kernel_size=1, padding=0 + ) + + def shortcut(self, x): + if self.preactivation: + if self.learnable_sc: + x = self.conv_sc(x) + if self.downsample: + x = self.downsample(x) + else: + if self.downsample: + x = self.downsample(x) + if self.learnable_sc: + x = self.conv_sc(x) + return x + + def forward(self, x): + if self.preactivation: + # h = self.activation(x) # NOT TODAY SATAN + # Andy's note: This line *must* be an out-of-place ReLU or it + # will negatively affect the shortcut connection. + h = F.relu(x) + else: + h = x + h = self.conv1(h) + h = self.conv2(self.activation(h)) + if self.downsample: + h = self.downsample(h) + + return h + self.shortcut(x) + + +# dogball diff --git a/BigGAN_PyTorch/logs/BigGAN_ch96_bs256x8.jsonl b/BigGAN_PyTorch/logs/BigGAN_ch96_bs256x8.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..cfa578f065f7f16496ee5f8fedebb3e1a0dc188d --- /dev/null +++ b/BigGAN_PyTorch/logs/BigGAN_ch96_bs256x8.jsonl @@ -0,0 +1,68 @@ +{"itr": 2000, "IS_mean": 2.806771755218506, "IS_std": 0.019480662420392036, "FID": 173.76484159711126, "_stamp": 1551403232.0425167} +{"itr": 4000, "IS_mean": 4.962374687194824, "IS_std": 0.07276841998100281, "FID": 113.86730514283107, "_stamp": 1551422228.743057} +{"itr": 6000, "IS_mean": 6.939817905426025, "IS_std": 0.11417163163423538, "FID": 101.63548498447199, "_stamp": 1551457139.3400874} +{"itr": 8000, "IS_mean": 8.142985343933105, "IS_std": 0.11931543797254562, "FID": 92.0014385772705, "_stamp": 1551476217.2409613} +{"itr": 10000, "IS_mean": 10.355518341064453, "IS_std": 0.09094739705324173, "FID": 83.58068997965364, "_stamp": 1551494854.2419689} +{"itr": 12000, "IS_mean": 11.288347244262695, "IS_std": 0.14952820539474487, "FID": 80.98066299357106, "_stamp": 1551513232.5049698} +{"itr": 14000, "IS_mean": 11.755794525146484, "IS_std": 0.17969024181365967, "FID": 76.80603924280956, "_stamp": 1551531425.150371} +{"itr": 18000, "IS_mean": 13.65534496307373, "IS_std": 0.11151058971881866, "FID": 65.95736694335938, "_stamp": 1551588271.9177916} +{"itr": 20000, "IS_mean": 14.817827224731445, "IS_std": 0.23588882386684418, "FID": 61.32061767578125, "_stamp": 1551606713.6567464} +{"itr": 22000, "IS_mean": 17.16551399230957, "IS_std": 0.19506946206092834, "FID": 53.387969970703125, "_stamp": 1551624876.6513028} +{"itr": 24000, "IS_mean": 19.60654067993164, "IS_std": 0.5591856837272644, "FID": 46.5386962890625, "_stamp": 1551642822.6126688} +{"itr": 26000, "IS_mean": 21.74416732788086, "IS_std": 0.2850531041622162, "FID": 41.595001220703125, "_stamp": 1551663522.6019194} +{"itr": 28000, "IS_mean": 23.923612594604492, "IS_std": 0.41587772965431213, "FID": 37.894744873046875, "_stamp": 1551681794.6567173} +{"itr": 30000, 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23.4940185546875, "_stamp": 1551897864.911537} +{"itr": 46000, "IS_mean": 40.239479064941406, "IS_std": 0.7761151194572449, "FID": 22.53167724609375, "_stamp": 1551915406.4840703} +{"itr": 48000, "IS_mean": 41.46656036376953, "IS_std": 1.1031498908996582, "FID": 21.5338134765625, "_stamp": 1551932899.6074848} +{"itr": 50000, "IS_mean": 43.31670379638672, "IS_std": 0.7796809077262878, "FID": 20.53253173828125, "_stamp": 1551950390.345334} +{"itr": 52000, "IS_mean": 45.1517333984375, "IS_std": 1.2925242185592651, "FID": 19.656646728515625, "_stamp": 1551967838.1501615} +{"itr": 54000, "IS_mean": 47.638771057128906, "IS_std": 1.0689665079116821, "FID": 18.898162841796875, "_stamp": 1552044534.5349634} +{"itr": 56000, "IS_mean": 48.87520217895508, "IS_std": 1.1317559480667114, "FID": 18.1248779296875, "_stamp": 1552061763.3080354} +{"itr": 58000, "IS_mean": 49.40987014770508, "IS_std": 1.1866596937179565, "FID": 17.751922607421875, "_stamp": 1552078939.9828825} +{"itr": 60000, "IS_mean": 51.051334381103516, "IS_std": 1.2281248569488525, "FID": 17.19964599609375, "_stamp": 1552096167.889482} +{"itr": 62000, "IS_mean": 52.0235481262207, "IS_std": 0.5391153693199158, "FID": 16.62115478515625, "_stamp": 1552113417.9520617} +{"itr": 64000, "IS_mean": 53.868492126464844, "IS_std": 1.327082633972168, "FID": 16.237335205078125, "_stamp": 1552142961.09602} +{"itr": 66000, "IS_mean": 54.978721618652344, "IS_std": 0.9502049088478088, "FID": 15.81170654296875, "_stamp": 1552162403.2232807} +{"itr": 68000, "IS_mean": 55.73248291015625, "IS_std": 1.0323851108551025, "FID": 15.545623779296875, "_stamp": 1552181112.676657} +{"itr": 70000, "IS_mean": 56.78422927856445, "IS_std": 1.211003303527832, "FID": 15.28369140625, "_stamp": 1552199498.887533} +{"itr": 72000, "IS_mean": 57.972999572753906, "IS_std": 0.8668608665466309, "FID": 14.86395263671875, "_stamp": 1552217782.2738616} +{"itr": 74000, "IS_mean": 58.845054626464844, "IS_std": 1.4297977685928345, "FID": 14.620635986328125, 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"IS_std": 1.6899267435073853, "FID": 12.586517333984375, "_stamp": 1552398550.2060475} +{"itr": 92000, "IS_mean": 68.63685607910156, "IS_std": 1.9431978464126587, "FID": 12.49505615234375, "_stamp": 1552430781.6406457} +{"itr": 94000, "IS_mean": 70.09907531738281, "IS_std": 1.0715738534927368, "FID": 12.047607421875, "_stamp": 1552449001.1950285} +{"itr": 96000, "IS_mean": 70.34623718261719, "IS_std": 1.7962944507598877, "FID": 11.896697998046875, "_stamp": 1552466989.3587568} +{"itr": 98000, "IS_mean": 71.08210754394531, "IS_std": 1.458209753036499, "FID": 11.73046875, "_stamp": 1552484800.7138846} +{"itr": 100000, "IS_mean": 72.24256896972656, "IS_std": 1.3259714841842651, "FID": 11.7386474609375, "_stamp": 1552502538.0269725} +{"itr": 102000, "IS_mean": 73.19488525390625, "IS_std": 1.3439149856567383, "FID": 11.50494384765625, "_stamp": 1552523284.4514356} +{"itr": 104000, "IS_mean": 73.38243103027344, "IS_std": 1.4162707328796387, "FID": 11.374542236328125, "_stamp": 1552541012.0651608} +{"itr": 106000, "IS_mean": 74.95563507080078, "IS_std": 1.089124083518982, "FID": 11.10479736328125, "_stamp": 1552558577.7458107} +{"itr": 108000, "IS_mean": 76.42997741699219, "IS_std": 1.9282453060150146, "FID": 10.998870849609375, "_stamp": 1552576111.9480467} +{"itr": 110000, "IS_mean": 76.89225769042969, "IS_std": 1.4771150350570679, "FID": 10.847015380859375, "_stamp": 1552593659.445132} +{"itr": 112000, "IS_mean": 78.04684448242188, "IS_std": 1.4850096702575684, "FID": 10.772552490234375, "_stamp": 1552616479.5201895} +{"itr": 114000, "IS_mean": 79.67677307128906, "IS_std": 2.0147368907928467, "FID": 10.528045654296875, "_stamp": 1552633850.9315467} +{"itr": 116000, "IS_mean": 79.8828125, "IS_std": 0.978247344493866, "FID": 10.626068115234375, "_stamp": 1552651198.9012825} +{"itr": 118000, "IS_mean": 79.95381164550781, "IS_std": 1.8608143329620361, "FID": 10.46771240234375, "_stamp": 1552668560.4420238} +{"itr": 120000, "IS_mean": 82.37217712402344, "IS_std": 1.8909310102462769, "FID": 10.259033203125, "_stamp": 1552749673.4319007} +{"itr": 122000, "IS_mean": 83.49666595458984, "IS_std": 2.38446044921875, "FID": 9.996185302734375, "_stamp": 1552766698.2706933} +{"itr": 124000, "IS_mean": 83.05189514160156, "IS_std": 1.8844469785690308, "FID": 10.164398193359375, "_stamp": 1552783762.891172} +{"itr": 126000, "IS_mean": 84.27763366699219, "IS_std": 0.9329544901847839, "FID": 10.03509521484375, "_stamp": 1552800953.5724175} +{"itr": 128000, "IS_mean": 85.84852600097656, "IS_std": 2.2698562145233154, "FID": 9.91644287109375, "_stamp": 1552818112.227726} +{"itr": 130000, "IS_mean": 87.356689453125, "IS_std": 2.0958640575408936, "FID": 9.771148681640625, "_stamp": 1552837539.995247} +{"itr": 132000, "IS_mean": 88.72562408447266, "IS_std": 1.7551432847976685, "FID": 9.8258056640625, "_stamp": 1552859685.9305944} +{"itr": 134000, "IS_mean": 88.0631103515625, "IS_std": 1.8199039697647095, "FID": 9.957183837890625, "_stamp": 1552880037.5408435} +{"itr": 136000, "IS_mean": 91.50938415527344, "IS_std": 1.9926033020019531, "FID": 9.876556396484375, "_stamp": 1552899854.652669} +{"itr": 138000, "IS_mean": 93.09217834472656, "IS_std": 2.3062736988067627, "FID": 9.908477783203125, "_stamp": 1552921580.958927} \ No newline at end of file diff --git a/BigGAN_PyTorch/logs/compare_IS.m b/BigGAN_PyTorch/logs/compare_IS.m new file mode 100644 index 0000000000000000000000000000000000000000..1194f1416ad16bbb02c4de1b1a6a0d2b1e74d3b9 --- /dev/null +++ b/BigGAN_PyTorch/logs/compare_IS.m @@ -0,0 +1,97 @@ +% Copyright (c) Facebook, Inc. and its affiliates. +% All rights reserved. +% +% All contributions by Andy Brock: +% Copyright (c) 2019 Andy Brock +% +% MIT License + +clc +clear all +close all +fclose all; + + + +%% Get All logs and sort them +s = {}; +d = dir(); +j = 1; +for i = 1:length(d) + if any(strfind(d(i).name,'.jsonl')) + s = [s; d(i).name]; + end +end + + +j = 1; +for i = 1:length(s) + fname = s{i,1}; + % Check if the Inception metrics log exists, and if so, plot it + [itr, IS, FID, t] = process_inception_log(fname(1:end - 10), 'log.jsonl'); + s{i,2} = itr; + s{i,3} = IS; + s{i,4} = FID; + s{i,5} = max(IS); + s{i,6} = min(FID); + s{i,7} = t; +end +% Sort by Inception Score? +[IS_sorted, IS_index] = sort(cell2mat(s(:,5))); +% Cutoff inception scores below a certain value? +threshold = 22; +IS_index = IS_index(IS_sorted > threshold); + +% Sort by FID? +[FID_sorted, FID_index] = sort(cell2mat(s(:,6))); +% Cutoff also based on IS? +% threshold = 0; +FID_index = FID_index(IS_sorted > threshold); + + + +%% Plot things? +cc = hsv(length(IS_index)); +legend1 = {}; +legend2 = {}; +make_axis=true;%false % Turn this on to see the axis out to 1e6 iterations +for i=1:length(IS_index) + legend1 = [legend1; s{IS_index(i), 1}]; + figure(1) + plot(s{IS_index(i),2}, s{IS_index(i),3}, 'color', cc(i,:),'linewidth',2) + hold on; + xlabel('itr'); ylabel('IS'); + grid on; + if make_axis + axis([0,1e6,0,80]); % 50% grid on; + end + legend(legend1,'Interpreter','none') + %pause(1) % Turn this on to animate stuff + legend2 = [legend2; s{IS_index(i), 1}]; + figure(2) + plot(s{IS_index(i),2}, s{IS_index(i),4}, 'color', cc(i,:),'linewidth',2) + hold on; + xlabel('itr'); ylabel('FID'); + j = j + 1; + grid on; + if make_axis + axis([0,1e6,0,50]);% grid on; + end + legend(legend2, 'Interpreter','none') + +end + +%% Quick script to plot IS versus timesteps +if 0 + figure(3); + this_index=4; + subplot(2,1,1); + %plot(s{this_index, 2}(2:end), s{this_index, 7}(2:end) - s{this_index, 7}(1:end-1), 'r*'); + % xlabel('Iteration');ylabel('\Delta T') + plot(s{this_index, 2}, s{this_index, 7}, 'r*'); + xlabel('Iteration');ylabel('T') + subplot(2,1,2); + plot(s{this_index, 2}, s{this_index, 3}, 'r', 'linewidth',2); + xlabel('Iteration'), ylabel('Inception score') + title(s{this_index,1}) +end diff --git a/BigGAN_PyTorch/logs/metalog.txt b/BigGAN_PyTorch/logs/metalog.txt new file mode 100644 index 0000000000000000000000000000000000000000..9214b1bbd13e0984c0a8b0e2fcc4cb14e47cf427 --- /dev/null +++ b/BigGAN_PyTorch/logs/metalog.txt @@ -0,0 +1,3 @@ +datetime: 2019-03-18 13:27:59.181225 +config: {'dataset': 'I128_hdf5', 'augment': False, 'num_workers': 8, 'pin_memory': True, 'shuffle': True, 'load_in_mem': True, 'use_multiepoch_sampler': True, 'model': 'model', 'G_param': 'SN', 'D_param': 'SN', 'G_ch': 96, 'D_ch': 96, 'G_depth': 1, 'D_depth': 1, 'D_wide': True, 'G_shared': True, 'shared_dim': 128, 'dim_z': 120, 'z_var': 1.0, 'hier': True, 'cross_replica': False, 'mybn': False, 'G_nl': 'inplace_relu', 'D_nl': 'inplace_relu', 'G_attn': '64', 'D_attn': '64', 'norm_style': 'bn', 'seed': 0, 'G_init': 'ortho', 'D_init': 'ortho', 'skip_init': True, 'G_lr': 0.0001, 'D_lr': 0.0004, 'G_B1': 0.0, 'D_B1': 0.0, 'G_B2': 0.999, 'D_B2': 0.999, 'batch_size': 256, 'G_batch_size': 0, 'num_G_accumulations': 8, 'num_D_steps': 1, 'num_D_accumulations': 8, 'split_D': False, 'num_epochs': 400, 'parallel': True, 'G_fp16': False, 'D_fp16': False, 'D_mixed_precision': False, 'G_mixed_precision': False, 'accumulate_stats': False, 'num_standing_accumulations': 16, 'G_eval_mode': True, 'save_every': 500, 'num_save_copies': 2, 'num_best_copies': 5, 'which_best': 'IS', 'no_fid': False, 'test_every': 2000, 'num_inception_images': 50000, 'hashname': False, 'base_root': '', 'dataset_root': 'data', 'weights_root': 'weights', 'logs_root': 'logs', 'samples_root': 'samples', 'pbar': 'mine', 'name_suffix': '', 'experiment_name': 'Jade_BigGAN_B1_bs256x8_fp32', 'config_from_name': False, 'ema': True, 'ema_decay': 0.9999, 'use_ema': True, 'ema_start': 20000, 'adam_eps': 1e-06, 'BN_eps': 1e-05, 'SN_eps': 1e-06, 'num_G_SVs': 1, 'num_D_SVs': 1, 'num_G_SV_itrs': 1, 'num_D_SV_itrs': 1, 'G_ortho': 0.0, 'D_ortho': 0.0, 'toggle_grads': True, 'which_train_fn': 'GAN', 'load_weights': '', 'resume': True, 'logstyle': '%3.3e', 'log_G_spectra': False, 'log_D_spectra': False, 'sv_log_interval': 10, 'resolution': 128, 'n_classes': 1000, 'G_activation': ReLU(inplace), 'D_activation': ReLU(inplace)} +state: {'itr': 137500, 'epoch': 2, 'save_num': 0, 'save_best_num': 1, 'best_IS': 91.509384, 'best_FID': tensor(9.7711, 'config': {'dataset': 'I128_hdf5', 'augment': False, 'num_workers': 8, 'pin_memory': True, 'shuffle': True, 'load_in_mem': True, 'use_multiepoch_sampler': True, 'model': 'model', 'G_param': 'SN', 'D_param': 'SN', 'G_ch': 96, 'D_ch': 96, 'D_wide': True, 'G_shared': True, 'shared_dim': 128, 'dim_z': 120, 'hier': True, 'cross_replica': False, 'mybn': False, 'G_nl': 'inplace_relu', 'D_nl': 'inplace_relu', 'G_attn': '64', 'D_attn': '64', 'norm_style': 'bn', 'seed': 0, 'G_init': 'ortho', 'D_init': 'ortho', 'skip_init': False, 'G_lr': 0.0001, 'D_lr': 0.0004, 'G_B1': 0.0, 'D_B1': 0.0, 'G_B2': 0.999, 'D_B2': 0.999, 'batch_size': 256, 'G_batch_size': 0, 'num_G_accumulations': 8, 'num_D_steps': 1, 'num_D_accumulations': 8, 'split_D': False, 'num_epochs': 100, 'parallel': True, 'G_fp16': False, 'D_fp16': False, 'D_mixed_precision': False, 'G_mixed_precision': False, 'accumulate_stats': False, 'num_standing_accumulations': 16, 'BN_sync': False, 'G_eval_mode': True, 'save_every': 500, 'num_save_copies': 2, 'num_best_copies': 5, 'which_best': 'IS', 'no_fid': False, 'test_every': 2000, 'num_inception_images': 50000, 'hashname': False, 'base_root': '', 'dataset_root': 'data', 'weights_root': 'weights', 'logs_root': 'logs', 'samples_root': 'samples', 'pbar': 'mine', 'name_suffix': '', 'experiment_name': 'Jade_BigGAN_B1_bs256x8_fp32', 'ema': True, 'ema_decay': 0.9999, 'use_ema': True, 'ema_start': 20000, 'adam_eps': 1e-06, 'BN_eps': 1e-05, 'SN_eps': 1e-06, 'num_G_SVs': 1, 'num_D_SVs': 1, 'num_G_SV_itrs': 1, 'num_D_SV_itrs': 1, 'G_ortho': 0.0, 'D_ortho': 0.0, 'toggle_grads': True, 'which_train_fn': 'GAN', 'load_weights': '', 'resume': False, 'logstyle': '%3.3e', 'log_G_spectra': False, 'log_D_spectra': False, 'sv_log_interval': 10, 'resolution': 128, 'n_classes': 1000, 'G_activation': ReLU(inplace), 'D_activation': ReLU(inplace)}} diff --git a/BigGAN_PyTorch/logs/process_inception_log.m b/BigGAN_PyTorch/logs/process_inception_log.m new file mode 100644 index 0000000000000000000000000000000000000000..6a5b9007e7a2977a4ef80f357f4cd0e65df6f7ed --- /dev/null +++ b/BigGAN_PyTorch/logs/process_inception_log.m @@ -0,0 +1,27 @@ +% Copyright (c) Facebook, Inc. and its affiliates. +% All rights reserved. +% +% All contributions by Andy Brock: +% Copyright (c) 2019 Andy Brock +% +% MIT License +% +function [itr, IS, FID, t] = process_inception_log(fname, which_log) +f = sprintf('%s_%s',fname, which_log);%'G_loss.log'); +fid = fopen(f,'r'); +itr = []; +IS = []; +FID = []; +t = []; +i = 1; +while ~feof(fid); + s = fgets(fid); + parsed = sscanf(s,'{"itr": %d, "IS_mean": %f, "IS_std": %f, "FID": %f, "_stamp": %f}'); + itr(i) = parsed(1); + IS(i) = parsed(2); + FID(i) = parsed(4); + t(i) = parsed(5); + i = i + 1; +end +fclose(fid); +end diff --git a/BigGAN_PyTorch/logs/process_training.m b/BigGAN_PyTorch/logs/process_training.m new file mode 100644 index 0000000000000000000000000000000000000000..437ef51bd4c47911b098536d58227c518b692e49 --- /dev/null +++ b/BigGAN_PyTorch/logs/process_training.m @@ -0,0 +1,117 @@ +% Copyright (c) Facebook, Inc. and its affiliates. +% All rights reserved. +% +% All contributions by Andy Brock: +% Copyright (c) 2019 Andy Brock +% +% MIT License +% +clc +clear all +close all +fclose all; + + + +%% Get all training logs for a given run +target_dir = '.'; +s = {}; +nm = {}; +d = dir(target_dir); +j = 1; +for i = 1:length(d) + if any(strfind(d(i).name,'.log')) + s = [s; sprintf('%s\\%s', target_dir, d(i).name)]; + nm = [nm; d(i).name]; + end +end +%% Loop over training logs and acquire data +D_count = 0; +G_count = 0; +for i = 1:length(s) + fname = s{i,1}; + fid = fopen(s{i,1},'r'); + % Prepare bookkeeping for sv0 + if any(strfind(s{i,1},'sv')) + if any(strfind(s{i,1},'G_')) + G_count = G_count +1; + else + D_count = D_count + 1; + end + end + itr = []; + val = []; + j = 1; + while ~feof(fid); + line = fgets(fid); + parsed = sscanf(line, '%d: %e'); + itr(j) = parsed(1); + val(j) = parsed(2); + j = j + 1; + end + s{i,2} = itr; + s{i,3} = val; + fclose(fid); +end + +%% Plot SVs and losses +close all; +Gcc = hsv(G_count); +Dcc = hsv(D_count); +gi = 1; +di = 1; +li = 1; +legendG = {}; +legendD = {}; +legendL = {}; +thresh=2; % wavelet denoising threshold +losses = {}; +for i=1:length(s) + if any(strfind(s{i,1},'D_loss_real.log')) || any(strfind(s{i,1},'D_loss_fake.log')) || any(strfind(s{i,1},'G_loss.log')) + % Select colors + if any(strfind(s{i,1},'D_loss_real.log')) + color1 = [0.7,0.7,1.0]; + color2 = [0, 0, 1]; + dlr = {s{i,2}, s{i,3}, wden(s{i,3},'sqtwolog','s','mln', thresh, 'sym4'), color1, color2}; + losses = [losses; dlr]; + elseif any(strfind(s{i,1},'D_loss_fake.log')) + color1 = [0.7,1.0,0.7]; + color2 = [0, 1, 0]; + dlf = {s{i,2},s{i,3} wden(s{i,3},'sqtwolog','s','mln', thresh, 'sym4'), color1, color2}; + losses = [losses; dlf]; + else % g loss + color1 = [1.0, 0.7,0.7]; + color2 = [1, 0, 0]; + gl = {s{i,2},s{i,3}, wden(s{i,3},'sqtwolog','s','mln', thresh, 'sym4'), color1 color2}; + losses = [losses; gl]; + end + figure(1); hold on; + % Plot the unsmoothed losses; we'll plot the smoothed losses later + plot(s{i,2},s{i,3},'color', color1, 'HandleVisibility','off'); + legendL = [legendL; nm{i}]; + continue + end + if any(strfind(s{i,1},'G_')) + legendG = [legendG; nm{i}]; + figure(2); hold on; + plot(s{i,2},s{i,3},'color',Gcc(gi,:),'linewidth',2); + gi = gi+1; + elseif any(strfind(s{i,1},'D_')) + legendD = [legendD; nm{i}]; + figure(3); hold on; + plot(s{i,2},s{i,3},'color',Dcc(di,:),'linewidth',2); + di = di+1; + else + s{i,1} % Debug print to show the name of the log that was not processed. + end +end +figure(1); +% Plot the smoothed losses last +for i = 1:3 +% plot(losses{i,1}, losses{i,2},'color', losses{i,4}, 'HandleVisibility','off'); +plot(losses{i,1},losses{i,3},'color',losses{i,5}); +end +legend(legendL, 'Interpreter', 'none'); title('Losses'); xlabel('Generator itr'); ylabel('loss'); axis([0, max(s{end,2}), -1, 4]); + +figure(2); legend(legendG,'Interpreter','none'); title('Singular Values in G'); xlabel('Generator itr'); ylabel('SV0'); +figure(3); legend(legendD, 'Interpreter', 'none'); title('Singular Values in D'); xlabel('Generator itr'); ylabel('SV0'); diff --git a/BigGAN_PyTorch/losses.py b/BigGAN_PyTorch/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..a87162c12006490e86d8f6f557985e5e72b1bdd2 --- /dev/null +++ b/BigGAN_PyTorch/losses.py @@ -0,0 +1,43 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +import torch +import torch.nn.functional as F + +# DCGAN loss +def loss_dcgan_dis(dis_fake, dis_real): + L1 = torch.mean(F.softplus(-dis_real)) + L2 = torch.mean(F.softplus(dis_fake)) + return L1, L2 + + +def loss_dcgan_gen(dis_fake): + loss = torch.mean(F.softplus(-dis_fake)) + return loss + + +# Hinge Loss +def loss_hinge_dis(dis_fake, dis_real): + loss_real = torch.mean(F.relu(1.0 - dis_real)) + loss_fake = torch.mean(F.relu(1.0 + dis_fake)) + return loss_real, loss_fake + + +# def loss_hinge_dis(dis_fake, dis_real): # This version returns a single loss +# loss = torch.mean(F.relu(1. - dis_real)) +# loss += torch.mean(F.relu(1. + dis_fake)) +# return loss + + +def loss_hinge_gen(dis_fake): + loss = -torch.mean(dis_fake) + return loss + + +# Default to hinge loss +generator_loss = loss_hinge_gen +discriminator_loss = loss_hinge_dis diff --git a/BigGAN_PyTorch/make_hdf5.py b/BigGAN_PyTorch/make_hdf5.py new file mode 100644 index 0000000000000000000000000000000000000000..cf71f73b9334942f976229eaa30ca261a951b2db --- /dev/null +++ b/BigGAN_PyTorch/make_hdf5.py @@ -0,0 +1,193 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +""" Convert dataset to HDF5 + This script preprocesses a dataset and saves it (images and labels) to + an HDF5 file for improved I/O. """ +import os +import sys +from argparse import ArgumentParser +from tqdm import tqdm, trange +import h5py as h5 + +import numpy as np +import torch +import torchvision.datasets as dset +import torchvision.transforms as transforms +from torchvision.utils import save_image +import torchvision.transforms as transforms +from torch.utils.data import DataLoader + +import utils + + +def prepare_parser(): + usage = "Parser for ImageNet HDF5 scripts." + parser = ArgumentParser(description=usage) + parser.add_argument( + "--resolution", + type=int, + default=128, + help="Which Dataset resolution to train on, out of 64, 128, 256, 512 (default: %(default)s)", + ) + parser.add_argument( + "--split", + type=str, + default="train", + help="Which Dataset to convert: train, val (default: %(default)s)", + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where data is stored (default: %(default)s)", + ) + parser.add_argument( + "--out_path", + type=str, + default="data", + help="Default location where data in hdf5 format will be stored (default: %(default)s)", + ) + parser.add_argument( + "--longtail", + action="store_true", + default=False, + help="Use long-tail version of the dataset", + ) + parser.add_argument( + "--batch_size", + type=int, + default=256, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--num_workers", + type=int, + default=16, + help="Number of dataloader workers (default: %(default)s)", + ) + parser.add_argument( + "--chunk_size", + type=int, + default=500, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--compression", + action="store_true", + default=False, + help="Use LZF compression? (default: %(default)s)", + ) + return parser + + +def run(config): + # Get image size + + # Update compression entry + config["compression"] = ( + "lzf" if config["compression"] else None + ) # No compression; can also use 'lzf' + + # Get dataset + kwargs = { + "num_workers": config["num_workers"], + "pin_memory": False, + "drop_last": False, + } + dataset = utils.get_dataset_images( + config["resolution"], + data_path=os.path.join(config["data_root"], config["split"]), + longtail=config["longtail"], + ) + train_loader = utils.get_dataloader( + dataset, config["batch_size"], shuffle=False, **kwargs + ) + + # HDF5 supports chunking and compression. You may want to experiment + # with different chunk sizes to see how it runs on your machines. + # Chunk Size/compression Read speed @ 256x256 Read speed @ 128x128 Filesize @ 128x128 Time to write @128x128 + # 1 / None 20/s + # 500 / None ramps up to 77/s 102/s 61GB 23min + # 500 / LZF 8/s 56GB 23min + # 1000 / None 78/s + # 5000 / None 81/s + # auto:(125,1,16,32) / None 11/s 61GB + + print( + "Starting to load dataset into an HDF5 file with chunk size %i and compression %s..." + % (config["chunk_size"], config["compression"]) + ) + # Loop over train loader + for i, (x, y) in enumerate(tqdm(train_loader)): + # Stick X into the range [0, 255] since it's coming from the train loader + x = (255 * ((x + 1) / 2.0)).byte().numpy() + # Numpyify y + y = y.numpy() + # If we're on the first batch, prepare the hdf5 + if i == 0: + with h5.File( + config["out_path"] + + "/ILSVRC%i%s_xy.hdf5" + % (config["resolution"], "" if not config["longtail"] else "longtail"), + "w", + ) as f: + print("Producing dataset of len %d" % len(train_loader.dataset)) + imgs_dset = f.create_dataset( + "imgs", + x.shape, + dtype="uint8", + maxshape=( + len(train_loader.dataset), + 3, + config["resolution"], + config["resolution"], + ), + chunks=( + config["chunk_size"], + 3, + config["resolution"], + config["resolution"], + ), + compression=config["compression"], + ) + print("Image chunks chosen as " + str(imgs_dset.chunks)) + imgs_dset[...] = x + labels_dset = f.create_dataset( + "labels", + y.shape, + dtype="int64", + maxshape=(len(train_loader.dataset),), + chunks=(config["chunk_size"],), + compression=config["compression"], + ) + print("Label chunks chosen as " + str(labels_dset.chunks)) + labels_dset[...] = y + # Else append to the hdf5 + else: + with h5.File( + config["out_path"] + + "/ILSVRC%i%s_xy.hdf5" + % (config["resolution"], "" if not config["longtail"] else "longtail"), + "a", + ) as f: + f["imgs"].resize(f["imgs"].shape[0] + x.shape[0], axis=0) + f["imgs"][-x.shape[0] :] = x + f["labels"].resize(f["labels"].shape[0] + y.shape[0], axis=0) + f["labels"][-y.shape[0] :] = y + + +def main(): + # parse command line and run + parser = prepare_parser() + config = vars(parser.parse_args()) + print(config) + run(config) + + +if __name__ == "__main__": + main() diff --git a/BigGAN_PyTorch/run.py b/BigGAN_PyTorch/run.py new file mode 100644 index 0000000000000000000000000000000000000000..be0d1e68cda56f747f605da1bef627756bfb3b29 --- /dev/null +++ b/BigGAN_PyTorch/run.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import utils +from trainer import run +from submitit.helpers import Checkpointable + +LOCAL = False +try: + import submitit +except: + print( + "No submitit package found! Defaulting to executing the script in the local machine" + ) + LOCAL = True +import json + + +class Trainer(Checkpointable): + def __call__(self, config): + if config["run_setup"] == "local_debug" or LOCAL: + run(config, "local_debug") + else: + run(config, "slurm", master_node=submitit.JobEnvironment().hostnames[0]) + + +if __name__ == "__main__": + parser = utils.prepare_parser() + config = vars(parser.parse_args()) + + if config["json_config"] != "": + data = json.load(open(config["json_config"])) + for key in data.keys(): + config[key] = data[key] + else: + print("Not using JSON configuration file!") + config["G_batch_size"] = config["batch_size"] + config["batch_size"] = ( + config["batch_size"] * config["num_D_accumulations"] * config["num_D_steps"] + ) + + trainer = Trainer() + if config["run_setup"] == "local_debug" or LOCAL: + trainer(config) + else: + print( + "Using ", + config["n_nodes"], + " nodes and ", + config["n_gpus_per_node"], + " GPUs per node.", + ) + executor = submitit.SlurmExecutor( + folder=config["slurm_logdir"], max_num_timeout=60 + ) + executor.update_parameters( + gpus_per_node=config["n_gpus_per_node"], + partition=config["partition"], + constraint="volta32gb", + nodes=config["n_nodes"], + ntasks_per_node=config["n_gpus_per_node"], + cpus_per_task=8, + mem=256000, + time=3200, + job_name=config["experiment_name"], + exclusive=True if config["n_gpus_per_node"] == 8 else False, + ) + + executor.submit(trainer, config) + import time + + time.sleep(1) diff --git a/BigGAN_PyTorch/scripts/launch_BigGAN_bs256x8.sh b/BigGAN_PyTorch/scripts/launch_BigGAN_bs256x8.sh new file mode 100644 index 0000000000000000000000000000000000000000..11dcc774d23cb801313d402b8cda4ae9025f64b2 --- /dev/null +++ b/BigGAN_PyTorch/scripts/launch_BigGAN_bs256x8.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +python train.py \ +--dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 --load_in_mem \ +--num_G_accumulations 8 --num_D_accumulations 8 \ +--num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ +--G_attn 64 --D_attn 64 \ +--G_nl inplace_relu --D_nl inplace_relu \ +--SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ +--G_ortho 0.0 \ +--G_shared \ +--G_init ortho --D_init ortho \ +--hier --dim_z 120 --shared_dim 128 \ +--G_eval_mode \ +--G_ch 96 --D_ch 96 \ +--ema --use_ema --ema_start 20000 \ +--test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ +--use_multiepoch_sampler \ diff --git a/BigGAN_PyTorch/scripts/launch_BigGAN_bs512x4.sh b/BigGAN_PyTorch/scripts/launch_BigGAN_bs512x4.sh new file mode 100644 index 0000000000000000000000000000000000000000..c0a01691490d7aa7ea48b98f3723fdb3b11cdcf5 --- /dev/null +++ b/BigGAN_PyTorch/scripts/launch_BigGAN_bs512x4.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +python train.py \ +--dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 512 --load_in_mem \ +--num_G_accumulations 4 --num_D_accumulations 4 \ +--num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ +--G_attn 64 --D_attn 64 \ +--G_nl inplace_relu --D_nl inplace_relu \ +--SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ +--G_ortho 0.0 \ +--G_shared \ +--G_init ortho --D_init ortho \ +--hier --dim_z 120 --shared_dim 128 \ +--G_eval_mode \ +--G_ch 96 --D_ch 96 \ +--ema --use_ema --ema_start 20000 \ +--test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ +--use_multiepoch_sampler \ diff --git a/BigGAN_PyTorch/scripts/launch_BigGAN_ch64_bs256x8.sh b/BigGAN_PyTorch/scripts/launch_BigGAN_ch64_bs256x8.sh new file mode 100644 index 0000000000000000000000000000000000000000..f76f02d5843409d895e9a1b085062ca67e2a4bd1 --- /dev/null +++ b/BigGAN_PyTorch/scripts/launch_BigGAN_ch64_bs256x8.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +python train.py \ +--dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 --load_in_mem \ +--num_G_accumulations 8 --num_D_accumulations 8 \ +--num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ +--G_attn 64 --D_attn 64 \ +--G_nl inplace_relu --D_nl inplace_relu \ +--SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ +--G_ortho 0.0 \ +--G_shared \ +--G_init ortho --D_init ortho \ +--hier --dim_z 120 --shared_dim 128 \ +--G_eval_mode \ +--G_ch 64 --G_ch 64 \ +--ema --use_ema --ema_start 20000 \ +--test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ +--use_multiepoch_sampler diff --git a/BigGAN_PyTorch/scripts/launch_BigGAN_deep.sh b/BigGAN_PyTorch/scripts/launch_BigGAN_deep.sh new file mode 100644 index 0000000000000000000000000000000000000000..a8e3fd3b72cf85716b80eee6f7040dff4ed83991 --- /dev/null +++ b/BigGAN_PyTorch/scripts/launch_BigGAN_deep.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +python train.py \ +--model BigGANdeep \ +--dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 \ +--num_G_accumulations 8 --num_D_accumulations 8 \ +--num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ +--G_attn 64 --D_attn 64 \ +--G_ch 128 --D_ch 128 \ +--G_depth 2 --D_depth 2 \ +--G_nl inplace_relu --D_nl inplace_relu \ +--SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ +--G_ortho 0.0 \ +--G_shared \ +--G_init ortho --D_init ortho \ +--hier --dim_z 128 --shared_dim 128 \ +--ema --use_ema --ema_start 20000 --G_eval_mode \ +--test_every 2000 --save_every 500 --num_best_copies 5 --num_save_copies 2 --seed 0 \ +--use_multiepoch_sampler \ diff --git a/BigGAN_PyTorch/scripts/launch_SAGAN_bs128x2_ema.sh b/BigGAN_PyTorch/scripts/launch_SAGAN_bs128x2_ema.sh new file mode 100644 index 0000000000000000000000000000000000000000..eb18d477c3f9e8193a826e2939e5d4a2aab673ff --- /dev/null +++ b/BigGAN_PyTorch/scripts/launch_SAGAN_bs128x2_ema.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +python train.py \ +--dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 128 \ +--num_G_accumulations 2 --num_D_accumulations 2 \ +--num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ +--G_attn 64 --D_attn 64 \ +--G_nl relu --D_nl relu \ +--SN_eps 1e-8 --BN_eps 1e-5 --adam_eps 1e-8 \ +--G_ortho 0.0 \ +--G_init xavier --D_init xavier \ +--ema --use_ema --ema_start 2000 --G_eval_mode \ +--test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ +--name_suffix SAGAN_ema \ diff --git a/BigGAN_PyTorch/scripts/launch_SNGAN.sh b/BigGAN_PyTorch/scripts/launch_SNGAN.sh new file mode 100644 index 0000000000000000000000000000000000000000..c16f3feea0739751ea7c080b9b38ca76b038a1c8 --- /dev/null +++ b/BigGAN_PyTorch/scripts/launch_SNGAN.sh @@ -0,0 +1,23 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +python train.py \ +--dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 64 \ +--num_G_accumulations 1 --num_D_accumulations 1 \ +--num_D_steps 5 --G_lr 2e-4 --D_lr 2e-4 --D_B2 0.900 --G_B2 0.900 \ +--G_attn 0 --D_attn 0 \ +--G_nl relu --D_nl relu \ +--SN_eps 1e-8 --BN_eps 1e-5 --adam_eps 1e-8 \ +--G_ortho 0.0 \ +--D_thin \ +--G_init xavier --D_init xavier \ + --G_eval_mode \ +--test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ +--name_suffix SNGAN \ diff --git a/BigGAN_PyTorch/scripts/launch_cifar_ema.sh b/BigGAN_PyTorch/scripts/launch_cifar_ema.sh new file mode 100644 index 0000000000000000000000000000000000000000..47c8688a9a59a871f9f6622f39fef14fe1ed92ca --- /dev/null +++ b/BigGAN_PyTorch/scripts/launch_cifar_ema.sh @@ -0,0 +1,20 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +CUDA_VISIBLE_DEVICES=0,1 python train.py \ +--shuffle --batch_size 50 --parallel \ +--num_G_accumulations 1 --num_D_accumulations 1 --num_epochs 500 \ +--num_D_steps 4 --G_lr 2e-4 --D_lr 2e-4 \ +--dataset C10 \ +--G_ortho 0.0 \ +--G_attn 0 --D_attn 0 \ +--G_init N02 --D_init N02 \ +--ema --use_ema --ema_start 1000 \ +--test_every 5000 --save_every 2000 --num_best_copies 5 --num_save_copies 2 --seed 0 diff --git a/BigGAN_PyTorch/scripts/sample_BigGAN_bs256x8.sh b/BigGAN_PyTorch/scripts/sample_BigGAN_bs256x8.sh new file mode 100644 index 0000000000000000000000000000000000000000..373653640cf8d0b93d572dd21a4dca3464dcc503 --- /dev/null +++ b/BigGAN_PyTorch/scripts/sample_BigGAN_bs256x8.sh @@ -0,0 +1,28 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +# use z_var to change the variance of z for all the sampling +# use --mybn --accumulate_stats --num_standing_accumulations 32 to +# use running stats +python sample.py \ +--dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 \ +--num_G_accumulations 8 --num_D_accumulations 8 \ +--num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ +--G_attn 64 --D_attn 64 \ +--G_ch 96 --D_ch 96 \ +--G_nl inplace_relu --D_nl inplace_relu \ +--SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ +--G_ortho 0.0 \ +--G_shared \ +--G_init ortho --D_init ortho --skip_init \ +--hier --dim_z 120 --shared_dim 128 \ +--ema --ema_start 20000 \ +--use_multiepoch_sampler \ +--test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ +--skip_init --G_batch_size 512 --use_ema --G_eval_mode --sample_trunc_curves 0.05_0.05_1.0 \ +--sample_inception_metrics --sample_npz --sample_random --sample_sheets --sample_interps diff --git a/BigGAN_PyTorch/scripts/sample_cifar_ema.sh b/BigGAN_PyTorch/scripts/sample_cifar_ema.sh new file mode 100644 index 0000000000000000000000000000000000000000..c5a9bdc64b5ecde6f7fbf2bbd27391dc42f60df5 --- /dev/null +++ b/BigGAN_PyTorch/scripts/sample_cifar_ema.sh @@ -0,0 +1,20 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +CUDA_VISIBLE_DEVICES=0,1 python sample.py \ +--shuffle --batch_size 50 --G_batch_size 256 --parallel \ +--num_G_accumulations 1 --num_D_accumulations 1 --num_epochs 500 \ +--num_D_steps 4 --G_lr 2e-4 --D_lr 2e-4 \ +--dataset C10 \ +--G_ortho 0.0 \ +--G_attn 0 --D_attn 0 \ +--G_init N02 --D_init N02 \ +--ema --use_ema --ema_start 1000 \ +--test_every 5000 --save_every 2000 --num_best_copies 5 --num_save_copies 2 --seed 0 diff --git a/BigGAN_PyTorch/scripts/utils/duplicate.sh b/BigGAN_PyTorch/scripts/utils/duplicate.sh new file mode 100644 index 0000000000000000000000000000000000000000..82f0a3c42cc0301a6c4c487e1a57071fd68b3e91 --- /dev/null +++ b/BigGAN_PyTorch/scripts/utils/duplicate.sh @@ -0,0 +1,22 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +#duplicate.sh +source=BigGAN_I128_hdf5_seed0_Gch64_Dch64_bs256_Glr1.0e-04_Dlr4.0e-04_Gnlinplace_relu_Dnlinplace_relu_Ginitxavier_Dinitxavier_Gshared_alex0 +target=BigGAN_I128_hdf5_seed0_Gch64_Dch64_bs256_Glr1.0e-04_Dlr4.0e-04_Gnlinplace_relu_Dnlinplace_relu_Ginitxavier_Dinitxavier_Gshared_alex0A +logs_root=logs +weights_root=weights +echo "copying ${source} to ${target}" +cp -r ${logs_root}/${source} ${logs_root}/${target} +cp ${logs_root}/${source}_log.jsonl ${logs_root}/${target}_log.jsonl +cp ${weights_root}/${source}_G.pth ${weights_root}/${target}_G.pth +cp ${weights_root}/${source}_G_ema.pth ${weights_root}/${target}_G_ema.pth +cp ${weights_root}/${source}_D.pth ${weights_root}/${target}_D.pth +cp ${weights_root}/${source}_G_optim.pth ${weights_root}/${target}_G_optim.pth +cp ${weights_root}/${source}_D_optim.pth ${weights_root}/${target}_D_optim.pth +cp ${weights_root}/${source}_state_dict.pth ${weights_root}/${target}_state_dict.pth diff --git a/BigGAN_PyTorch/scripts/utils/prepare_data.sh b/BigGAN_PyTorch/scripts/utils/prepare_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..31c2dcc8a1e2602e50fc556b803e87a0ffccef92 --- /dev/null +++ b/BigGAN_PyTorch/scripts/utils/prepare_data.sh @@ -0,0 +1,21 @@ +#!/bin/bash +# +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +# ImageNet +python make_hdf5.py --resolution 64 --split 'train' --data_root '../../anyshot_longtail/data/Imagenet_all/' --out_path 'mock_data' +python calculate_inception_moments.py --resolution 64 --split 'train' --data_root 'mock_data' --load_in_mem --out_path 'mock_data' +python make_hdf5.py --resolution 64 --split 'val' --data_root '../../anyshot_longtail/data/Imagenet_all/' --out_path 'mock_data' +python calculate_inception_moments.py --resolution 64 --split 'val' --data_root 'mock_data' --load_in_mem --out_path 'mock_data' + +# ImageNet-LT +python make_hdf5.py --resolution 64 --split 'train' --data_root '../../anyshot_longtail/data/Imagenet_all/' --out_path 'mock_data' --longtail +python calculate_inception_moments.py --resolution 64 --split 'train' --data_root 'mock_data' --longtail --load_in_mem +python make_hdf5.py --resolution 64 --split 'val' --data_root '../../anyshot_longtail/data/Imagenet_all/' --out_path 'mock_data' +python calculate_inception_moments.py --resolution 64 --split 'val' --data_root 'mock_data' --load_in_mem --out_path 'mock_data' --stratified_moments diff --git a/BigGAN_PyTorch/sync_batchnorm/__init__.py b/BigGAN_PyTorch/sync_batchnorm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0f3df594c2035a1bd2111e6458d577c813fc775c --- /dev/null +++ b/BigGAN_PyTorch/sync_batchnorm/__init__.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import ( + SynchronizedBatchNorm1d, + SynchronizedBatchNorm2d, + SynchronizedBatchNorm3d, +) +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/BigGAN_PyTorch/sync_batchnorm/batchnorm.py b/BigGAN_PyTorch/sync_batchnorm/batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..fefb02dfa04dc2d8e8b05fe0b1d2b0ab333f2031 --- /dev/null +++ b/BigGAN_PyTorch/sync_batchnorm/batchnorm.py @@ -0,0 +1,382 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock + +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm +from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast + +from .comm import SyncMaster + +__all__ = [ + "SynchronizedBatchNorm1d", + "SynchronizedBatchNorm2d", + "SynchronizedBatchNorm3d", +] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dementions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple("_ChildMessage", ["sum", "ssum", "sum_size"]) +_MasterMessage = collections.namedtuple("_MasterMessage", ["sum", "inv_std"]) +# _MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'ssum', 'sum_size']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): + super(_SynchronizedBatchNorm, self).__init__( + num_features, eps=eps, momentum=momentum, affine=affine + ) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input, gain=None, bias=None): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + out = F.batch_norm( + input, + self.running_mean, + self.running_var, + self.weight, + self.bias, + self.training, + self.momentum, + self.eps, + ) + if gain is not None: + out = out + gain + if bias is not None: + out = out + bias + return out + + # Resize the input to (B, C, -1). + input_shape = input.size() + # print(input_shape) + input = input.view(input.size(0), input.size(1), -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + # Reduce-and-broadcast the statistics. + # print('it begins') + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master( + _ChildMessage(input_sum, input_ssum, sum_size) + ) + else: + mean, inv_std = self._slave_pipe.run_slave( + _ChildMessage(input_sum, input_ssum, sum_size) + ) + # if self._parallel_id == 0: + # # print('here') + # sum, ssum, num = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + # else: + # # print('there') + # sum, ssum, num = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # print('how2') + # num = sum_size + # print('Sum: %f, ssum: %f, sumsize: %f, insum: %f' %(float(sum.sum().cpu()), float(ssum.sum().cpu()), float(sum_size), float(input_sum.sum().cpu()))) + # Fix the graph + # sum = (sum.detach() - input_sum.detach()) + input_sum + # ssum = (ssum.detach() - input_ssum.detach()) + input_ssum + + # mean = sum / num + # var = ssum / num - mean ** 2 + # # var = (ssum - mean * sum) / num + # inv_std = torch.rsqrt(var + self.eps) + + # Compute the output. + if gain is not None: + # print('gaining') + # scale = _unsqueeze_ft(inv_std) * gain.squeeze(-1) + # shift = _unsqueeze_ft(mean) * scale - bias.squeeze(-1) + # output = input * scale - shift + output = (input - _unsqueeze_ft(mean)) * ( + _unsqueeze_ft(inv_std) * gain.squeeze(-1) + ) + bias.squeeze(-1) + elif self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft( + inv_std * self.weight + ) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + # print('a') + # print(type(sum_), type(ssum), type(sum_size), sum_.shape, ssum.shape, sum_size) + # broadcasted = Broadcast.apply(target_gpus, sum_, ssum, torch.tensor(sum_size).float().to(sum_.device)) + # print('b') + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i * 2 : i * 2 + 2]))) + # outputs.append((rec[0], _MasterMessage(*broadcasted[i*3:i*3+3]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert ( + size > 1 + ), "BatchNorm computes unbiased standard-deviation, which requires size > 1." + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + self.running_mean = ( + 1 - self.momentum + ) * self.running_mean + self.momentum * mean.data + self.running_var = ( + 1 - self.momentum + ) * self.running_var + self.momentum * unbias_var.data + return mean, torch.rsqrt(bias_var + self.eps) + # return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError( + "expected 2D or 3D input (got {}D input)".format(input.dim()) + ) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError("expected 4D input (got {}D input)".format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError("expected 5D input (got {}D input)".format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/BigGAN_PyTorch/sync_batchnorm/batchnorm_reimpl.py b/BigGAN_PyTorch/sync_batchnorm/batchnorm_reimpl.py new file mode 100644 index 0000000000000000000000000000000000000000..a7cb4a665a870600968fe817db5d73a049f5b4fd --- /dev/null +++ b/BigGAN_PyTorch/sync_batchnorm/batchnorm_reimpl.py @@ -0,0 +1,82 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +#! /usr/bin/env python3 +# -*- coding: utf-8 -*- +# File : batchnorm_reimpl.py +# Author : acgtyrant +# Date : 11/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import torch +import torch.nn as nn +import torch.nn.init as init + +__all__ = ["BatchNormReimpl"] + + +class BatchNorm2dReimpl(nn.Module): + """ + A re-implementation of batch normalization, used for testing the numerical + stability. + + Author: acgtyrant + See also: + https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14 + """ + + def __init__(self, num_features, eps=1e-5, momentum=0.1): + super().__init__() + + self.num_features = num_features + self.eps = eps + self.momentum = momentum + self.weight = nn.Parameter(torch.empty(num_features)) + self.bias = nn.Parameter(torch.empty(num_features)) + self.register_buffer("running_mean", torch.zeros(num_features)) + self.register_buffer("running_var", torch.ones(num_features)) + self.reset_parameters() + + def reset_running_stats(self): + self.running_mean.zero_() + self.running_var.fill_(1) + + def reset_parameters(self): + self.reset_running_stats() + init.uniform_(self.weight) + init.zeros_(self.bias) + + def forward(self, input_): + batchsize, channels, height, width = input_.size() + numel = batchsize * height * width + input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel) + sum_ = input_.sum(1) + sum_of_square = input_.pow(2).sum(1) + mean = sum_ / numel + sumvar = sum_of_square - sum_ * mean + + self.running_mean = ( + 1 - self.momentum + ) * self.running_mean + self.momentum * mean.detach() + unbias_var = sumvar / (numel - 1) + self.running_var = ( + 1 - self.momentum + ) * self.running_var + self.momentum * unbias_var.detach() + + bias_var = sumvar / numel + inv_std = 1 / (bias_var + self.eps).pow(0.5) + output = (input_ - mean.unsqueeze(1)) * inv_std.unsqueeze( + 1 + ) * self.weight.unsqueeze(1) + self.bias.unsqueeze(1) + + return ( + output.view(channels, batchsize, height, width) + .permute(1, 0, 2, 3) + .contiguous() + ) diff --git a/BigGAN_PyTorch/sync_batchnorm/comm.py b/BigGAN_PyTorch/sync_batchnorm/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..5239963152228c015f64ad8d5087a7d4eb15d1b6 --- /dev/null +++ b/BigGAN_PyTorch/sync_batchnorm/comm.py @@ -0,0 +1,145 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ["FutureResult", "SlavePipe", "SyncMaster"] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, "Previous result has't been fetched." + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple("MasterRegistry", ["result"]) +_SlavePipeBase = collections.namedtuple( + "_SlavePipeBase", ["identifier", "queue", "result"] +) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {"master_callback": self._master_callback} + + def __setstate__(self, state): + self.__init__(state["master_callback"]) + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), "Queue is not clean before next initialization." + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, "The first result should belongs to the master." + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/BigGAN_PyTorch/sync_batchnorm/replicate.py b/BigGAN_PyTorch/sync_batchnorm/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..644a7a9a2387b3f1e7c53175eff2d92fe928f394 --- /dev/null +++ b/BigGAN_PyTorch/sync_batchnorm/replicate.py @@ -0,0 +1,100 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + "CallbackContext", + "execute_replication_callbacks", + "DataParallelWithCallback", + "patch_replication_callback", +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, "__data_parallel_replicate__"): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/BigGAN_PyTorch/sync_batchnorm/unittest.py b/BigGAN_PyTorch/sync_batchnorm/unittest.py new file mode 100644 index 0000000000000000000000000000000000000000..c5dc32b4ca0245f8788e07823fc65dea240de828 --- /dev/null +++ b/BigGAN_PyTorch/sync_batchnorm/unittest.py @@ -0,0 +1,32 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest +import torch + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, x, y): + adiff = float((x - y).abs().max()) + if (y == 0).all(): + rdiff = "NaN" + else: + rdiff = float((adiff / y).abs().max()) + + message = ("Tensor close check failed\n" "adiff={}\n" "rdiff={}\n").format( + adiff, rdiff + ) + self.assertTrue(torch.allclose(x, y), message) diff --git a/BigGAN_PyTorch/train_fns.py b/BigGAN_PyTorch/train_fns.py new file mode 100644 index 0000000000000000000000000000000000000000..8e3994fef1ca77cd91154f92b822788d36d397f7 --- /dev/null +++ b/BigGAN_PyTorch/train_fns.py @@ -0,0 +1,362 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock + +# MIT License +""" train_fns.py +Functions for the main loop of training different conditional image models +""" +import torch + +import utils +import losses + + +# Dummy training function for debugging +def dummy_training_function(): + def train(x, y): + return {} + + return train + + +def GAN_training_function( + G, + D, + GD, + ema, + state_dict, + config, + sample_conditionings, + embedded_optimizers=True, + device="cuda", + batch_size=0, +): + def train(x, y=None, features=None): + if embedded_optimizers: + G.optim.zero_grad() + D.optim.zero_grad() + else: + GD.optimizer_D.zero_grad() + GD.optimizer_G.zero_grad() + # How many chunks to split x and y into? + x = torch.split(x, batch_size) + if y is not None: + y = torch.split(y, batch_size) + if features is not None: + f_ = torch.split(features, batch_size) + else: + f_ = None + counter = 0 + + # Optionally toggle D and G's "require_grad" + if config["toggle_grads"]: + utils.toggle_grad(D, True) + utils.toggle_grad(G, False) + + for step_index in range(config["num_D_steps"]): + # If accumulating gradients, loop multiple times before an optimizer step + if embedded_optimizers: + D.optim.zero_grad() + else: + GD.optimizer_D.zero_grad() + for accumulation_index in range(config["num_D_accumulations"]): + # Sample conditioning for G + sampled_cond = sample_conditionings() + labels_g, f_g = None, None + if features is not None and y is not None: + z_, labels_g, f_g = sampled_cond + elif y is not None: + z_, labels_g = sampled_cond + elif features is not None: + z_, f_g = sampled_cond + # Tensors to device + if labels_g is not None: + labels_g = ( + labels_g[:batch_size].to(device, non_blocking=True).long() + ) + if f_g is not None: + f_g = f_g[:batch_size].to(device, non_blocking=True) + z_ = z_[:batch_size].to(device, non_blocking=True) + # Obtain discriminator scores + D_fake, D_real = GD( + z_, + labels_g, + f_g, + x[counter], + y[counter] if y is not None else None, + f_[counter] if f_ is not None else None, + train_G=False, + split_D=config["split_D"], + policy=config["DiffAugment"], + DA=config["DA"], + ) + + # Compute components of D's loss, average them, and divide by + # the number of gradient accumulations + D_loss_real, D_loss_fake = losses.discriminator_loss(D_fake, D_real) + D_loss = (D_loss_real + D_loss_fake) / float( + config["num_D_accumulations"] + ) + D_loss.backward() + counter += 1 + + # Optionally apply ortho reg in D + if config["D_ortho"] > 0.0: + # Debug print to indicate we're using ortho reg in D. + print("using modified ortho reg in D") + utils.ortho(D, config["D_ortho"]) + + if embedded_optimizers: + D.optim.step() + else: + GD.optimizer_D.step() + + # Optionally toggle "requires_grad" + if config["toggle_grads"]: + utils.toggle_grad(D, False) + utils.toggle_grad(G, True) + + # Zero G's gradients by default before training G, for safety + if embedded_optimizers: + G.optim.zero_grad() + else: + GD.optimizer_G.zero_grad() + + counter = 0 + # If accumulating gradients, loop multiple times + for accumulation_index in range(config["num_G_accumulations"]): + # Sample conditioning for G + sampled_cond = sample_conditionings() + labels_g, f_g = None, None + if features is not None and y is not None: + z_, labels_g, f_g = sampled_cond + elif y is not None: + z_, labels_g = sampled_cond + elif features is not None: + z_, f_g = sampled_cond + # Tensors to device + if labels_g is not None: + labels_g = labels_g.to(device, non_blocking=True).long() + if f_g is not None: + f_g = f_g.to(device, non_blocking=True) + z_ = z_.to(device, non_blocking=True) + # Obtain discriminator scores + D_fake = GD( + z_, + labels_g, + f_g, + train_G=True, + split_D=config["split_D"], + policy=config["DiffAugment"], + DA=config["DA"], + ) + G_loss = losses.generator_loss(D_fake) / float( + config["num_G_accumulations"] + ) + G_loss.backward() + counter += 1 + + # Optionally apply modified ortho reg in G + if config["G_ortho"] > 0.0: + print( + "using modified ortho reg in G" + ) # Debug print to indicate we're using ortho reg in G + # Don't ortho reg shared, it makes no sense. Really we should blacklist any embeddings for this + utils.ortho( + G, + config["G_ortho"], + blacklist=[param for param in G.shared.parameters()], + ) + if embedded_optimizers: + G.optim.step() + else: + GD.optimizer_G.step() + + # If we have an ema, update it, regardless of if we test with it or not + if config["ema"]: + ema.update(state_dict["itr"]) + + out = { + "G_loss": float(G_loss.item()), + "D_loss_real": float(D_loss_real.item()), + "D_loss_fake": float(D_loss_fake.item()), + } + # Return G's loss and the components of D's loss. + return out + + return train + + +def save_weights( + G, + D, + G_ema, + state_dict, + config, + experiment_name, + embedded_optimizers=True, + G_optim=None, + D_optim=None, +): + utils.save_weights( + G, + D, + state_dict, + config["weights_root"], + experiment_name, + None, + G_ema if config["ema"] else None, + embedded_optimizers=embedded_optimizers, + G_optim=G_optim, + D_optim=D_optim, + ) + # Save an additional copy to mitigate accidental corruption if process + # is killed during a save (it's happened to me before -.-) + if config["num_save_copies"] > 0: + utils.save_weights( + G, + D, + state_dict, + config["weights_root"], + experiment_name, + "copy%d" % state_dict["save_num"], + G_ema if config["ema"] else None, + embedded_optimizers=embedded_optimizers, + G_optim=G_optim, + D_optim=D_optim, + ) + state_dict["save_num"] = (state_dict["save_num"] + 1) % config[ + "num_save_copies" + ] + + +""" This function takes in the model, saves the weights (multiple copies if + requested), and prepares sample sheets: one consisting of samples given + a fixed noise seed (to show how the model evolves throughout training), + a set of full conditional sample sheets, and a set of interp sheets. """ + + +def save_and_sample( + G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name +): + utils.save_weights( + G, + D, + state_dict, + config["weights_root"], + experiment_name, + None, + G_ema if config["ema"] else None, + ) + # Save an additional copy to mitigate accidental corruption if process + # is killed during a save (it's happened to me before -.-) + if config["num_save_copies"] > 0: + utils.save_weights( + G, + D, + state_dict, + config["weights_root"], + experiment_name, + "copy%d" % state_dict["save_num"], + G_ema if config["ema"] else None, + ) + state_dict["save_num"] = (state_dict["save_num"] + 1) % config[ + "num_save_copies" + ] + + # Accumulate standing statistics? + if config["accumulate_stats"]: + utils.accumulate_standing_stats( + G_ema if config["ema"] and config["use_ema"] else G, + z_, + y_, + config["n_classes"], + config["num_standing_accumulations"], + ) + + +""" This function runs the inception metrics code, checks if the results + are an improvement over the previous best (either in IS or FID, + user-specified), logs the results, and saves a best_ copy if it's an + improvement. """ + + +def test( + G, + D, + G_ema, + z_, + y_, + state_dict, + config, + sample, + get_inception_metrics, + experiment_name, + test_log, + loader=None, + embedded_optimizers=True, + G_optim=None, + D_optim=None, + rank=0, +): + print("Gathering inception metrics...") + if config["accumulate_stats"]: + utils.accumulate_standing_stats( + G_ema if config["ema"] and config["use_ema"] else G, + z_, + y_, + config["n_classes"], + config["num_standing_accumulations"], + ) + if loader is not None: + IS_mean, IS_std, FID, stratified_FID, prdc_metrics = get_inception_metrics( + sample, config["num_inception_images"], num_splits=10, loader_ref=loader + ) + else: + IS_mean, IS_std, FID, stratified_FID = get_inception_metrics( + sample, config["num_inception_images"], num_splits=10 + ) + print( + "Itr %d: PYTORCH UNOFFICIAL Inception Score is %3.3f +/- %3.3f, PYTORCH UNOFFICIAL FID is %5.4f" + % (state_dict["itr"], IS_mean, IS_std, FID) + ) + # If improved over previous best metric, save approrpiate copy + if rank == 0: + if (config["which_best"] == "IS" and IS_mean > state_dict["best_IS"]) or ( + config["which_best"] == "FID" and FID < state_dict["best_FID"] + ): + print( + "%s improved over previous best, saving checkpoint..." + % config["which_best"] + ) + utils.save_weights( + G, + D, + state_dict, + config["weights_root"], + experiment_name, + "best%d" % state_dict["save_best_num"], + G_ema if config["ema"] else None, + embedded_optimizers=embedded_optimizers, + G_optim=G_optim, + D_optim=D_optim, + ) + state_dict["save_best_num"] = (state_dict["save_best_num"] + 1) % config[ + "num_best_copies" + ] + state_dict["best_IS"] = max(state_dict["best_IS"], IS_mean) + state_dict["best_FID"] = min(state_dict["best_FID"], FID) + # Log results to file + test_log.log( + itr=int(state_dict["itr"]), + IS_mean=float(IS_mean), + IS_std=float(IS_std), + FID=float(FID), + ) + return IS_mean, FID diff --git a/BigGAN_PyTorch/trainer.py b/BigGAN_PyTorch/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6f3df0984fd0f0477c3f547397b4483d484b4ba0 --- /dev/null +++ b/BigGAN_PyTorch/trainer.py @@ -0,0 +1,541 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License + +import os +import functools +import math +from tqdm import tqdm, trange +import argparse +import time +import subprocess +import re +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +import numpy as np + +import torch +import torch.nn as nn + +import torch.distributed as dist +import torch.multiprocessing as mp +from torch.nn.parallel import DistributedDataParallel as DDP +import torch.optim as optim + +# Import my stuff +import data_utils.inception_utils as inception_utils +import utils +import train_fns +from sync_batchnorm import patch_replication_callback +from data_utils import utils as data_utils + + +def run(config, ddp_setup="slurm", master_node=""): + config["n_classes"] = 1000 # utils.nclass_dict[self.config['dataset']] + config["G_activation"] = utils.activation_dict[config["G_nl"]] + config["D_activation"] = utils.activation_dict[config["D_nl"]] + config = utils.update_config_roots(config) + + # Prepare root folders if necessary + utils.prepare_root(config) + + if config["ddp_train"]: + if ddp_setup == "slurm": + n_nodes = int(os.environ.get("SLURM_JOB_NUM_NODES")) + n_gpus_per_node = int(os.environ.get("SLURM_TASKS_PER_NODE").split("(")[0]) + world_size = n_gpus_per_node * n_nodes + print( + "Master node is ", + master_node, + " World size is ", + world_size, + " with ", + n_gpus_per_node, + "gpus per node.", + ) + dist_url = "tcp://" + dist_url += master_node + port = 40000 + dist_url += ":" + str(port) + print("Dist url ", dist_url) + train(-1, world_size, config, dist_url) + else: + world_size = torch.cuda.device_count() + dist_url = "env://" + mp.spawn( + train, args=(world_size, config, dist_url), nprocs=world_size, join=True + ) + else: + train(0, -1, config, None) + + +def train(rank, world_size, config, dist_url): + print("Rank of this job is ", rank) + copy_locally = False + tmp_dir = "" + if config["ddp_train"]: + if dist_url == "env://": + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12355" + local_rank = rank + else: + rank = int(os.environ.get("SLURM_PROCID")) + local_rank = int(os.environ.get("SLURM_LOCALID")) + copy_locally = True + tmp_dir = "/scratch/slurm_tmpdir/" + str(os.environ.get("SLURM_JOB_ID")) + + print("Before setting process group") + print(dist_url, rank) + dist.init_process_group( + backend="nccl", init_method=dist_url, rank=rank, world_size=world_size + ) + print("After setting process group") + device = "cuda:{}".format(local_rank) # rank % 8) + print(dist_url, rank, " /Device is ", device) + else: + device = "cuda" + local_rank = "cuda" + + # Update the config dict as necessary + # This is for convenience, to add settings derived from the user-specified + # configuration into the config-dict (e.g. inferring the number of classes + # and size of the images from the dataset, passing in a pytorch object + # for the activation specified as a string)' + + # Seed RNG + utils.seed_rng(config["seed"] + rank) + + # Setup cudnn.benchmark for free speed + torch.backends.cudnn.benchmark = True + if config["deterministic_run"]: + torch.backends.cudnn.deterministic = True + + # Import the model--this line allows us to dynamically select different files. + model = __import__(config["model"]) + experiment_name = ( + config["experiment_name"] + if config["experiment_name"] + else utils.name_from_config(config) + ) + print("Experiment name is %s" % experiment_name) + + if config["ddp_train"]: + torch.cuda.set_device(device) + # Next, build the model + G = model.Generator(**{**config, "embedded_optimizers": False}).to(device) + D = model.Discriminator(**{**config, "embedded_optimizers": False}).to(device) + + # If using EMA, prepare it + if config["ema"]: + print("Preparing EMA for G with decay of {}".format(config["ema_decay"])) + G_ema = model.Generator(**{**config, "skip_init": True, "no_optim": True}).to( + device + ) + ema = utils.ema(G, G_ema, config["ema_decay"], config["ema_start"]) + else: + G_ema, ema = None, None + + print( + "Number of params in G: {} D: {}".format( + *[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]] + ) + ) + + # Setup the optimizers + if config["D_fp16"]: + print("Using fp16 adam ") + optim_type = utils.Adam16 + else: + optim_type = optim.Adam + optimizer_D = optim_type( + params=D.parameters(), + lr=config["D_lr"], + betas=(config["D_B1"], config["D_B2"]), + weight_decay=0, + eps=config["adam_eps"], + ) + optimizer_G = optim_type( + params=G.parameters(), + lr=config["G_lr"], + betas=(config["G_B1"], config["G_B2"]), + weight_decay=0, + eps=config["adam_eps"], + ) + + # Prepare state dict, which holds things like epoch # and itr # + state_dict = { + "itr": 0, + "epoch": 0, + "save_num": 0, + "save_best_num": 0, + "best_IS": 0, + "best_FID": 999999, + "es_epoch": 0, + "config": config, + } + + # FP16? + if config["G_fp16"]: + print("Casting G to float16...") + G = G.half() + if config["ema"]: + G_ema = G_ema.half() + if config["D_fp16"]: + print("Casting D to fp16...") + D = D.half() + + ## DDP the models + if config["ddp_train"]: + print("before G DDP ") + G = DDP( + G, + device_ids=[local_rank], + output_device=local_rank, + find_unused_parameters=True, + ) + print("After G DDP ") + D = DDP( + D, + device_ids=[local_rank], + output_device=local_rank, + find_unused_parameters=True, + ) + + # If loading from a pre-trained model, load weights + print("Loading weights...") + if config["ddp_train"]: + dist.barrier() + map_location = device + else: + map_location = None + + utils.load_weights( + G, + D, + state_dict, + config["weights_root"], + experiment_name, + config["load_weights"] if config["load_weights"] else None, + G_ema if config["ema"] else None, + map_location=map_location, + embedded_optimizers=False, + G_optim=optimizer_G, + D_optim=optimizer_D, + ) + + # wrapper class + GD = model.G_D(G, D, optimizer_G=optimizer_G, optimizer_D=optimizer_D) + + if config["parallel"] and world_size > -1: + GD = nn.DataParallel(GD) + if config["cross_replica"]: + patch_replication_callback(GD) + + # Prepare loggers for stats; metrics holds test metrics, + # lmetrics holds any desired training metrics. + if rank == 0: + test_metrics_fname = "%s/%s_log.jsonl" % (config["logs_root"], experiment_name) + train_metrics_fname = "%s/%s" % (config["logs_root"], experiment_name) + print("Inception Metrics will be saved to {}".format(test_metrics_fname)) + test_log = utils.MetricsLogger(test_metrics_fname, reinitialize=False) + print("Training Metrics will be saved to {}".format(train_metrics_fname)) + train_log = utils.MyLogger( + train_metrics_fname, reinitialize=False, logstyle=config["logstyle"] + ) + # Write metadata + utils.write_metadata(config["logs_root"], experiment_name, config, state_dict) + else: + test_log = None + train_log = None + + D_batch_size = ( + config["batch_size"] * config["num_D_steps"] * config["num_D_accumulations"] + ) + + if config["longtail"]: + samples_per_class = np.load( + "imagenet_lt/imagenet_lt_samples_per_class.npy", allow_pickle=True + ) + class_probabilities = np.load( + "imagenet_lt/imagenet_lt_class_prob.npy", allow_pickle=True + ) + else: + samples_per_class, class_probabilities = None, None + + train_dataset = data_utils.get_dataset_hdf5( + **{ + **config, + "data_path": config["data_root"], + "batch_size": D_batch_size, + "augment": config["hflips"], + "local_rank": local_rank, + "copy_locally": copy_locally, + "tmp_dir": tmp_dir, + "ddp": config["ddp_train"], + } + ) + train_loader = data_utils.get_dataloader( + **{ + **config, + "dataset": train_dataset, + "batch_size": config["batch_size"], + "start_epoch": state_dict["epoch"], + "start_itr": state_dict["itr"], + "longtail_temperature": config["longtail_temperature"], + "samples_per_class": samples_per_class, + "class_probabilities": class_probabilities, + "rank": rank, + "world_size": world_size, + "shuffle": True, + "drop_last": True, + } + ) + + # Prepare inception metrics: FID and IS + is_moments_prefix = "I" if config["which_dataset"] == "imagenet" else "COCO" + + im_filename = "%s%i_%s" % ( + is_moments_prefix, + config["resolution"], + "" if not config["longtail"] else "longtail", + ) + print("Using ", im_filename, "for Inception metrics.") + + get_inception_metrics = inception_utils.prepare_inception_metrics( + im_filename, + samples_per_class, + config["parallel"], + config["no_fid"], + config["data_root"], + device=device, + ) + + G_batch_size = config["G_batch_size"] + + z_, y_ = data_utils.prepare_z_y( + G_batch_size, + G.module.dim_z if config["ddp_train"] else G.dim_z, + config["n_classes"], + device=device, + fp16=config["G_fp16"], + longtail_gen=config["longtail_gen"], + custom_distrib=config["custom_distrib_gen"], + longtail_temperature=config["longtail_temperature"], + class_probabilities=class_probabilities, + ) + + # Balance instance sampling for ImageNet-LT + weights_sampling = None + if ( + config["longtail"] + and config["use_balanced_sampler"] + and config["instance_cond"] + ): + if config["which_knn_balance"] == "center_balance": + print( + "Balancing the instance features." "Using custom temperature distrib?", + config["custom_distrib_gen"], + " with temperature", + config["longtail_temperature"], + ) + weights_sampling = data_utils.make_weights_for_balanced_classes( + samples_per_class, + train_loader.dataset.labels, + 1000, + config["custom_distrib_gen"], + config["longtail_temperature"], + class_probabilities=class_probabilities, + ) + # Balancing the NN classes (p(y)) + elif config["which_knn_balance"] == "nnclass_balance": + print( + "Balancing the class distribution (classes drawn from the neighbors)." + " Using custom temperature distrib?", + config["custom_distrib_gen"], + " with temperature", + config["longtail_temperature"], + ) + weights_sampling = torch.exp( + class_probabilities / config["longtail_temperature"] + ) / torch.sum( + torch.exp(class_probabilities / config["longtail_temperature"]) + ) + + # Configure conditioning sampling function to train G + sample_conditioning = functools.partial( + data_utils.sample_conditioning_values, + z_=z_, + y_=y_, + dataset=train_dataset, + batch_size=G_batch_size, + weights_sampling=weights_sampling, + ddp=config["ddp_train"], + constant_conditioning=config["constant_conditioning"], + class_cond=config["class_cond"], + instance_cond=config["instance_cond"], + nn_sampling_strategy=config["which_knn_balance"], + ) + + print("G batch size ", G_batch_size) + # Loaders are loaded, prepare the training function + train = train_fns.GAN_training_function( + G, + D, + GD, + ema, + state_dict, + config, + sample_conditioning, + embedded_optimizers=False, + device=device, + batch_size=G_batch_size, + ) + + # Prepare Sample function for use with inception metrics + sample = functools.partial( + utils.sample, + G=(G_ema if config["ema"] and config["use_ema"] else G), + sample_conditioning_func=sample_conditioning, + config=config, + class_cond=config["class_cond"], + instance_cond=config["instance_cond"], + ) + + print("Beginning training at epoch %d..." % state_dict["epoch"]) + # Train for specified number of epochs, although we mostly track G iterations. + best_FID_run = state_dict["best_FID"] + FID = state_dict["best_FID"] + + for epoch in range(state_dict["epoch"], config["num_epochs"]): + # Set epoch for distributed loader + if config["ddp_train"]: + train_loader.sampler.set_epoch(epoch) + # Initialize seeds at every epoch (useful for conditioning and + # noise sampling, as well as data order in the sampler) + if config["deterministic_run"]: + utils.seed_rng(config["seed"] + rank + state_dict["epoch"]) + # Which progressbar to use? TQDM or my own? + if config["pbar"] == "mine": + pbar = utils.progress( + train_loader, + displaytype="s1k" if config["use_multiepoch_sampler"] else "eta", + ) + else: + pbar = tqdm(train_loader) + s = time.time() + print("Before iteration, dataloader length", len(train_loader)) + for i, batch in enumerate(pbar): + # if i> 5: + # break + in_label, in_feat = None, None + if config["instance_cond"] and config["class_cond"]: + x, in_label, in_feat, _ = batch + elif config["instance_cond"]: + x, in_feat, _ = batch + elif config["class_cond"]: + x, in_label = batch + if config["constant_conditioning"]: + in_label = torch.zeros_like(in_label) + else: + x = batch + + x = x.to(device, non_blocking=True) + if in_label is not None: + in_label = in_label.to(device, non_blocking=True) + if in_feat is not None: + in_feat = in_feat.float().to(device, non_blocking=True) + # Increment the iteration counter + state_dict["itr"] += 1 + # Make sure G and D are in training mode, just in case they got set to eval + # For D, which typically doesn't have BN, this shouldn't matter much. + G.train() + D.train() + if config["ema"]: + G_ema.train() + + metrics = train(x, in_label, in_feat) + # print('After training step ', time.time() - s_stratified) + # s_stratified = time.time() + if rank == 0: + train_log.log(itr=int(state_dict["itr"]), **metrics) + + # If using my progbar, print metrics. + if config["pbar"] == "mine" and rank == 0: + print( + ", ".join( + ["itr: %d" % state_dict["itr"]] + + ["%s : %+4.3f" % (key, metrics[key]) for key in metrics] + ), + end=" ", + ) + # Test every specified interval + + print("Iteration time ", time.time() - s) + s = time.time() + # Increment epoch counter at end of epoch + state_dict["epoch"] += 1 + + if not (state_dict["epoch"] % config["test_every"]): + if config["G_eval_mode"]: + print("Switching G to eval mode...") + G.eval() + D.eval() + + # Compute IS and FID using training dataset as reference + test_time = time.time() + IS, FID = train_fns.test( + G, + D, + G_ema, + z_, + y_, + state_dict, + config, + sample, + get_inception_metrics, + experiment_name, + test_log, + loader=None, + embedded_optimizers=False, + G_optim=optimizer_G, + D_optim=optimizer_D, + rank=rank, + ) + print("Testing took ", time.time() - test_time) + + if 2 * IS < state_dict["best_IS"] and config["stop_when_diverge"]: + print("Experiment diverged!") + break + else: + print("IS is ", IS, " and 2x best is ", 2 * state_dict["best_IS"]) + + if not (state_dict["epoch"] % config["save_every"]) and rank == 0: + train_fns.save_weights( + G, + D, + G_ema, + state_dict, + config, + experiment_name, + embedded_optimizers=False, + G_optim=optimizer_G, + D_optim=optimizer_D, + ) + if rank == 0: + if FID < best_FID_run: + best_FID_run = FID + state_dict["es_epoch"] = 0 + else: + state_dict["es_epoch"] += 1 + if state_dict["es_epoch"] >= config["es_patience"]: + print("reached Early stopping!") + return FID + return FID diff --git a/BigGAN_PyTorch/utils.py b/BigGAN_PyTorch/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a92e2449de827624d264368d725ea8d5915c3323 --- /dev/null +++ b/BigGAN_PyTorch/utils.py @@ -0,0 +1,1772 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License + +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" Utilities file +This file contains utility functions for bookkeeping, logging, and data loading. +Methods which directly affect training should either go in layers, the model, +or train_fns.py. +""" + +from __future__ import print_function +import sys +import os +import numpy as np +import time +import datetime +import json +import pickle +from argparse import ArgumentParser +import random + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision +import torchvision.transforms as transforms + + +def prepare_parser(): + usage = "Parser for all scripts." + parser = ArgumentParser(description=usage) + + parser.add_argument( + "--json_config", + type=str, + default="", + help="Json config from where to load the configuration parameters.", + ) + + ### Dataset/Dataloader stuff ### + parser.add_argument( + "--resolution", + type=int, + default=64, + help="Resolution to train with " "(default: %(default)s)", + ) + parser.add_argument( + "--augment", + action="store_true", + default=False, + help="Augment with random crops and flips (default: %(default)s)", + ) + parser.add_argument( + "--num_workers", + type=int, + default=8, + help="Number of dataloader workers; consider using less for HDF5 " + "(default: %(default)s)", + ) + parser.add_argument( + "--no_pin_memory", + action="store_false", + dest="pin_memory", + default=True, + help="Pin data into memory through dataloader? (default: %(default)s)", + ) + parser.add_argument( + "--shuffle", + action="store_true", + default=False, + help="Shuffle the data (strongly recommended)? (default: %(default)s)", + ) + parser.add_argument( + "--load_in_mem", + action="store_true", + default=False, + help="Load all data into memory? (default: %(default)s)", + ) + parser.add_argument( + "--use_multiepoch_sampler", + action="store_true", + default=False, + help="Use the multi-epoch sampler for dataloader? (default: %(default)s)", + ) + parser.add_argument( + "--use_checkpointable_sampler", + action="store_true", + default=False, + help="Use the checkpointable sampler for dataloader? (default: %(default)s)", + ) + parser.add_argument( + "--use_balanced_sampler", + action="store_true", + default=False, + help="Use the class balanced sampler for dataloader? (default: %(default)s)", + ) + parser.add_argument( + "--longtail_temperature", + type=int, + default=1, + help="Temperature to relax longtail_distribution", + ) + + parser.add_argument( + "--longtail", + action="store_true", + default=False, + help="Use long-tail version of the dataset", + ) + parser.add_argument( + "--longtail_gen", + action="store_true", + default=False, + help="Use long-tail version of class conditioning sampling for generator.", + ) + parser.add_argument( + "--custom_distrib_gen", + action="store_true", + default=False, + help="Use custom distribution for sampling class conditionings in generator.", + ) + + ### Data augmentation ### + parser.add_argument( + "--DiffAugment", type=str, default="", help="DiffAugment policy" + ) + parser.add_argument( + "--DA", + action="store_true", + default=False, + help="Diff Augment for GANs (default: %(default)s)", + ) + parser.add_argument( + "--hflips", + action="store_true", + default=False, + help="Use horizontal flips in data augmentation." "(default: %(default)s)", + ) + + ### IC-GAN arguments ### + parser.add_argument( + "--instance_cond", + action="store_true", + default=False, + help="Use instance features as conditioning", + ) + parser.add_argument( + "--feature_augmentation", + action="store_true", + default=False, + help="use hflips in instance conditionings (default: %(default)s)", + ) + parser.add_argument( + "--which_knn_balance", + type=str, + default="instance_balance", + choices=["instance_balance", "nnclass_balance"], + help="Class balancing either done at the instance level or at the class level.", + ) + parser.add_argument( + "--G_shared_feat", + action="store_true", + default=False, + help="Use fully connected layer for conditioning instance features in G? (default: %(default)s)", + ) + parser.add_argument( + "--shared_dim_feat", + type=int, + default=2048, + help="G" + "s fully connected layer output dimensionality for instance features" + "(default: %(default)s)", + ) + parser.add_argument( + "--k_nn", + type=int, + default=50, + help="Number of neigbors for each instance" "(default: %(default)s)", + ) + parser.add_argument( + "--feature_extractor", + type=str, + default="classification", + choices=["classification", "selfsupervised"], + help="Choice of feature extractor", + ) + parser.add_argument( + "--backbone_feature_extractor", + type=str, + default="resnet50", + choices=["resnet50"], + help="Choice of feature extractor backbone", + ) + + parser.add_argument( + "--eval_instance_set", + type=str, + default="train", + help="(Eval) Dataset split from which to draw conditioning instances (default: %(default)s)", + ) + parser.add_argument( + "--kmeans_subsampled", + type=int, + default=-1, + help="Number of kmeans centers if using subsampled training instances (default: %(default)s)", + ) + parser.add_argument( + "--n_subsampled_data", + type=float, + default=-1, + help="Percent of instances used at test time", + ) + + ### COCO_Stuff evaluation ### + parser.add_argument( + "--filter_hd", + type=int, + default=-1, + help="Hamming distance to filter val test in COCO_Stuff (by default no filtering) (default: %(default)s)", + ) + + ### Model stuff ### + parser.add_argument( + "--model", + type=str, + default="BigGAN", + help="Name of the model module (default: %(default)s)", + ) + parser.add_argument( + "--G_param", + type=str, + default="SN", + help="Parameterization style to use for G, spectral norm (SN) or SVD (SVD)" + " or None (default: %(default)s)", + ) + parser.add_argument( + "--D_param", + type=str, + default="SN", + help="Parameterization style to use for D, spectral norm (SN) or SVD (SVD)" + " or None (default: %(default)s)", + ) + parser.add_argument( + "--G_ch", + type=int, + default=64, + help="Channel multiplier for G (default: %(default)s)", + ) + parser.add_argument( + "--D_ch", + type=int, + default=64, + help="Channel multiplier for D (default: %(default)s)", + ) + parser.add_argument( + "--G_depth", + type=int, + default=1, + help="Number of resblocks per stage in G? (default: %(default)s)", + ) + parser.add_argument( + "--D_depth", + type=int, + default=1, + help="Number of resblocks per stage in D? (default: %(default)s)", + ) + parser.add_argument( + "--D_thin", + action="store_false", + dest="D_wide", + default=True, + help="Use the SN-GAN channel pattern for D? (default: %(default)s)", + ) + parser.add_argument( + "--G_shared", + action="store_true", + default=True, + help="Use shared embeddings in G? (default: %(default)s)", + ) + parser.add_argument( + "--shared_dim", + type=int, + default=0, + help="G" + "s shared embedding dimensionality; if 0, will be equal to dim_z. " + "(default: %(default)s)", + ) + parser.add_argument( + "--dim_z", type=int, default=120, help="Noise dimensionality: %(default)s)" + ) + parser.add_argument( + "--z_var", type=float, default=1.0, help="Noise variance: %(default)s)" + ) + parser.add_argument( + "--hier", + action="store_true", + default=False, + help="Use hierarchical z in G? (default: %(default)s)", + ) + parser.add_argument( + "--syncbn", + action="store_true", + default=False, + help="Sync batch norm? (default: %(default)s)", + ) + parser.add_argument( + "--cross_replica", + action="store_true", + default=False, + help="Cross_replica batchnorm in G?(default: %(default)s)", + ) + parser.add_argument( + "--mybn", + action="store_true", + default=False, + help="Use my batchnorm (which supports standing stats?) %(default)s)", + ) + parser.add_argument( + "--G_nl", + type=str, + default="relu", + help="Activation function for G (default: %(default)s)", + ) + parser.add_argument( + "--D_nl", + type=str, + default="relu", + help="Activation function for D (default: %(default)s)", + ) + parser.add_argument( + "--G_attn", + type=str, + default="64", + help="What resolutions to use attention on for G (underscore separated) " + "(default: %(default)s)", + ) + parser.add_argument( + "--D_attn", + type=str, + default="64", + help="What resolutions to use attention on for D (underscore separated) " + "(default: %(default)s)", + ) + parser.add_argument( + "--norm_style", + type=str, + default="bn", + help="Normalizer style for G, one of bn [batchnorm], in [instancenorm], " + "ln [layernorm], gn [groupnorm] (default: %(default)s)", + ) + + ### Model init stuff ### + parser.add_argument( + "--seed", + type=int, + default=0, + help="Random seed to use; affects both initialization and " + " dataloading. (default: %(default)s)", + ) + parser.add_argument( + "--G_init", + type=str, + default="ortho", + help="Init style to use for G (default: %(default)s)", + ) + parser.add_argument( + "--D_init", + type=str, + default="ortho", + help="Init style to use for D(default: %(default)s)", + ) + parser.add_argument( + "--skip_init", + action="store_true", + default=False, + help="Skip initialization, ideal for testing when ortho init was used " + "(default: %(default)s)", + ) + + ### Optimizer stuff ### + parser.add_argument( + "--G_lr", + type=float, + default=5e-5, + help="Learning rate to use for Generator (default: %(default)s)", + ) + parser.add_argument( + "--D_lr", + type=float, + default=2e-4, + help="Learning rate to use for Discriminator (default: %(default)s)", + ) + parser.add_argument( + "--G_B1", + type=float, + default=0.0, + help="Beta1 to use for Generator (default: %(default)s)", + ) + parser.add_argument( + "--D_B1", + type=float, + default=0.0, + help="Beta1 to use for Discriminator (default: %(default)s)", + ) + parser.add_argument( + "--G_B2", + type=float, + default=0.999, + help="Beta2 to use for Generator (default: %(default)s)", + ) + parser.add_argument( + "--D_B2", + type=float, + default=0.999, + help="Beta2 to use for Discriminator (default: %(default)s)", + ) + + ### Batch size, parallel, and precision stuff ### + parser.add_argument( + "--batch_size", + type=int, + default=64, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--G_batch_size", + type=int, + default=0, + help="Batch size to use for G; if 0, same as D (default: %(default)s)", + ) + parser.add_argument( + "--num_G_accumulations", + type=int, + default=1, + help="Number of passes to accumulate G" + "s gradients over " + "(default: %(default)s)", + ) + parser.add_argument( + "--num_D_steps", + type=int, + default=2, + help="Number of D steps per G step (default: %(default)s)", + ) + parser.add_argument( + "--num_D_accumulations", + type=int, + default=1, + help="Number of passes to accumulate D" + "s gradients over " + "(default: %(default)s)", + ) + parser.add_argument( + "--split_D", + action="store_true", + default=False, + help="Run D twice rather than concatenating inputs? (default: %(default)s)", + ) + parser.add_argument( + "--num_epochs", + type=int, + default=100, + help="Number of epochs to train for (default: %(default)s)", + ) + parser.add_argument( + "--parallel", + action="store_true", + default=False, + help="Train with multiple GPUs (default: %(default)s)", + ) + parser.add_argument( + "--G_fp16", + action="store_true", + default=False, + help="Train with half-precision in G? (default: %(default)s)", + ) + parser.add_argument( + "--D_fp16", + action="store_true", + default=False, + help="Train with half-precision in D? (default: %(default)s)", + ) + parser.add_argument( + "--D_mixed_precision", + action="store_true", + default=False, + help="Train with half-precision activations but fp32 params in D? " + "(default: %(default)s)", + ) + parser.add_argument( + "--G_mixed_precision", + action="store_true", + default=False, + help="Train with half-precision activations but fp32 params in G? " + "(default: %(default)s)", + ) + parser.add_argument( + "--accumulate_stats", + action="store_true", + default=False, + help='Accumulate "standing" batchnorm stats? (default: %(default)s)', + ) + parser.add_argument( + "--num_standing_accumulations", + type=int, + default=16, + help="Number of forward passes to use in accumulating standing stats? " + "(default: %(default)s)", + ) + + ### Bookkeping stuff ### + parser.add_argument( + "--slurm_logdir", + help="Where to save the logs from SLURM", + required=False, + default="biggan-training-runs", + metavar="DIR", + ) + + parser.add_argument( + "--G_eval_mode", + action="store_true", + default=False, + help="Run G in eval mode (running/standing stats?) at sample/test time? " + "(default: %(default)s)", + ) + parser.add_argument( + "--save_every", + type=int, + default=2000, + help="Save every X iterations (default: %(default)s)", + ) + parser.add_argument( + "--num_save_copies", + type=int, + default=2, + help="How many copies to save (default: %(default)s)", + ) + parser.add_argument( + "--num_best_copies", + type=int, + default=2, + help="How many previous best checkpoints to save (default: %(default)s)", + ) + parser.add_argument( + "--which_best", + type=str, + default="IS", + help='Which metric to use to determine when to save new "best"' + "checkpoints, one of IS or FID (default: %(default)s)", + ) + parser.add_argument( + "--no_fid", + action="store_true", + default=False, + help="Calculate IS only, not FID? (default: %(default)s)", + ) + parser.add_argument( + "--test_every", + type=int, + default=5000, + help="Test every X iterations (default: %(default)s)", + ) + parser.add_argument( + "--num_inception_images", + type=int, + default=50000, + help="Number of samples to compute inception metrics with " + "(default: %(default)s)", + ) + parser.add_argument( + "--hashname", + action="store_true", + default=False, + help="Use a hash of the experiment name instead of the full config " + "(default: %(default)s)", + ) + parser.add_argument( + "--base_root", + type=str, + default="", + help="Default location to store all weights, samples, data, and logs " + " (default: %(default)s)", + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where data is stored (default: %(default)s)", + ) + parser.add_argument( + "--weights_root", + type=str, + default="weights", + help="Default location to store weights (default: %(default)s)", + ) + parser.add_argument( + "--logs_root", + type=str, + default="logs", + help="Default location to store logs (default: %(default)s)", + ) + parser.add_argument( + "--samples_root", + type=str, + default="samples", + help="Default location to store samples (default: %(default)s)", + ) + parser.add_argument( + "--pbar", + type=str, + default="mine", + help='Type of progressbar to use; one of "mine" or "tqdm" ' + "(default: %(default)s)", + ) + parser.add_argument( + "--name_suffix", + type=str, + default="", + help="Suffix for experiment name for loading weights for sampling " + '(consider "best0") (default: %(default)s)', + ) + parser.add_argument( + "--experiment_name", + type=str, + default="", + help="Optionally override the automatic experiment naming with this arg. " + "(default: %(default)s)", + ) + parser.add_argument( + "--config_from_name", + action="store_true", + default=False, + help="Use a hash of the experiment name instead of the full config " + "(default: %(default)s)", + ) + + ### EMA Stuff ### + parser.add_argument( + "--ema", + action="store_true", + default=False, + help="Keep an ema of G" "s weights? (default: %(default)s)", + ) + parser.add_argument( + "--ema_decay", + type=float, + default=0.9999, + help="EMA decay rate (default: %(default)s)", + ) + parser.add_argument( + "--use_ema", + action="store_true", + default=False, + help="Use the EMA parameters of G for evaluation? (default: %(default)s)", + ) + parser.add_argument( + "--ema_start", + type=int, + default=20000, + help="When to start updating the EMA weights (default: %(default)s)", + ) + + ### Numerical precision and SV stuff ### + parser.add_argument( + "--adam_eps", + type=float, + default=1e-6, + help="epsilon value to use for Adam (default: %(default)s)", + ) + parser.add_argument( + "--BN_eps", + type=float, + default=1e-5, + help="epsilon value to use for BatchNorm (default: %(default)s)", + ) + parser.add_argument( + "--SN_eps", + type=float, + default=1e-6, + help="epsilon value to use for Spectral Norm(default: %(default)s)", + ) + parser.add_argument( + "--num_G_SVs", + type=int, + default=1, + help="Number of SVs to track in G (default: %(default)s)", + ) + parser.add_argument( + "--num_D_SVs", + type=int, + default=1, + help="Number of SVs to track in D (default: %(default)s)", + ) + parser.add_argument( + "--num_G_SV_itrs", + type=int, + default=1, + help="Number of SV itrs in G (default: %(default)s)", + ) + parser.add_argument( + "--num_D_SV_itrs", + type=int, + default=1, + help="Number of SV itrs in D (default: %(default)s)", + ) + + parser.add_argument( + "--class_cond", + action="store_true", + default=False, + help="Use classes as conditioning", + ) + parser.add_argument( + "--constant_conditioning", + action="store_true", + default=False, + help="Use a a class-conditioning vector where the input label is always 0? (default: %(default)s)", + ) + + parser.add_argument( + "--which_dataset", + type=str, + default="imagenet", + # choices=['imagenet','coco', 'coco_40k'], + help="Dataset choice.", + ) + + ### Ortho reg stuff ### + parser.add_argument( + "--G_ortho", + type=float, + default=0.0, # 1e-4 is default for BigGAN + help="Modified ortho reg coefficient in G(default: %(default)s)", + ) + parser.add_argument( + "--D_ortho", + type=float, + default=0.0, + help="Modified ortho reg coefficient in D (default: %(default)s)", + ) + parser.add_argument( + "--toggle_grads", + action="store_true", + default=True, + help="Toggle D and G" + 's "requires_grad" settings when not training them? ' + " (default: %(default)s)", + ) + + ### Which train functions/setup ### + parser.add_argument( + "--partition", + help="Partition name for SLURM", + required=False, + default="learnlab", + ) + parser.add_argument( + "--which_train_fn", + type=str, + default="GAN", + help="How2trainyourbois (default: %(default)s)", + ) + parser.add_argument( + "--run_setup", + type=str, + default="slurm", + help="If local_debug or slurm (default: %(default)s)", + ) + parser.add_argument( + "--ddp_train", + action="store_true", + default=False, + help="If use DDP for training", + ) + parser.add_argument( + "--n_nodes", + type=int, + default=1, + help="Number of nodes for ddp (default: %(default)s)", + ) + parser.add_argument( + "--n_gpus_per_node", + type=int, + default=1, + help="Number of gpus per node for ddp (default: %(default)s)", + ) + parser.add_argument( + "--stop_when_diverge", + action="store_true", + default=False, + help="Stop the experiment if there is signs of divergence. " + "(default: %(default)s)", + ) + parser.add_argument( + "--es_patience", type=int, default=50, help="Epochs for early stopping patience" + ) + parser.add_argument( + "--deterministic_run", + action="store_true", + default=False, + help="Set deterministic cudnn and set the seed at each epoch" + "(default: %(default)s)", + ) + + ### Testing parameters ### + parser.add_argument( + "--eval_prdc", + action="store_true", + default=False, + help="(Eval) Evaluate prdc " " (default: %(default)s)", + ) + parser.add_argument( + "--eval_reference_set", + type=str, + default="train", + help="(Eval) Reference dataset to use for FID computation (default: %(default)s)", + ) + + ### Resume training stuff + parser.add_argument( + "--load_weights", + type=str, + default="", + help="Suffix for which weights to load (e.g. best0, copy0) " + "(default: %(default)s)", + ) + parser.add_argument( + "--resume", + action="store_true", + default=False, + help="Resume training? (default: %(default)s)", + ) + + ### Log stuff ### + parser.add_argument( + "--logstyle", + type=str, + default="%3.3e", + help="What style to use when logging training metrics?" + "One of: %#.#f/ %#.#e (float/exp, text)," + "pickle (python pickle)," + "npz (numpy zip)," + "mat (MATLAB .mat file) (default: %(default)s)", + ) + parser.add_argument( + "--log_G_spectra", + action="store_true", + default=False, + help="Log the top 3 singular values in each SN layer in G? " + "(default: %(default)s)", + ) + parser.add_argument( + "--log_D_spectra", + action="store_true", + default=False, + help="Log the top 3 singular values in each SN layer in D? " + "(default: %(default)s)", + ) + parser.add_argument( + "--sv_log_interval", + type=int, + default=10, + help="Iteration interval for logging singular values " + " (default: %(default)s)", + ) + + return parser + + +# Arguments for sample.py; not presently used in train.py +def add_sample_parser(parser): + parser.add_argument( + "--sample_npz", + action="store_true", + default=False, + help='Sample "sample_num_npz" images and save to npz? ' + "(default: %(default)s)", + ) + parser.add_argument( + "--sample_num_npz", + type=int, + default=50000, + help="Number of images to sample when sampling NPZs " "(default: %(default)s)", + ) + parser.add_argument( + "--sample_sheets", + action="store_true", + default=False, + help="Produce class-conditional sample sheets and stick them in " + "the samples root? (default: %(default)s)", + ) + parser.add_argument( + "--sample_interps", + action="store_true", + default=False, + help="Produce interpolation sheets and stick them in " + "the samples root? (default: %(default)s)", + ) + parser.add_argument( + "--sample_sheet_folder_num", + type=int, + default=-1, + help="Number to use for the folder for these sample sheets " + "(default: %(default)s)", + ) + parser.add_argument( + "--sample_random", + action="store_true", + default=False, + help="Produce a single random sheet? (default: %(default)s)", + ) + parser.add_argument( + "--sample_trunc_curves", + type=str, + default="", + help="Get inception metrics with a range of variances?" + "To use this, specify a startpoint, step, and endpoint, e.g. " + "--sample_trunc_curves 0.2_0.1_1.0 for a startpoint of 0.2, " + "endpoint of 1.0, and stepsize of 1.0. Note that this is " + "not exactly identical to using tf.truncated_normal, but should " + "have approximately the same effect. (default: %(default)s)", + ) + parser.add_argument( + "--sample_inception_metrics", + action="store_true", + default=False, + help="Calculate Inception metrics with sample.py? (default: %(default)s)", + ) + return parser + + +activation_dict = { + "inplace_relu": nn.ReLU(inplace=True), + "relu": nn.ReLU(inplace=False), + "ir": nn.ReLU(inplace=True), +} + + +class CenterCropLongEdge(object): + """Crops the given PIL Image on the long edge. + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. + """ + + def __call__(self, img): + """ + Args: + img (PIL Image): Image to be cropped. + Returns: + PIL Image: Cropped image. + """ + return transforms.functional.center_crop(img, min(img.size)) + + def __repr__(self): + return self.__class__.__name__ + + +class RandomCropLongEdge(object): + """Crops the given PIL Image on the long edge with a random start point. + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. + """ + + def __call__(self, img): + """ + Args: + img (PIL Image): Image to be cropped. + Returns: + PIL Image: Cropped image. + """ + size = (min(img.size), min(img.size)) + # Only step forward along this edge if it's the long edge + i = ( + 0 + if size[0] == img.size[0] + else np.random.randint(low=0, high=img.size[0] - size[0]) + ) + j = ( + 0 + if size[1] == img.size[1] + else np.random.randint(low=0, high=img.size[1] - size[1]) + ) + return transforms.functional.crop(img, i, j, size[0], size[1]) + + def __repr__(self): + return self.__class__.__name__ + + +# Utility file to seed rngs +def seed_rng(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + + +def seed_worker(worker_id): + worker_seed = torch.initial_seed() + worker_id + + +# Utility to peg all roots to a base root +# If a base root folder is provided, peg all other root folders to it. +def update_config_roots(config, change_weight_folder=True): + if config["base_root"]: + print("Pegging all root folders to base root %s" % config["base_root"]) + for key in ["weights", "logs", "samples"]: + if change_weight_folder: + config["%s_root" % key] = "%s/%s" % (config["base_root"], key) + else: + config["%s_root" % key] = "%s" % (config["base_root"]) + return config + + +# Utility to prepare root folders if they don't exist; parent folder must exist +def prepare_root(config): + for key in ["weights_root", "logs_root", "samples_root"]: + if not os.path.exists(config[key]): + print("Making directory %s for %s..." % (config[key], key)) + os.mkdir(config[key]) + + +# Simple wrapper that applies EMA to a model. COuld be better done in 1.0 using +# the parameters() and buffers() module functions, but for now this works +# with state_dicts using .copy_ +class ema(object): + def __init__(self, source, target, decay=0.9999, start_itr=0): + self.source = source + self.target = target + self.decay = decay + # Optional parameter indicating what iteration to start the decay at + self.start_itr = start_itr + # Initialize target's params to be source's + self.source_dict = self.source.state_dict() + self.target_dict = self.target.state_dict() + print("Initializing EMA parameters to be source parameters...") + with torch.no_grad(): + for key in self.source_dict: + self.target_dict[key].data.copy_(self.source_dict[key].data) + # target_dict[key].data = source_dict[key].data # Doesn't work! + + def update(self, itr=None): + # If an iteration counter is provided and itr is less than the start itr, + # peg the ema weights to the underlying weights. + if itr and itr < self.start_itr: + decay = 0.0 + else: + decay = self.decay + with torch.no_grad(): + for key in self.source_dict: + self.target_dict[key].data.copy_( + self.target_dict[key].data * decay + + self.source_dict[key].data * (1 - decay) + ) + + +# Apply modified ortho reg to a model +# This function is an optimized version that directly computes the gradient, +# instead of computing and then differentiating the loss. +def ortho(model, strength=1e-4, blacklist=[]): + with torch.no_grad(): + for param in model.parameters(): + # Only apply this to parameters with at least 2 axes, and not in the blacklist + if len(param.shape) < 2 or any([param is item for item in blacklist]): + continue + w = param.view(param.shape[0], -1) + grad = 2 * torch.mm( + torch.mm(w, w.t()) * (1.0 - torch.eye(w.shape[0], device=w.device)), w + ) + param.grad.data += strength * grad.view(param.shape) + + +# Default ortho reg +# This function is an optimized version that directly computes the gradient, +# instead of computing and then differentiating the loss. +def default_ortho(model, strength=1e-4, blacklist=[]): + with torch.no_grad(): + for param in model.parameters(): + # Only apply this to parameters with at least 2 axes & not in blacklist + if len(param.shape) < 2 or param in blacklist: + continue + w = param.view(param.shape[0], -1) + grad = 2 * torch.mm( + torch.mm(w, w.t()) - torch.eye(w.shape[0], device=w.device), w + ) + param.grad.data += strength * grad.view(param.shape) + + +# Convenience utility to switch off requires_grad +def toggle_grad(model, on_or_off): + for param in model.parameters(): + param.requires_grad = on_or_off + + +# Function to join strings or ignore them +# Base string is the string to link "strings," while strings +# is a list of strings or Nones. +def join_strings(base_string, strings): + return base_string.join([item for item in strings if item]) + + +# Save a model's weights, optimizer, and the state_dict +def save_weights( + G, + D, + state_dict, + weights_root, + experiment_name, + name_suffix=None, + G_ema=None, + embedded_optimizers=True, + G_optim=None, + D_optim=None, +): + root = "/".join([weights_root, experiment_name]) + if not os.path.exists(root): + os.mkdir(root) + if name_suffix: + print("Saving weights to %s/%s..." % (root, name_suffix)) + else: + print("Saving weights to %s..." % root) + torch.save( + G.state_dict(), "%s/%s.pth" % (root, join_strings("_", ["G", name_suffix])) + ) + torch.save( + D.state_dict(), "%s/%s.pth" % (root, join_strings("_", ["D", name_suffix])) + ) + torch.save( + state_dict, "%s/%s.pth" % (root, join_strings("_", ["state_dict", name_suffix])) + ) + + if embedded_optimizers: + torch.save( + G.optim.state_dict(), + "%s/%s.pth" % (root, join_strings("_", ["G_optim", name_suffix])), + ) + torch.save( + D.optim.state_dict(), + "%s/%s.pth" % (root, join_strings("_", ["D_optim", name_suffix])), + ) + else: + torch.save( + G_optim.state_dict(), + "%s/%s.pth" % (root, join_strings("_", ["G_optim", name_suffix])), + ) + torch.save( + D_optim.state_dict(), + "%s/%s.pth" % (root, join_strings("_", ["D_optim", name_suffix])), + ) + if G_ema is not None: + torch.save( + G_ema.state_dict(), + "%s/%s.pth" % (root, join_strings("_", ["G_ema", name_suffix])), + ) + + +# Load a model's weights, optimizer, and the state_dict +def load_weights( + G, + D, + state_dict, + weights_root, + experiment_name, + name_suffix=None, + G_ema=None, + strict=True, + load_optim=True, + eval=False, + map_location=None, + embedded_optimizers=True, + G_optim=None, + D_optim=None, +): + root = "/".join([weights_root, experiment_name]) + if not os.path.exists(root): + print("Not loading data, experiment folder does not exist yet!") + print(root) + if eval: + raise ValueError("Make sure foder exists") + return + + if name_suffix: + print("Loading %s weights from %s..." % (name_suffix, root)) + else: + print("Loading weights from %s..." % root) + if G is not None: + G.load_state_dict( + torch.load( + "%s/%s.pth" % (root, join_strings("_", ["G", name_suffix])), + map_location=map_location, + ), + strict=strict, + ) + if load_optim: + if embedded_optimizers: + G.optim.load_state_dict( + torch.load( + "%s/%s.pth" + % (root, join_strings("_", ["G_optim", name_suffix])), + map_location=map_location, + ) + ) + else: + G_optim.load_state_dict( + torch.load( + "%s/%s.pth" + % (root, join_strings("_", ["G_optim", name_suffix])), + map_location=map_location, + ) + ) + if D is not None: + D.load_state_dict( + torch.load( + "%s/%s.pth" % (root, join_strings("_", ["D", name_suffix])), + map_location=map_location, + ), + strict=strict, + ) + if load_optim: + if embedded_optimizers: + D.optim.load_state_dict( + torch.load( + "%s/%s.pth" + % (root, join_strings("_", ["D_optim", name_suffix])), + map_location=map_location, + ) + ) + else: + D_optim.load_state_dict( + torch.load( + "%s/%s.pth" + % (root, join_strings("_", ["D_optim", name_suffix])), + map_location=map_location, + ) + ) + # Load state dict + for item in state_dict: + try: + state_dict[item] = torch.load( + "%s/%s.pth" % (root, join_strings("_", ["state_dict", name_suffix])), + map_location=map_location, + )[item] + except: + print("No values to load") + if G_ema is not None: + G_ema.load_state_dict( + torch.load( + "%s/%s.pth" % (root, join_strings("_", ["G_ema", name_suffix])), + map_location=map_location, + ), + strict=strict, + ) + + +""" MetricsLogger originally stolen from VoxNet source code. + Used for logging inception metrics""" + + +class MetricsLogger(object): + def __init__(self, fname, reinitialize=False): + self.fname = fname + self.reinitialize = reinitialize + if os.path.exists(self.fname): + if self.reinitialize: + print("{} exists, deleting...".format(self.fname)) + os.remove(self.fname) + + def log(self, record=None, **kwargs): + """ + Assumption: no newlines in the input. + """ + if record is None: + record = {} + record.update(kwargs) + record["_stamp"] = time.time() + with open(self.fname, "a") as f: + f.write(json.dumps(record, ensure_ascii=True) + "\n") + + +# Logstyle is either: +# '%#.#f' for floating point representation in text +# '%#.#e' for exponent representation in text +# 'npz' for output to npz # NOT YET SUPPORTED +# 'pickle' for output to a python pickle # NOT YET SUPPORTED +# 'mat' for output to a MATLAB .mat file # NOT YET SUPPORTED +class MyLogger(object): + def __init__(self, fname, reinitialize=False, logstyle="%3.3f"): + self.root = fname + if not os.path.exists(self.root): + os.mkdir(self.root) + self.reinitialize = reinitialize + self.metrics = [] + self.logstyle = logstyle # One of '%3.3f' or like '%3.3e' + + # Delete log if re-starting and log already exists + def reinit(self, item): + if os.path.exists("%s/%s.log" % (self.root, item)): + if self.reinitialize: + # Only print the removal mess + if "sv" in item: + if not any("sv" in item for item in self.metrics): + print("Deleting singular value logs...") + else: + print( + "{} exists, deleting...".format("%s_%s.log" % (self.root, item)) + ) + os.remove("%s/%s.log" % (self.root, item)) + + # Log in plaintext; this is designed for being read in MATLAB(sorry not sorry) + def log(self, itr, **kwargs): + for arg in kwargs: + if arg not in self.metrics: + if self.reinitialize: + self.reinit(arg) + self.metrics += [arg] + if self.logstyle == "pickle": + print("Pickle not currently supported...") + # with open('%s/%s.log' % (self.root, arg), 'a') as f: + # pickle.dump(kwargs[arg], f) + elif self.logstyle == "mat": + print(".mat logstyle not currently supported...") + else: + with open("%s/%s.log" % (self.root, arg), "a") as f: + f.write("%d: %s\n" % (itr, self.logstyle % kwargs[arg])) + + +# Write some metadata to the logs directory +def write_metadata(logs_root, experiment_name, config, state_dict): + with open(("%s/%s/metalog.txt" % (logs_root, experiment_name)), "w") as writefile: + writefile.write("datetime: %s\n" % str(datetime.datetime.now())) + writefile.write("config: %s\n" % str(config)) + writefile.write("state: %s\n" % str(state_dict)) + + +""" +Very basic progress indicator to wrap an iterable in. + +Author: Jan Schlüter +Andy's adds: time elapsed in addition to ETA, makes it possible to add +estimated time to 1k iters instead of estimated time to completion. +""" + + +def progress(items, desc="", total=None, min_delay=0.1, displaytype="s1k"): + """ + Returns a generator over `items`, printing the number and percentage of + items processed and the estimated remaining processing time before yielding + the next item. `total` gives the total number of items (required if `items` + has no length), and `min_delay` gives the minimum time in seconds between + subsequent prints. `desc` gives an optional prefix text (end with a space). + """ + total = total or len(items) + t_start = time.time() + t_last = 0 + for n, item in enumerate(items): + t_now = time.time() + if t_now - t_last > min_delay: + print( + "\r%s%d/%d (%6.2f%%)" % (desc, n + 1, total, n / float(total) * 100), + end=" ", + ) + if n > 0: + + if displaytype == "s1k": # minutes/seconds for 1000 iters + next_1000 = n + (1000 - n % 1000) + t_done = t_now - t_start + t_1k = t_done / n * next_1000 + outlist = list(divmod(t_done, 60)) + list(divmod(t_1k - t_done, 60)) + print("(TE/ET1k: %d:%02d / %d:%02d)" % tuple(outlist), end=" ") + else: # displaytype == 'eta': + t_done = t_now - t_start + t_total = t_done / n * total + outlist = list(divmod(t_done, 60)) + list( + divmod(t_total - t_done, 60) + ) + print("(TE/ETA: %d:%02d / %d:%02d)" % tuple(outlist), end=" ") + + sys.stdout.flush() + t_last = t_now + yield item + t_total = time.time() - t_start + print( + "\r%s%d/%d (100.00%%) (took %d:%02d)" + % ((desc, total, total) + divmod(t_total, 60)) + ) + + +# Sample function for use with inception metrics +def sample( + G, + sample_conditioning_func, + config, + class_cond=True, + instance_cond=False, + device="cuda", +): + conditioning = sample_conditioning_func() + with torch.no_grad(): + if class_cond and not instance_cond: + z_, y_ = conditioning + y_ = y_.long() + y_ = y_.to(device, non_blocking=True) + feats_ = None + elif instance_cond and not class_cond: + z_, feats_ = conditioning + feats_ = feats_.to(device, non_blocking=True) + y_ = None + elif instance_cond and class_cond: + z_, y_, feats_ = conditioning + y_, feats_ = ( + y_.to(device, non_blocking=True), + feats_.to(device, non_blocking=True), + ) + z_ = z_.to(device, non_blocking=True) + + if config["parallel"]: + G_z = nn.parallel.data_parallel(G, (z_, y_, feats_)) + else: + G_z = G(z_, y_, feats_) + return G_z, y_, feats_ + + +# Sample function for sample sheets +def sample_sheet( + G, + classes_per_sheet, + num_classes, + samples_per_class, + parallel, + samples_root, + experiment_name, + folder_number, + z_=None, +): + # Prepare sample directory + if not os.path.isdir("%s/%s" % (samples_root, experiment_name)): + os.mkdir("%s/%s" % (samples_root, experiment_name)) + if not os.path.isdir("%s/%s/%d" % (samples_root, experiment_name, folder_number)): + os.mkdir("%s/%s/%d" % (samples_root, experiment_name, folder_number)) + # loop over total number of sheets + for i in range(num_classes // classes_per_sheet): + ims = [] + y = torch.arange( + i * classes_per_sheet, (i + 1) * classes_per_sheet, device="cuda" + ) + for j in range(samples_per_class): + if ( + (z_ is not None) + and hasattr(z_, "sample_") + and classes_per_sheet <= z_.size(0) + ): + z_.sample_() + else: + z_ = torch.randn(classes_per_sheet, G.dim_z, device="cuda") + with torch.no_grad(): + if parallel: + o = nn.parallel.data_parallel( + G, (z_[:classes_per_sheet], G.shared(y)) + ) + else: + o = G(z_[:classes_per_sheet], G.shared(y)) + + ims += [o.data.cpu()] + # This line should properly unroll the images + out_ims = ( + torch.stack(ims, 1) + .view(-1, ims[0].shape[1], ims[0].shape[2], ims[0].shape[3]) + .data.float() + .cpu() + ) + # The path for the samples + image_filename = "%s/%s/%d/samples%d.jpg" % ( + samples_root, + experiment_name, + folder_number, + i, + ) + torchvision.utils.save_image( + out_ims, image_filename, nrow=samples_per_class, normalize=True + ) + + +# Interp function; expects x0 and x1 to be of shape (shape0, 1, rest_of_shape..) +def interp(x0, x1, num_midpoints): + lerp = torch.linspace(0, 1.0, num_midpoints + 2, device="cuda").to(x0.dtype) + return (x0 * (1 - lerp.view(1, -1, 1))) + (x1 * lerp.view(1, -1, 1)) + + +# interp sheet function +# Supports full, class-wise and intra-class interpolation +def interp_sheet( + G, + num_per_sheet, + num_midpoints, + num_classes, + parallel, + samples_root, + experiment_name, + folder_number, + sheet_number=0, + fix_z=False, + fix_y=False, + device="cuda", +): + # Prepare zs and ys + if fix_z: # If fix Z, only sample 1 z per row + zs = torch.randn(num_per_sheet, 1, G.dim_z, device=device) + zs = zs.repeat(1, num_midpoints + 2, 1).view(-1, G.dim_z) + else: + zs = interp( + torch.randn(num_per_sheet, 1, G.dim_z, device=device), + torch.randn(num_per_sheet, 1, G.dim_z, device=device), + num_midpoints, + ).view(-1, G.dim_z) + if fix_y: # If fix y, only sample 1 z per row + ys = sample_1hot(num_per_sheet, num_classes) + ys = G.shared(ys).view(num_per_sheet, 1, -1) + ys = ys.repeat(1, num_midpoints + 2, 1).view( + num_per_sheet * (num_midpoints + 2), -1 + ) + else: + ys = interp( + G.shared(sample_1hot(num_per_sheet, num_classes)).view( + num_per_sheet, 1, -1 + ), + G.shared(sample_1hot(num_per_sheet, num_classes)).view( + num_per_sheet, 1, -1 + ), + num_midpoints, + ).view(num_per_sheet * (num_midpoints + 2), -1) + # Run the net--note that we've already passed y through G.shared. + if G.fp16: + zs = zs.half() + with torch.no_grad(): + if parallel: + out_ims = nn.parallel.data_parallel(G, (zs, ys)).data.cpu() + else: + out_ims = G(zs, ys).data.cpu() + interp_style = "" + ("Z" if not fix_z else "") + ("Y" if not fix_y else "") + image_filename = "%s/%s/%d/interp%s%d.jpg" % ( + samples_root, + experiment_name, + folder_number, + interp_style, + sheet_number, + ) + torchvision.utils.save_image( + out_ims, image_filename, nrow=num_midpoints + 2, normalize=True + ) + + +# Convenience debugging function to print out gradnorms and shape from each layer +# May need to rewrite this so we can actually see which parameter is which +def print_grad_norms(net): + gradsums = [ + [ + float(torch.norm(param.grad).item()), + float(torch.norm(param).item()), + param.shape, + ] + for param in net.parameters() + ] + order = np.argsort([item[0] for item in gradsums]) + print( + [ + "%3.3e,%3.3e, %s" + % ( + gradsums[item_index][0], + gradsums[item_index][1], + str(gradsums[item_index][2]), + ) + for item_index in order + ] + ) + + +# Get singular values to log. This will use the state dict to find them +# and substitute underscores for dots. +def get_SVs(net, prefix): + d = net.state_dict() + return { + ("%s_%s" % (prefix, key)).replace(".", "_"): float(d[key].item()) + for key in d + if "sv" in key + } + + +# Name an experiment based on its config +def name_from_config(config): + name = "_".join( + [ + item + for item in [ + "Big%s" % config["which_train_fn"], + config["dataset"], + config["model"] if config["model"] != "BigGAN" else None, + "seed%d" % config["seed"], + "Gch%d" % config["G_ch"], + "Dch%d" % config["D_ch"], + "Gd%d" % config["G_depth"] if config["G_depth"] > 1 else None, + "Dd%d" % config["D_depth"] if config["D_depth"] > 1 else None, + "bs%d" % config["batch_size"], + "Gfp16" if config["G_fp16"] else None, + "Dfp16" if config["D_fp16"] else None, + "nDs%d" % config["num_D_steps"] if config["num_D_steps"] > 1 else None, + "nDa%d" % config["num_D_accumulations"] + if config["num_D_accumulations"] > 1 + else None, + "nGa%d" % config["num_G_accumulations"] + if config["num_G_accumulations"] > 1 + else None, + "Glr%2.1e" % config["G_lr"], + "Dlr%2.1e" % config["D_lr"], + "GB%3.3f" % config["G_B1"] if config["G_B1"] != 0.0 else None, + "GBB%3.3f" % config["G_B2"] if config["G_B2"] != 0.999 else None, + "DB%3.3f" % config["D_B1"] if config["D_B1"] != 0.0 else None, + "DBB%3.3f" % config["D_B2"] if config["D_B2"] != 0.999 else None, + "Gnl%s" % config["G_nl"], + "Dnl%s" % config["D_nl"], + "Ginit%s" % config["G_init"], + "Dinit%s" % config["D_init"], + "G%s" % config["G_param"] if config["G_param"] != "SN" else None, + "D%s" % config["D_param"] if config["D_param"] != "SN" else None, + "Gattn%s" % config["G_attn"] if config["G_attn"] != "0" else None, + "Dattn%s" % config["D_attn"] if config["D_attn"] != "0" else None, + "Gortho%2.1e" % config["G_ortho"] if config["G_ortho"] > 0.0 else None, + "Dortho%2.1e" % config["D_ortho"] if config["D_ortho"] > 0.0 else None, + config["norm_style"] if config["norm_style"] != "bn" else None, + "cr" if config["cross_replica"] else None, + "Gshared" if config["G_shared"] else None, + "hier" if config["hier"] else None, + "ema" if config["ema"] else None, + config["name_suffix"] if config["name_suffix"] else None, + ] + if item is not None + ] + ) + + +# Get GPU memory, -i is the index +def query_gpu(indices): + os.system("nvidia-smi -i 0 --query-gpu=memory.free --format=csv") + + +# Convenience function to count the number of parameters in a module +def count_parameters(module): + print( + "Number of parameters: {}".format( + sum([p.data.nelement() for p in module.parameters()]) + ) + ) + + +# Convenience function to sample an index, not actually a 1-hot +def sample_1hot(batch_size, num_classes, device="cuda"): + return torch.randint( + low=0, + high=num_classes, + size=(batch_size,), + device=device, + dtype=torch.int64, + requires_grad=False, + ) + + +def initiate_standing_stats(net): + for module in net.modules(): + if hasattr(module, "accumulate_standing"): + module.reset_stats() + module.accumulate_standing = True + + +def accumulate_standing_stats(net, z, y, nclasses, num_accumulations=16): + initiate_standing_stats(net) + net.train() + for i in range(num_accumulations): + with torch.no_grad(): + z.normal_() + y.random_(0, nclasses) + x = net(z, net.shared(y)) # No need to parallelize here unless using syncbn + # Set to eval mode + net.eval() + + +# This version of Adam keeps an fp32 copy of the parameters and +# does all of the parameter updates in fp32, while still doing the +# forwards and backwards passes using fp16 (i.e. fp16 copies of the +# parameters and fp16 activations). +# +# Note that this calls .float().cuda() on the params. +import math +from torch.optim.optimizer import Optimizer + + +class Adam16(Optimizer): + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + params = list(params) + super(Adam16, self).__init__(params, defaults) + + # Safety modification to make sure we floatify our state + def load_state_dict(self, state_dict): + super(Adam16, self).load_state_dict(state_dict) + for group in self.param_groups: + for p in group["params"]: + self.state[p]["exp_avg"] = self.state[p]["exp_avg"].float() + self.state[p]["exp_avg_sq"] = self.state[p]["exp_avg_sq"].float() + self.state[p]["fp32_p"] = self.state[p]["fp32_p"].float() + + def step(self, closure=None): + """Performs a single optimization step. + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + + grad = p.grad.data.float() + state = self.state[p] + + # State initialization + if len(state) == 0: + state["step"] = 0 + # Exponential moving average of gradient values + state["exp_avg"] = grad.new().resize_as_(grad).zero_() + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = grad.new().resize_as_(grad).zero_() + # Fp32 copy of the weights + state["fp32_p"] = p.data.float() + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + beta1, beta2 = group["betas"] + + state["step"] += 1 + + if group["weight_decay"] != 0: + grad = grad.add(group["weight_decay"], state["fp32_p"]) + + # Decay the first and second moment running average coefficient + exp_avg.mul_(beta1).add_(1 - beta1, grad) + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + + denom = exp_avg_sq.sqrt().add_(group["eps"]) + + bias_correction1 = 1 - beta1 ** state["step"] + bias_correction2 = 1 - beta2 ** state["step"] + step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 + + state["fp32_p"].addcdiv_(-step_size, exp_avg, denom) + p.data = state["fp32_p"].half() + + return loss diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..83f431e8feeb7e80d571f39c9f6c1b96857b5f85 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,80 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic +address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a +professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +This Code of Conduct also applies outside the project spaces when there is a +reasonable belief that an individual's behavior may have a negative impact on +the project or its community. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..4307269a571a11d41a4c44d280cd76b82b01b814 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,32 @@ +# Contributing to IC-GAN +We want to make contributing to this project as easy and transparent as +possible. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from `main`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. +5. Make sure your code lints. +6. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe +disclosure of security bugs. 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IC-GAN: Instance-Conditioned GAN

+Official Pytorch code of [Instance-Conditioned GAN](https://arxiv.org/abs/2109.05070) by Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano. +![IC-GAN results](./figures/github_image.png?raw=true) + +## Generate images with IC-GAN in a Colab Notebook +We provide a [Google Colab notebook](https://colab.research.google.com/github/facebookresearch/ic_gan/blob/main/inference/icgan_colab.ipynb) to generate images with IC-GAN and its class-conditional counter part. We also invite users to check out the [demo on Replicate](https://replicate.ai/arantxacasanova/ic_gan), courtesy of [Replicate](https://replicate.ai/home). + +The figure below depicts two instances, unseen during training and downloaded from [Creative Commons search](https://search.creativecommons.org), and the generated images with IC-GAN and class-conditional IC-GAN when conditioning on the class "castle": +

+ +

+ +Additionally, and inspired by [this Colab](https://colab.research.google.com/github/eyaler/clip_biggan/blob/main/ClipBigGAN.ipynb), we provide the funcionality in the same Colab notebook to guide generations with text captions, using the [CLIP model](https://github.com/openai/CLIP). +As an example, the following Figure shows three instance conditionings and a text caption (top), followed by the resulting generated images with IC-GAN (bottom), when optimizing the noise vector following CLIP's gradient for 100 iterations. +

+ +

+ + +*Credit for the three instance conditionings, from left to right, that were modified with a resize and central crop:* [1: "Landscape in Bavaria" by shining.darkness, licensed under CC BY 2.0](https://search.creativecommons.org/photos/92ef279c-4469-49a5-aa4b-48ad746f2dc4), [2: "Fantasy Landscape - slolsss" by Douglas Tofoli is marked with CC PDM 1.0](https://search.creativecommons.org/photos/13646adc-f1df-437a-a0dd-8223452ee46c), [3: "How to Draw Landscapes Simply" by Kuwagata Keisai is marked with CC0 1.0](https://search.creativecommons.org/photos/2ab9c3b7-de99-4536-81ed-604ee988bd5f) + + +## Requirements +* Python 3.8 +* Cuda v10.2 / Cudnn v7.6.5 +* gcc v7.3.0 +* Pytorch 1.8.0 +* A conda environment can be created from `environment.yaml` by entering the command: `conda env create -f environment.yml`, that contains the aforemention version of Pytorch and other required packages. +* Faiss: follow the instructions in the [original repository](https://github.com/facebookresearch/faiss). + + +## Overview + +This repository consists of four main folders: +* `data_utils`: A common folder to obtain and format the data needed to train and test IC-GAN, agnostic of the specific backbone. +* `inference`: Scripts to test the models both qualitatively and quantitatively. +* `BigGAN_PyTorch`: It provides the training, evaluation and sampling scripts for IC-GAN with a BigGAN backbone. The code base comes from [Pytorch BigGAN repository](https://github.com/ajbrock/BigGAN-PyTorch), made available under the MIT License. It has been modified to [add additional utilities](#biggan-changelog) and it enables IC-GAN training on top of it. +* `stylegan2_ada_pytorch`: It provides the training, evaluation and sampling scripts for IC-GAN with a StyleGAN2 backbone. The code base comes from [StyleGAN2 Pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch), made available under the [Nvidia Source Code License](https://nvlabs.github.io/stylegan2-ada-pytorch/license.html). It has been modified to [add additional utilities](#stylegan-changelog) and it enables IC-GAN training on top of it. + + +## (Python script) Generate images with IC-GAN +Alternatively, we can generate images with IC-GAN models directly from a python script, by following the next steps: +1) Download the desired pretrained models (links below) and the [pre-computed 1000 instance features from ImageNet](https://dl.fbaipublicfiles.com/ic_gan/stored_instances.tar.gz) and extract them into a folder `pretrained_models_path`. + +| model | backbone | class-conditional? | training dataset | resolution | url | +|-------------------|-------------------|-------------------|---------------------|--------------------|--------------------| +| IC-GAN | BigGAN | No | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res256.tar.gz) | +| IC-GAN (half capacity) | BigGAN | No | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res256_halfcap.tar.gz) | +| IC-GAN | BigGAN | No | ImageNet | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res128.tar.gz) | +| IC-GAN | BigGAN | No | ImageNet | 64x64 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res64.tar.gz) | +| IC-GAN | BigGAN | Yes | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res256.tar.gz) | +| IC-GAN (half capacity) | BigGAN | Yes | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res256_halfcap.tar.gz) | +| IC-GAN | BigGAN | Yes | ImageNet | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res128.tar.gz) | +| IC-GAN | BigGAN | Yes | ImageNet | 64x64 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res64.tar.gz) | +| IC-GAN | BigGAN | Yes | ImageNet-LT | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenetlt_res256.tar.gz) | +| IC-GAN | BigGAN | Yes | ImageNet-LT | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenetlt_res128.tar.gz) | +| IC-GAN | BigGAN | Yes | ImageNet-LT | 64x64 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenetlt_res64.tar.gz) | +| IC-GAN | BigGAN | No | COCO-Stuff | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_coco_res256.tar.gz) | +| IC-GAN | BigGAN | No | COCO-Stuff | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_coco_res128.tar.gz) | +| IC-GAN | StyleGAN2 | No | COCO-Stuff | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_stylegan2_coco_res256.tar.gz) | +| IC-GAN | StyleGAN2 | No | COCO-Stuff | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_stylegan2_coco_res128.tar.gz) | + +2) Execute: +``` +python inference/generate_images.py --root_path [pretrained_models_path] --model [model] --model_backbone [backbone] --resolution [res] +``` +* `model` can be chosen from `["icgan", "cc_icgan"]` to use the IC-GAN or the class-conditional IC-GAN model respectively. +* `backbone` can be chosen from `["biggan", "stylegan2"]`. +* `res` indicates the resolution at which the model has been trained. For ImageNet, choose one in `[64, 128, 256]`, and for COCO-Stuff, one in `[128, 256]`. + +This script results in a .PNG file where several generated images are shown, given an instance feature (each row), and a sampled noise vector (each grid position). + +Additional and optional parameters: +* `index`: (None by default), is an integer from 0 to 999 that choses a specific instance feature vector out of the 1000 instances that have been selected with k-means on the ImageNet dataset and stored in `pretrained_models_path/stored_instances`. +* `swap_target`: (None by default) is an integer from 0 to 999 indicating an ImageNet class label. This label will be used to condition the class-conditional IC-GAN, regardless of which instance features are being used. +* `which_dataset`: (ImageNet by default) can be chosen from `["imagenet", "coco"]` to indicate which dataset (training split) to sample the instances from. +* `trained_dataset`: (ImageNet by default) can be chosen from `["imagenet", "coco"]` to indicate the dataset in which the IC-GAN model has been trained on. +* `num_imgs_gen`: (5 by default), it changes the number of noise vectors to sample per conditioning. Increasing this number results in a bigger .PNG file to save and load. +* `num_conditionings_gen`: (5 by default), it changes the number of conditionings to sample. Increasing this number results in a bigger .PNG file to save and load. +* `z_var`: (1.0 by default) controls the truncation factor for the generation. +* Optionally, the script can be run with the following additional options `--visualize_instance_images --dataset_path [dataset_path]` to visualize the ground-truth images corresponding to the conditioning instance features, given a path to the dataset's ground-truth images `dataset_path`. Ground-truth instances will be plotted as the leftmost image for each row. + +## Data preparation +
+
+ImageNet +
+
    +
  1. Download dataset from here . +
    2. +
  2. Download SwAV feature extractor weights from here .
    3. +
  3. Replace the paths in data_utils/prepare_data.sh: out_path by the path where hdf5 files will be stored, path_imnet by the path where ImageNet dataset is downloaded, and path_swav by the path where SwAV weights are stored.
    4. +
  4. Execute ./data_utils/prepare_data.sh imagenet [resolution], where [resolution] can be an integer in {64,128,256}. This script will create several hdf5 files: +
    • ILSVRC[resolution]_xy.hdf5 and ILSVRC[resolution]_val_xy.hdf5, where images and labels are stored for the training and validation set respectively.
      • +
    • ILSVRC[resolution]_feats_[feature_extractor]_resnet50.hdf5 that contains the instance features for each image.
      • +
    • ILSVRC[resolution]_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5 that contains the list of [k_nn] neighbors for each of the instance features.
    5. +

+
+ +
+ImageNet-LT +
+
    +
  1. Download ImageNet dataset from here . Following ImageNet-LT , the file ImageNet_LT_train.txt can be downloaded from this link and later stored in the folder ./BigGAN_PyTorch/imagenet_lt. +
    2. +
  2. Download the pre-trained weights of the ResNet on ImageNet-LT from this link, provided by the classifier-balancing repository .
    3. +
  3. Replace the paths in data_utils/prepare_data.sh: out_path by the path where hdf5 files will be stored, path_imnet by the path where ImageNet dataset is downloaded, and path_classifier_lt by the path where the pre-trained ResNet50 weights are stored.
    4. +
  4. Execute ./data_utils/prepare_data.sh imagenet_lt [resolution], where [resolution] can be an integer in {64,128,256}. This script will create several hdf5 files: +
    • ILSVRC[resolution]longtail_xy.hdf5, where images and labels are stored for the training and validation set respectively.
      • +
    • ILSVRC[resolution]longtail_feats_[feature_extractor]_resnet50.hdf5 that contains the instance features for each image.
      • +
    • ILSVRC[resolution]longtail_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5 that contains the list of [k_nn] neighbors for each of the instance features.
    5. +

+
+ +
+COCO-Stuff +
+
    +
  1. Download the dataset following the LostGANs' repository instructions . +
    2. +
  2. Download SwAV feature extractor weights from here .
    3. +
  3. Replace the paths in data_utils/prepare_data.sh: out_path by the path where hdf5 files will be stored, path_imnet by the path where ImageNet dataset is downloaded, and path_swav by the path where SwAV weights are stored.
    4. +
  4. Execute ./data_utils/prepare_data.sh coco [resolution], where [resolution] can be an integer in {128,256}. This script will create several hdf5 files: +
    • COCO[resolution]_xy.hdf5 and COCO[resolution]_val_test_xy.hdf5, where images and labels are stored for the training and evaluation set respectively.
      • +
    • COCO[resolution]_feats_[feature_extractor]_resnet50.hdf5 that contains the instance features for each image.
      • +
    • COCO[resolution]_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5 that contains the list of [k_nn] neighbors for each of the instance features.
    5. +

+
+ +
+Other datasets +
+
    +
  1. Download the corresponding dataset and store in a folder dataset_path. +
    2. +
  2. Download SwAV feature extractor weights from here .
    3. +
  3. Replace the paths in data_utils/prepare_data.sh: out_path by the path where hdf5 files will be stored and path_swav by the path where SwAV weights are stored.
    4. +
  4. Execute ./data_utils/prepare_data.sh [dataset_name] [resolution] [dataset_path], where [dataset_name] will be the dataset name, [resolution] can be an integer, for example 128 or 256, and dataset_path contains the dataset images. This script will create several hdf5 files: +
    • [dataset_name][resolution]_xy.hdf5, where images and labels are stored for the training set.
      • +
    • [dataset_name][resolution]_feats_[feature_extractor]_resnet50.hdf5 that contains the instance features for each image.
      • +
    • [dataset_name][resolution]_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5 that contains the list of k_nn neighbors for each of the instance features.
    5. +

+
+ + +
+How to subsample an instance feature dataset with k-means +
+To downsample the instance feature vector dataset, after we have prepared the data, we can use the k-means algorithm: + +python data_utils/store_kmeans_indexes.py --resolution [resolution] --which_dataset [dataset_name] --data_root [data_path] + +
  • Adding --gpu allows the faiss library to compute k-means leveraging GPUs, resulting in faster execution.
    • +
  • Adding the parameter --feature_extractor [feature_extractor] chooses which feature extractor to use, with feature_extractor in ['selfsupervised', 'classification'] , if we are using swAV as feature extactor or the ResNet pretrained on the classification task on ImageNet, respectively.
    • +
  • The number of k-means clusters can be set with --kmeans_subsampled [centers], where centers is an integer.
+
+
+
+ +## How to train the models + +#### BigGAN or StyleGAN2 backbone +Training parameters are stored in JSON files in `[backbone_folder]/config_files/[dataset]/*.json`, where `[backbone_folder]` is either BigGAN_Pytorch or stylegan2_ada_pytorch and `[dataset]` can either be ImageNet, ImageNet-LT or COCO_Stuff. +``` +cd BigGAN_PyTorch +python run.py --json_config config_files//.json --data_root [data_root] --base_root [base_root] +``` +or +``` +cd stylegan_ada_pytorch +python run.py --json_config config_files//.json --data_root [data_root] --base_root [base_root] +``` +where: +* `data_root` path where the data has been prepared and stored, following the previous section (Data preparation). +* `base_root` path where to store the model weights and logs. + + +Note that one can create other JSON files to modify the training parameters. + +#### Other backbones +To be able to run IC-GAN with other backbones, we provide some orientative steps: +* Place the new backbone code in a new folder under `ic_gan` (`ic_gan/new_backbone`). +* Modify the relevant piece of code in the GAN architecture to allow instance features as conditionings (for both generator and discriminator). +* Create a `trainer.py` file with the training loop to train an IC-GAN with the new backbone. The `data_utils` folder provides the tools to prepare the dataset, load the data and conditioning sampling to train an IC-GAN. The IC-GAN with BigGAN backbone [`trainer.py`](BigGAN_PyTorch/trainer.py) file can be used as an inspiration. + + + +## How to test the models +To obtain the FID and IS metrics on ImageNet and ImageNet-LT: +1) Execute: +``` +python inference/test.py --json_config [BigGAN-PyTorch or stylegan-ada-pytorch]/config_files//.json --num_inception_images [num_imgs] --sample_num_npz [num_imgs] --eval_reference_set [ref_set] --sample_npz --base_root [base_root] --data_root [data_root] --kmeans_subsampled [kmeans_centers] --model_backbone [backbone] +``` +To obtain the tensorflow IS and FID metrics, use an environment with the Python <3.7 and Tensorflow 1.15. Then: + +2) Obtain Inception Scores and pre-computed FID moments: + ``` + python ../data_utils/inception_tf13.py --experiment_name [exp_name] --experiment_root [base_root] --kmeans_subsampled [kmeans_centers] + ``` + +For stratified FIDs in the ImageNet-LT dataset, the following parameters can be added `--which_dataset 'imagenet_lt' --split 'val' --strat_name [stratified_split]`, where `stratified_split` can be in `[few,low, many]`. + +3) (Only needed once) Pre-compute reference moments with tensorflow code: + ``` + python ../data_utils/inception_tf13.py --use_ground_truth_data --data_root [data_root] --split [ref_set] --resolution [res] --which_dataset [dataset] + ``` + +4) (Using this [repository](https://github.com/bioinf-jku/TTUR)) FID can be computed using the pre-computed statistics obtained in 2) and the pre-computed ground-truth statistics obtain in 3). For example, to compute the FID with reference ImageNet validation set: +```python TTUR/fid.py [base_root]/[exp_name]/TF_pool_.npz [data_root]/imagenet_val_res[res]_tf_inception_moments_ground_truth.npz ``` + +To obtain the FID metric on COCO-Stuff: +1) Obtain ground-truth jpeg images: ```python data_utils/store_coco_jpeg_images.py --resolution [res] --split [ref_set] --data_root [data_root] --out_path [gt_coco_images] --filter_hd [filter_hd] ``` +2) Store generated images as jpeg images: ```python sample.py --json_config ../[BigGAN-PyTorch or stylegan-ada-pytorch]/config_files//.json --data_root [data_root] --base_root [base_root] --sample_num_npz [num_imgs] --which_dataset 'coco' --eval_instance_set [ref_set] --eval_reference_set [ref_set] --filter_hd [filter_hd] --model_backbone [backbone] ``` +3) Using this [repository](https://github.com/bioinf-jku/TTUR), compute FID on the two folders of ground-truth and generated images. + +where: +* `dataset`: option to select the dataset in `['imagenet', 'imagenet_lt', 'coco'] +* `exp_name`: name of the experiment folder. +* `data_root`: path where the data has been prepared and stored, following the previous section ["Data preparation"](#data-preparation). +* `base_root`: path where to find the model (for example, where the pretrained models have been downloaded). +* `num_imgs`: needs to be set to 50000 for ImageNet and ImageNet-LT (with validation set as reference) and set to 11500 for ImageNet-LT (with training set as reference). For COCO-Stuff, set to 75777, 2050, 675, 1375 if using the training, evaluation, evaluation seen or evaluation unseen set as reference. +* `ref_set`: set to `'val'` for ImageNet, ImageNet-LT (and COCO) to obtain metrics with the validation (evaluation) set as reference, or set to `'train'` for ImageNet-LT or COCO to obtain metrics with the training set as reference. +* `kmeans_centers`: set to 1000 for ImageNet and to -1 for ImageNet-LT. +* `backbone`: model backbone architecture in `['biggan','stylegan2']`. +* `res`: integer indicating the resolution of the images (64,128,256). +* `gt_coco_images`: folder to store the ground-truth JPEG images of that specific split. +* `filter_hd`: only valid for `ref_set=val`. If -1, use the entire evaluation set; if 0, use only conditionings and their ground-truth images with seen class combinations during training (eval seen); if 1, use only conditionings and their ground-truth images with unseen class combinations during training (eval unseen). + + +## Utilities for GAN backbones +We change and provide extra utilities to facilitate the training, for both BigGAN and StyleGAN2 base repositories. + +### BigGAN change log +The following changes were made: + +* BigGAN architecture: + * In `train_fns.py`: option to either have the optimizers inside the generator and discriminator class, or directly in the `G_D` wrapper module. Additionally, added an option to augment both generated and real images with augmentations from [DiffAugment](https://github.com/mit-han-lab/data-efficient-gans). + * In `BigGAN.py`: added a function `get_condition_embeddings` to handle the conditioning separately. + * Small modifications to `layers.py` to adapt the batchnorm function calls to the pytorch 1.8 version. + +* Training utilities: + * Added `trainer.py` file (replacing train.py): + * Training now allows the usage of DDP for faster single-node and multi-node training. + * Training is performed by epochs instead of by iterations. + * Option to stop the training by using early stopping or when experiments diverge. + * In `utils.py`: + * Replaced `MultiEpochSampler` for `CheckpointedSampler` to allow experiments to be resumable when using epochs and fixing a bug where `MultiEpochSampler` would require a long time to fetch data permutations when the number of epochs increased. + * ImageNet-LT: Added option to use different class distributions when sampling a class label for the generator. + * ImageNet-LT: Added class balancing (uniform and temperature annealed). + * Added data augmentations from [DiffAugment](https://github.com/mit-han-lab/data-efficient-gans). + +* Testing utilities: + * In `calculate_inception_moments.py`: added option to obtain moments for ImageNet-LT dataset, as well as stratified moments for many, medium and few-shot classes (stratified FID computation). + * In `inception_utils.py`: added option to compute [Precision, Recall, Density, Coverage](https://github.com/clovaai/generative-evaluation-prdc) and stratified FID. + +* Data utilities: + * In `datasets.py`, added option to load ImageNet-LT dataset. + * Added ImageNet-LT.txt files with image indexes for training and validation split. + * In `utils.py`: + * Separate functions to obtain the data from hdf5 files (`get_dataset_hdf5`) or from directory (`get_dataset_images`), as well as a function to obtain only the data loader (`get_dataloader`). + * Added the function `sample_conditionings` to handle possible different conditionings to train G with. + +* Experiment utilities: + * Added JSON files to launch experiments with the proposed hyper-parameter configuration. + * Script to launch experiments with either the [submitit tool](https://github.com/facebookincubator/submitit) or locally in the same machine (run.py). + +### StyleGAN2 change log +
+
    +
  • Multi-node DistributedDataParallel training.
    • +
  • Added early stopping based on the training FID metric.
    • +
  • Automatic checkpointing when jobs are automatically rescheduled on a cluster.
    • +
  • Option to load dataset from hdf5 file.
    • +
  • Replaced the usage of Click python package by an `ArgumentParser`.
    • +
  • Only saving best and last model weights.
    • +
+
+ +## Acknowledgements +We would like to thanks the authors of the [Pytorch BigGAN repository](https://github.com/ajbrock/BigGAN-PyTorch) and [StyleGAN2 Pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch), as our model requires their repositories to train IC-GAN with BigGAN or StyleGAN2 bakcbone respectively. +Moreover, we would like to further thank the authors of [generative-evaluation-prdc](https://github.com/clovaai/generative-evaluation-prdc), [data-efficient-gans](https://github.com/mit-han-lab/data-efficient-gans), [faiss](https://github.com/facebookresearch/faiss) and [sg2im](https://github.com/google/sg2im) as some components were borrowed and modified from their code bases. Finally, we thank the author of [WanderCLIP](https://colab.research.google.com/github/eyaler/clip_biggan/blob/main/WanderCLIP.ipynb) as well as the following repositories, that we use in our Colab notebook: [pytorch-pretrained-BigGAN](https://github.com/huggingface/pytorch-pretrained-BigGAN) and [CLIP](https://github.com/openai/CLIP). + +## License +The majority of IC-GAN is licensed under CC-BY-NC, however portions of the project are available under separate license terms: BigGAN and [PRDC](https://github.com/facebookresearch/ic_gan/blob/main/data_utils/compute_pdrc.py) are licensed under the MIT license; [COCO-Stuff loader](https://github.com/facebookresearch/ic_gan/blob/main/data_utils/cocostuff_dataset.py) is licensed under Apache License 2.0; [DiffAugment](https://github.com/facebookresearch/ic_gan/blob/main/BigGAN_PyTorch/diffaugment_utils.py) is licensed under BSD 2-Clause Simplified license; StyleGAN2 is licensed under a NVIDIA license, available here: https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/LICENSE.txt. In the Colab notebook, [CLIP](https://github.com/openai/CLIP) and [pytorch-pretrained-BigGAN](https://github.com/huggingface/pytorch-pretrained-BigGAN) code is used, both licensed under the MIT license. + +## Disclaimers +THE DIFFAUGMENT SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +THE CLIP SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +THE PYTORCH-PRETRAINED-BIGGAN SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +## Cite the paper +If this repository, the paper or any of its content is useful for your research, please cite: +``` +@inproceedings{casanova2021instanceconditioned, + title={Instance-Conditioned GAN}, + author={Arantxa Casanova and Marlène Careil and Jakob Verbeek and Michal Drozdzal and Adriana Romero-Soriano}, + booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, + year={2021} +} +``` diff --git a/cog.yaml b/cog.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c84c8de86c27d5a406f793b2042f04062152d16 --- /dev/null +++ b/cog.yaml @@ -0,0 +1,25 @@ +build: + gpu: true + python_version: "3.8" + system_packages: + - "libgl1-mesa-glx" + - "libglib2.0-0" + python_packages: + - "torch==1.8.0" + - "torchvision==0.9.0" + - "numpy==1.21.1" + - "ipython==7.21.0" + - "pytorch-pretrained-biggan==0.1.1" + - "ftfy==6.0.3" + - "cma==3.1.0" + - "scikit-learn==0.24.2" + - "imageio==2.9.0" + - "Pillow==8.3.1" + - "regex==2021.8.28" + - "nltk==3.6.3" + - "scipy==1.7.1" + - "h5py==3.4.0" + - "matplotlib==3.4.3" + - "faiss-gpu==1.7.1.post2" + +predict: "predict.py:Predictor" diff --git a/data_utils/__init__.py b/data_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data_utils/calculate_inception_moments.py b/data_utils/calculate_inception_moments.py new file mode 100644 index 0000000000000000000000000000000000000000..a286d0fbe89ad26de78ace3fd9d1728cd574f9bb --- /dev/null +++ b/data_utils/calculate_inception_moments.py @@ -0,0 +1,248 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +""" Calculate Inception Moments + This script iterates over the dataset and calculates the moments of the + activations of the Inception net (needed for FID), and also returns + the Inception Score of the training data. + + Note that if you don't shuffle the data, the IS of true data will be under- + estimated as it is label-ordered. By default, the data is not shuffled + so as to reduce non-determinism. """ +import os +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +import data_utils.utils as utils +import data_utils.inception_utils as inception_utils +from tqdm import tqdm +from argparse import ArgumentParser + + +def prepare_parser(): + usage = "Calculate and store inception metrics." + parser = ArgumentParser(description=usage) + parser.add_argument( + "--resolution", + type=int, + default=128, + help="Which Dataset resolution, out of 64, 128, 256" " (default: %(default)s)", + ) + parser.add_argument( + "--split", + type=str, + default="train", + help="Which Dataset to convert: train, val (default: %(default)s)", + ) + parser.add_argument( + "--stratified_moments", + action="store_true", + default=False, + help="Compute moments for FID computation stratifying by many, medium and few-shot classes" + " (ImageNet-LT)", + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where data is stored and where dataset hdf5 is found" + " (default: %(default)s)", + ) + parser.add_argument( + "--out_path", + type=str, + default="data", + help="Default location where data in hdf5 format will be stored (default: %(default)s)", + ) + parser.add_argument( + "--batch_size", + type=int, + default=64, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--parallel", + action="store_true", + default=False, + help="Use multiple GPUs (default: %(default)s)", + ) + parser.add_argument( + "--num_workers", + type=int, + default=8, + help="Number of dataloader workers (default: %(default)s)", + ) + parser.add_argument( + "--shuffle", + action="store_true", + default=False, + help="Shuffle the data? (default: %(default)s)", + ) + parser.add_argument("--seed", type=int, default=0, help="Random seed to use.") + parser.add_argument( + "--load_in_mem", + action="store_true", + default=False, + help="Load all data into memory? (default: %(default)s)", + ) + + parser.add_argument( + "--which_dataset", + type=str, + default="imagenet", + choices=["imagenet", "imagenet_lt", "coco"], + help="Dataset choice.", + ) + + return parser + + +def run(config): + # Get dataset and loader + kwargs = { + "num_workers": config["num_workers"], + "pin_memory": False, + "drop_last": False, + "load_in_mem": config["load_in_mem"], + } + if config["which_dataset"] in ["imagenet", "imagenet_lt"]: + dataset_name_prefix = "ILSVRC" + elif config["which_dataset"] == "coco": + dataset_name_prefix = "COCO" + test_part = False + if config["which_dataset"] == "coco" and config["split"] == "val": + test_part = True + + # Using hdf5 filename + dataset = utils.get_dataset_hdf5( + config["resolution"], + data_path=config["data_root"], + longtail=config["which_dataset"] == "imagenet_lt" + and config["split"] == "train", + split=config["split"], + load_in_mem=config["load_in_mem"], + which_dataset=config["which_dataset"], + test_part=test_part, + ) + + loader = utils.get_dataloader( + dataset, config["batch_size"], shuffle=False, **kwargs + ) + + # Load inception net + net = inception_utils.load_inception_net(parallel=config["parallel"]) + device = "cuda" + + # Accumulate logits + pool, logits, labels = [], [], [] + for i, batch in enumerate(tqdm(loader)): + (x, y) = (batch[0], batch[1]) + x = x.to(device) + with torch.no_grad(): + pool_val, logits_val = net(x) + pool += [np.asarray(pool_val.cpu())] + logits += [np.asarray(F.softmax(logits_val, 1).cpu())] + labels += [np.asarray(y.cpu())] + + pool, logits, labels = [np.concatenate(item, 0) for item in [pool, logits, labels]] + + print("Calculating inception metrics...") + IS_mean, IS_std = inception_utils.calculate_inception_score(logits) + print( + "Training data from dataset %s has IS of %5.5f +/- %5.5f" + % (config["which_dataset"], IS_mean, IS_std) + ) + # Prepare mu and sigma, save to disk. Remove "hdf5" by default + # (the FID code also knows to strip "hdf5") + print("Calculating means and covariances...") + mu, sigma = np.mean(pool, axis=0), np.cov(pool, rowvar=False) + print("Saving calculated means and covariances to disk...") + if config["which_dataset"] in ["imagenet", "imagenet_lt"]: + dataset_name_prefix = "I" + elif config["which_dataset"] == "coco": + dataset_name_prefix = "COCO" + np.savez( + os.path.join( + config["out_path"], + "%s%i_%s%s%s_inception_moments.npz" + % ( + dataset_name_prefix, + config["resolution"], + "longtail" + if config["which_dataset"] == "imagenet_lt" + and config["split"] == "train" + else "", + "_val" if config["split"] == "val" else "", + "_test" if test_part else "", + ), + ), + **{"mu": mu, "sigma": sigma} + ) + # Compute stratified moments for ImageNet-LT dataset + if config["stratified_moments"]: + samples_per_class = np.load( + "BigGAN_PyTorch/imagenet_lt/imagenet_lt_samples_per_class.npy", + allow_pickle=True, + ) + for strat_name in ["_many", "_low", "_few"]: + if strat_name == "_many": + logits_ = logits[samples_per_class[labels] >= 100] + pool_ = pool[samples_per_class[labels] >= 100] + elif strat_name == "_low": + logits_ = logits[samples_per_class[labels] < 100] + pool_ = pool[samples_per_class[labels] < 100] + labels_ = labels[samples_per_class[labels] < 100] + logits_ = logits_[samples_per_class[labels_] > 20] + pool_ = pool_[samples_per_class[labels_] > 20] + elif strat_name == "_few": + logits_ = logits[samples_per_class[labels] <= 20] + pool_ = pool[samples_per_class[labels] <= 20] + print( + "Calculating inception metrics for strat ", + strat_name, + " with number of samples ", + len(logits_), + "...", + ) + IS_mean, IS_std = inception_utils.calculate_inception_score(logits_) + print( + "Training data from dataset %s has IS of %5.5f +/- %5.5f" + % (config["which_dataset"], IS_mean, IS_std) + ) + # Prepare mu and sigma, save to disk. Remove "hdf5" by default + # (the FID code also knows to strip "hdf5") + print("Calculating means and covariances...") + mu, sigma = np.mean(pool_, axis=0), np.cov(pool_, rowvar=False) + print("Saving calculated means and covariances to disk...") + np.savez( + os.path.join( + config["data_root"], + "%s%i__val%s_inception_moments.npz" + % (dataset_name_prefix, config["resolution"], strat_name), + ), + **{"mu": mu, "sigma": sigma} + ) + + +def main(): + # parse command line + parser = prepare_parser() + config = vars(parser.parse_args()) + print(config) + run(config) + + +if __name__ == "__main__": + main() diff --git a/data_utils/cocostuff_dataset.py b/data_utils/cocostuff_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..9c5c9a8d5ee982777ef54edf5ead2aaaff00b99d --- /dev/null +++ b/data_utils/cocostuff_dataset.py @@ -0,0 +1,281 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +#!/usr/bin/python +# +# Copyright 2018 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +from collections import defaultdict +import torchvision.transforms as T +from torch.utils.data import Dataset +import PIL +import numpy as np + +PREDICATES_VALUES = ["left of", "right of", "above", "below", "inside", "surrounding"] +MEAN = [0.5, 0.5, 0.5] +STD = [0.5, 0.5, 0.5] + + +def imagenet_preprocess(): + return T.Normalize(mean=MEAN, std=STD) + + +class Resize(object): + def __init__(self, size, interp=PIL.Image.BILINEAR): + if isinstance(size, tuple): + H, W = size + self.size = (W, H) + else: + self.size = (size, size) + self.interp = interp + + def __call__(self, img): + return img.resize(self.size, self.interp) + + +class CocoStuff(Dataset): + """ + A PyTorch Dataset for loading Coco and Coco-Stuff annotations. + + Parameters + ---------- + image_dir: str + Path to a directory where images are held. + instances_json: str + Path to a JSON file giving COCO annotations. + stuff_json: str,optional + Path to a JSON file giving COCO_Stuff annotations. + stuff_only: bool, optional + If True then only iterate over images which appear in stuff_json; + if False then iterate over all images in instances_json. + image_size: tuple, optional + Size (H, W) at which to load images. Default (64, 64). + normalize_image: bool, optional + If True then normalize images by subtracting ImageNet mean pixel and dividing by + ImageNet std pixel. + max_samples: int, optional + If None use all images. Other wise only use images in the range [0, max_samples). + Default None. + min_object_size: float, optional + Ignore objects whose bounding box takes up less than this fraction of the image. + min_objects_per_image: int, optional + Ignore images which have fewer than this many object annotations. + max_objects_per_image: int, optional + Ignore images which have more than this many object annotations. + instance_whitelist: list, optional + None means use all instance categories. Otherwise a list giving a whitelist of + instance category names to use. + stuff_whitelist: list, optional + None means use all stuff categories. Otherwise a list giving a whitelist of stuff + category names to use. + """ + + def __init__( + self, + image_dir, + instances_json, + stuff_json=None, + stuff_only=True, + image_size=64, + normalize_images=True, + max_samples=None, + min_object_size=0.02, + min_objects_per_image=3, + max_objects_per_image=8, + instance_whitelist=None, + stuff_whitelist=None, + no__img__=False, + test_part=False, + split="train", + iscrowd=True, + mode="train", + **kwargs + ): + super(Dataset, self).__init__() + if stuff_only and stuff_json is None: + print("WARNING: Got stuff_only=True but stuff_json=None.") + print("Falling back to stuff_only=False.") + + self.image_dir = image_dir + self.max_samples = max_samples + self.normalize_images = normalize_images + self.iscrowd = iscrowd + # self.transform = transform + + self.left_right_flip = False # True if split == 'train' else False + self.max_objects_per_image = max_objects_per_image + self.mode = mode + + if image_size is not None: + self.set_image_size(image_size) + print(self.transform) + self.no__img__ = no__img__ + + with open(instances_json, "r") as f: + instances_data = json.load(f) + + self.image_id_to_sentences = {} + stuff_data = None + if stuff_json is not None and stuff_json != "": + with open(stuff_json, "r") as f: + stuff_data = json.load(f) + + self.image_ids = [] + self.image_id_to_filename = {} + self.image_id_to_size = {} + for image_data in instances_data["images"]: + image_id = image_data["id"] + filename = image_data["file_name"] + width = image_data["width"] + height = image_data["height"] + self.image_ids.append(image_id) + self.image_id_to_filename[image_id] = filename + self.image_id_to_size[image_id] = (width, height) + + object_idx_to_name = {} + # Get categories names and ids + all_instance_categories = self.populate_categories( + instances_data, object_idx_to_name + ) + all_stuff_categories = self.populate_categories(stuff_data, object_idx_to_name) + + if instance_whitelist is None: + instance_whitelist = all_instance_categories + if stuff_whitelist is None: + stuff_whitelist = all_stuff_categories + + category_whitelist = set(instance_whitelist) | set(stuff_whitelist) + + # Add object data from instances and stuff + self.image_id_to_objects = defaultdict(list) + self.add_object_instances( + instances_data, min_object_size, object_idx_to_name, category_whitelist + ) + image_ids_with_stuff = self.add_object_instances( + stuff_data, min_object_size, object_idx_to_name, category_whitelist + ) + if stuff_only: + new_image_ids = [] + for image_id in self.image_ids: + if image_id in image_ids_with_stuff: + new_image_ids.append(image_id) + self.image_ids = new_image_ids + + all_image_ids = set(self.image_id_to_filename.keys()) + image_ids_to_remove = all_image_ids - image_ids_with_stuff + for image_id in image_ids_to_remove: + self.image_id_to_filename.pop(image_id, None) + self.image_id_to_size.pop(image_id, None) + self.image_id_to_objects.pop(image_id, None) + + # Prune images that have too few or too many objects + new_image_ids = [] + total_objs = 0 + for image_id in self.image_ids: + num_objs = len(self.image_id_to_objects[image_id]) + total_objs += num_objs + if min_objects_per_image <= num_objs <= max_objects_per_image: + new_image_ids.append(image_id) + self.image_ids = new_image_ids + + if split == "val": + if test_part: + self.image_ids = self.image_ids[1024:] + else: + print("Entering in val part") + self.image_ids = self.image_ids[:1024] + + def populate_categories(self, data, object_idx_to_name): + all_categories = [] + for category_data in data["categories"]: + category_id = category_data["id"] + category_name = category_data["name"] + all_categories.append(category_name) + object_idx_to_name[category_id] = category_name + return all_categories + + def add_object_instances( + self, data, min_object_size, object_idx_to_name, category_whitelist + ): + image_ids_present = set() + for object_data in data["annotations"]: + image_id = object_data["image_id"] + _, _, w, h = object_data["bbox"] + image_ids_present.add(image_id) + W, H = self.image_id_to_size[image_id] + box_area = (w * h) / (W * H) + box_ok = box_area > min_object_size + object_name = object_idx_to_name[object_data["category_id"]] + category_ok = object_name in category_whitelist + other_ok = object_name != "other" + + condition = box_ok and category_ok and other_ok + if self.iscrowd: + condition = condition and (object_data["iscrowd"] != 1) + if condition: + self.image_id_to_objects[image_id].append(object_data) + return image_ids_present + + def set_image_size(self, image_size): + print("called set_image_size", image_size) + transform = [Resize(image_size), T.ToTensor()] + if self.normalize_images: + transform.append(imagenet_preprocess()) + self.transform = T.Compose(transform) + self.image_size = image_size + + def total_objects(self): + total_objs = 0 + for i, image_id in enumerate(self.image_ids): + if self.max_samples and i >= self.max_samples: + break + num_objs = len(self.image_id_to_objects[image_id]) + total_objs += num_objs + return total_objs + + def __len__(self): + if self.max_samples is None: + if self.left_right_flip: + return len(self.image_ids) * 2 + return len(self.image_ids) + return min(len(self.image_ids), self.max_samples) + + def __getitem__(self, index): + """ Get an image, a void label and the image index. + Returns a tuple of: image (FloatTensor of shape (C, H, W)), void label 0 and image index. + + """ + flip = False + if self.mode == "train": + if index >= len(self.image_ids): + index = index - len(self.image_ids) + flip = True + + image_id = self.image_ids[index] + + filename = self.image_id_to_filename[image_id] + image_path = os.path.join(self.image_dir, filename) + with open(image_path, "rb") as f: + with PIL.Image.open(f) as image: + if flip and self.mode == "train": + image = PIL.ImageOps.mirror(image) + image = self.transform(image.convert("RGB")) + + return image, int(0), image_id diff --git a/data_utils/compute_pdrc.py b/data_utils/compute_pdrc.py new file mode 100644 index 0000000000000000000000000000000000000000..f510768291abb26ae0d78822591dd2f84f3fff53 --- /dev/null +++ b/data_utils/compute_pdrc.py @@ -0,0 +1,112 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# prdc +# Copyright (c) 2020-present NAVER Corp. +# MIT license + +import numpy as np +import sklearn.metrics + +__all__ = ["compute_prdc"] + + +def compute_pairwise_distance(data_x, data_y=None): + """ + Parameters + ---------- + data_x: numpy.ndarray([N, feature_dim], dtype=np.float32) + data_y: numpy.ndarray([N, feature_dim], dtype=np.float32) + Returns + ------- + numpy.ndarray([N, N], dtype=np.float32) of pairwise distances. + """ + if data_y is None: + data_y = data_x + dists = sklearn.metrics.pairwise_distances( + data_x, data_y, metric="euclidean", n_jobs=8 + ) + return dists + + +def get_kth_value(unsorted, k, axis=-1): + """ + Parameters + ---------- + unsorted: numpy.ndarray of any dimensionality. + k: int + axis: int + Returns + ------- + kth values along the designated axis. + """ + indices = np.argpartition(unsorted, k, axis=axis)[..., :k] + k_smallests = np.take_along_axis(unsorted, indices, axis=axis) + kth_values = k_smallests.max(axis=axis) + return kth_values + + +def compute_nearest_neighbour_distances(input_features, nearest_k): + """ + Parameters + ---------- + input_features: numpy.ndarray([N, feature_dim], dtype=np.float32) + nearest_k: int + Returns + ------- + Distances to kth nearest neighbours. + """ + distances = compute_pairwise_distance(input_features) + radii = get_kth_value(distances, k=nearest_k + 1, axis=-1) + return radii + + +def compute_prdc(real_features, fake_features, nearest_k): + """ + Computes precision, recall, density, and coverage given two manifolds. + + Parameters + ---------- + real_features: numpy.ndarray([N, feature_dim], dtype=np.float32) + fake_features: numpy.ndarray([N, feature_dim], dtype=np.float32) + nearest_k: int. + Returns + ------- + dict of precision, recall, density, and coverage. + """ + + print( + "Num real: {} Num fake: {}".format( + real_features.shape[0], fake_features.shape[0] + ) + ) + + real_nearest_neighbour_distances = compute_nearest_neighbour_distances( + real_features, nearest_k + ) + fake_nearest_neighbour_distances = compute_nearest_neighbour_distances( + fake_features, nearest_k + ) + distance_real_fake = compute_pairwise_distance(real_features, fake_features) + + precision = ( + (distance_real_fake < np.expand_dims(real_nearest_neighbour_distances, axis=1)) + .any(axis=0) + .mean() + ) + + recall = ( + (distance_real_fake < np.expand_dims(fake_nearest_neighbour_distances, axis=0)) + .any(axis=1) + .mean() + ) + + density = (1.0 / float(nearest_k)) * ( + distance_real_fake < np.expand_dims(real_nearest_neighbour_distances, axis=1) + ).sum(axis=0).mean() + + coverage = ( + distance_real_fake.min(axis=1) < real_nearest_neighbour_distances + ).mean() + + return dict(precision=precision, recall=recall, density=density, coverage=coverage) diff --git a/data_utils/datasets_common.py b/data_utils/datasets_common.py new file mode 100644 index 0000000000000000000000000000000000000000..6a269e9e71e152270157654feed2df65b41fcd68 --- /dev/null +++ b/data_utils/datasets_common.py @@ -0,0 +1,821 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# All contributions made by NAVER Corp.: +# Copyright (c) 2020-present NAVER Corp. +# +# MIT license + +import sys +import os +import os.path + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +from data_utils import utils as data_utils +from PIL import Image +import numpy as np +from tqdm import tqdm +import random +import sklearn.metrics +import torch.utils.data as data +try: + import faiss + USE_FAISS = 1 +except: + print('Faiss library not found!') + USE_FAISS = 0 +import h5py as h5 +import torch + +IMG_EXTENSIONS = [".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm"] + + +def is_image_file(filename): + """Checks if a file is an image. + + Args: + filename (string): path to a file + + Returns: + bool: True if the filename ends with a known image extension + """ + filename_lower = filename.lower() + return any(filename_lower.endswith(ext) for ext in IMG_EXTENSIONS) + + +def find_classes(dir): + classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))] + classes.sort() + class_to_idx = {classes[i]: i for i in range(len(classes))} + return classes, class_to_idx + + +def make_dataset(dir, class_to_idx): + images = [] + dir = os.path.expanduser(dir) + for target in tqdm(sorted(os.listdir(dir))): + d = os.path.join(dir, target) + if not os.path.isdir(d): + continue + + for root, _, fnames in sorted(os.walk(d)): + for fname in sorted(fnames): + if is_image_file(fname): + path = os.path.join(root, fname) + item = (path, class_to_idx[target]) + images.append(item) + + return images + + +def pil_loader(path): + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + + +def accimage_loader(path): + import accimage + + try: + return accimage.Image(path) + except IOError: + # Potentially a decoding problem, fall back to PIL.Image + return pil_loader(path) + + +def default_loader(path): + from torchvision import get_image_backend + + if get_image_backend() == "accimage": + return accimage_loader(path) + else: + return pil_loader(path) + + +class ImageFolder(data.Dataset): + """A generic data loader where the images are arranged in this way: :: + + root/dogball/xxx.png + root/dogball/xxy.png + root/dogball/xxz.png + + root/cat/123.png + root/cat/nsdf3.png + root/cat/asd932_.png + + Parameters + ---------- + root: string. Root directory path. + transform: callable, optional. A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform: callable, optional. A function/transform that takes in the + target and transforms it. + loader: callable, optional. A function to load an image given its path. + + Attributes + ---------- + classes: list. List of the class names. + class_to_idx: dict. Dict with items (class_name, class_index). + imgs: list. List of (image path, class_index) tuples + """ + + def __init__( + self, + root, + transform=None, + target_transform=None, + loader=default_loader, + load_in_mem=False, + index_filename="imagenet_imgs.npz", + longtail=False, + subsampled=False, + split="train", + **kwargs + ): + + classes, class_to_idx = find_classes(root) + # Load pre-computed image directory walk + if False: # os.path.exists(os.path.join(index_filename)): + print("Loading pre-saved Index file %s..." % index_filename) + imgs = np.load(os.path.join(index_filename))["imgs"] + # If first time, walk the folder directory and save the + # results to a pre-computed file. + else: + print("Generating Index file %s..." % index_filename) + if not longtail: + imgs = make_dataset(root, class_to_idx) + if subsampled: + # Same number of samples as in ImageNet-LT + imgs = random.sample(imgs, 115846) + else: + imgs = [] + print("Using long-tail version of the dataset with split ", split, "!") + with open( + "BigGAN_PyTorch/imagenet_lt/ImageNet_LT_" + split + ".txt" + ) as f: + for line in f: + imgs.append( + ( + os.path.join( + root, "/".join(line.split()[0].split("/")[1:]) + ), + int(line.split()[1]), + ) + ) + np.savez_compressed(os.path.join(index_filename), **{"imgs": imgs}) + if len(imgs) == 0: + raise ( + RuntimeError( + "Found 0 images in subfolders of: " + root + "\n" + "Supported image extensions are: " + ",".join(IMG_EXTENSIONS) + ) + ) + + self.root = root + self.imgs = imgs + self.classes = classes + self.class_to_idx = class_to_idx + self.transform = transform + self.target_transform = target_transform + self.loader = loader + self.load_in_mem = load_in_mem + + if self.load_in_mem: + print("Loading all images into memory...") + self.data, self.labels = [], [] + for index in tqdm(range(len(self.imgs))): + path, target = imgs[index][0], imgs[index][1] + self.data.append(self.transform(self.loader(path))) + self.labels.append(target) + + def __getitem__(self, index): + """ + Parameters + ---------- + index: int. Index + + Returns + ------- + tuple: (image, target) where target is class_index of the target class. + """ + if self.load_in_mem: + img = self.data[index] + target = self.labels[index] + else: + path, target = self.imgs[index] + img = self.loader(str(path)) + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + return img, int(target), index + + def __len__(self): + return len(self.imgs) + + def __repr__(self): + fmt_str = "Dataset " + self.__class__.__name__ + "\n" + fmt_str += " Number of datapoints: {}\n".format(self.__len__()) + fmt_str += " Root Location: {}\n".format(self.root) + tmp = " Transforms (if any): " + fmt_str += "{0}{1}\n".format( + tmp, self.transform.__repr__().replace("\n", "\n" + " " * len(tmp)) + ) + tmp = " Target Transforms (if any): " + fmt_str += "{0}{1}".format( + tmp, self.target_transform.__repr__().replace("\n", "\n" + " " * len(tmp)) + ) + return fmt_str + + +class ILSVRC_HDF5_feats(data.Dataset): + """ ILSVRC_HDF5_feats: A dataset to support I/O from an HDF5. + + Parameters + ---------- + root :str + Path to the hdf5 file containing images and labels. + root_feats: str, optional + Path to the hdf5 file containing the instance features. + root_nns: str, optional + Path to the hdf5 file containing the list of nearest neighbors for each instance. + transform : callable, optional + A function/transform that takes in an PIL image and returns a transformed version. + E.g, ``transforms.RandomCrop`` + target_transform: callable, optional + A function/transform that takes in the target and transforms it. + load_labels: bool, optional + Return labels for each example. + load_features: bool, optional + Return instance features and its neighbors (needed for IC-GAN). + load_in_mem_images: bool, optional + Load all images in memory. + load_in_mem_labels: bool, optional + Load all labels in memory. + load_in_mem_feats: bool, optional + Load all instance features in memory. + k_nn: int, optional + Size of the neighborhood obtained with the k-NN algorithm. + which_nn_balance: str, optional + Whether to sample an instance or a neighbor class first. By default, + ``instance_balance`` is used. + Using ``nnclass_balance`` allows class balancing to be applied. + kmeans_file: str, optional + Path to a file where only the dataset indexes selected with k-means are stored. + It reduces the amount of available data to train or test the model. + n_subsampled_data: int, optional + If other than -1, that number of data points are randomly selected from the dataset. + It reduces the amount of available data to train or test the model. + filter_hd: int, optional + Only used for COCO-Stuff dataset. If -1, all COCO-Stuff evaluation set is used. + If 0, only images with seen class combinations are used. + If 1, only images with unseen class combinations are used. + label_dim: int, optional + Dimensionality of label embeddings. Useful for the StyleGAN2 backbone code. + feature_dim: int, optional + Dimensionality of instance features embeddings. Useful for the StyleGAN2 backbone + code. + feature_augmentation: bool, optional + Use the instance features of the flipped ground-truth image instances as + conditioning, with a 50% probability. + gpu_knn: bool, optional + Accelerate k-NN faiss computation with GPUs. + apply_norm: bool, optional + Normalize images between [-0.5, 0.5]. + label_onehot: bool, optional + Return labels as a one hot encoding. Useful for StyleGAN2 backbone code. + + Attributes + --------- + root: str + Path to the hdf5 file containing images and labels. + root_feats: str + Path to the hdf5 file containing the instance features. + root_nns: str + Path to the hdf5 file containing the list of nearest neighbors for each + instance. + transform : callable + A function/transform that takes in an PIL image and returns a transformed version. + E.g, ``transforms.RandomCrop`` + target_transform: callable + A function/transform that takes in the target and transforms it. + load_labels: bool + Return labels for each example. + load_features: bool + Return instance features and its neighbors (needed for IC-GAN). + load_in_mem_images: bool + Load all images in memory. + load_in_mem_labels: bool + Load all labels in memory. + load_in_mem_feats: bool + Load all instance features in memory. + feature_augmentation: bool + Use the instance features of the flipped ground-truth image instances as conditioning, + with a 50% probability. + which_nn_balance: str + Whether to sample an instance or a neighbor class first. By default, + ``instance_balance`` is used. Using ``nnclass_balance`` allows class balancing to be + applied. + apply_norm: bool + Normalize images between [-0.5, 0.5]. + label_onehot: bool + Return labels as a one hot encoding. Useful for StyleGAN2 backbone code. + num_imgs: int. + Number of data points in the dataset. + data: NumPy array + Image data, with the shape (num_imgs, w, h, 3), where w: width and h: height. + labels: NumPy array + Label data, with the shape (num_imgs, 1). + feats: NumPy array + Instance features data, with the shape (num_imgs, 2048). + sample_nns: list + List with length ``num_imgs``, that contains a list of the ``k_nn`` neighbor indexes + for each instance. + sample_nn_radius: NumPy array + Array of size (num_imgs) that stores the distance between each instance and its + farthest(k-th) neighbor. + possible_sampling_idxs: list + List of all effective possible data samples. By default, it is a range(0, num_imgs). + kmeans_samples: list + List of indexes for samples selected with k-means algorithm. + kth_values: NumPy array + Distances between instances and its k-th neighbor. + """ + + def __init__( + self, + root, + root_feats=None, + root_nns=None, + transform=None, + target_transform=None, + load_labels=True, + load_features=True, + load_in_mem_images=False, + load_in_mem_labels=False, + load_in_mem_feats=False, + k_nn=4, + which_nn_balance="instance_balance", + kmeans_file=None, + n_subsampled_data=-1, + filter_hd=-1, + label_dim=0, + feature_dim=2048, + feature_augmentation=False, + gpu_knn=True, + apply_norm=True, + label_onehot=False, + **kwargs + ): + self.root = root + self.root_feats = root_feats + self.root_nns = root_nns + + self.load_labels = load_labels + self.load_features = load_features + self._label_dim = label_dim + self._feature_dim = feature_dim + self.label_onehot = label_onehot + self.feature_augmentation = feature_augmentation + + # Set the transform here + self.transform = transform + self.target_transform = target_transform + # Normalization of images between -0.5 and 0.5 used in BigGAN + self.apply_norm = apply_norm + + # load the entire dataset into memory? + self.load_in_mem_images = load_in_mem_images + self.load_in_mem_labels = load_in_mem_labels + self.load_in_mem_feats = load_in_mem_feats + + self.which_nn_balance = which_nn_balance + + self.num_imgs = len(h5.File(root, "r")["labels"]) + + self.labels, self.feats = None, None + self.kth_values = None + # If loading into memory, do so now + print( + "Load in mem? Images: %r, Labels: %r, Features: %r." + % (self.load_in_mem_images, self.load_in_mem_labels, self.load_in_mem_feats) + ) + if self.load_in_mem_images: + print("Loading images from %s into memory..." % root) + with h5.File(root, "r") as f: + self.data = f["imgs"][:] + if load_labels and self.load_in_mem_labels: + print("Loading labels from %s into memory..." % root) + with h5.File(root, "r") as f: + self.labels = f["labels"][:] + if load_features and self.load_in_mem_feats: + print("Loading features from %s into memory..." % root_feats) + with h5.File(root_feats, "r") as f: + self.feats = f["feats"][:] + # Normalizing features + print("Normalizing features by their norm") + self.feats /= np.linalg.norm(self.feats, axis=1, keepdims=True) + self.feats = torch.from_numpy(self.feats) + self.feats.share_memory_() + + if load_features: + if root_nns is None and self.load_in_mem_feats: + # We compute NNs only if we are loading features and there is no root_nns file. + self._obtain_nns(k_nn, gpu=gpu_knn, faiss_lib=USE_FAISS) + elif root_nns is not None: + # Still loading the NNs indexes! + print("Loading %s into memory..." % root_nns) + with h5.File(root_nns, "r") as f: + self.sample_nns = f["sample_nns"][:] + self.sample_nn_radius = f["sample_nns_radius"][:] + else: + raise ValueError( + "If no file with pre-computed neighborhoods is provided, " + "the features need to be loaded in memory to extract them." + " Set the load_in_mem_feats=True." + ) + + # Reducing the number of available samples according to different criteria + self.possible_sampling_idxs = range(self.num_imgs) + self.kmeans_samples = None + if kmeans_file is not None: + print("Loading file with just a few centroids (kmeans)... ", kmeans_file) + self.kmeans_samples = np.load(kmeans_file, allow_pickle=True).item()[ + "center_examples" + ][:, 0] + self.possible_sampling_idxs = self.kmeans_samples + elif n_subsampled_data > -1: + self.possible_sampling_idxs = np.random.choice( + np.array(self.possible_sampling_idxs), + int(n_subsampled_data), + replace=False, + ) + elif filter_hd > -1: + # For COCO_Stuff, we can divide the evaluation set in seen class combinations + # (filter_hd=0) + # or unseen class combinations (filter_hd=1) + allowed_idxs = data_utils.filter_by_hd(filter_hd) + self.possible_sampling_idxs = allowed_idxs + # Change the size of the dataset if only a subset of the data is used + self.possible_sampling_idxs = np.array(self.possible_sampling_idxs) + self.num_imgs = len(self.possible_sampling_idxs) + + print( + "All possible conditioning instances are ", len(self.possible_sampling_idxs) + ) + + def __getitem__(self, index): + """ + Parameters + ---------- + index: int + + Returns + ------- + If the dataset loads both features and labels, return 4 elements: neighbor image, + neighbor class label, instance features and instance radius + If the dataset loads only features (no labels), return 4 elements: neighbor image, + instance features, instance radius + If the dataset loads ony labels (no features), return 2 elements: neighbor image and + neighbor class label. + If the dataset does not load features nor labels, return only an image. + """ + # This only changes the index if possible_sampling_idx contains only a subset of the data + # (k-means/random sampling or evaluation sets in COCO-Stuff) + index = self.possible_sampling_idxs[index] + img = self._get_image(index) + target = self.get_label(index) + if self.load_features: + img_nn, label_nn, feats, radii = self._get_instance_features_and_nn(index) + img = img_nn + target = label_nn + else: + feats, radii = None, None + + # Apply transform + img = torch.from_numpy(img) + if self.apply_norm: + img = ((img.float() / 255) - 0.5) * 2 + if self.transform is not None: + img = self.transform(img) + if self.target_transform is not None: + target = self.target_transform(target) + + if not self.label_onehot: + target = int(target) + + if self.load_features and self.load_labels: + return img, target, feats, radii + elif self.load_features: + return img, feats, radii + elif self.load_labels: + return img, target + else: + return img + + def sample_conditioning_instance_balance(self, batch_size, weights=None): + """ + It samples a batch size of conditionings. + + First, by first sampling an instance, and then one of the neighbor's class. + + Parameters + ---------- + batch_size: int + Number of conditioning to sample. + weights: NumPy array, optional + Array of size len(self.possible_sampling_idxs), indicating the weight for each instance, + used for sampling. + + Returns + ------- + labels_gen: torch.LongTensor + Tensor of shape (batch_size, label_dim). Batch of neighbor labels. + instance_gen: torch.LongTensor + Tensor of shape (batch_size, label_dim). Batch of instance features. + """ + # Control instance (center of k-NN) balancing with weights + # Sampling from p(h) + if weights is None: + # np.random.randint is a faster function than np.random.choice. + # If there is no sampling weights, use this one. + sel_idxs = np.random.randint(0, len(self.possible_sampling_idxs), size=batch_size) + sel_idxs = self.possible_sampling_idxs[sel_idxs] + else: + sel_idxs = np.random.choice( + self.possible_sampling_idxs, batch_size, replace=True, p=weights + ) + + # Features from center example + instance_gen = self.get_instance_features(sel_idxs) + # Get labels from neighbor + labels_gen = [] + for idx_ in sel_idxs: + # Sampling neighbor from p(x_nn, y_nn| h) + chosen_idx = np.random.choice(self.sample_nns[idx_]) + # Labels from neighbors + if self.load_labels: + labels_gen.append(self.get_label(chosen_idx)[np.newaxis, ...]) + if self.load_labels: + labels_gen = np.concatenate(labels_gen, 0) + labels_gen = torch.LongTensor(labels_gen) + else: + labels_gen = None + + instance_gen = torch.FloatTensor(instance_gen) + + return labels_gen, instance_gen + + def sample_conditioning_nnclass_balance( + self, batch_size, weights=None, num_classes=1000 + ): + """ + It samples a batch size of conditionings. + + First, by sampling a class, then an image from this class, and finally an instance feature + that would have this image as a neighbor in feature space. + + Parameters + ---------- + batch_size: int + Number of conditioning to sample. + weights: NumPy array, optional + Array of size num_classes, indicating the weight for each instance, used for sampling. + num_classes: int, optional + Number of classes in the dataset + + Returns + ------- + labels_gen: torch.LongTensor + Tensor of shape (batch_size, label_dim). Batch of neighbor labels. + instance_gen: torch.LongTensor + Tensor of shape (batch_size, label_dim). Batch of instance features. + """ + if weights is not None: + weights = np.array(weights) / sum(weights) + + # Sampling from p(y) + chosen_class = np.random.choice( + range(num_classes), batch_size, replace=True, p=weights + ) + nn_idxs = [] + for lab_ in chosen_class: + # Sampling from p(x_nn|y) + chosen_xnn = np.random.choice((self.labels == lab_).nonzero()[0]) + # Sampling from p(h| x_nn,y) + nn_idxs.append(np.random.choice(self.sample_nns[chosen_xnn])) + + instance_gen = self.get_instance_features(nn_idxs) + + instance_gen = torch.FloatTensor(instance_gen) + labels_gen = torch.LongTensor(chosen_class) + + return labels_gen, instance_gen + + def get_label(self, index): + """Obtain a label as an int or as a one-hot vector.""" + if not self.load_labels: + if self.label_onehot: + return np.zeros(self.label_dim, dtype=np.float32).copy() + else: + return 0 + + if self.load_labels: + if self.load_in_mem_labels: + target = self.labels[index] + else: + with h5.File(self.root, "r") as f: + target = f["labels"][index] + else: + target = None + if self.label_onehot: + onehot_vec = np.zeros(self.label_dim, dtype=np.float32) + onehot_vec[target] = 1 + target = onehot_vec.copy() + + return target + + def get_instance_features(self, index): + """Obtain an instance feature vector.""" + if not self.load_features: + return np.zeros(self.feature_dim, dtype=np.float32).copy() + + if self.load_in_mem_feats: + feat = self.feats[index].clone().float() # .astype('float') + else: + with h5.File(self.root_feats, "r") as f: + if isinstance(index, (int, np.int64)): + hflip = np.random.randint(2) == 1 + if self.feature_augmentation and hflip: + feat = f["feats_hflip"][index].astype("float") + else: + feat = f["feats"][index].astype("float") + feat /= np.linalg.norm(feat, keepdims=True) + else: + feat = [] + for sl_idx in index: + hflip = np.random.randint(2) == 1 + if self.feature_augmentation and hflip: + feat.append( + f["feats_hflip"][sl_idx].astype("float")[ + np.newaxis, ... + ] + ) + else: + feat.append( + f["feats"][sl_idx].astype("float")[np.newaxis, ...] + ) + feat = np.concatenate(feat) + feat /= np.linalg.norm(feat, axis=1, keepdims=True) + return feat + + @property + def resolution(self): + with h5.File(self.root, "r") as f: + sze = list(f["imgs"][0].shape) + return sze[1] + + @property + def label_dim(self): + return self._label_dim + + @property + def feature_dim(self): + return self._feature_dim + + def _obtain_nns(self, k_nn=20, faiss_lib=True, feat_sz=2048, gpu=True): + """ + It obtains the neighborhoods for all instances using the k-NN algorithm. + + Parameters + ---------- + k_nn: int, optional + Number of neighbors (k). + faiss_lib: bool, optional + If True, use the faiss library implementation of k-NN. If not, use the slower + implementation of sklearn. + feat_sz: int, optional + Feature dimensionality. + gpu: bool, optional + If True, leverage GPU resources to speed up computation with the faiss library. + + """ + # K_nn computation takes into account the input sample as the first NN, + # so we add an extra NN to later remove the input sample. + k_nn += 1 + + self.sample_nns = [[] for _ in range(self.num_imgs)] + self.sample_nn_radius = np.zeros(self.num_imgs, dtype=float) + + if faiss_lib: + cpu_index = faiss.IndexFlatL2(feat_sz) + if gpu: + gpu_index = faiss.index_cpu_to_all_gpus(cpu_index) # build the index + index = gpu_index + else: + index = cpu_index + index.add(self.feats.float().numpy().astype("float32")) + kth_values, kth_values_arg = index.search( + self.feats.numpy().astype("float32"), k_nn + ) + self.kth_values = np.sqrt(kth_values) + knn_radii = np.sqrt(kth_values[:, -1]) + + else: + dists = sklearn.metrics.pairwise_distances( + self.feats, self.feats, metric="euclidean", n_jobs=-1 + ) + print("Computed distances.") + knn_radii, kth_values_arg = self._get_kth_value_accurate(dists, k_nn) + for i_sample in range(self.num_imgs): + knns = kth_values_arg[i_sample] + # Discarding the input sample, also seen as the 0-NN. + knns = np.delete(knns, np.where(knns == i_sample)[0], 0) + self.sample_nns[i_sample] = knns.tolist() + self.sample_nn_radius[i_sample] = knn_radii[i_sample] + print("Computed NNs.") + + @staticmethod + def _get_kth_value_accurate(distances, k, axis=-1): + """ Find k nearest neighbor + Parameters + --------- + distances: NumPy array + Matrix of size (M, M) of unordered distances. + k: int + Neighborhood size + axis: int + + Returns + ------- + kth values: NumPy array + Distances of the k-th nearest neighbor along the designated axis. + indices: NumPy array + Array positions in the input matrix indicating all neighbors up until the k-th. + + """ + indices = np.argpartition(distances, k - 1, axis=axis)[..., :k] + k_smallests = np.take_along_axis(distances, indices, axis=axis) + kth_values = k_smallests.max(axis=axis) + return kth_values, indices + + def _get_image(self, index): + """Obtain an image array.""" + if self.load_in_mem_images: + img = self.data[index] + else: + with h5.File(self.root, "r") as f: + img = f["imgs"][index] + return img + + def _get_instance_features_and_nn(self, index): + """ Builds a quadruplet of neighbor image, its label, conditioning instance features, radii. + + Returns + ---------- + img_nn: NumPy array + Neighbor image. + label_nn: NumPy array + Neighbor label. + feats: NumPy array + Conditioning instance features. + radii: float + Distance between conditioning instance and farthest (k-th) neighbor. + """ + # Standard sampling: Obtain a feature vector for the input index, + # and image/class label for a neighbor. + if self.which_nn_balance == "instance_balance": + idx_h = index + # If we are only using a selected number of instances (kmeans), re-choose the index + if self.kmeans_samples is not None: + index = np.random.choice(self.kmeans_samples) + idx_nn = np.random.choice(self.sample_nns[index]) + + # Reverse sampling, used when we want to perform class balancing (long-tail setup). + # In class-conditional IC-GAN, the classes are taken from the neighbors. + # The reverse sampling allows us to control the class balancing by using extra weights + # in the DataLoader. + elif self.which_nn_balance == "nnclass_balance": + idx_h = np.random.choice(self.sample_nns[index]) + idx_nn = index + + # Index selects the instance feature vector + radii = self.sample_nn_radius[idx_h] + + img_nn = self._get_image(idx_nn) + label_nn = self.get_label(idx_nn) + feats = self.get_instance_features(idx_h) + + return img_nn, label_nn, feats, radii + + def __len__(self): + return self.num_imgs diff --git a/data_utils/inception_tf13.py b/data_utils/inception_tf13.py new file mode 100644 index 0000000000000000000000000000000000000000..b26a68f8cbde2ddedd9a6b989756830c3db0f1d4 --- /dev/null +++ b/data_utils/inception_tf13.py @@ -0,0 +1,342 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +# +""" Tensorflow inception score code +Derived from https://github.com/openai/improved-gan +Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py +THIS CODE REQUIRES TENSORFLOW 1.3 or EARLIER to run in PARALLEL BATCH MODE + +To use this code, run sample.py on your model with --sample_npz, and then +pass the experiment name in the --experiment_name. +This code also saves pool3 stats to an npz file for FID calculation +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os.path +import sys +import tarfile +import math +from tqdm import tqdm, trange +from argparse import ArgumentParser + +import numpy as np +from six.moves import urllib +import tensorflow as tf +import pickle +import h5py as h5 +import json + +MODEL_DIR = "../inception_net" +DATA_URL = ( + "http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz" +) +softmax = None + + +def prepare_parser(): + usage = "Parser for TF1.3- Inception Score scripts." + parser = ArgumentParser(description=usage) + parser.add_argument( + "--experiment_name", + type=str, + default="", + help="Which experiment" "s samples.npz file to pull and evaluate", + ) + parser.add_argument( + "--experiment_root", + type=str, + default="samples", + help="Default location where samples are stored (default: %(default)s)", + ) + parser.add_argument( + "--batch_size", + type=int, + default=500, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--kmeans_subsampled", + type=int, + default=-1, + help="Reduced number of instances to test with (using this number of centroids).", + ) + parser.add_argument( + "--seed", type=int, default=0, help="Seed (default: %(default)s)" + ) + + ## Ground-truth data arguments ## + parser.add_argument( + "--use_ground_truth_data", + action="store_true", + default=False, + help="Use ground truth data to store its reference inception moments", + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where data is stored (default: %(default)s)", + ) + parser.add_argument( + "--which_dataset", + type=str, + default="imagenet", + choices=["imagenet", "imagenet_lt", "coco"], + help="Dataset choice.", + ) + parser.add_argument( + "--resolution", + type=int, + default=64, + help="Resolution to train with " "(default: %(default)s)", + ) + parser.add_argument( + "--split", type=str, default="train", help="Data split (default: %(default)s)" + ) + parser.add_argument( + "--strat_name", + type=str, + default="", + choices=["", "few", "low", "many"], + help="Stratified split for FID in ImageNet-LT validation (default: %(default)s)", + ) + return parser + + +def run(config): + assert ( + config["strat_name"] != "" + and config["which_dataset"] == "imagenet_lt" + and config["split"] == "val" + ) or config["strat_name"] == "" + # Inception with TF1.3 or earlier. + # Call this function with list of images. Each of elements should be a + # numpy array with values ranging from 0 to 255. + def get_inception_score(images, splits=10, normalize=True): + assert type(images) == list + assert type(images[0]) == np.ndarray + assert len(images[0].shape) == 3 + # assert(np.max(images[0]) > 10) + # assert(np.min(images[0]) >= 0.0) + inps = [] + for img in images: + if normalize: + img = np.uint8(255 * (img + 1) / 2.0) + img = img.astype(np.float32) + inps.append(np.expand_dims(img, 0)) + bs = config["batch_size"] + with tf.Session() as sess: + preds, pools = [], [] + n_batches = int(math.ceil(float(len(inps)) / float(bs))) + for i in trange(n_batches): + inp = inps[(i * bs) : min((i + 1) * bs, len(inps))] + inp = np.concatenate(inp, 0) + pred, pool = sess.run([softmax, pool3], {"ExpandDims:0": inp}) + preds.append(pred) + pools.append(pool) + preds = np.concatenate(preds, 0) + scores = [] + for i in range(splits): + part = preds[ + (i * preds.shape[0] // splits) : ( + (i + 1) * preds.shape[0] // splits + ), + :, + ] + kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0))) + kl = np.mean(np.sum(kl, 1)) + scores.append(np.exp(kl)) + return np.mean(scores), np.std(scores), np.squeeze(np.concatenate(pools, 0)) + + # Init inception + def _init_inception(): + global softmax, pool3 + if not os.path.exists(MODEL_DIR): + os.makedirs(MODEL_DIR) + filename = DATA_URL.split("/")[-1] + filepath = os.path.join(MODEL_DIR, filename) + if not os.path.exists(filepath): + + def _progress(count, block_size, total_size): + sys.stdout.write( + "\r>> Downloading %s %.1f%%" + % (filename, float(count * block_size) / float(total_size) * 100.0) + ) + sys.stdout.flush() + + filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) + print() + statinfo = os.stat(filepath) + print("Succesfully downloaded", filename, statinfo.st_size, "bytes.") + tarfile.open(filepath, "r:gz").extractall(MODEL_DIR) + with tf.gfile.FastGFile( + os.path.join(MODEL_DIR, "classify_image_graph_def.pb"), "rb" + ) as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + _ = tf.import_graph_def(graph_def, name="") + # Works with an arbitrary minibatch size. + with tf.Session() as sess: + pool3 = sess.graph.get_tensor_by_name("pool_3:0") + ops = pool3.graph.get_operations() + for op_idx, op in enumerate(ops): + for o in op.outputs: + shape = o.get_shape() + shape = [s.value for s in shape] + new_shape = [] + for j, s in enumerate(shape): + if s == 1 and j == 0: + new_shape.append(None) + else: + new_shape.append(s) + o.__dict__["_shape_val"] = tf.TensorShape(new_shape) + w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1] + logits = tf.matmul(tf.squeeze(pool3, [1, 2]), w) + softmax = tf.nn.softmax(logits) + + # if softmax is None: # No need to functionalize like this. + _init_inception() + + if config["use_ground_truth_data"]: + # HDF5 file name + if config["which_dataset"] in ["imagenet", "imagenet_lt"]: + dataset_name_prefix = "ILSVRC" + elif config["which_dataset"] == "coco": + dataset_name_prefix = "COCO" + hdf5_filename = "%s%i%s%s%s_xy.hdf5" % ( + dataset_name_prefix, + config["resolution"], + "longtail" + if config["which_dataset"] == "imagenet_lt" and config["split"] == "train" + else "", + "_val" if config["split"] == "val" else "", + "_test" + if config["split"] == "val" and config["which_dataset"] == "coco" + else "", + ) + with h5.File(os.path.join(config["data_root"], hdf5_filename), "r") as f: + data_imgs = f["imgs"][:] + data_labels = f["labels"][:] + ims = data_imgs.transpose(0, 2, 3, 1) + + else: + if config["strat_name"] != "": + fname = "%s/%s/samples%s_seed%i_strat_%s.pickle" % ( + config["experiment_root"], + config["experiment_name"], + "_kmeans" + str(config["kmeans_subsampled"]) + if config["kmeans_subsampled"] > -1 + else "", + config["seed"], + config["strat_name"], + ) + else: + fname = "%s/%s/samples%s_seed%i.pickle" % ( + config["experiment_root"], + config["experiment_name"], + "_kmeans" + str(config["kmeans_subsampled"]) + if config["kmeans_subsampled"] > -1 + else "", + config["seed"], + ) + print("loading %s ..." % fname) + file_to_read = open(fname, "rb") + ims = pickle.load(file_to_read)["x"] + print("loading %s ..." % fname) + print("number of images saved are ", len(ims)) + file_to_read.close() + ims = ims.swapaxes(1, 2).swapaxes(2, 3) + + import time + + t0 = time.time() + inc_mean, inc_std, pool_activations = get_inception_score( + list(ims), splits=10, normalize=not config["use_ground_truth_data"] + ) + t1 = time.time() + print("Saving pool to numpy file for FID calculations...") + mu = np.mean(pool_activations, axis=0) + sigma = np.cov(pool_activations, rowvar=False) + if config["use_ground_truth_data"]: + np.savez( + "%s/%s%s_res%i_tf_inception_moments_ground_truth.npz" + % ( + config["data_root"], + config["which_dataset"], + "_val" if config["split"] == "val" else "", + config["resolution"], + ), + **{"mu": mu, "sigma": sigma} + ) + else: + np.savez( + "%s/%s/TF_pool%s_%s.npz" + % ( + config["experiment_root"], + config["experiment_name"], + "_val" if config["split"] == "val" else "", + "_strat_" + config["strat_name"] if config["strat_name"] != "" else "", + ), + **{"mu": mu, "sigma": sigma} + ) + print( + "Inception took %3f seconds, score of %3f +/- %3f." + % (t1 - t0, inc_mean, inc_std) + ) + + # If ground-truth data moments, also compute the moments for stratified FID. + if ( + config["split"] == "val" + and config["which_dataset"] == "imagenet_lt" + and config["use_ground_truth_data"] + ): + samples_per_class = np.load( + "BigGAN_PyTorch/imagenet_lt/imagenet_lt_samples_per_class.npy", + allow_pickle=True, + ) + for strat_name in ["_many", "_low", "_few"]: + if strat_name == "_many": + pool_ = pool_activations[samples_per_class[data_labels] >= 100] + elif strat_name == "_low": + pool_ = pool_activations[samples_per_class[data_labels] < 100] + labels_ = data_labels[samples_per_class[data_labels] < 100] + pool_ = pool_[samples_per_class[labels_] > 20] + elif strat_name == "_few": + pool_ = pool_activations[samples_per_class[data_labels] <= 20] + print("Size for strat ", strat_name, " is ", len(pool_)) + mu = np.mean(pool_, axis=0) + sigma = np.cov(pool_, rowvar=False) + + np.savez( + "%s/%s%s_res%i_tf_inception_moments%s_ground_truth.npz" + % ( + config["data_root"], + config["which_dataset"], + "_val" if config["split"] == "val" else "", + config["resolution"], + strat_name, + ), + **{"mu": mu, "sigma": sigma} + ) + + +def main(): + # parse command line and run + parser = prepare_parser() + config = vars(parser.parse_args()) + print(config) + run(config) + + +if __name__ == "__main__": + main() diff --git a/data_utils/inception_utils.py b/data_utils/inception_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..85a6bc5142f25c9c1150288746e6a16b23c8a5b4 --- /dev/null +++ b/data_utils/inception_utils.py @@ -0,0 +1,479 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +""" Inception utilities + This file contains methods for calculating IS and FID, using either + the original numpy code or an accelerated fully-pytorch version that + uses a fast newton-schulz approximation for the matrix sqrt. There are also + methods for acquiring a desired number of samples from the Generator, + and parallelizing the inbuilt PyTorch inception network. + + NOTE that Inception Scores and FIDs calculated using these methods will + *not* be directly comparable to values calculated using the original TF + IS/FID code. You *must* use the TF model if you wish to report and compare + numbers. This code tends to produce IS values that are 5-10% lower than + those obtained through TF. +""" +import os +import numpy as np +from scipy import linalg # For numpy FID +import time + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Parameter as P +from torchvision.models.inception import inception_v3 + +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +from data_utils.compute_pdrc import compute_prdc + +# Module that wraps the inception network to enable use with dataparallel and +# returning pool features and logits. +class WrapInception(nn.Module): + def __init__(self, net): + super(WrapInception, self).__init__() + self.net = net + self.mean = P( + torch.tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1), requires_grad=False + ) + self.std = P( + torch.tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1), requires_grad=False + ) + + def forward(self, x): + # Normalize x + x = (x + 1.0) / 2.0 + x = (x - self.mean) / self.std + # Upsample if necessary + if x.shape[2] != 299 or x.shape[3] != 299: + x = F.interpolate(x, size=(299, 299), mode="bilinear", align_corners=True) + # 299 x 299 x 3 + x = self.net.Conv2d_1a_3x3(x) + # 149 x 149 x 32 + x = self.net.Conv2d_2a_3x3(x) + # 147 x 147 x 32 + x = self.net.Conv2d_2b_3x3(x) + # 147 x 147 x 64 + x = F.max_pool2d(x, kernel_size=3, stride=2) + # 73 x 73 x 64 + x = self.net.Conv2d_3b_1x1(x) + # 73 x 73 x 80 + x = self.net.Conv2d_4a_3x3(x) + # 71 x 71 x 192 + x = F.max_pool2d(x, kernel_size=3, stride=2) + # 35 x 35 x 192 + x = self.net.Mixed_5b(x) + # 35 x 35 x 256 + x = self.net.Mixed_5c(x) + # 35 x 35 x 288 + x = self.net.Mixed_5d(x) + # 35 x 35 x 288 + x = self.net.Mixed_6a(x) + # 17 x 17 x 768 + x = self.net.Mixed_6b(x) + # 17 x 17 x 768 + x = self.net.Mixed_6c(x) + # 17 x 17 x 768 + x = self.net.Mixed_6d(x) + # 17 x 17 x 768 + x = self.net.Mixed_6e(x) + # 17 x 17 x 768 + # 17 x 17 x 768 + x = self.net.Mixed_7a(x) + # 8 x 8 x 1280 + x = self.net.Mixed_7b(x) + # 8 x 8 x 2048 + x = self.net.Mixed_7c(x) + # 8 x 8 x 2048 + pool = torch.mean(x.view(x.size(0), x.size(1), -1), 2) + # 1 x 1 x 2048 + logits = self.net.fc(F.dropout(pool, training=False).view(pool.size(0), -1)) + # 1000 (num_classes) + return pool, logits + + +# A pytorch implementation of cov, from Modar M. Alfadly +# https://discuss.pytorch.org/t/covariance-and-gradient-support/16217/2 +def torch_cov(m, rowvar=False): + """Estimate a covariance matrix given data. + + Covariance indicates the level to which two variables vary together. + If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`, + then the covariance matrix element `C_{ij}` is the covariance of + `x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`. + + Parameters + ---------- + m: A 1-D or 2-D array containing multiple variables and observations. + Each row of `m` represents a variable, and each column a single + observation of all those variables. + rowvar: If `rowvar` is True, then each row represents a + variable, with observations in the columns. Otherwise, the + relationship is transposed: each column represents a variable, + while the rows contain observations. + + Returns + ------- + The covariance matrix of the variables. + """ + if m.dim() > 2: + raise ValueError("m has more than 2 dimensions") + if m.dim() < 2: + m = m.view(1, -1) + if not rowvar and m.size(0) != 1: + m = m.t() + # m = m.type(torch.double) # uncomment this line if desired + fact = 1.0 / (m.size(1) - 1) + m -= torch.mean(m, dim=1, keepdim=True) + mt = m.t() # if complex: mt = m.t().conj() + return fact * m.matmul(mt).squeeze() + + +# Pytorch implementation of matrix sqrt, from Tsung-Yu Lin, and Subhransu Maji +# https://github.com/msubhransu/matrix-sqrt +def sqrt_newton_schulz(A, numIters, dtype=None): + with torch.no_grad(): + if dtype is None: + dtype = A.type() + batchSize = A.shape[0] + dim = A.shape[1] + normA = A.mul(A).sum(dim=1).sum(dim=1).sqrt() + Y = A.div(normA.view(batchSize, 1, 1).expand_as(A)) + I = torch.eye(dim, dim).view(1, dim, dim).repeat(batchSize, 1, 1).type(dtype) + Z = torch.eye(dim, dim).view(1, dim, dim).repeat(batchSize, 1, 1).type(dtype) + for i in range(numIters): + T = 0.5 * (3.0 * I - Z.bmm(Y)) + Y = Y.bmm(T) + Z = T.bmm(Z) + sA = Y * torch.sqrt(normA).view(batchSize, 1, 1).expand_as(A) + return sA + + +# FID calculator from TTUR--consider replacing this with GPU-accelerated cov +# calculations using torch? +def numpy_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): + """Numpy implementation of the Frechet Distance. + Taken from https://github.com/bioinf-jku/TTUR + The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) + and X_2 ~ N(mu_2, C_2) is + d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). + Stable version by Dougal J. Sutherland. + Parameters + ---------- + mu1 : Numpy array containing the activations of a layer of the + inception net (like returned by the function 'get_predictions') + for generated samples. + mu2 : The sample mean over activations, precalculated on an + representive data set. + sigma1: The covariance matrix over activations for generated samples. + sigma2: The covariance matrix over activations, precalculated on an + representive data set. + Returns + ------- + The Frechet Distance (float). + """ + + mu1 = np.atleast_1d(mu1) + mu2 = np.atleast_1d(mu2) + + sigma1 = np.atleast_2d(sigma1) + sigma2 = np.atleast_2d(sigma2) + + assert ( + mu1.shape == mu2.shape + ), "Training and test mean vectors have different lengths" + assert ( + sigma1.shape == sigma2.shape + ), "Training and test covariances have different dimensions" + + diff = mu1 - mu2 + + # Product might be almost singular + covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) + if not np.isfinite(covmean).all(): + msg = ( + "fid calculation produces singular product; " + "adding %s to diagonal of cov estimates" + ) % eps + print(msg) + offset = np.eye(sigma1.shape[0]) * eps + covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) + + # Numerical error might give slight imaginary component + if np.iscomplexobj(covmean): + print("wat") + if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): + m = np.max(np.abs(covmean.imag)) + raise ValueError("Imaginary component {}".format(m)) + covmean = covmean.real + + tr_covmean = np.trace(covmean) + + out = diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean + return out + + +def torch_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): + """Pytorch implementation of the Frechet Distance. + Taken from https://github.com/bioinf-jku/TTUR + The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) + and X_2 ~ N(mu_2, C_2) is + d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). + Stable version by Dougal J. Sutherland. + + Parameters + ---------- + mu1 : Numpy array containing the activations of a layer of the + inception net (like returned by the function 'get_predictions') + for generated samples. + mu2 : The sample mean over activations, precalculated on an + representive data set. + sigma1: The covariance matrix over activations for generated samples. + sigma2: The covariance matrix over activations, precalculated on an + representive data set. + Returns + ------- + The Frechet Distance (float). + """ + + assert ( + mu1.shape == mu2.shape + ), "Training and test mean vectors have different lengths" + assert ( + sigma1.shape == sigma2.shape + ), "Training and test covariances have different dimensions" + + diff = mu1 - mu2 + # Run 50 itrs of newton-schulz to get the matrix sqrt of sigma1 dot sigma2 + covmean = sqrt_newton_schulz(sigma1.mm(sigma2).unsqueeze(0), 50).squeeze() + out = ( + diff.dot(diff) + + torch.trace(sigma1) + + torch.trace(sigma2) + - 2 * torch.trace(covmean) + ) + return out + + +# Calculate Inception Score mean + std given softmax'd logits and number of splits +def calculate_inception_score(pred, num_splits=10): + scores = [] + for index in range(num_splits): + pred_chunk = pred[ + index + * (pred.shape[0] // num_splits) : (index + 1) + * (pred.shape[0] // num_splits), + :, + ] + kl_inception = pred_chunk * ( + np.log(pred_chunk) - np.log(np.expand_dims(np.mean(pred_chunk, 0), 0)) + ) + kl_inception = np.mean(np.sum(kl_inception, 1)) + scores.append(np.exp(kl_inception)) + return np.mean(scores), np.std(scores) + + +# Loop and run the sampler and the net until it accumulates num_inception_images +# activations. Return the pool, the logits, and the labels (if one wants +# Inception Accuracy the labels of the generated class will be needed) +def accumulate_inception_activations(sample, net, num_inception_images=50000, model_backbone='biggan'): + pool, logits, labels = [], [], [] + while (torch.cat(logits, 0).shape[0] if len(logits) else 0) < num_inception_images: + with torch.no_grad(): + images, labels_val, _ = sample() + if model_backbone == 'stylegan2': + images = torch.clamp((images * 127.5 + 128), 0, 255) + images = ((images / 255) - 0.5) * 2 + if labels_val is not None: + labels_val = labels_val.long() + pool_val, logits_val = net(images.float()) + pool += [pool_val] + logits += [F.softmax(logits_val, 1)] + labels += [labels_val] + return ( + torch.cat(pool, 0), + torch.cat(logits, 0), + torch.cat(labels, 0) + if labels[0] is not None + else torch.zeros(torch.cat(logits, 0).shape[0]).long(), + ) + + +### Iterates over the real data in the loader to return the labels and pool activations. +def accumulate_features(net, loader, num_inception_images=50000, device="cuda"): + pool_real = [] + for i, batch in enumerate(loader): + x = batch[0] + with torch.no_grad(): + x = x.to(device).float() + pool_real += [net(x)[0].cpu()] + if ( + torch.cat(pool_real, 0).shape[0] if len(pool_real) else 0 + ) >= num_inception_images: + break + + return torch.cat(pool_real, 0).cpu()[:num_inception_images] + + +# Load and wrap the Inception model +def load_inception_net(parallel=False, device="cuda"): + inception_model = inception_v3(pretrained=True, transform_input=False) + inception_model = WrapInception(inception_model.eval()).to(device) + if parallel: + print("Parallelizing Inception module...") + inception_model = nn.DataParallel(inception_model) + return inception_model + + +# This produces a function which takes in an iterator which returns a set number of samples +# and iterates until it accumulates config['num_inception_images'] images. +# The iterator can return samples with a different batch size than used in +# training, using the setting confg['inception_batchsize'] +def prepare_inception_metrics( + dataset, + samples_per_class, + parallel, + no_fid=False, + data_root="", + split_name="", + stratified_fid=False, + prdc=False, + device="cuda", + backbone='biggan', +): + # Load metrics; this is intentionally not in a try-except loop so that + # the script will crash here if it cannot find the Inception moments. + # By default, remove the "hdf5" from dataset + print( + "Loading dataset inception moments from ", + os.path.join( + data_root, dataset + "_" + "inception_moments" + split_name + ".npz" + ), + ) + stats = np.load( + os.path.join( + data_root, dataset + "_" + "inception_moments" + split_name + ".npz" + ) + ) + data_mu = stats["mu"] + data_sigma = stats["sigma"] + if stratified_fid: + many_stats = np.load( + os.path.join(data_root, dataset + "_many_inception_moments.npz") + ) + low_stats = np.load( + os.path.join(data_root, dataset + "_low_inception_moments.npz") + ) + few_stats = np.load( + os.path.join(data_root, dataset + "_few_inception_moments.npz") + ) + # Load network + net = load_inception_net(parallel, device=device) + + def get_inception_metrics( + sample, + num_inception_images, + num_splits=10, + prints=True, + use_torch=True, + loader_ref=None, + num_pr_images=10000 + ): + if prints: + print("Gathering activations...") + pool, logits, labels_val = accumulate_inception_activations( + sample, net, num_inception_images, backbone + ) + # Obtain features for the real ground-truth data + if prdc and loader_ref is not None: + pool_real = accumulate_features( + net, loader_ref, num_inception_images, device=device + ) + print("Subsampling %i samples for prdc metrics!" % (num_pr_images)) + idxs_selected = np.random.choice( + range(len(pool_real)), num_pr_images, replace=False + ) + + prdc_metrics = compute_prdc( + pool_real[idxs_selected], pool[idxs_selected].cpu(), 5 + ) + if prints: + print("Calculating Inception Score...") + IS_mean, IS_std = calculate_inception_score(logits.cpu().numpy(), num_splits) + if no_fid: + FID = 9999.0 + else: + if prints: + print("Calculating means and covariances...") + FID = compute_fid( + pool.clone(), data_mu, data_sigma, prints, use_torch, device=device + ) + # Obtain stratified FID metrics for ImageNet-LT dataset. + stratified_fid_list = [] + if stratified_fid: + labels_val = labels_val.cpu() + pool = pool.cpu() + for stats, strat_name in zip( + [many_stats, low_stats, few_stats], ["_many", "_low", "_few"] + ): + if strat_name == "_many": + pool_ = pool[samples_per_class[labels_val] >= 100] + print("For many-shot, selecting ", len(pool_), " samples.") + elif strat_name == "_low": + pool_ = pool[samples_per_class[labels_val] < 100] + labels_ = labels_val[samples_per_class[labels_val] < 100] + pool_ = pool_[samples_per_class[labels_] > 20] + print("For low-shot, selecting ", len(pool_), " samples.") + + elif strat_name == "_few": + pool_ = pool[samples_per_class[labels_val] <= 20] + print("For few-shot, selecting ", len(pool_), " samples.") + # import pdb + # pdb.set_trace() + stratified_fid_list.append( + compute_fid(pool_, stats["mu"], stats["sigma"], prints, False) + ) + + del pool_ + # Delete mu, sigma, pool, logits, and labels, just in case + del pool, logits, labels_val + if prdc: + return IS_mean, IS_std, FID, stratified_fid_list, prdc_metrics + else: + return IS_mean, IS_std, FID, stratified_fid_list + + return get_inception_metrics + + +def compute_fid(pool, data_mu, data_sigma, prints, use_torch, device="cuda"): + if use_torch: + mu, sigma = torch.mean(pool, 0), torch_cov(pool, rowvar=False) + else: + mu, sigma = ( + np.mean(pool.cpu().numpy(), axis=0), + np.cov(pool.cpu().numpy(), rowvar=False), + ) + if prints: + print("Covariances calculated, getting FID...") + if use_torch: + FID = torch_calculate_frechet_distance( + mu, + sigma, + torch.tensor(data_mu).float().to(device), + torch.tensor(data_sigma).float().to(device), + ) + FID = float(FID.cpu().numpy()) + else: + FID = numpy_calculate_frechet_distance(mu, sigma, data_mu, data_sigma) + del mu, sigma + return FID diff --git a/data_utils/make_hdf5.py b/data_utils/make_hdf5.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b4a9eccd6c0fa44c2ff6d8eaf2dabb174f7d82 --- /dev/null +++ b/data_utils/make_hdf5.py @@ -0,0 +1,351 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +""" Convert dataset to HDF5 + This script preprocesses a dataset and saves it (images and labels) to + an HDF5 file for improved I/O. """ +import os +import sys +from argparse import ArgumentParser +from tqdm import tqdm, trange +import h5py as h5 + +import numpy as np +import torch + +import utils +import torchvision.transforms.functional as tv_f + + +def prepare_parser(): + usage = "Parser for ImageNet HDF5 scripts." + parser = ArgumentParser(description=usage) + parser.add_argument( + "--resolution", + type=int, + default=128, + help="Which Dataset resolution to train on, out of 64, 128, 256 (default: %(default)s)", + ) + parser.add_argument( + "--split", + type=str, + default="train", + help="Which Dataset to convert: train, val (default: %(default)s)", + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where data is stored (default: %(default)s)", + ) + parser.add_argument( + "--out_path", + type=str, + default="data", + help="Default location where data in hdf5 format will be stored (default: %(default)s)", + ) + parser.add_argument( + "--pretrained_model_path", + type=str, + default="", + help="Location where the pretrained model (to extract features) can be found (default: %(default)s)", + ) + parser.add_argument( + "--save_features_only", + action="store_true", + default=False, + help="Only save features in hdf5 file.", + ) + parser.add_argument( + "--save_images_only", + action="store_true", + default=False, + help="Only save images and their labels in hdf5 file.", + ) + parser.add_argument( + "--feature_augmentation", + action="store_true", + default=False, + help="Additioally store instance features with horizontally flipped input images.", + ) + parser.add_argument( + "--feature_extractor", + type=str, + default="classification", + choices=["classification", "selfsupervised"], + help="Choice of feature extractor", + ) + parser.add_argument( + "--backbone_feature_extractor", + type=str, + default="resnet50", + choices=["resnet50"], + help="Choice of feature extractor backbone", + ) + parser.add_argument( + "--which_dataset", type=str, default="imagenet", help="Dataset choice." + ) + parser.add_argument( + "--instance_json", + type=str, + default="", + help="Path to JSON containing instance segmentations for COCO_Stuff", + ) + parser.add_argument( + "--stuff_json", + type=str, + default="", + help="Path to JSON containing instance segmentations for COCO_Stuff", + ) + parser.add_argument( + "--batch_size", + type=int, + default=256, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--num_workers", + type=int, + default=16, + help="Number of dataloader workers (default: %(default)s)", + ) + parser.add_argument( + "--chunk_size", + type=int, + default=500, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--compression", + action="store_true", + default=False, + help="Use LZF compression? (default: %(default)s)", + ) + return parser + + +def run(config): + # Load pretrained feature extractor + net = utils.load_pretrained_feature_extractor( + config["pretrained_model_path"], + config["feature_extractor"], + config["backbone_feature_extractor"], + ) + net.eval() + + # Update compression entry + config["compression"] = ( + "lzf" if config["compression"] else None + ) # No compression; can also use 'lzf' + + # Get dataset + kwargs = { + "num_workers": config["num_workers"], + "pin_memory": False, + "drop_last": False, + } + test_part = False + if config["split"] == "test": + config["split"] = "val" + test_part = True + if config["which_dataset"] in ["imagenet", "imagenet_lt"]: + data_path = os.path.join(config["data_root"], config["split"]) + else: + data_path = config["data_root"] + dataset = utils.get_dataset_images( + config["resolution"], + data_path=data_path, + longtail=config["which_dataset"] == "imagenet_lt", + split=config["split"], + test_part=test_part, + which_dataset=config["which_dataset"], + instance_json=config["instance_json"], + stuff_json=config["stuff_json"], + ) + train_loader = utils.get_dataloader( + dataset, config["batch_size"], shuffle=False, **kwargs + ) + + # HDF5 supports chunking and compression. You may want to experiment + # with different chunk sizes to see how it runs on your machines. + # Chunk Size/compression Read speed @ 256x256 Read speed @ 128x128 Filesize @ 128x128 Time to write @128x128 + # 1 / None 20/s + # 500 / None ramps up to 77/s 102/s 61GB 23min + # 500 / LZF 8/s 56GB 23min + # 1000 / None 78/s + # 5000 / None 81/s + # auto:(125,1,16,32) / None 11/s 61GB + + # Use imagenet statistics to preprocess images for the feature extractor (instance features) + norm_mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).cuda() + norm_std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).cuda() + + if config["which_dataset"] in ["imagenet", "imagenet_lt"]: + dataset_name_prefix = "ILSVRC" + elif config["which_dataset"] == "coco": + dataset_name_prefix = "COCO" + else: + dataset_name_prefix = config["which_dataset"] + + if not config["save_features_only"]: + h5file_name = config["out_path"] + "/%s%i%s%s%s_xy.hdf5" % ( + dataset_name_prefix, + config["resolution"], + "" if config["which_dataset"] != "imagenet_lt" else "longtail", + "_val" if config["split"] == "val" else "", + "_test" if test_part else "", + ) + print("Filenames are ", h5file_name) + + if not config["save_images_only"]: + h5file_name_feats = config["out_path"] + "/%s%i%s%s%s_feats_%s_%s.hdf5" % ( + dataset_name_prefix, + config["resolution"], + "" if config["which_dataset"] != "imagenet_lt" else "longtail", + "_val" if config["split"] == "val" else "", + "_test" if test_part else "", + config["feature_extractor"], + config["backbone_feature_extractor"], + ) + print("Filenames are ", h5file_name_feats) + + print( + "Starting to load dataset into an HDF5 file with chunk size %i and compression %s..." + % (config["chunk_size"], config["compression"]) + ) + + # Save original COCO image indexes in order for the evaluation set + if config["which_dataset"] == "coco" and test_part: + all_image_ids = [] + # Loop over loader + for i, (x, y, image_id) in enumerate(tqdm(train_loader)): + if config["which_dataset"] == "coco" and test_part: + all_image_ids.append(image_id) + if not config["save_images_only"]: + with torch.no_grad(): + x_tf = x.cuda() + x_tf = x_tf * 0.5 + 0.5 + x_tf = (x_tf - norm_mean) / norm_std + x_tf = torch.nn.functional.upsample(x_tf, 224, mode="bicubic") + + x_feat, _ = net(x_tf) + x_feat = x_feat.cpu().numpy() + if config["feature_augmentation"]: + x_tf_hflip = tv_f.hflip(x_tf) + x_feat_hflip, _ = net(x_tf_hflip) + x_feat_hflip = x_feat_hflip.cpu().numpy() + else: + x_feat_hflip = None + else: + x_feat, x_feat_hflip = None, None + # Stick X into the range [0, 255] since it's coming from the train loader + x = (255 * ((x + 1) / 2.0)).byte().numpy() + # Numpyify y + y = y.numpy() + # If we're on the first batch, prepare the hdf5 + if i == 0: + # Save images and labels in hdf5 file + if not config["save_features_only"]: + with h5.File(h5file_name, "w") as f: + print("Producing dataset of len %d" % len(train_loader.dataset)) + imgs_dset = f.create_dataset( + "imgs", + x.shape, + dtype="uint8", + maxshape=( + len(train_loader.dataset), + 3, + config["resolution"], + config["resolution"], + ), + chunks=( + config["chunk_size"], + 3, + config["resolution"], + config["resolution"], + ), + compression=config["compression"], + ) + print("Image chunks chosen as " + str(imgs_dset.chunks)) + imgs_dset[...] = x + labels_dset = f.create_dataset( + "labels", + y.shape, + dtype="int64", + maxshape=(len(train_loader.dataset),), + chunks=(config["chunk_size"],), + compression=config["compression"], + ) + print("Label chunks chosen as " + str(labels_dset.chunks)) + labels_dset[...] = y + + # Save features in hdf5 file + if not config["save_images_only"]: + with h5.File(h5file_name_feats, "w") as f: + features_dset = f.create_dataset( + "feats", + x_feat.shape, + dtype="float", + maxshape=(len(train_loader.dataset), x_feat.shape[1]), + chunks=(config["chunk_size"], x_feat.shape[1]), + compression=config["compression"], + ) + features_dset[...] = x_feat + if config["feature_augmentation"]: + features_dset_hflips = f.create_dataset( + "feats_hflip", + x_feat.shape, + dtype="float", + maxshape=(len(train_loader.dataset), x_feat.shape[1]), + chunks=(config["chunk_size"], x_feat.shape[1]), + compression=config["compression"], + ) + features_dset_hflips[...] = x_feat_hflip + + # Else append to the hdf5 + else: + if not config["save_features_only"]: + with h5.File(h5file_name, "a") as f: + f["imgs"].resize(f["imgs"].shape[0] + x.shape[0], axis=0) + f["imgs"][-x.shape[0] :] = x + f["labels"].resize(f["labels"].shape[0] + y.shape[0], axis=0) + f["labels"][-y.shape[0] :] = y + + if not config["save_images_only"]: + with h5.File(h5file_name_feats, "a") as f: + f["feats"].resize(f["feats"].shape[0] + x_feat.shape[0], axis=0) + f["feats"][-x_feat.shape[0] :] = x_feat + if config["feature_augmentation"]: + with h5.File(h5file_name_feats, "a") as f: + f["feats_hflip"].resize( + f["feats_hflip"].shape[0] + x_feat_hflip.shape[0], axis=0 + ) + f["feats_hflip"][-x_feat_hflip.shape[0] :] = x_feat_hflip + + if config["which_dataset"] == "coco" and test_part: + print( + "Saved COCO index images for evaluation set (in order of appearance in the hdf5 file)" + ) + np.save( + os.path.join("coco_stuff_val_indexes", "cocostuff_val2_all_idxs"), + np.concatenate(all_image_ids), + ) + + +def main(): + # parse command line and run + parser = prepare_parser() + config = vars(parser.parse_args()) + print(config) + run(config) + + +if __name__ == "__main__": + main() diff --git a/data_utils/make_hdf5_nns.py b/data_utils/make_hdf5_nns.py new file mode 100644 index 0000000000000000000000000000000000000000..c6a0a9c8c8371dd55909b7f0cbeff7a81b3a4abd --- /dev/null +++ b/data_utils/make_hdf5_nns.py @@ -0,0 +1,184 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +""" Obtain nearest neighbors and store them in a HDF5 file. """ +import os +import sys +from argparse import ArgumentParser +from tqdm import tqdm, trange +import h5py as h5 + +import numpy as np +import torch +import utils + + +def prepare_parser(): + usage = "Parser for ImageNet HDF5 scripts." + parser = ArgumentParser(description=usage) + parser.add_argument( + "--resolution", + type=int, + default=128, + help="Which Dataset resolution to train on, out of 64, 128, 256 (default: %(default)s)", + ) + parser.add_argument( + "--split", + type=str, + default="train", + help="Which Dataset to convert: train, val (default: %(default)s)", + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where data is stored (default: %(default)s)", + ) + parser.add_argument( + "--out_path", + type=str, + default="data", + help="Default location where data in hdf5 format will be stored (default: %(default)s)", + ) + parser.add_argument( + "--num_workers", + type=int, + default=16, + help="Number of dataloader workers (default: %(default)s)", + ) + parser.add_argument( + "--chunk_size", + type=int, + default=500, + help="Default overall batchsize (default: %(default)s)", + ) + parser.add_argument( + "--compression", + action="store_true", + default=False, + help="Use LZF compression? (default: %(default)s)", + ) + + parser.add_argument( + "--feature_extractor", + type=str, + default="classification", + choices=["classification", "selfsupervised"], + help="Choice of feature extractor", + ) + parser.add_argument( + "--backbone_feature_extractor", + type=str, + default="resnet50", + choices=["resnet50"], + help="Choice of feature extractor backbone", + ) + parser.add_argument( + "--k_nn", + type=int, + default=100, + help="Number of nearest neighbors (default: %(default)s)", + ) + + parser.add_argument( + "--which_dataset", type=str, default="imagenet", help="Dataset choice." + ) + + return parser + + +def run(config): + # Update compression entry + config["compression"] = ( + "lzf" if config["compression"] else None + ) # No compression; can also use 'lzf' + + test_part = False + if config["split"] == "test": + config["split"] = "val" + test_part = True + if config["which_dataset"] in ["imagenet", "imagenet_lt"]: + dataset_name_prefix = "ILSVRC" + elif config["which_dataset"] == "coco": + dataset_name_prefix = "COCO" + else: + dataset_name_prefix = config["which_dataset"] + + train_dataset = utils.get_dataset_hdf5( + **{ + "resolution": config["resolution"], + "data_path": config["data_root"], + "load_in_mem_feats": True, + "compute_nns": True, + "longtail": config["which_dataset"] == "imagenet_lt", + "split": config["split"], + "instance_cond": True, + "feature_extractor": config["feature_extractor"], + "backbone_feature_extractor": config["backbone_feature_extractor"], + "k_nn": config["k_nn"], + "ddp": False, + "which_dataset": config["which_dataset"], + "test_part": test_part, + } + ) + + all_nns = np.array(train_dataset.sample_nns)[:, : config["k_nn"]] + all_nns_radii = train_dataset.kth_values[:, config["k_nn"]] + print("NNs shape ", all_nns.shape, all_nns_radii.shape) + labels_ = torch.Tensor(train_dataset.labels) + acc = np.array( + [(labels_[all_nns[:, i_nn]] == labels_).sum() for i_nn in range(config["k_nn"])] + ).sum() / (len(labels_) * config["k_nn"]) + print("For k ", config["k_nn"], " accuracy:", acc) + + h5file_name_nns = config["out_path"] + "/%s%i%s%s%s_feats_%s_%s_nn_k%i.hdf5" % ( + dataset_name_prefix, + config["resolution"], + "" if config["which_dataset"] != "imagenet_lt" else "longtail", + "_val" if config["split"] == "val" else "", + "_test" if test_part else "", + config["feature_extractor"], + config["backbone_feature_extractor"], + config["k_nn"], + ) + print("Filename is ", h5file_name_nns) + + with h5.File(h5file_name_nns, "w") as f: + nns_dset = f.create_dataset( + "sample_nns", + all_nns.shape, + dtype="int64", + maxshape=all_nns.shape, + chunks=(config["chunk_size"], all_nns.shape[1]), + compression=config["compression"], + ) + nns_dset[...] = all_nns + + nns_radii_dset = f.create_dataset( + "sample_nns_radius", + all_nns_radii.shape, + dtype="float", + maxshape=all_nns_radii.shape, + chunks=(config["chunk_size"],), + compression=config["compression"], + ) + nns_radii_dset[...] = all_nns_radii + + +def main(): + # parse command line and run + parser = prepare_parser() + config = vars(parser.parse_args()) + print(config) + run(config) + + +if __name__ == "__main__": + main() diff --git a/data_utils/prepare_data.sh b/data_utils/prepare_data.sh new file mode 100755 index 0000000000000000000000000000000000000000..66d802154ace2fe48835240e10a40ffa3c9fdc90 --- /dev/null +++ b/data_utils/prepare_data.sh @@ -0,0 +1,64 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +resolution=$2 # 64,128,256 +dataset=$1 #'imagenet', 'imagenet_lt', 'coco', [a transfer dataset, such as 'cityscapes'] +out_path='' +path_imnet='' +path_swav='swav_800ep_pretrain.pth.tar' +path_classifier_lt='resnet50_uniform_e90.pth' + + +################## +#### ImageNet #### +################## +if [ $dataset = 'imagenet' ]; then + python data_utils/make_hdf5.py --resolution $resolution --split 'train' --data_root $path_imnet --out_path $out_path --feature_extractor 'classification' --feature_augmentation + python data_utils/make_hdf5.py --resolution $resolution --split 'train' --data_root $path_imnet --out_path $out_path --save_features_only --feature_extractor 'selfsupervised' --feature_augmentation --pretrained_model_path $path_swav + python data_utils/make_hdf5.py --resolution $resolution --split 'val' --data_root $path_imnet --out_path $out_path --save_images_only + ## Calculate inception moments + for split in 'train' 'val'; do + python data_utils/calculate_inception_moments.py --resolution $resolution --split 'train' --data_root $out_path --load_in_mem --out_path $out_path + done + # Compute NNs + python data_utils/make_hdf5_nns.py --resolution $resolution --split 'train' --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 50 + python data_utils/make_hdf5_nns.py --resolution $resolution --split 'train' --feature_extractor 'selfsupervised' --data_root $out_path --out_path $out_path --k_nn 50 + +elif [ $dataset = 'imagenet_lt' ]; then + python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'train' --data_root $path_imnet --out_path $out_path --feature_extractor 'classification' --feature_augmentation --pretrained_model_path $path_classifier_lt + python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'val' --data_root $path_imnet --out_path $out_path --save_images_only + # Calculate inception moments + python data_utils/calculate_inception_moments.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'train' --data_root $out_path --out_path $out_path + python data_utils/calculate_inception_moments.py --resolution $resolution --split 'val' --data_root $out_path --out_path $out_path --stratified_moments + # Compute NNs + python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'train' --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 5 + +elif [ $dataset = 'coco' ]; then + path_split=("train" "val") + split=("train" "test") + for i in "${!path_split[@]}"; do + coco_data_path='COCO/022719/'${path_split[i]}'2017' + coco_instances_path='datasets/coco/annotations/instances_'${path_split[i]}'2017.json' + coco_stuff_path='datasets/coco/annotations/stuff_'${path_split[i]}'2017.json' + python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --data_root $coco_data_path --instance_json $coco_instances_path --stuff_json $coco_stuff_path --out_path $out_path --feature_extractor 'selfsupervised' --feature_augmentation --pretrained_model_path $path_swav + python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --data_root $coco_data_path --instance_json $coco_instances_path --stuff_json $coco_stuff_path --out_path $out_path --feature_extractor 'classification' --feature_augmentation + + # Calculate inception moments + python data_utils/calculate_inception_moments.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --data_root $out_path --load_in_mem --out_path $out_path + # Compute NNs + python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --feature_extractor 'selfsupervised' --data_root $out_path --out_path $out_path --k_nn 5 + python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 5 + + done +# Transfer datasets +else + python data_utils/make_hdf5.py --resolution $resolution --which_dataset $dataset --split 'train' --data_root $3 --feature_extractor 'classification' --out_path $out_path + # Compute NNs + python data_utils/make_hdf5.py --resolution $resolution --which_dataset $dataset --split 'train' --data_root $3 --feature_extractor 'selfsupervised' --pretrained_model_path $path_swav --save_features_only --out_path $out_path + # Compute NNs + # Compute NNs + python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset $dataset --split 'train' --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 5 + python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset $dataset --split 'train' --feature_extractor 'selfsupervised' --data_root $out_path --out_path $out_path --k_nn 5 + +fi diff --git a/data_utils/resnet.py b/data_utils/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..0ad13421433533102f7ff518ebfeef045d5d64e0 --- /dev/null +++ b/data_utils/resnet.py @@ -0,0 +1,153 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# From PyTorch: +# +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) +# +# From Caffe2: +# +# Copyright (c) 2016-present, Facebook Inc. All rights reserved. +# +# All contributions by Facebook: +# Copyright (c) 2016 Facebook Inc. +# +# All contributions by Google: +# Copyright (c) 2015 Google Inc. +# All rights reserved. +# +# All contributions by Yangqing Jia: +# Copyright (c) 2015 Yangqing Jia +# All rights reserved. +# +# All contributions by Kakao Brain: +# Copyright 2019-2020 Kakao Brain +# +# All contributions from Caffe: +# Copyright(c) 2013, 2014, 2015, the respective contributors +# All rights reserved. +# +# All other contributions: +# Copyright(c) 2015, 2016 the respective contributors +# All rights reserved. +import torch +from torchvision.models.utils import load_state_dict_from_url +from typing import Type, Any, Callable, Union, List, Optional +from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet + + +__all__ = [ + "ResNet", + "resnet18", + "resnet34", + "resnet50", + "resnet101", + "resnet152", + "resnext50_32x4d", + "resnext101_32x8d", + "wide_resnet50_2", + "wide_resnet101_2", +] + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", + "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", + "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth", + "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", + "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", + "wide_resnet50_2": "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", + "wide_resnet101_2": "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", +} + + +class ResNet_mine(ResNet): + def __init__(self, block, layers, classifier_run=True, **kwargs): + super().__init__(block, layers, **kwargs) + self.classifier_run = classifier_run + + def _forward_impl(self, x: torch.Tensor) -> (torch.Tensor, torch.Tensor): + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x_ = self.layer4(x) + + x = self.avgpool(x_) + x = torch.flatten(x, 1) + if self.classifier_run: + x = self.fc(x) + + return x, x_ + + def forward(self, x: torch.Tensor) -> (torch.Tensor, torch.Tensor): + return self._forward_impl(x) + + +def pnorm(weights, p): + normB = torch.norm(weights, 2, 1) + ws = weights.clone() + for i in range(weights.size(0)): + ws[i] = ws[i] / torch.pow(normB[i], p) + return ws + + +def _resnet( + arch: str, + block: Type[Union[BasicBlock, Bottleneck]], + layers: List[int], + pretrained: bool, + progress: bool, + **kwargs: Any +) -> ResNet: + model = ResNet_mine(block, layers, **kwargs) + if pretrained: + print("Inside resnet function, using ImageNet pretrained from model url!") + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def resnext50_32x4d( + pretrained: bool = False, progress: bool = True, **kwargs: Any +) -> ResNet: + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["groups"] = 32 + kwargs["width_per_group"] = 4 + return _resnet( + "resnext50_32x4d", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs + ) + + +def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) diff --git a/data_utils/store_coco_jpeg_images.py b/data_utils/store_coco_jpeg_images.py new file mode 100644 index 0000000000000000000000000000000000000000..bf129eb67abad9fc6de910763b5aa4a00c2a11fe --- /dev/null +++ b/data_utils/store_coco_jpeg_images.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Store JPEG images from a hdf5 file, in order to compute FID scores (COCO-Stuff).""" +from argparse import ArgumentParser +import numpy as np +import os +import h5py as h5 +from imageio import imwrite as imsave + +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +from data_utils.utils import filter_by_hd + + +def main(args): + dataset_name_prefix = "COCO" + test_part = True if args["split"] == "val" else False + + # HDF5 file name + hdf5_filename = "%s%i%s%s" % ( + dataset_name_prefix, + args["resolution"], + "_val" if args["split"] == "val" else "", + "_test" if test_part else "", + ) + data_path_xy = os.path.join(args["data_root"], hdf5_filename + "_xy.hdf5") + # Load data + print("Loading images %s..." % (data_path_xy)) + with h5.File(data_path_xy, "r") as f: + imgs = f["imgs"][:] + + # Filter images + if args["filter_hd"] > -1: + filtered_idxs = filter_by_hd(args["filter_hd"]) + else: + filtered_idxs = range(len(imgs)) + + # Save images + counter_i = 0 + for i, im in enumerate(imgs): + if i in filtered_idxs: + imsave( + "%s/%06d.%s" % (args["out_path"], counter_i, "jpg"), + im.transpose(1, 2, 0), + ) + counter_i += 1 + + +if __name__ == "__main__": + parser = ArgumentParser( + description="Storing ground-truth COCO-Stuff images to compute FID metric." + ) + parser.add_argument( + "--resolution", + type=int, + default=64, + help="Data resolution (default: %(default)s)", + ) + parser.add_argument( + "--split", + type=str, + default="train", + choices=["train", "val"], + help="Data split (default: %(default)s)", + ) + parser.add_argument( + "--filter_hd", + type=int, + default=-1, + help="Hamming distance to filter val test in COCO_Stuff (by default no filtering) (default: %(default)s)", + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where the hdf5 file is stored (default: %(default)s)", + ) + parser.add_argument( + "--out_path", + type=str, + default="data", + help="Default location where the resulting images will be stored (default: %(default)s)", + ) + + args = vars(parser.parse_args()) + main(args) diff --git a/data_utils/store_kmeans_indexes.py b/data_utils/store_kmeans_indexes.py new file mode 100644 index 0000000000000000000000000000000000000000..c56e81e80b9caed3db5600ddbb8cc958f425902d --- /dev/null +++ b/data_utils/store_kmeans_indexes.py @@ -0,0 +1,133 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Store dataset indexes of datapoints selected by k-means algorithm.""" +from argparse import ArgumentParser +import numpy as np +import os +import h5py as h5 +import faiss + + +def main(args): + if args["which_dataset"] == "imagenet": + dataset_name_prefix = "ILSVRC" + im_prefix = "IN" + elif args["which_dataset"] == "coco": + dataset_name_prefix = "COCO" + im_prefix = "COCO" + else: + dataset_name_prefix = args["which_dataset"] + im_prefix = args["which_dataset"] + # HDF5 filename + filename = os.path.join( + args["data_root"], + "%s%s_feats_%s_%s.hdf5" + % ( + dataset_name_prefix, + args["resolution"], + args["feature_extractor"], + args["backbone_feature_extractor"], + ), + ) + # Load features + print("Loading features %s..." % (filename)) + with h5.File(filename, "r") as f: + features = f["feats"][:] + features = np.array(features) + # Normalize features + features /= np.linalg.norm(features, axis=1, keepdims=True) + + feat_dim = 2048 + # k-means + print("Training k-means with %i centers..." % (args["kmeans_subsampled"])) + kmeans = faiss.Kmeans( + feat_dim, + args["kmeans_subsampled"], + niter=100, + verbose=True, + gpu=args["gpu"], + min_points_per_centroid=200, + spherical=False, + ) + kmeans.train(features.astype(np.float32)) + + # Find closest instances to each k-means cluster + print("Finding closest instances to centers...") + index = faiss.IndexFlatL2(feat_dim) + index.add(features.astype(np.float32)) + D, closest_sample = index.search(kmeans.centroids, 1) + + net_str = ( + "rn50" + if args["backbone_feature_extractor"] + else args["backbone_feature_extractor"] + ) + stored_filename = "%s_res%i_%s_%s_kmeans_k%i" % ( + im_prefix, + args["resolution"], + net_str, + args["feature_extractor"], + args["kmeans_subsampled"], + ) + np.save( + os.path.join(args["data_root"], stored_filename), + {"center_examples": closest_sample}, + ) + print( + "Instance indexes resulting from a subsampling based on k-means have been saved in file %s!" + % (stored_filename) + ) + + +if __name__ == "__main__": + parser = ArgumentParser( + description="Storing cluster indexes for k-means-based data subsampling" + ) + parser.add_argument( + "--resolution", + type=int, + default=64, + help="Data resolution (default: %(default)s)", + ) + parser.add_argument( + "--which_dataset", type=str, default="imagenet", help="Dataset choice." + ) + parser.add_argument( + "--data_root", + type=str, + default="data", + help="Default location where data is stored (default: %(default)s)", + ) + parser.add_argument( + "--feature_extractor", + type=str, + default="classification", + choices=["classification", "selfsupervised"], + help="Choice of feature extractor", + ) + parser.add_argument( + "--backbone_feature_extractor", + type=str, + default="resnet50", + choices=["resnet50"], + help="Choice of feature extractor backbone", + ) + parser.add_argument( + "--kmeans_subsampled", + type=int, + default=-1, + help="Number of k-means centers if using subsampled training instances" + " (default: %(default)s)", + ) + parser.add_argument( + "--gpu", + action="store_true", + default=False, + help="Use faiss with GPUs (default: %(default)s)", + ) + args = vars(parser.parse_args()) + main(args) diff --git a/data_utils/utils.py b/data_utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8f9845b00356fe5cb46d7fb888631c9480ceeefa --- /dev/null +++ b/data_utils/utils.py @@ -0,0 +1,1029 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock + +# MIT License +import sys +import os +import numpy as np +import time +import datetime +import json +import math +import torch +import torchvision.transforms as transforms +from torch.utils.data import DataLoader + +import shutil +import torch.distributed as dist + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +from data_utils.resnet import resnet50 +import data_utils.datasets_common as dset +from data_utils.cocostuff_dataset import CocoStuff + + +class CenterCropLongEdge(object): + """Crops the given PIL Image on the long edge. + Parameters + ---------- + size: sequence or int + Desired output size of the crop. If size is an int instead of sequence like (h, w), + a square crop (size, size) is made. + """ + + def __call__(self, img): + """ + Args: + img (PIL Image): Image to be cropped. + Returns: + PIL Image: Cropped image. + """ + return transforms.functional.center_crop(img, min(img.size)) + + def __repr__(self): + return self.__class__.__name__ + + +# Modified to be able to do class-balancing +class DistributedSampler(torch.utils.data.sampler.Sampler): + """Sampler that restricts data loading to a subset of the dataset. + + It is especially useful in conjunction with + :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each + process can pass a DistributedSampler instance as a DataLoader sampler, + and load a subset of the original dataset that is exclusive to it. + + .. note:: + Dataset is assumed to be of constant size. + + Arguments: + dataset: Dataset used for sampling. + num_replicas (optional): Number of processes participating in + distributed training. + rank (optional): Rank of the current process within num_replicas. + shuffle (optional): If true (default), sampler will shuffle the indices + """ + + def __init__( + self, dataset, num_replicas=None, rank=None, shuffle=True, weights=None + ): + if num_replicas is None: + if not torch.dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + num_replicas = torch.dist.get_world_size() + if rank is None: + if not torch.dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + rank = torch.dist.get_rank() + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) + self.total_size = self.num_samples * self.num_replicas + self.shuffle = shuffle + self.weights = weights + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + if self.shuffle: + if self.weights is not None: + print("using class balanced!") + indices = torch.multinomial( + self.weights, len(self.dataset), replacement=True, generator=g + ).tolist() + else: + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = list(range(len(self.dataset))) + + # add extra samples to make it evenly divisible + indices += indices[: (self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + indices = indices[self.rank : self.total_size : self.num_replicas] + assert len(indices) == self.num_samples + + return iter(indices) + + def __len__(self): + return self.num_samples + + def set_epoch(self, epoch): + self.epoch = epoch + + +class CheckpointedSampler(torch.utils.data.Sampler): + """Resumable sample with a random generated initialized with a given seed. + + Arguments + --------- + data_source: Dataset + Dataset to sample from. + start_itr: int, optional + Number of iteration to start (or restart) the sampling. + start_epoch: int, optional + Number of epoch to start (or restart) the sampling. + batch_size: int, optional + Batch size. + class_balanced: bool, optional + Sample the data with a class balancing approach. + custom_distrib_gen: bool, optional + Use a temperature controlled class balancing. + samples_per_class: list, optional + A list of int values that indicate the number of samples per class. + class_probabilities: list, optional + A list of float values indicating the probability of a class in the dataset. + longtail_temperature: float, optional + Temperature value to smooth the longtail distribution with a softmax function. + seed: int, optional + Random seed used. + + """ + + def __init__( + self, + data_source, + start_itr=0, + start_epoch=0, + batch_size=128, + class_balanced=False, + custom_distrib_gen=False, + samples_per_class=None, + class_probabilities=None, + longtail_temperature=1, + seed=0, + ): + self.data_source = data_source + self.num_samples = len(self.data_source) + self.start_itr = start_itr % (len(self.data_source) // batch_size) + self.start_epoch = start_epoch + self.batch_size = batch_size + self.class_balanced = class_balanced + self.custom_distrib_gen = custom_distrib_gen + self.generator = torch.Generator() + self.generator.manual_seed(seed) + + if self.class_balanced: + print("Class balanced sampling.") + self.weights = make_weights_for_balanced_classes( + samples_per_class, + self.data_source.labels, + 1000, + self.custom_distrib_gen, + longtail_temperature, + class_probabilities=class_probabilities, + ) + self.weights = torch.DoubleTensor(self.weights) + + # Resumable data loader + print( + "Using the generator ", + self.start_epoch, + " times to resume where we left off.", + ) + # print('Later, we will resume at iteration ', self.start_itr) + for epoch in range(self.start_epoch): + self._sample_epoch_perm() + + if not isinstance(self.num_samples, int) or self.num_samples <= 0: + raise ValueError( + "num_samples should be a positive integeral " + "value, but got num_samples={}".format(self.num_samples) + ) + + def _sample_epoch_perm(self): + if self.class_balanced: + out = [ + torch.multinomial( + self.weights, + len(self.data_source), + replacement=True, + generator=self.generator, + ) + ] + else: + out = [torch.randperm(len(self.data_source), generator=self.generator)] + return out + + def __iter__(self): + out = self._sample_epoch_perm() + output = torch.cat(out).tolist() + return iter(output) + + def __len__(self): + return len(self.data_source) + + +def make_weights_for_balanced_classes( + samples_per_class, + labels=None, + nclasses=None, + custom_distrib_gen=False, + longtail_temperature=1, + class_probabilities=None, +): + """It prepares the sampling weights for the DataLoader. + + Arguments + --------- + samples_per_class: list + A list of int values (size C) that indicate the number of samples per class, + for all C classes. + labels: list/ NumPy array/ torch Tensor, optional + A list of size N that contains a class label for each sample. + nclasses: int, optional + Number of classes in the dataset. + custom_distrib_gen: bool, optional + Use a temperature controlled class balancing. + longtail_temperature: float, optional + Temperature value to smooth the longtail distribution with a softmax function. + class_probabilities: list + A list of float values (size C) indicating the probability of a class in the dataset. + seed: int + Random seed used. + Returns + ------- + If custom_distrib_gen is True, a torch Tensor with size C, where C is the number of classes, + that contains the sampling weights for each class. + If custom_distrib_gen is False, a list with size N (dataset size) that contains the sampling + weights for each individual data sample. + + """ + if custom_distrib_gen: + # temperature controlled distribution + print( + "Temperature controlled distribution for balanced classes! " "Temperature:", + longtail_temperature, + ) + class_prob = torch.log(torch.DoubleTensor(class_probabilities)) + weight_per_class = torch.exp(class_prob / longtail_temperature) / torch.sum( + torch.exp(class_prob / longtail_temperature) + ) + else: + count = [0] * nclasses + for item in labels: + count[item] += 1 + weight_per_class = [0.0] * nclasses + N = float(sum(count)) + for i in range(nclasses): + # Standard class balancing + weight_per_class[i] = N / float(count[i]) + # Convert weighting per class to weighting per example + weight = [0] * len(labels) + for idx, val in enumerate(labels): + # Uniform probability of selecting a sample, given a class + # p(x|y)p(y) + weight[idx] = (1 / samples_per_class[val]) * weight_per_class[val] + return weight + + +def load_pretrained_feature_extractor( + pretrained_path="", + feature_extractor="classification", + backbone_feature_extractor="resnet50", +): + """It loads a pre-trained feature extractor. + + Arguments + --------- + pretrained_path: str, optional + Path to the feature extractor's weights. + feature_extractor: str, optional + If "classification" a network trained on ImageNet for classification will be used. If + "selfsupervised", a network trained on ImageNet with self-supervision will be used. + backbone_feature_extractor: str, optional + Name of the backbone for the feature extractor. Currently, only ResNet50 is supported. + Returns + ------- + A Pytorch network initialized with pre-trained weights. + + """ + if backbone_feature_extractor == "resnet50": + print("using resnet50 to extract features") + net = resnet50( + pretrained=False if pretrained_path != "" else True, classifier_run=False + ).cuda() + else: + raise ValueError("Not implemented for backbones other than ResNet50.") + if pretrained_path != "": + print("Loading pretrained weights from: ", pretrained_path) + + # original saved file with DataParallel + state_dict = torch.load(pretrained_path) + if not feature_extractor == "selfsupervised": + state_dict = state_dict["state_dict_best"]["feat_model"] + + # create new OrderedDict that does not contain `module.` + from collections import OrderedDict + + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if "module." in k: + name = k[7:] # remove `module.` + elif "_feature_blocks." in k: + name = k.replace("_feature_blocks.", "") + else: + name = k + if name in net.state_dict().keys(): + new_state_dict[name] = v + else: + print("key ", name, " not in dict") + + for key in net.state_dict().keys(): + if key not in new_state_dict.keys(): + print("Network key ", key, " not in dict to load") + if not feature_extractor == "selfsupervised": + state_dict = torch.load(pretrained_path)["state_dict_best"]["classifier"] + # create new OrderedDict that does not contain `module.` + for k, v in state_dict.items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + # load params + net.load_state_dict( + new_state_dict, + strict=False if feature_extractor == "selfsupervised" else True, + ) + else: + print("Using pretrained weights on full ImageNet.") + return net + + +def get_dataset_images( + resolution, + data_path, + load_in_mem=False, + augment=False, + longtail=False, + split="train", + test_part=False, + which_dataset="imagenet", + instance_json="", + stuff_json="", + **kwargs +): + """It prepares a dataset that reads the files from a folder. + + Arguments + --------- + resolution: int + Dataset resolution. + data_path: str + Path where to find the data. + load_in_mem: bool, optional + If True, load all data in memory. + augment: bool, optional + If True, use horizontal flips as data augmentation. + longtail: bool, optional + If True, use the longtailed version of ImageNet (ImageNet-LT). + split: str, optional + Split name to use. + test_part: bool, optional + Only used for COCO-Stuff. If True, use the evaluation set instead of the validation set. + which_dataset: str, optional + Dataset name. + instance_json: str, optional + Path where to find the JSON data for COCO-Stuff instances. + stuff_json: str, optional + Path where to find the JSON data for COCO-Stuff stuff. + Returns + ------- + A Dataset class. + + """ + # Data transforms + norm_mean = [0.5, 0.5, 0.5] + norm_std = [0.5, 0.5, 0.5] + if which_dataset not in ["coco"]: + transform_list = [CenterCropLongEdge(), transforms.Resize(resolution)] + else: + transform_list = [transforms.Resize(resolution)] + transform_list = transforms.Compose( + transform_list + + [transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std)] + ) + if augment: + transform_list = transforms.Compose( + transform_list + [transforms.RandomHorizontalFlip()] + ) + + if which_dataset not in ["coco"]: + which_dataset_file = dset.ImageFolder + dataset_kwargs = {} + else: + print("Using coco-stuff dataset class") + which_dataset_file = CocoStuff + dataset_kwargs = { + "image_dir": data_path, + "instances_json": instance_json, + "stuff_json": stuff_json, + "image_size": resolution, + "iscrowd": True if split == "train" else False, + "test_part": test_part, + } + dataset = which_dataset_file( + root=data_path, + transform=transform_list, + load_in_mem=load_in_mem, + split=split, + longtail=longtail, + **dataset_kwargs + ) + return dataset + + +def get_dataset_hdf5( + resolution, + data_path, + augment=False, + longtail=False, + local_rank=0, + copy_locally=False, + ddp=True, + tmp_dir="", + class_cond=True, + instance_cond=False, + feature_extractor="classification", + backbone_feature_extractor="resnext50", + which_nn_balance="instance_balance", + which_dataset="imagenet", + split="train", + test_part=False, + kmeans_subsampled=-1, + n_subsampled_data=-1, + feature_augmentation=False, + filter_hd=-1, + k_nn=50, + load_in_mem_feats=False, + compute_nns=False, + **kwargs +): + """It prepares a dataset that reads the data from HDF5 files. + + Arguments + --------- + resolution: int + Dataset resolution. + data_path: str + Path where to find the data. + load_in_mem: bool, optional + If True, load all data in memory. + augment: bool, optional + If True, use horizontal flips as data augmentation. + longtail: bool, optional + If True, use the longtailed version of ImageNet (ImageNet-LT). + local_rank: int, optional + Index indicating the rank of the DistributedDataParallel (DDP) process in the local + machine. It is set to 0 by default or if DDP is not used. + copy_locally: bool, optional + If true, the HDF5 files will be copied locally to the machine. + Useful if the data is in a server. + ddp: bool, optional + If True, use DistributedDataParallel (DDP). + tmp_dir: str, optional + Path where to copy the dataset HDF5 files locally. + class_cond: bool, optional + If True, the dataset will load the labels of the neighbor real samples. + instance_cond: bool, optional + If True, the dataset will load the instance features. + feature_extractor: str, optional + If "classification" a network trained on ImageNet for classification will be used. If + "selfsupervised", a network trained on ImageNet with self-supervision will be used. + backbone_feature_extractor: str, optional + Name of the backbone for the feature extractor. Currently, only ResNet50 is supported. + which_nn_balance: str, optional + Whether to sample an instance or a neighbor class first. By default, + ``instance_balance`` is used. Using ``nnclass_balance`` allows class balancing + to be applied. + split: str, optional + Split name to use. + test_part: bool, optional + Only used for COCO-Stuff. If True, use the evaluation set instead of the validation set. + kmeans_subsampled: int, optional + If other than -1, that number of data points are selected with k-means from the dataset. + It reduces the amount of available data to train or test the model. + n_subsampled_data: int, optional + If other than -1, that number of data points are randomly selected from the dataset. + It reduces the amount of available data to train or test the model. + feature_augmentation: bool, optional + Use the instance features of the flipped ground-truth image instances as + conditioning, with a 50% probability. + filter_hd: int, optional + Only used for COCO-Stuff dataset. If -1, all COCO-Stuff evaluation set is used. + If 0, only images with seen class combinations are used. + If 1, only images with unseen class combinations are used. + k_nn: int, optional + Size of the neighborhood obtained with the k-NN algorithm. + load_in_mem_feats: bool, optional + Load all instance features in memory. + compute_nns: bool, optional + If True, compute the nearest neighbors. If False, load them from a file with + pre-computed neighbors. + Returns + ------- + A Dataset class. + + """ + + if which_dataset in ["imagenet", "imagenet_lt"]: + dataset_name_prefix = "ILSVRC" + elif which_dataset == "coco": + dataset_name_prefix = "COCO" + else: + dataset_name_prefix = which_dataset + # HDF5 file name + hdf5_filename = "%s%i%s%s%s" % ( + dataset_name_prefix, + resolution, + "" if not longtail else "longtail", + "_val" if split == "val" else "", + "_test" if test_part else "", + ) + + # Data paths + data_path_xy = os.path.join(data_path, hdf5_filename + "_xy.hdf5") + data_path_feats, data_path_nns, kmeans_file = None, None, None + if instance_cond: + data_path_feats = os.path.join( + data_path, + hdf5_filename + + "_feats_%s_%s.hdf5" % (feature_extractor, backbone_feature_extractor), + ) + if not compute_nns: + data_path_nns = os.path.join( + data_path, + hdf5_filename + + "_feats_%s_%s_nn_k%i.hdf5" + % (feature_extractor, backbone_feature_extractor, k_nn), + ) + # Load a file with indexes of the samples selected with k-means. + if kmeans_subsampled > -1: + if which_dataset == "imagenet": + d_name = "IN" + elif which_dataset == "coco": + d_name = "COCO" + else: + d_name = which_dataset + kmeans_file = ( + d_name + + "_res" + + str(resolution) + + "_rn50_" + + feature_extractor + + "_kmeans_k" + + str(kmeans_subsampled) + + ".npy" + ) + kmeans_file = os.path.join(data_path, kmeans_file) + + # Optionally copy the data locally in the cluster. + if copy_locally: + tmp_file = os.path.join(tmp_dir, hdf5_filename + "_xy.hdf5") + print(tmp_file) + if instance_cond: + tmp_file_feats = os.path.join( + tmp_dir, + hdf5_filename + + "_feats_%s_%s.hdf5" % (feature_extractor, backbone_feature_extractor), + ) + print(tmp_file_feats) + + # Only copy locally for the first device in each machine + if local_rank == 0: # device == 'cuda:0': + shutil.copy2(data_path_xy, tmp_file) + if instance_cond: + shutil.copy2(data_path_feats, tmp_file_feats) + data_path_xy = tmp_file + if instance_cond: + data_path_feats = tmp_file_feats + + # Wait for the main process to copy the data locally + if ddp: + dist.barrier() + + # Data transforms + if augment: + transform_list = transforms.RandomHorizontalFlip() + else: + transform_list = None + + dataset = dset.ILSVRC_HDF5_feats( + root=data_path_xy, + root_feats=data_path_feats, + root_nns=data_path_nns, + transform=transform_list, + load_labels=class_cond, + load_features=instance_cond, + load_in_mem_images=False, + load_in_mem_labels=True, + load_in_mem_feats=load_in_mem_feats, + k_nn=k_nn, + which_nn_balance=which_nn_balance, + kmeans_file=kmeans_file, + n_subsampled_data=n_subsampled_data, + feature_augmentation=feature_augmentation, + filter_hd=filter_hd, + ) + return dataset + + +def filter_by_hd(ood_distance): + """Pre-select image indexes in COCO-Stuff evaluation set according to its class composition. + + Parameters + ---------- + ood_distance: int + Minimum hamming distance (HD) between the set of classes present in the evaluation image + and all training images. + If 0, pre-selected images will be the ones that only contain class sets already seen + during training. + If other than 0, all other images with unseen class sets will be selected, + regardless of the hamming distance (HD>0). + Returns + ------- + List of pre-selected images. + """ + + image_ids_original = np.load( + "../coco_stuff_val_indexes/cocostuff_val2_all_idxs.npy", allow_pickle=True + ) + print("Filtering new ids!") + odd_image_ids = np.load( + os.path.join( + "../coco_stuff_val_indexes", "val2" + "_image_ids_by_hd_75ktraining_im.npy" + ), + allow_pickle=True, + ) + if ood_distance == 0: + image_ids = odd_image_ids[ood_distance] + else: + total_img_ids = [] + for ood_dist in range(1, len(odd_image_ids)): + total_img_ids += odd_image_ids[ood_dist] + image_ids = total_img_ids + + allowed_idxs = [] + for i_idx, id in enumerate(image_ids_original): + if id in image_ids: + allowed_idxs.append(i_idx) + allowed_idxs = np.array(allowed_idxs) + print("Num images after filtering ", len(allowed_idxs)) + return allowed_idxs + + +def get_dataloader( + dataset, + batch_size=64, + num_workers=8, + shuffle=True, + pin_memory=True, + drop_last=True, + start_itr=0, + start_epoch=0, + use_checkpointable_sampler=False, + use_balanced_sampler=False, + custom_distrib_gen=False, + samples_per_class=None, + class_probabilities=None, + seed=0, + longtail_temperature=1, + rank=0, + world_size=-1, + **kwargs +): + """Get DataLoader to iterate over the dataset. + + Parameters + ---------- + dataset: Dataset + Class with the specified dataset characteristics. + batch_size: int, optional + Batch size. + num_workers: int, optional + Number of workers for the dataloader. + shuffle: bool, optional + If True, the data is shuffled. If a sampler is used (use_checkpointable_sampler=True, + use_balanced_sampler=True or world_size>-1), this parameter is not used. + pin_memory: bool, optional + Pin memory in the dataloader. + drop_last: bool, optional + Drop last incomplete batch in the dataloader. + start_itr: int, optional + Iteration number to resume the sample from. Only used with + use_checkpointable_sampler=True. + start_epoch: int, optional + Epoch number to resume the sample from. Only used with + use_checkpointable_sampler=True. + use_checkpointable_sampler: bool, optional + If True, use the CheckpointedSampler class to resume jobs from the last seen batch + (deterministic). + use_balanced_sampler: bool, optional + If True, balance the data according to a specific class distribution. Use in conjunction + with ``custom_distrib_gen``, ``samples_per_class``, ``class_probabilities`` and + ``longtail_temperature``. + custom_distrib_gen: bool, optional + Use a temperature controlled class balancing. + samples_per_class: list, optional + A list of int values that indicate the number of samples per class. + class_probabilities: list, optional + A list of float values indicating the probability of a class in the dataset. + longtail_temperature: float, optional + Temperature value to smooth the longtail distribution with a softmax function. + seed: int, optional + Random seed used. + rank: int, optional + Rank of the current process (if using DistributedDataParallel training). + world_size: int, optional + World size (if using DistributedDataParallel training). + Returns + ------- + An instance of DataLoader. + """ + + # Prepare loader; the loaders list is for forward compatibility with + # using validation / test splits. + # if use_multiepoch_sampler: + loader_kwargs = { + "num_workers": num_workers, + "pin_memory": pin_memory, + "drop_last": drop_last, + } + print("Dropping last batch? ", drop_last) + # Otherwise, it has issues dividing the batch for accumulations + # if longtail: + # loader_kwargs.update({'drop_last': drop_last}) + if use_checkpointable_sampler: + print( + "Using checkpointable sampler from start_itr %d..., using seed %d" + % (start_itr, seed) + ) + + sampler = CheckpointedSampler( + dataset, + start_itr, + start_epoch, + batch_size, + class_balanced=use_balanced_sampler, + custom_distrib_gen=custom_distrib_gen, + longtail_temperature=longtail_temperature, + samples_per_class=samples_per_class, + class_probabilities=class_probabilities, + seed=seed, + ) + loader = DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + shuffle=False, + worker_init_fn=seed_worker, + **loader_kwargs + ) + else: + if use_balanced_sampler: + print("Balancing real data! Custom? ", custom_distrib_gen) + weights = make_weights_for_balanced_classes( + samples_per_class, + dataset.labels, + 1000, + custom_distrib_gen, + longtail_temperature, + class_probabilities=class_probabilities, + ) + weights = torch.DoubleTensor(weights) + else: + weights = None + if world_size == -1: + if use_balanced_sampler: + sampler = torch.utils.data.sampler.WeightedRandomSampler( + weights, len(weights) + ) + shuffle = False + else: + sampler = None + else: + sampler = DistributedSampler( + dataset, num_replicas=world_size, rank=rank, weights=weights + ) + shuffle = False + print("Loader workers?", loader_kwargs, " with shuffle?", shuffle) + loader = DataLoader( + dataset, + batch_size=batch_size, + shuffle=shuffle, + sampler=sampler, + worker_init_fn=seed_worker if use_checkpointable_sampler else None, + **loader_kwargs + ) + + return loader + + +def sample_conditioning_values( + z_, + y_, + ddp=False, + batch_size=1, + weights_sampling=None, + dataset=None, + constant_conditioning=False, + class_cond=True, + instance_cond=False, + nn_sampling_strategy="instance_balance", +): + """It samples conditionings from the noise distribution and dataset statistics. + + Arguments + --------- + z_: Distribution + Noise distribution. + y_: Distribution + Labels distribution ( + ddp: bool, optional + If True, use DistributedDataParallel (DDP). + batch_size: int, optional + Batch size. + weights_sampling: NumPy array, optional + Weights to balance the sampling of the conditionings. + dataset: Dataset + Instance of a dataset. + constant_conditioning: bool, optional + If True, set all labels to zero. + class_cond: bool, optional + If True, the dataset will load the labels of the neighbor real samples. + instance_cond: bool, optional + If True, the dataset will load the instance features. + nn_sampling_strategy: str, optional + Whether to sample an instance or a neighbor class first. By default, + ``instance_balance`` is used. Using ``nnclass_balance`` allows class balancing + to be applied. + Returns + ------- + If not using labels (class_cond=False) nor instance features (instance_cond=False), + return the sampled noise vectors. + If not using labels (class_cond=False), return the sampled noise vectors and instance + feature vectors, sampled according to the ``nn_sampling_strategy`` and ``weights_sampling``. + If using labels (class_cond=True), return the sampled noise vectors, instance feature + vectors and the neighbor class labels. + + """ + with torch.no_grad(): + z_.sample_() + if not class_cond and not instance_cond: + return z_ + elif class_cond and not instance_cond: + y_.sample_() + if constant_conditioning: + return z_, torch.zeros_like(y_) + else: + if ddp: + return z_, y_ + else: + return z_, y_.data.clone() + else: + if nn_sampling_strategy == "instance_balance": + sampling_funct_name = dataset.sample_conditioning_instance_balance + elif nn_sampling_strategy == "nnclass_balance": + sampling_funct_name = dataset.sample_conditioning_nnclass_balance + + labels_g, f_g = sampling_funct_name(batch_size, weights_sampling) + if instance_cond and not class_cond: + return z_, f_g + elif instance_cond and class_cond: + return z_, labels_g, f_g + + +# Convenience function to prepare a z and y vector +def prepare_z_y( + G_batch_size, + dim_z, + nclasses, + device="cuda", + fp16=False, + z_var=1.0, + longtail_gen=False, + custom_distrib=False, + longtail_temperature=1, + class_probabilities=None, +): + """Prepare the noise and label distributions. + + Arguments + --------- + G_batch_size: int + Batch size for the generator. + dim_z: int + Noise vector dimensionality. + nclasses: int + Number of classes in the dataset + fp16: bool, optional + Float16. + z_var: float, optional + Variance for the noise normal distribution. + longtail_gen: bool, optional + If true, use the longtail distribution for the classes (ImageNet-LT) + custom_distrib: bool, optional + If true, use a temperature annealed class distribution. + longtail_temperature: float, optional + Temperature value to smooth the longtail distribution with a softmax function. + class_probabilities: list, optional + A list of float values indicating the probability of a class in the dataset. + + Returns + ------- + The noise and class distributions. + """ + z_ = Distribution(torch.randn(G_batch_size, dim_z, requires_grad=False)) + z_.init_distribution("normal", mean=0, var=z_var) + # z_ = z_.to(device, torch.float16 if fp16 else torch.float32) + + if fp16: + z_ = z_.half() + + y_ = Distribution(torch.zeros(G_batch_size, requires_grad=False)) + if longtail_gen: + y_.init_distribution( + "categorical_longtail", + num_categories=nclasses, + class_prob=class_probabilities, + ) + elif custom_distrib: + y_.init_distribution( + "categorical_longtail_temperature", + num_categories=nclasses, + temperature=longtail_temperature, + class_prob=class_probabilities, + ) + else: + y_.init_distribution("categorical", num_categories=nclasses) + # y_ = y_.to(device, torch.int64) + return z_, y_ + + +# A highly simplified convenience class for sampling from distributions +# One could also use PyTorch's inbuilt distributions package. +# Note that this class requires initialization to proceed as +# x = Distribution(torch.randn(size)) +# x.init_distribution(dist_type, **dist_kwargs) +# x = x.to(device,dtype) +# This is partially based on https://discuss.pytorch.org/t/subclassing-torch-tensor/23754/2 +class Distribution(torch.Tensor): + # Init the params of the distribution + def init_distribution(self, dist_type, class_prob=None, **kwargs): + self.dist_type = dist_type + self.dist_kwargs = kwargs + if self.dist_type == "normal": + self.mean, self.var = kwargs["mean"], kwargs["var"] + elif self.dist_type == "categorical": + self.num_categories = kwargs["num_categories"] + elif self.dist_type == "categorical_longtail": + print("(class conditioning sampler) using longtail distribution") + self.num_categories = kwargs["num_categories"] + self.class_prob = torch.DoubleTensor(class_prob) + elif self.dist_type == "categorical_longtail_temperature": + print( + "(class conditioning sampler) Softening the long-tail distribution with temperature ", + kwargs["temperature"], + ) + self.num_categories = kwargs["num_categories"] + self.class_prob = torch.log(torch.DoubleTensor(class_prob)) + self.class_prob = torch.exp( + self.class_prob / kwargs["temperature"] + ) / torch.sum(torch.exp(self.class_prob / kwargs["temperature"])) + + def seed_generator(self, seed): + self.generator.manual_seed(seed) + + def sample_(self): + if self.dist_type == "normal": + self.normal_(self.mean, self.var) + elif self.dist_type == "categorical": + self.random_(0, self.num_categories) + elif ( + "categorical_longtail" in self.dist_type + or "categorical_longtail_temperature" in self.dist_type + ): + self.data = torch.multinomial( + self.class_prob, len(self), replacement=True + ).to(self.device) + # return self.variable + + # Silly hack: overwrite the to() method to wrap the new object + # in a distribution as well + # def to(self, *args, **kwargs): + # new_obj = Distribution(self) + # new_obj.init_distribution(self.dist_type, **self.dist_kwargs) + # new_obj.data = super().to(*args, **kwargs) + # return new_obj + + +def seed_worker(worker_id): + worker_seed = torch.initial_seed() + worker_id diff --git a/download-weights.sh b/download-weights.sh new file mode 100644 index 0000000000000000000000000000000000000000..e2deb6e71652b12f4f2e55b6fa7b8f54d745ce05 --- /dev/null +++ b/download-weights.sh @@ -0,0 +1,9 @@ +#!/bin/sh + +wget https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res256.tar.gz +tar -xvf cc_icgan_biggan_imagenet_res256.tar.gz +wget https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res256.tar.gz +tar -xvf icgan_biggan_imagenet_res256.tar.gz +wget https://dl.fbaipublicfiles.com/ic_gan/stored_instances.tar.gz +tar -xvf stored_instances.tar.gz +curl -L -o swav_pretrained.pth.tar -C - 'https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar' diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..280e21b3c2930f17aa740ca71b1dbf4d04215ae7 --- /dev/null +++ b/environment.yml @@ -0,0 +1,155 @@ +name: ic_gan_ddp_1.8.0 +channels: + - conda-forge + - pytorch + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - blas=1.0=mkl + - bzip2=1.0.8=h7b6447c_0 + - ca-certificates=2021.5.25=h06a4308_1 + - certifi=2021.5.30=py38h06a4308_0 + - click=7.1.2=pyhd3eb1b0_0 + - cloudpickle=1.6.0=py_0 + - cudatoolkit=10.2.89=hfd86e86_1 + - cycler=0.10.0=py38_0 + - cytoolz=0.11.0=py38h7b6447c_0 + - dask-core=2021.4.0=pyhd3eb1b0_0 + - decorator=4.4.2=pyhd3eb1b0_0 + - faiss-gpu=1.7.0=py3.8_h080d439_0_cuda10.2 + - ffmpeg=4.3=hf484d3e_0 + - freetype=2.10.4=h5ab3b9f_0 + - fsspec=0.9.0=pyhd3eb1b0_0 + - gmp=6.2.1=h2531618_2 + - gnutls=3.6.15=he1e5248_0 + - h5py=2.10.0=py38hd6299e0_1 + - hdf5=1.10.6=hb1b8bf9_0 + - imageio=2.9.0=pyhd3eb1b0_0 + - intel-openmp=2020.2=254 + - joblib=1.0.1=pyhd3eb1b0_0 + - jpeg=9b=h024ee3a_2 + - kiwisolver=1.3.1=py38h2531618_0 + - lame=3.100=h7b6447c_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.33.1=h53a641e_7 + - libfaiss=1.7.0=h4fe19ad_0_cuda10.2 + - libffi=3.3=he6710b0_2 + - libgcc-ng=9.1.0=hdf63c60_0 + - libgfortran-ng=7.3.0=hdf63c60_0 + - libiconv=1.15=h63c8f33_5 + - libidn2=2.3.0=h27cfd23_0 + - libpng=1.6.37=hbc83047_0 + - libstdcxx-ng=9.1.0=hdf63c60_0 + - libtasn1=4.16.0=h27cfd23_0 + - libtiff=4.1.0=h2733197_1 + - libunistring=0.9.10=h27cfd23_0 + - libuv=1.40.0=h7b6447c_0 + - lmdb=0.9.28=h2531618_0 + - locket=0.2.1=py38h06a4308_1 + - lz4-c=1.9.3=h2531618_0 + - matplotlib-base=3.3.4=py38h62a2d02_0 + - mkl=2020.2=256 + - mkl-service=2.3.0=py38he904b0f_0 + - mkl_fft=1.3.0=py38h54f3939_0 + - mkl_random=1.1.1=py38h0573a6f_0 + - ncurses=6.2=he6710b0_1 + - nettle=3.7.2=hbbd107a_1 + - networkx=2.5.1=pyhd3eb1b0_0 + - ninja=1.10.2=hff7bd54_1 + - numpy=1.19.2=py38h54aff64_0 + - numpy-base=1.19.2=py38hfa32c7d_0 + - olefile=0.46=py_0 + - openh264=2.1.0=hd408876_0 + - openssl=1.1.1k=h27cfd23_0 + - partd=1.2.0=pyhd3eb1b0_0 + - pillow=8.2.0=py38he98fc37_0 + - pip=21.0.1=py38h06a4308_0 + - psutil=5.8.0=py38h27cfd23_1 + - pyparsing=2.4.7=pyhd3eb1b0_0 + - python=3.8.8=hdb3f193_5 + - python-dateutil=2.8.1=pyhd3eb1b0_0 + - python-lmdb=1.1.1=py38h2531618_1 + - python_abi=3.8=1_cp38 + - pytorch=1.8.0=py3.8_cuda10.2_cudnn7.6.5_0 + - pywavelets=1.1.1=py38h7b6447c_2 + - pyyaml=5.4.1=py38h27cfd23_1 + - readline=8.1=h27cfd23_0 + - scikit-image=0.18.1=py38ha9443f7_0 + - scikit-learn=0.24.1=py38ha9443f7_0 + - scipy=1.6.2=py38h91f5cce_0 + - setuptools=52.0.0=py38h06a4308_0 + - six=1.15.0=py38h06a4308_0 + - sqlite=3.35.4=hdfb4753_0 + - threadpoolctl=2.1.0=pyh5ca1d4c_0 + - tifffile=2020.10.1=py38hdd07704_2 + - tk=8.6.10=hbc83047_0 + - toolz=0.11.1=pyhd3eb1b0_0 + - torchaudio=0.8.0=py38 + - torchvision=0.9.0=py38_cu102 + - tornado=6.1=py38h27cfd23_0 + - tqdm=4.59.0=pyhd3eb1b0_1 + - typing_extensions=3.7.4.3=pyha847dfd_0 + - wheel=0.36.2=pyhd3eb1b0_0 + - xz=5.2.5=h7b6447c_0 + - yaml=0.2.5=h7b6447c_0 + - zlib=1.2.11=h7b6447c_3 + - zstd=1.4.9=haebb681_0 + - pip: + - argon2-cffi==20.1.0 + - async-generator==1.10 + - attrs==21.2.0 + - backcall==0.2.0 + - bleach==3.3.0 + - cffi==1.14.5 + - chardet==4.0.0 + - defusedxml==0.7.1 + - entrypoints==0.3 + - filelock==3.0.12 + - gdown==3.13.0 + - idna==2.10 + - ipykernel==5.5.4 + - ipython==7.23.1 + - ipython-genutils==0.2.0 + - ipywidgets==7.6.3 + - jedi==0.18.0 + - jinja2==2.11.3 + - jsonschema==3.2.0 + - jupyter==1.0.0 + - jupyter-client==6.1.12 + - jupyter-console==6.4.0 + - jupyter-core==4.7.1 + - jupyterlab-pygments==0.1.2 + - jupyterlab-widgets==1.0.0 + - markupsafe==1.1.1 + - matplotlib-inline==0.1.2 + - mistune==0.8.4 + - nbclient==0.5.3 + - nbconvert==6.0.7 + - nbformat==5.1.3 + - nest-asyncio==1.5.1 + - notebook==6.3.0 + - packaging==20.9 + - pandocfilters==1.4.3 + - parso==0.8.2 + - pexpect==4.8.0 + - pickleshare==0.7.5 + - prometheus-client==0.10.1 + - prompt-toolkit==3.0.18 + - ptyprocess==0.7.0 + - pycparser==2.20 + - pygments==2.9.0 + - pyrsistent==0.17.3 + - pysocks==1.7.1 + - pyzmq==22.0.3 + - qtconsole==5.1.0 + - qtpy==1.9.0 + - requests==2.25.1 + - send2trash==1.5.0 + - submitit==1.3.3 + - terminado==0.9.4 + - testpath==0.4.4 + - traitlets==5.0.5 + - urllib3==1.26.4 + - wcwidth==0.2.5 + - webencodings==0.5.1 + - widgetsnbextension==3.5.1 diff --git a/figures/github_image.png b/figures/github_image.png new file mode 100644 index 0000000000000000000000000000000000000000..cf18487e5dde6f2d2141b728faafdb4103f7dcb2 Binary files /dev/null and b/figures/github_image.png differ diff --git a/figures/icgan_clip.png b/figures/icgan_clip.png new file mode 100644 index 0000000000000000000000000000000000000000..a34b0b32e3e561d868d681455d579b7e5a2b4740 Binary files /dev/null and b/figures/icgan_clip.png differ diff --git a/figures/icgan_transfer_all_github.png b/figures/icgan_transfer_all_github.png new file mode 100644 index 0000000000000000000000000000000000000000..846adb4e365322243387a1f51dc8916b77f81eb5 Binary files /dev/null and b/figures/icgan_transfer_all_github.png differ diff --git a/inference/.ipynb_checkpoints/icgan_colab-checkpoint.ipynb b/inference/.ipynb_checkpoints/icgan_colab-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..86496d21d9cb1f90c6587490f122b9314291a269 --- /dev/null +++ b/inference/.ipynb_checkpoints/icgan_colab-checkpoint.ipynb @@ -0,0 +1,707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bb75a2b9", + "metadata": {}, + "source": [ + "Copyright (c) Facebook, Inc. and its affiliates.\n", + "All rights reserved.\n", + "\n", + "This source code is licensed under the license found in the\n", + "LICENSE file in the root directory of this source tree." + ] + }, + { + "cell_type": "markdown", + "id": "81a8ddb6", + "metadata": {}, + "source": [ + "# IC-GAN\n", + "\n", + "\n", + "Official Colab notebook from the paper \"Instance-Conditioned GAN\" by Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.\n", + "\n", + "This Colab provides the code to generate images with IC-GAN, with the option of further guiding the generation with captions (CLIP). \n", + "\n", + "Based on the Colab [WanderClip](https://j.mp/wanderclip) by Eyal Gruss [@eyaler](https://twitter.com/eyaler) [eyalgruss.com](https://eyalgruss.com)\n", + "\n", + "Using the work from [our repository](https://github.com/facebookresearch/ic_gan)\n", + "\n", + "https://github.com/openai/CLIP, Copyright (c) 2021 OpenAI\n", + "\n", + "https://github.com/huggingface/pytorch-pretrained-BigGAN, Copyright (c) 2019 Thomas Wolf\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9442671e", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Restart after running this cell!\n", + "\n", + "!nvidia-smi -L\n", + "\n", + "import subprocess\n", + "\n", + "CUDA_version = [s for s in subprocess.check_output([\"nvcc\", \"--version\"]).decode(\"UTF-8\").split(\", \") if s.startswith(\"release\")][0].split(\" \")[-1]\n", + "print(\"CUDA version:\", CUDA_version)\n", + "\n", + "if CUDA_version == \"10.1\":\n", + " torch_version_suffix = \"+cu101\"\n", + "elif CUDA_version == \"10.2\":\n", + " torch_version_suffix = \"+cu102\"\n", + "else:\n", + " torch_version_suffix = \"+cu111\"\n", + "\n", + "!pip install torch==1.8.0{torch_version_suffix} torchvision==0.8.2{torch_version_suffix} -f https://download.pytorch.org/whl/torch_stable.html ftfy regex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b01f51f1", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Setup\n", + "!git clone https://github.com/facebookresearch/ic_gan.git\n", + "\n", + "%cd /content\n", + "# Uncompress required files\n", + "!wget https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res256.tar.gz\n", + "!tar -xvf cc_icgan_biggan_imagenet_res256.tar.gz\n", + "!wget https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res256.tar.gz\n", + "!tar -xvf icgan_biggan_imagenet_res256.tar.gz\n", + "!wget https://dl.fbaipublicfiles.com/ic_gan/stored_instances.tar.gz\n", + "!tar -xvf stored_instances.tar.gz\n", + "\n", + "!pip install pytorch-pretrained-biggan\n", + "\n", + "!git clone --depth 1 https://github.com/openai/CLIP\n", + "!pip install ftfy\n", + "%cd /content/CLIP\n", + "import clip\n", + "last_clip_model = 'ViT-B/32'\n", + "perceptor, preprocess = clip.load(last_clip_model)\n", + "\n", + "import nltk\n", + "nltk.download('wordnet')\n", + "\n", + "!pip install cma\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa6c7629", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Prepare functions\n", + "from pytorch_pretrained_biggan import BigGAN, convert_to_images, one_hot_from_names, utils\n", + "\n", + "%cd /content/ic_gan/\n", + "import sys\n", + "import os\n", + "sys.path[0] = '/content/ic_gan/inference'\n", + "sys.path.insert(1, os.path.join(sys.path[0], \"..\"))\n", + "import torch \n", + "\n", + "import numpy as np\n", + "import torch\n", + "import torchvision\n", + "import sys\n", + "torch.manual_seed(np.random.randint(sys.maxsize))\n", + "import imageio\n", + "from IPython.display import HTML, Image, clear_output\n", + "from PIL import Image as Image_PIL\n", + "from scipy.stats import truncnorm, dirichlet\n", + "from torch import nn\n", + "from nltk.corpus import wordnet as wn\n", + "from base64 import b64encode\n", + "from time import time\n", + "import cma\n", + "from cma.sigma_adaptation import CMAAdaptSigmaCSA, CMAAdaptSigmaTPA\n", + "import warnings\n", + "warnings.simplefilter(\"ignore\", cma.evolution_strategy.InjectionWarning)\n", + "import torchvision.transforms as transforms\n", + "import inference.utils as inference_utils\n", + "import data_utils.utils as data_utils\n", + "from BigGAN_PyTorch.BigGAN import Generator as generator\n", + "import sklearn.metrics\n", + "\n", + "def replace_to_inplace_relu(model): #saves memory; from https://github.com/minyoungg/pix2latent/blob/master/pix2latent/model/biggan.py\n", + " for child_name, child in model.named_children():\n", + " if isinstance(child, nn.ReLU):\n", + " setattr(model, child_name, nn.ReLU(inplace=False))\n", + " else:\n", + " replace_to_inplace_relu(child)\n", + " return\n", + " \n", + "def save(out,name=None, torch_format=True):\n", + " if torch_format:\n", + " with torch.no_grad():\n", + " out = out.cpu().numpy()\n", + " img = convert_to_images(out)[0]\n", + " if name:\n", + " imageio.imwrite(name, np.asarray(img))\n", + " return img\n", + "\n", + "hist = []\n", + "def checkin(i, best_ind, total_losses, losses, regs, out, noise=None, emb=None, probs=None):\n", + " global sample_num, hist\n", + " name = None\n", + " if save_every and i%save_every==0:\n", + " name = '/content/output/frame_%05d.jpg'%sample_num\n", + " pil_image = save(out, name)\n", + " vals0 = [sample_num, i, total_losses[best_ind], losses[best_ind], regs[best_ind], np.mean(total_losses), np.mean(losses), np.mean(regs), np.std(total_losses), np.std(losses), np.std(regs)]\n", + " stats = 'sample=%d iter=%d best: total=%.2f cos=%.2f reg=%.3f avg: total=%.2f cos=%.2f reg=%.3f std: total=%.2f cos=%.2f reg=%.3f'%tuple(vals0)\n", + " vals1 = []\n", + " if noise is not None:\n", + " vals1 = [np.mean(noise), np.std(noise)]\n", + " stats += ' noise: avg=%.2f std=%.3f'%tuple(vals1)\n", + " vals2 = []\n", + " if emb is not None:\n", + " vals2 = [emb.mean(),emb.std()]\n", + " stats += ' emb: avg=%.2f std=%.3f'%tuple(vals2)\n", + " elif probs:\n", + " best = probs[best_ind]\n", + " inds = np.argsort(best)[::-1]\n", + " probs = np.array(probs)\n", + " vals2 = [ind2name[inds[0]], best[inds[0]], ind2name[inds[1]], best[inds[1]], ind2name[inds[2]], best[inds[2]], np.sum(probs >= 0.5)/pop_size,np.sum(probs >= 0.3)/pop_size,np.sum(probs >= 0.1)/pop_size]\n", + " stats += ' 1st=%s(%.2f) 2nd=%s(%.2f) 3rd=%s(%.2f) components: >=0.5:%.0f, >=0.3:%.0f, >=0.1:%.0f'%tuple(vals2)\n", + " hist.append(vals0+vals1+vals2)\n", + " if show_every and i%show_every==0:\n", + " clear_output()\n", + " display(pil_image) \n", + " print(stats)\n", + " sample_num += 1\n", + "\n", + "def load_icgan(experiment_name, root_ = '/content'):\n", + " root = os.path.join(root_, experiment_name)\n", + " config = torch.load(\"%s/%s.pth\" %\n", + " (root, \"state_dict_best0\"))['config']\n", + "\n", + " config[\"weights_root\"] = root_\n", + " config[\"model_backbone\"] = 'biggan'\n", + " config[\"experiment_name\"] = experiment_name\n", + " # TODO: delete this line\n", + " G, config = inference_utils.load_model_inference(config)\n", + " G.cuda()\n", + " G.eval()\n", + " return G\n", + "\n", + "def get_output(noise_vector, input_label, input_features): \n", + " if stochastic_truncation: #https://arxiv.org/abs/1702.04782\n", + " with torch.no_grad():\n", + " trunc_indices = noise_vector.abs() > 2*truncation\n", + " size = torch.count_nonzero(trunc_indices).cpu().numpy()\n", + " trunc = truncnorm.rvs(-2*truncation, 2*truncation, size=(1,size)).astype(np.float32)\n", + " noise_vector.data[trunc_indices] = torch.tensor(trunc, requires_grad=requires_grad, device='cuda')\n", + " else:\n", + " noise_vector = noise_vector.clamp(-2*truncation, 2*truncation)\n", + " if input_label is not None:\n", + " input_label = torch.LongTensor(input_label)\n", + " else:\n", + " input_label = None\n", + "\n", + " out = model(noise_vector, input_label.cuda() if input_label is not None else None, input_features.cuda() if input_features is not None else None)\n", + " \n", + " if channels==1:\n", + " out = out.mean(dim=1, keepdim=True)\n", + " out = out.repeat(1,3,1,1)\n", + " return out\n", + "\n", + "def normality_loss(vec): #https://arxiv.org/abs/1903.00925\n", + " mu2 = vec.mean().square()\n", + " sigma2 = vec.var()\n", + " return mu2+sigma2-torch.log(sigma2)-1\n", + " \n", + "\n", + "def load_generative_model(gen_model, last_gen_model, experiment_name, model):\n", + " # Load generative model\n", + " if gen_model != last_gen_model:\n", + " model = load_icgan(experiment_name, root_ = '/content')\n", + " last_gen_model = gen_model\n", + " return model, last_gen_model\n", + "\n", + "def load_feature_extractor(gen_model, last_feature_extractor, feature_extractor):\n", + " # Load feature extractor to obtain instance features\n", + " feat_ext_name = 'classification' if gen_model == 'cc_icgan' else 'selfsupervised'\n", + " if last_feature_extractor != feat_ext_name:\n", + " if feat_ext_name == 'classification':\n", + " feat_ext_path = ''\n", + " else:\n", + " !curl -L -o /content/swav_pretrained.pth.tar -C - 'https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar' \n", + " feat_ext_path = '/content/swav_pretrained.pth.tar'\n", + " last_feature_extractor = feat_ext_name\n", + " feature_extractor = data_utils.load_pretrained_feature_extractor(feat_ext_path, feature_extractor = feat_ext_name)\n", + " feature_extractor.eval()\n", + " return feature_extractor, last_feature_extractor\n", + "\n", + "norm_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3, 1, 1)\n", + "norm_std = torch.Tensor([0.229, 0.224, 0.225]).view(3, 1, 1)\n", + "\n", + "def preprocess_input_image(input_image_path, size): \n", + " pil_image = Image_PIL.open(input_image_path).convert('RGB')\n", + " transform_list = transforms.Compose([data_utils.CenterCropLongEdge(), transforms.Resize((size,size)), transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std)])\n", + " tensor_image = transform_list(pil_image)\n", + " tensor_image = torch.nn.functional.interpolate(tensor_image.unsqueeze(0), 224, mode=\"bicubic\", align_corners=True)\n", + " return tensor_image\n", + "\n", + "def preprocess_generated_image(image): \n", + " transform_list = transforms.Normalize(norm_mean, norm_std)\n", + " image = transform_list(image*0.5 + 0.5)\n", + " image = torch.nn.functional.interpolate(image, 224, mode=\"bicubic\", align_corners=True)\n", + " return image\n", + "\n", + "last_gen_model = None\n", + "last_feature_extractor = None\n", + "model = None\n", + "feature_extractor = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17278e04", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Generate images with IC-GAN!\n", + "#@markdown 1. Select type of IC-GAN model with **gen_model**: \"icgan\" is conditioned on an instance; \"cc_icgan\" is conditioned on both instance and a class index.\n", + "#@markdown 1. Select which instance to condition on, following one of the following options:\n", + "#@markdown 1. **input_image_instance** is the path to an input image, from either the mounted Google Drive or a manually uploaded image to \"Files\" (left part of the screen).\n", + "#@markdown 1. **input_feature_index** write an integer from 0 to 1000. This will change the instance conditioning and therefore the style and semantics of the generated images. This will select one of the 1000 instance features pre-selected from ImageNet using k-means.\n", + "#@markdown 1. For **class_index** (only valid for gen_model=\"cc_icgan\") write an integer from 0 to 1000. This will change the ImageNet class to condition on. Consult [this link](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) for a correspondence between class name and indexes.\n", + "#@markdown 1. **num_samples_ranked** (default=16) indicates the number of generated images to output in a mosaic. These generated images are the ones that scored a higher cosine similarity with the conditioning instance, out of **num_samples_total** (default=160) generated samples. Increasing \"num_samples_total\" will result in higher run times, but more generated images to choose the top \"num_samples_ranked\" from, and therefore higher chance of better image quality. Reducing \"num_samples_total\" too much could result in generated images with poorer visual quality. A ratio of 10:1 (num_samples_total:num_samples_ranked) is recommended.\n", + "#@markdown 1. Vary **truncation** (default=0.7) from 0 to 1 to apply the [truncation trick](https://arxiv.org/abs/1809.11096). Truncation=1 will provide more diverse but possibly poorer quality images. Trucation values between 0.7 and 0.9 seem to empirically work well.\n", + "#@markdown 1. **seed**=0 means no seed.\n", + "\n", + "gen_model = 'icgan' #@param ['icgan', 'cc_icgan']\n", + "if gen_model == 'icgan': \n", + " experiment_name = 'icgan_biggan_imagenet_res256'\n", + "else:\n", + " experiment_name = 'cc_icgan_biggan_imagenet_res256'\n", + "#last_gen_model = experiment_name\n", + "size = '256'\n", + "input_image_instance = \"\"#@param {type:\"string\"}\n", + "input_feature_index = 3#@param {type:'integer'}\n", + "class_index = 538#@param {type:'integer'}\n", + "num_samples_ranked = 16#@param {type:'integer'}\n", + "num_samples_total = 160#@param {type:'integer'}\n", + "truncation = 0.7#@param {type:'number'}\n", + "stochastic_truncation = False #@param {type:'boolean'}\n", + "download_file = True #@param {type:'boolean'}\n", + "seed = 50#@param {type:'number'}\n", + "if seed == 0:\n", + " seed = None\n", + "noise_size = 128\n", + "class_size = 1000\n", + "channels = 3\n", + "batch_size = 4\n", + "if gen_model == 'icgan':\n", + " class_index = None\n", + "if 'biggan' in gen_model:\n", + " input_feature_index = None\n", + " input_image_instance = None\n", + "\n", + "assert(num_samples_ranked <=num_samples_total)\n", + "import numpy as np\n", + "state = None if not seed else np.random.RandomState(seed)\n", + "np.random.seed(seed)\n", + "\n", + "feature_extractor_name = 'classification' if gen_model == 'cc_icgan' else 'selfsupervised'\n", + "\n", + "# Load feature extractor (outlier filtering and optionally input image feature extraction)\n", + "feature_extractor, last_feature_extractor = load_feature_extractor(gen_model, last_feature_extractor, feature_extractor)\n", + "# Load features \n", + "if input_image_instance not in ['None', \"\"]:\n", + " print('Obtaining instance features from input image!')\n", + " input_feature_index = None\n", + " input_image_tensor = preprocess_input_image(input_image_instance, int(size))\n", + " print('Displaying instance conditioning:')\n", + " display(convert_to_images(((input_image_tensor*norm_std + norm_mean)-0.5) / 0.5)[0])\n", + " with torch.no_grad():\n", + " input_features, _ = feature_extractor(input_image_tensor.cuda())\n", + " input_features/=torch.linalg.norm(input_features,dim=-1, keepdims=True)\n", + "elif input_feature_index is not None:\n", + " print('Selecting an instance from pre-extracted vectors!')\n", + " input_features = np.load('/content/stored_instances/imagenet_res'+str(size)+'_rn50_'+feature_extractor_name+'_kmeans_k1000_instance_features.npy', allow_pickle=True).item()[\"instance_features\"][input_feature_index:input_feature_index+1]\n", + "else:\n", + " input_features = None\n", + "\n", + "# Load generative model\n", + "model, last_gen_model = load_generative_model(gen_model, last_gen_model, experiment_name, model)\n", + "# Prepare other variables\n", + "name_file = '%s_class_index%s_instance_index%s'%(gen_model, str(class_index) if class_index is not None else 'None', str(input_feature_index) if input_feature_index is not None else 'None')\n", + "\n", + "!rm -rf /content/output\n", + "!mkdir -p /content/output\n", + "\n", + "replace_to_inplace_relu(model)\n", + "ind2name = {index: wn.of2ss('%08dn'%offset).lemma_names()[0] for offset, index in utils.IMAGENET.items()}\n", + "\n", + "from google.colab import files, output\n", + "\n", + "eps = 1e-8\n", + "\n", + "# Create noise, instance and class vector\n", + "noise_vector = truncnorm.rvs(-2*truncation, 2*truncation, size=(num_samples_total, noise_size), random_state=state).astype(np.float32) #see https://github.com/tensorflow/hub/issues/214\n", + "noise_vector = torch.tensor(noise_vector, requires_grad=False, device='cuda')\n", + "if input_features is not None:\n", + " instance_vector = torch.tensor(input_features, requires_grad=False, device='cuda').repeat(num_samples_total, 1)\n", + "else: \n", + " instance_vector = None\n", + "if class_index is not None:\n", + " print('Conditioning on class: ', ind2name[class_index])\n", + " input_label = torch.LongTensor([class_index]*num_samples_total)\n", + "else:\n", + " input_label = None\n", + "if input_feature_index is not None:\n", + " print('Conditioning on instance with index: ', input_feature_index)\n", + "\n", + "size = int(size)\n", + "all_outs, all_dists = [], []\n", + "for i_bs in range(num_samples_total//batch_size+1):\n", + " start = i_bs*batch_size\n", + " end = min(start+batch_size, num_samples_total)\n", + " if start == end:\n", + " break\n", + " out = get_output(noise_vector[start:end], input_label[start:end] if input_label is not None else None, instance_vector[start:end] if instance_vector is not None else None)\n", + "\n", + " if instance_vector is not None:\n", + " # Get features from generated images + feature extractor\n", + " out_ = preprocess_generated_image(out)\n", + " with torch.no_grad():\n", + " out_features, _ = feature_extractor(out_.cuda())\n", + " out_features/=torch.linalg.norm(out_features,dim=-1, keepdims=True)\n", + " dists = sklearn.metrics.pairwise_distances(\n", + " out_features.cpu(), instance_vector[start:end].cpu(), metric=\"euclidean\", n_jobs=-1)\n", + " all_dists.append(np.diagonal(dists))\n", + " all_outs.append(out.detach().cpu())\n", + " del (out)\n", + "all_outs = torch.cat(all_outs)\n", + "all_dists = np.concatenate(all_dists)\n", + "\n", + "# Order samples by distance to conditioning feature vector and select only num_samples_ranked images\n", + "selected_idxs =np.argsort(all_dists)[:num_samples_ranked]\n", + "#print('All distances re-ordered ', np.sort(all_dists))\n", + "# Create figure \n", + "row_i, col_i, i_im = 0, 0, 0\n", + "all_images_mosaic = np.zeros((3,size*(int(np.sqrt(num_samples_ranked))), size*(int(np.sqrt(num_samples_ranked)))))\n", + "for j in selected_idxs:\n", + " all_images_mosaic[:,row_i*size:row_i*size+size, col_i*size:col_i*size+size] = all_outs[j]\n", + " if row_i == int(np.sqrt(num_samples_ranked))-1:\n", + " row_i = 0\n", + " if col_i == int(np.sqrt(num_samples_ranked))-1:\n", + " col_i = 0\n", + " else:\n", + " col_i +=1\n", + " else:\n", + " row_i+=1\n", + " i_im +=1\n", + "\n", + "name = '/content/%s_seed%i.png'%(name_file,seed if seed is not None else -1)\n", + "pil_image = save(all_images_mosaic[np.newaxis,...],name, torch_format=False) \n", + "print('Displaying generated images')\n", + "display(pil_image)\n", + "\n", + "if download_file:\n", + " files.download(name)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da5ee254", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Generate images with IC-GAN + CLIP!\n", + "#@markdown 1. For **prompt** OpenAI suggest to use the template \"A photo of a X.\" or \"A photo of a X, a type of Y.\" [[paper]](https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.pdf)\n", + "#@markdown 1. Select type of IC-GAN model with **gen_model**: \"icgan\" is conditioned on an instance; \"cc_icgan\" is conditioned on both instance and a class index.\n", + "#@markdown 1. Select which instance to condition on, following one of the following options:\n", + "#@markdown 1. **input_image_instance** is the path to an input image, from either the mounted Google Drive or a manually uploaded image to \"Files\" (left part of the screen).\n", + "#@markdown 1. **input_feature_index** write an integer from 0 to 1000. This will change the instance conditioning and therefore the style and semantics of the generated images. This will select one of the 1000 instance features pre-selected from ImageNet using k-means.\n", + "#@markdown 1. For **class_index** (only valid for gen_model=\"cc_icgan\") write an integer from 0 to 1000. This will change the ImageNet class to condition on. Consult [this link](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) for a correspondence between class name and indexes.\n", + "#@markdown 1. Vary **truncation** from 0 to 1 to apply the [truncation trick](https://arxiv.org/abs/1809.11096). Truncation=1 will provide more diverse but possibly poorer quality images. Trucation values between 0.7 and 0.9 seem to empirically work well.\n", + "#@markdown 4. **seed**=0 means no seed.\n", + "prompt = 'A dragon' #@param {type:'string'}\n", + "gen_model = 'icgan' #@param ['icgan', 'cc_icgan']\n", + "if gen_model == 'icgan': \n", + " experiment_name = 'icgan_biggan_imagenet_res256_nofeataug'\n", + "else:\n", + " experiment_name = 'cc_icgan_biggan_imagenet_res256_nofeataug'\n", + "#last_gen_model = experiment_name\n", + "size = '256'\n", + "input_image_instance = \"\"#@param {type:\"string\"}\n", + "\n", + "input_feature_index = 500#@param {type:'integer'}\n", + "class_index = 627 #@param {type:'integer'} (only with cc_icgan)\n", + "download_image = False #@param {type:'boolean'}\n", + "download_video = False #@param {type:'boolean'}\n", + "truncation = 0.7 #@param {type:'number'}\n", + "stochastic_truncation = False #@param {type:'boolean'}\n", + "optimizer = 'CMA-ES' #@param ['SGD','Adam','CMA-ES','CMA-ES + SGD interleaved','CMA-ES + Adam interleaved','CMA-ES + terminal SGD','CMA-ES + terminal Adam']\n", + "pop_size = 50 #@param {type:'integer'}\n", + "clip_model = 'ViT-B/32' #@param ['ViT-B/32','RN50','RN101','RN50x4']\n", + "augmentations = 64#@param {type:'integer'}\n", + "learning_rate = 0.1#@param {type:'number'}\n", + "noise_normality_loss = 0#@param {type:'number'}\n", + "minimum_entropy_loss = 0.0001 #@param {type:'number'}\n", + "total_variation_loss = 0.1 #@param {type:'number'}\n", + "iterations = 100#@param {type:'integer'}\n", + "terminal_iterations = 100#@param {type:'integer'}\n", + "show_every = 1 #@param {type:'integer'}\n", + "save_every = 1 #@param {type:'integer'}\n", + "fps = 2#@param {type:'number'}\n", + "freeze_secs = 0 #@param {type:'number'}\n", + "seed = 10#@param {type:'number'}\n", + "if seed == 0:\n", + " seed = None\n", + "\n", + "softmax_temp = 1\n", + "emb_factor = 0.067 #calculated empirically \n", + "loss_factor = 100\n", + "sigma0 = 0.5 #http://cma.gforge.inria.fr/cmaes_sourcecode_page.html#practical\n", + "cma_adapt = True\n", + "cma_diag = False\n", + "cma_active = True\n", + "cma_elitist = False\n", + "noise_size = 128\n", + "class_size = 1000\n", + "channels = 3\n", + "if gen_model == 'icgan':\n", + " class_index = None\n", + "\n", + "import numpy as np\n", + "state = None if not seed else np.random.RandomState(seed)\n", + "np.random.seed(seed)\n", + "# Load features \n", + "if input_image_instance not in ['None',\"\"]:\n", + " print('Obtaining instance features from input image!')\n", + " input_feature_index = None\n", + " feature_extractor, last_feature_extractor = load_feature_extractor(gen_model, last_feature_extractor, feature_extractor)\n", + " input_image_tensor = preprocess_input_image(input_image_instance, int(size))\n", + " input_features, _ = feature_extractor(input_image_tensor.cuda())\n", + " input_features/=torch.linalg.norm(input_features,dim=-1, keepdims=True)\n", + "elif input_feature_index is not None:\n", + " print('Selecting an instance from pre-extracted vectors!')\n", + " feature_extractor_name = 'classification' if gen_model == 'cc_icgan' else 'selfsupervised'\n", + " input_features = np.load('/content/stored_instances/imagenet_res'+str(size)+'_rn50_'+feature_extractor_name+'_kmeans_k1000_instance_features.npy', allow_pickle=True).item()[\"instance_features\"][input_feature_index:input_feature_index+1]\n", + "else:\n", + " input_features = None\n", + "\n", + "\n", + "# Load generative model\n", + "model, last_gen_model = load_generative_model(gen_model, last_gen_model, experiment_name, model)\n", + "\n", + "# Load CLIP model\n", + "if clip_model != last_clip_model:\n", + " perceptor, preprocess = clip.load(clip_model)\n", + " last_clip_model = clip_model\n", + "clip_res = perceptor.visual.input_resolution\n", + "sideX = sideY = int(size)\n", + "if sideX<=clip_res and sideY<=clip_res:\n", + " augmentations = 1\n", + "if 'CMA' not in optimizer:\n", + " pop_size = 1\n", + "\n", + "# Prepare other variables\n", + "name_file = '%s_%s_class_index%s_instance_index%s'%(gen_model, prompt, str(class_index) if class_index is not None else 'None', str(input_feature_index) if input_feature_index is not None else 'None')\n", + "requires_grad = ('SGD' in optimizer or 'Adam' in optimizer) and ('terminal' not in optimizer or terminal_iterations>0)\n", + "total_iterations = iterations + terminal_iterations*('terminal' in optimizer)\n", + "\n", + "!rm -rf /content/output\n", + "!mkdir -p /content/output\n", + "\n", + "replace_to_inplace_relu(model)\n", + "replace_to_inplace_relu(perceptor)\n", + "ind2name = {index: wn.of2ss('%08dn'%offset).lemma_names()[0] for offset, index in utils.IMAGENET.items()}\n", + "eps = 1e-8\n", + "\n", + "# Create noise and instance vector\n", + "noise_vector = truncnorm.rvs(-2*truncation, 2*truncation, size=(pop_size, noise_size), random_state=state).astype(np.float32) #see https://github.com/tensorflow/hub/issues/214\n", + "noise_vector = torch.tensor(noise_vector, requires_grad=requires_grad, device='cuda')\n", + "if input_features is not None:\n", + " instance_vector = torch.tensor(input_features, requires_grad=False, device='cuda')\n", + "else: \n", + " instance_vector = None\n", + "if class_index is not None:\n", + " print('Conditioning on class: ', ind2name[class_index])\n", + "if input_feature_index is not None:\n", + " print('Conditioning on instance with index: ', input_feature_index)\n", + "\n", + "# Prepare optimizer\n", + "if requires_grad:\n", + " params = [noise_vector]\n", + " if 'SGD' in optimizer:\n", + " optim = torch.optim.SGD(params, lr=learning_rate, momentum=0.9) \n", + " else:\n", + " optim = torch.optim.Adam(params, lr=learning_rate)\n", + "\n", + "def ascend_txt(i, grad_step=False, show_save=False):\n", + " global global_best_loss, global_best_iteration, global_best_noise_vector, global_best_class_vector\n", + " regs = []\n", + " losses = []\n", + " total_losses = []\n", + " best_loss = np.inf\n", + " global_reg = torch.tensor(0, device='cuda', dtype=torch.float32, requires_grad=grad_step)\n", + " if noise_normality_loss:\n", + " global_reg = global_reg+noise_normality_loss*normality_loss(noise_vector)\n", + " global_reg = loss_factor*global_reg \n", + " if grad_step:\n", + " global_reg.backward()\n", + " for j in range(pop_size):\n", + " p_s = []\n", + " out = get_output(noise_vector[j:j+1], [class_index] if class_index is not None else None, instance_vector)\n", + " for aug in range(augmentations):\n", + " if sideX<=clip_res and sideY<=clip_res or augmentations==1:\n", + " apper = out \n", + " else:\n", + " size = torch.randint(int(.7*sideX), int(.98*sideX), ())\n", + " offsetx = torch.randint(0, sideX - size, ())\n", + " offsety = torch.randint(0, sideX - size, ())\n", + " apper = out[:, :, offsetx:offsetx + size, offsety:offsety + size]\n", + " apper = (apper+1)/2\n", + " apper = nn.functional.interpolate(apper, clip_res, mode='bilinear')\n", + " #apper = apper.clamp(0,1)\n", + " p_s.append(apper)\n", + " into = nom(torch.cat(p_s, 0))\n", + " predict_clip = perceptor.encode_image(into)\n", + " loss = loss_factor*(1-torch.cosine_similarity(predict_clip, target_clip).mean())\n", + " total_loss = loss\n", + " regs.append(global_reg.item())\n", + "\n", + " with torch.no_grad():\n", + " losses.append(loss.item())\n", + " total_losses.append(total_loss.item()+global_reg.item())\n", + " if total_losses[-1]=iterations):\n", + " losses, best_ind = ascend_txt(i, grad_step=True, show_save='CMA' not in optimizer or i>=iterations)\n", + " assert noise_vector.requires_grad and noise_vector.is_leaf and (not optimize_class or class_vector.requires_grad and class_vector.is_leaf), (noise_vector.requires_grad, noise_vector.is_leaf, class_vector.requires_grad, class_vector.is_leaf)\n", + " if 'CMA' in optimizer and i\n", + " \n", + " \"\"\" % data_url))\n", + "\n", + " from google.colab import files, output\n", + " output.eval_js('new Audio(\"https://freesound.org/data/previews/80/80921_1022651-lq.ogg\").play()')\n", + " files.download(file_name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/inference/generate_images.py b/inference/generate_images.py new file mode 100644 index 0000000000000000000000000000000000000000..3760fff17924b37cdf9426db862bdeb1c403188b --- /dev/null +++ b/inference/generate_images.py @@ -0,0 +1,347 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) + +import numpy as np +import matplotlib.pyplot as plt +from argparse import ArgumentParser +import torch + +import data_utils.utils as data_utils +import inference.utils as inference_utils +import BigGAN_PyTorch.utils as biggan_utils +from data_utils.datasets_common import pil_loader +import torchvision.transforms as transforms +import time + + +def get_data(root_path, model, resolution, which_dataset, visualize_instance_images): + data_path = os.path.join(root_path, "stored_instances") + if model == "cc_icgan": + feature_extractor = "classification" + else: + feature_extractor = "selfsupervised" + filename = "%s_res%i_rn50_%s_kmeans_k1000_instance_features.npy" % ( + which_dataset, + resolution, + feature_extractor, + ) + # Load conditioning instances from files + data = np.load(os.path.join(data_path, filename), allow_pickle=True).item() + + transform_list = None + if visualize_instance_images: + # Transformation used for ImageNet images. + transform_list = transforms.Compose( + [data_utils.CenterCropLongEdge(), transforms.Resize(resolution)] + ) + return data, transform_list + + +def get_model(exp_name, root_path, backbone, device="cuda"): + parser = biggan_utils.prepare_parser() + parser = biggan_utils.add_sample_parser(parser) + parser = inference_utils.add_backbone_parser(parser) + + args = ["--experiment_name", exp_name] + args += ["--base_root", root_path] + args += ["--model_backbone", backbone] + + config = vars(parser.parse_args(args=args)) + + # Load model and overwrite configuration parameters if stored in the model + config = biggan_utils.update_config_roots(config, change_weight_folder=False) + generator, config = inference_utils.load_model_inference(config, device=device) + biggan_utils.count_parameters(generator) + generator.eval() + + return generator + + +def get_conditionings(test_config, generator, data): + # Obtain noise vectors + z = torch.empty( + test_config["num_imgs_gen"] * test_config["num_conditionings_gen"], + generator.z_dim if config["model_backbone"] == "stylegan2" else generator.dim_z, + ).normal_(mean=0, std=test_config["z_var"]) + + # Subsampling some instances from the 1000 k-means centers file + if test_config["num_conditionings_gen"] > 1: + total_idxs = np.random.choice( + range(1000), test_config["num_conditionings_gen"], replace=False + ) + + # Obtain features, labels and ground truth image paths + all_feats, all_img_paths, all_labels = [], [], [] + for counter in range(test_config["num_conditionings_gen"]): + # Index in 1000 k-means centers file + if test_config["index"] is not None: + idx = test_config["index"] + else: + idx = total_idxs[counter] + # Image paths to visualize ground-truth instance + if test_config["visualize_instance_images"]: + all_img_paths.append(data["image_path"][idx]) + # Instance features + all_feats.append( + torch.FloatTensor(data["instance_features"][idx : idx + 1]).repeat( + test_config["num_imgs_gen"], 1 + ) + ) + # Obtain labels + if test_config["swap_target"] is not None: + # Swap label for a manually specified one + label_int = test_config["swap_target"] + else: + # Use the label associated to the instance feature + label_int = int(data["labels"][idx]) + # Format labels according to the backbone + labels = None + if test_config["model_backbone"] == "stylegan2": + dim_labels = 1000 + labels = torch.eye(dim_labels)[torch.LongTensor([label_int])].repeat( + test_config["num_imgs_gen"], 1 + ) + else: + if test_config["model"] == "cc_icgan": + labels = torch.LongTensor([label_int]).repeat( + test_config["num_imgs_gen"] + ) + all_labels.append(labels) + # Concatenate all conditionings + all_feats = torch.cat(all_feats) + if all_labels[0] is not None: + all_labels = torch.cat(all_labels) + else: + all_labels = None + return z, all_feats, all_labels, all_img_paths + + +def main(test_config): + suffix = ( + "_nofeataug" + if test_config["resolution"] == 256 + and test_config["trained_dataset"] == "imagenet" + else "" + ) + exp_name = "%s_%s_%s_res%i%s" % ( + test_config["model"], + test_config["model_backbone"], + test_config["trained_dataset"], + test_config["resolution"], + suffix, + ) + device = "cuda" + ### -- Data -- ### + data, transform_list = get_data( + test_config["root_path"], + test_config["model"], + test_config["resolution"], + test_config["which_dataset"], + test_config["visualize_instance_images"], + ) + + ### -- Model -- ### + generator = get_model( + exp_name, test_config["root_path"], test_config["model_backbone"], device=device + ) + + ### -- Generate images -- ### + # Prepare input and conditioning: different noise vector per sample but the same conditioning + # Sample noise vector + z, all_feats, all_labels, all_img_paths = get_conditionings( + test_config, generator, data + ) + + ## Generate the images + all_generated_images = [] + with torch.no_grad(): + num_batches = 1 + (z.shape[0]) // test_config["batch_size"] + for i in range(num_batches): + start = test_config["batch_size"] * i + end = min( + test_config["batch_size"] * i + test_config["batch_size"], z.shape[0] + ) + if all_labels is not None: + labels_ = all_labels[start:end].to(device) + else: + labels_ = None + gen_img = generator( + z[start:end].to(device), labels_, all_feats[start:end].to(device) + ) + if test_config["model_backbone"] == "biggan": + gen_img = ((gen_img * 0.5 + 0.5) * 255).int() + elif test_config["model_backbone"] == "stylegan2": + gen_img = torch.clamp((gen_img * 127.5 + 128), 0, 255).int() + all_generated_images.append(gen_img.cpu()) + all_generated_images = torch.cat(all_generated_images) + all_generated_images = all_generated_images.permute(0, 2, 3, 1).numpy() + + big_plot = [] + for i in range(0, test_config["num_conditionings_gen"]): + row = [] + for j in range(0, test_config["num_imgs_gen"]): + subplot_idx = (i * test_config["num_imgs_gen"]) + j + row.append(all_generated_images[subplot_idx]) + row = np.concatenate(row, axis=1) + big_plot.append(row) + big_plot = np.concatenate(big_plot, axis=0) + + # (Optional) Show ImageNet ground-truth conditioning instances + if test_config["visualize_instance_images"]: + all_gt_imgs = [] + for i in range(0, len(all_img_paths)): + all_gt_imgs.append( + np.array( + transform_list( + pil_loader( + os.path.join(test_config["dataset_path"], all_img_paths[i]) + ) + ) + ).astype(np.uint8) + ) + all_gt_imgs = np.concatenate(all_gt_imgs, axis=0) + white_space = ( + np.ones((all_gt_imgs.shape[0], 20, all_gt_imgs.shape[2])) * 255 + ).astype(np.uint8) + big_plot = np.concatenate([all_gt_imgs, white_space, big_plot], axis=1) + + plt.figure( + figsize=( + 5 * test_config["num_imgs_gen"], + 5 * test_config["num_conditionings_gen"], + ) + ) + plt.imshow(big_plot) + plt.axis("off") + + fig_path = "%s_Generations_with_InstanceDataset_%s%s%s_zvar%0.2f.png" % ( + exp_name, + test_config["which_dataset"], + "_index" + str(test_config["index"]) + if test_config["index"] is not None + else "", + "_class_idx" + str(test_config["swap_target"]) + if test_config["swap_target"] is not None + else "", + test_config["z_var"], + ) + plt.savefig(fig_path, dpi=600, bbox_inches="tight", pad_inches=0) + + print("Done! Figure saved as %s" % (fig_path)) + + +if __name__ == "__main__": + parser = ArgumentParser( + description="Generate and save images using pre-trained models" + ) + + parser.add_argument( + "--root_path", + type=str, + required=True, + help="Path where pretrained models + instance features have been downloaded.", + ) + parser.add_argument( + "--which_dataset", + type=str, + default="imagenet", + choices=["imagenet", "coco"], + help="Dataset to sample instances from.", + ) + parser.add_argument( + "--trained_dataset", + type=str, + default="imagenet", + choices=["imagenet", "coco"], + help="Dataset in which the model has been trained on.", + ) + parser.add_argument( + "--model", + type=str, + default="icgan", + choices=["icgan", "cc_icgan"], + help="Model type.", + ) + parser.add_argument( + "--model_backbone", + type=str, + default="biggan", + choices=["biggan", "stylegan2"], + help="Model backbone type.", + ) + parser.add_argument( + "--resolution", + type=int, + default=256, + help="Resolution to generate images with " "(default: %(default)s)", + ) + parser.add_argument( + "--z_var", type=float, default=1.0, help="Noise variance: %(default)s)" + ) + parser.add_argument("--batch_size", type=int, default=16, help="Batch size.") + parser.add_argument( + "--num_imgs_gen", + type=int, + default=5, + help="Number of images to generate with different noise vectors, " + "given an input conditioning.", + ) + parser.add_argument( + "--num_conditionings_gen", + type=int, + default=5, + help="Number of conditionings to generate with." + " Use `num_imgs_gen` to control the number of generated samples per conditioning", + ) + parser.add_argument( + "--index", + type=int, + default=None, + help="Index of the stored instance to use as conditioning [0,1000)." + " Mutually exclusive with `num_conditionings_gen!=1`", + ) + parser.add_argument( + "--swap_target", + type=int, + default=None, + help="For class-conditional IC-GAN, we can choose to swap the target for a different one." + " If swap_target=None, the original label from the instance is used. " + "If swap_target is in [0,1000), a specific ImageNet class is used instead.", + ) + + parser.add_argument( + "--visualize_instance_images", + action="store_true", + default=False, + help="Also visualize the ground-truth image corresponding to the instance conditioning " + "(requires a path to the ImageNet dataset)", + ) + parser.add_argument( + "--dataset_path", + type=str, + default="", + help="Only needed if visualize_instance_images=True." + " Folder where to find the dataset ground-truth images.", + ) + + config = vars(parser.parse_args()) + + if config["index"] is not None and config["num_conditionings_gen"] != 1: + raise ValueError( + "If a specific feature vector (specificed by --index) " + "wants to be used to sample images from, num_conditionings_gen" + " needs to be set to 1" + ) + if config["swap_target"] is not None and config["model"] == "icgan": + raise ValueError( + 'Cannot specify a class label for IC-GAN! Only use "swap_target" with --model=cc_igan. ' + ) + main(config) diff --git a/inference/icgan_colab.ipynb b/inference/icgan_colab.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3c6e150485f985983dca21fc784045a8453e69c7 --- /dev/null +++ b/inference/icgan_colab.ipynb @@ -0,0 +1,713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bb75a2b9", + "metadata": { + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "source": [ + "Copyright (c) Facebook, Inc. and its affiliates.\n", + "All rights reserved.\n", + "\n", + "This source code is licensed under the license found in the\n", + "LICENSE file in the root directory of this source tree." + ] + }, + { + "cell_type": "markdown", + "id": "81a8ddb6", + "metadata": {}, + "source": [ + "# IC-GAN\n", + "\n", + "\n", + "Official Colab notebook from the paper \"Instance-Conditioned GAN\" by Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.\n", + "\n", + "This Colab provides the code to generate images with IC-GAN, with the option of further guiding the generation with captions (CLIP). \n", + "\n", + "Based on the Colab [WanderClip](https://j.mp/wanderclip) by Eyal Gruss [@eyaler](https://twitter.com/eyaler) [eyalgruss.com](https://eyalgruss.com)\n", + "\n", + "Using the work from [our repository](https://github.com/facebookresearch/ic_gan)\n", + "\n", + "https://github.com/openai/CLIP, Copyright (c) 2021 OpenAI\n", + "\n", + "https://github.com/huggingface/pytorch-pretrained-BigGAN, Copyright (c) 2019 Thomas Wolf\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9442671e", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Restart after running this cell!\n", + "\n", + "!nvidia-smi -L\n", + "\n", + "import subprocess\n", + "\n", + "CUDA_version = [s for s in subprocess.check_output([\"nvcc\", \"--version\"]).decode(\"UTF-8\").split(\", \") if s.startswith(\"release\")][0].split(\" \")[-1]\n", + "print(\"CUDA version:\", CUDA_version)\n", + "\n", + "if CUDA_version == \"10.1\":\n", + " torch_version_suffix = \"+cu101\"\n", + "elif CUDA_version == \"10.2\":\n", + " torch_version_suffix = \"+cu102\"\n", + "else:\n", + " torch_version_suffix = \"+cu111\"\n", + "\n", + "!pip install torch==1.8.0{torch_version_suffix} torchvision==0.9.0{torch_version_suffix} -f https://download.pytorch.org/whl/torch_stable.html ftfy regex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b01f51f1", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Setup\n", + "!git clone https://github.com/facebookresearch/ic_gan.git\n", + "\n", + "%cd /content\n", + "# Uncompress required files\n", + "!wget https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res256.tar.gz\n", + "!tar -xvf cc_icgan_biggan_imagenet_res256.tar.gz\n", + "!wget https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res256.tar.gz\n", + "!tar -xvf icgan_biggan_imagenet_res256.tar.gz\n", + "!wget https://dl.fbaipublicfiles.com/ic_gan/stored_instances.tar.gz\n", + "!tar -xvf stored_instances.tar.gz\n", + "\n", + "!pip install pytorch-pretrained-biggan\n", + "\n", + "!git clone --depth 1 https://github.com/openai/CLIP\n", + "!pip install ftfy\n", + "%cd /content/CLIP\n", + "import clip\n", + "last_clip_model = 'ViT-B/32'\n", + "perceptor, preprocess = clip.load(last_clip_model)\n", + "\n", + "import nltk\n", + "nltk.download('wordnet')\n", + "\n", + "!pip install cma\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa6c7629", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Prepare functions\n", + "from pytorch_pretrained_biggan import BigGAN, convert_to_images, one_hot_from_names, utils\n", + "\n", + "%cd /content/ic_gan/\n", + "import sys\n", + "import os\n", + "sys.path[0] = '/content/ic_gan/inference'\n", + "sys.path.insert(1, os.path.join(sys.path[0], \"..\"))\n", + "import torch \n", + "\n", + "import numpy as np\n", + "import torch\n", + "import torchvision\n", + "import sys\n", + "torch.manual_seed(np.random.randint(sys.maxsize))\n", + "import imageio\n", + "from IPython.display import HTML, Image, clear_output\n", + "from PIL import Image as Image_PIL\n", + "from scipy.stats import truncnorm, dirichlet\n", + "from torch import nn\n", + "from nltk.corpus import wordnet as wn\n", + "from base64 import b64encode\n", + "from time import time\n", + "import cma\n", + "from cma.sigma_adaptation import CMAAdaptSigmaCSA, CMAAdaptSigmaTPA\n", + "import warnings\n", + "warnings.simplefilter(\"ignore\", cma.evolution_strategy.InjectionWarning)\n", + "import torchvision.transforms as transforms\n", + "import inference.utils as inference_utils\n", + "import data_utils.utils as data_utils\n", + "from BigGAN_PyTorch.BigGAN import Generator as generator\n", + "import sklearn.metrics\n", + "\n", + "def replace_to_inplace_relu(model): #saves memory; from https://github.com/minyoungg/pix2latent/blob/master/pix2latent/model/biggan.py\n", + " for child_name, child in model.named_children():\n", + " if isinstance(child, nn.ReLU):\n", + " setattr(model, child_name, nn.ReLU(inplace=False))\n", + " else:\n", + " replace_to_inplace_relu(child)\n", + " return\n", + " \n", + "def save(out,name=None, torch_format=True):\n", + " if torch_format:\n", + " with torch.no_grad():\n", + " out = out.cpu().numpy()\n", + " img = convert_to_images(out)[0]\n", + " if name:\n", + " imageio.imwrite(name, np.asarray(img))\n", + " return img\n", + "\n", + "hist = []\n", + "def checkin(i, best_ind, total_losses, losses, regs, out, noise=None, emb=None, probs=None):\n", + " global sample_num, hist\n", + " name = None\n", + " if save_every and i%save_every==0:\n", + " name = '/content/output/frame_%05d.jpg'%sample_num\n", + " pil_image = save(out, name)\n", + " vals0 = [sample_num, i, total_losses[best_ind], losses[best_ind], regs[best_ind], np.mean(total_losses), np.mean(losses), np.mean(regs), np.std(total_losses), np.std(losses), np.std(regs)]\n", + " stats = 'sample=%d iter=%d best: total=%.2f cos=%.2f reg=%.3f avg: total=%.2f cos=%.2f reg=%.3f std: total=%.2f cos=%.2f reg=%.3f'%tuple(vals0)\n", + " vals1 = []\n", + " if noise is not None:\n", + " vals1 = [np.mean(noise), np.std(noise)]\n", + " stats += ' noise: avg=%.2f std=%.3f'%tuple(vals1)\n", + " vals2 = []\n", + " if emb is not None:\n", + " vals2 = [emb.mean(),emb.std()]\n", + " stats += ' emb: avg=%.2f std=%.3f'%tuple(vals2)\n", + " elif probs:\n", + " best = probs[best_ind]\n", + " inds = np.argsort(best)[::-1]\n", + " probs = np.array(probs)\n", + " vals2 = [ind2name[inds[0]], best[inds[0]], ind2name[inds[1]], best[inds[1]], ind2name[inds[2]], best[inds[2]], np.sum(probs >= 0.5)/pop_size,np.sum(probs >= 0.3)/pop_size,np.sum(probs >= 0.1)/pop_size]\n", + " stats += ' 1st=%s(%.2f) 2nd=%s(%.2f) 3rd=%s(%.2f) components: >=0.5:%.0f, >=0.3:%.0f, >=0.1:%.0f'%tuple(vals2)\n", + " hist.append(vals0+vals1+vals2)\n", + " if show_every and i%show_every==0:\n", + " clear_output()\n", + " display(pil_image) \n", + " print(stats)\n", + " sample_num += 1\n", + "\n", + "def load_icgan(experiment_name, root_ = '/content'):\n", + " root = os.path.join(root_, experiment_name)\n", + " config = torch.load(\"%s/%s.pth\" %\n", + " (root, \"state_dict_best0\"))['config']\n", + "\n", + " config[\"weights_root\"] = root_\n", + " config[\"model_backbone\"] = 'biggan'\n", + " config[\"experiment_name\"] = experiment_name\n", + " G, config = inference_utils.load_model_inference(config)\n", + " G.cuda()\n", + " G.eval()\n", + " return G\n", + "\n", + "def get_output(noise_vector, input_label, input_features): \n", + " if stochastic_truncation: #https://arxiv.org/abs/1702.04782\n", + " with torch.no_grad():\n", + " trunc_indices = noise_vector.abs() > 2*truncation\n", + " size = torch.count_nonzero(trunc_indices).cpu().numpy()\n", + " trunc = truncnorm.rvs(-2*truncation, 2*truncation, size=(1,size)).astype(np.float32)\n", + " noise_vector.data[trunc_indices] = torch.tensor(trunc, requires_grad=requires_grad, device='cuda')\n", + " else:\n", + " noise_vector = noise_vector.clamp(-2*truncation, 2*truncation)\n", + " if input_label is not None:\n", + " input_label = torch.LongTensor(input_label)\n", + " else:\n", + " input_label = None\n", + "\n", + " out = model(noise_vector, input_label.cuda() if input_label is not None else None, input_features.cuda() if input_features is not None else None)\n", + " \n", + " if channels==1:\n", + " out = out.mean(dim=1, keepdim=True)\n", + " out = out.repeat(1,3,1,1)\n", + " return out\n", + "\n", + "def normality_loss(vec): #https://arxiv.org/abs/1903.00925\n", + " mu2 = vec.mean().square()\n", + " sigma2 = vec.var()\n", + " return mu2+sigma2-torch.log(sigma2)-1\n", + " \n", + "\n", + "def load_generative_model(gen_model, last_gen_model, experiment_name, model):\n", + " # Load generative model\n", + " if gen_model != last_gen_model:\n", + " model = load_icgan(experiment_name, root_ = '/content')\n", + " last_gen_model = gen_model\n", + " return model, last_gen_model\n", + "\n", + "def load_feature_extractor(gen_model, last_feature_extractor, feature_extractor):\n", + " # Load feature extractor to obtain instance features\n", + " feat_ext_name = 'classification' if gen_model == 'cc_icgan' else 'selfsupervised'\n", + " if last_feature_extractor != feat_ext_name:\n", + " if feat_ext_name == 'classification':\n", + " feat_ext_path = ''\n", + " else:\n", + " !curl -L -o /content/swav_pretrained.pth.tar -C - 'https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar' \n", + " feat_ext_path = '/content/swav_pretrained.pth.tar'\n", + " last_feature_extractor = feat_ext_name\n", + " feature_extractor = data_utils.load_pretrained_feature_extractor(feat_ext_path, feature_extractor = feat_ext_name)\n", + " feature_extractor.eval()\n", + " return feature_extractor, last_feature_extractor\n", + "\n", + "norm_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3, 1, 1)\n", + "norm_std = torch.Tensor([0.229, 0.224, 0.225]).view(3, 1, 1)\n", + "\n", + "def preprocess_input_image(input_image_path, size): \n", + " pil_image = Image_PIL.open(input_image_path).convert('RGB')\n", + " transform_list = transforms.Compose([data_utils.CenterCropLongEdge(), transforms.Resize((size,size)), transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std)])\n", + " tensor_image = transform_list(pil_image)\n", + " tensor_image = torch.nn.functional.interpolate(tensor_image.unsqueeze(0), 224, mode=\"bicubic\", align_corners=True)\n", + " return tensor_image\n", + "\n", + "def preprocess_generated_image(image): \n", + " transform_list = transforms.Normalize(norm_mean, norm_std)\n", + " image = transform_list(image*0.5 + 0.5)\n", + " image = torch.nn.functional.interpolate(image, 224, mode=\"bicubic\", align_corners=True)\n", + " return image\n", + "\n", + "last_gen_model = None\n", + "last_feature_extractor = None\n", + "model = None\n", + "feature_extractor = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17278e04", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Generate images with IC-GAN!\n", + "#@markdown 1. Select type of IC-GAN model with **gen_model**: \"icgan\" is conditioned on an instance; \"cc_icgan\" is conditioned on both instance and a class index.\n", + "#@markdown 1. Select which instance to condition on, following one of the following options:\n", + "#@markdown 1. **input_image_instance** is the path to an input image, from either the mounted Google Drive or a manually uploaded image to \"Files\" (left part of the screen).\n", + "#@markdown 1. **input_feature_index** write an integer from 0 to 1000. This will change the instance conditioning and therefore the style and semantics of the generated images. This will select one of the 1000 instance features pre-selected from ImageNet using k-means.\n", + "#@markdown 1. For **class_index** (only valid for gen_model=\"cc_icgan\") write an integer from 0 to 1000. This will change the ImageNet class to condition on. Consult [this link](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) for a correspondence between class name and indexes.\n", + "#@markdown 1. **num_samples_ranked** (default=16) indicates the number of generated images to output in a mosaic. These generated images are the ones that scored a higher cosine similarity with the conditioning instance, out of **num_samples_total** (default=160) generated samples. Increasing \"num_samples_total\" will result in higher run times, but more generated images to choose the top \"num_samples_ranked\" from, and therefore higher chance of better image quality. Reducing \"num_samples_total\" too much could result in generated images with poorer visual quality. A ratio of 10:1 (num_samples_total:num_samples_ranked) is recommended.\n", + "#@markdown 1. Vary **truncation** (default=0.7) from 0 to 1 to apply the [truncation trick](https://arxiv.org/abs/1809.11096). Truncation=1 will provide more diverse but possibly poorer quality images. Trucation values between 0.7 and 0.9 seem to empirically work well.\n", + "#@markdown 1. **seed**=0 means no seed.\n", + "\n", + "gen_model = 'icgan' #@param ['icgan', 'cc_icgan']\n", + "if gen_model == 'icgan': \n", + " experiment_name = 'icgan_biggan_imagenet_res256'\n", + "else:\n", + " experiment_name = 'cc_icgan_biggan_imagenet_res256'\n", + "#last_gen_model = experiment_name\n", + "size = '256'\n", + "input_image_instance = \"\"#@param {type:\"string\"}\n", + "input_feature_index = 3#@param {type:'integer'}\n", + "class_index = 538#@param {type:'integer'}\n", + "num_samples_ranked = 16#@param {type:'integer'}\n", + "num_samples_total = 160#@param {type:'integer'}\n", + "truncation = 0.7#@param {type:'number'}\n", + "stochastic_truncation = False #@param {type:'boolean'}\n", + "download_file = True #@param {type:'boolean'}\n", + "seed = 50#@param {type:'number'}\n", + "if seed == 0:\n", + " seed = None\n", + "noise_size = 128\n", + "class_size = 1000\n", + "channels = 3\n", + "batch_size = 4\n", + "if gen_model == 'icgan':\n", + " class_index = None\n", + "if 'biggan' in gen_model:\n", + " input_feature_index = None\n", + " input_image_instance = None\n", + "\n", + "assert(num_samples_ranked <=num_samples_total)\n", + "import numpy as np\n", + "state = None if not seed else np.random.RandomState(seed)\n", + "np.random.seed(seed)\n", + "\n", + "feature_extractor_name = 'classification' if gen_model == 'cc_icgan' else 'selfsupervised'\n", + "\n", + "# Load feature extractor (outlier filtering and optionally input image feature extraction)\n", + "feature_extractor, last_feature_extractor = load_feature_extractor(gen_model, last_feature_extractor, feature_extractor)\n", + "# Load features \n", + "if input_image_instance not in ['None', \"\"]:\n", + " print('Obtaining instance features from input image!')\n", + " input_feature_index = None\n", + " input_image_tensor = preprocess_input_image(input_image_instance, int(size))\n", + " print('Displaying instance conditioning:')\n", + " display(convert_to_images(((input_image_tensor*norm_std + norm_mean)-0.5) / 0.5)[0])\n", + " with torch.no_grad():\n", + " input_features, _ = feature_extractor(input_image_tensor.cuda())\n", + " input_features/=torch.linalg.norm(input_features,dim=-1, keepdims=True)\n", + "elif input_feature_index is not None:\n", + " print('Selecting an instance from pre-extracted vectors!')\n", + " input_features = np.load('/content/stored_instances/imagenet_res'+str(size)+'_rn50_'+feature_extractor_name+'_kmeans_k1000_instance_features.npy', allow_pickle=True).item()[\"instance_features\"][input_feature_index:input_feature_index+1]\n", + "else:\n", + " input_features = None\n", + "\n", + "# Load generative model\n", + "model, last_gen_model = load_generative_model(gen_model, last_gen_model, experiment_name, model)\n", + "# Prepare other variables\n", + "name_file = '%s_class_index%s_instance_index%s'%(gen_model, str(class_index) if class_index is not None else 'None', str(input_feature_index) if input_feature_index is not None else 'None')\n", + "\n", + "!rm -rf /content/output\n", + "!mkdir -p /content/output\n", + "\n", + "replace_to_inplace_relu(model)\n", + "ind2name = {index: wn.of2ss('%08dn'%offset).lemma_names()[0] for offset, index in utils.IMAGENET.items()}\n", + "\n", + "from google.colab import files, output\n", + "\n", + "eps = 1e-8\n", + "\n", + "# Create noise, instance and class vector\n", + "noise_vector = truncnorm.rvs(-2*truncation, 2*truncation, size=(num_samples_total, noise_size), random_state=state).astype(np.float32) #see https://github.com/tensorflow/hub/issues/214\n", + "noise_vector = torch.tensor(noise_vector, requires_grad=False, device='cuda')\n", + "if input_features is not None:\n", + " instance_vector = torch.tensor(input_features, requires_grad=False, device='cuda').repeat(num_samples_total, 1)\n", + "else: \n", + " instance_vector = None\n", + "if class_index is not None:\n", + " print('Conditioning on class: ', ind2name[class_index])\n", + " input_label = torch.LongTensor([class_index]*num_samples_total)\n", + "else:\n", + " input_label = None\n", + "if input_feature_index is not None:\n", + " print('Conditioning on instance with index: ', input_feature_index)\n", + "\n", + "size = int(size)\n", + "all_outs, all_dists = [], []\n", + "for i_bs in range(num_samples_total//batch_size+1):\n", + " start = i_bs*batch_size\n", + " end = min(start+batch_size, num_samples_total)\n", + " if start == end:\n", + " break\n", + " out = get_output(noise_vector[start:end], input_label[start:end] if input_label is not None else None, instance_vector[start:end] if instance_vector is not None else None)\n", + "\n", + " if instance_vector is not None:\n", + " # Get features from generated images + feature extractor\n", + " out_ = preprocess_generated_image(out)\n", + " with torch.no_grad():\n", + " out_features, _ = feature_extractor(out_.cuda())\n", + " out_features/=torch.linalg.norm(out_features,dim=-1, keepdims=True)\n", + " dists = sklearn.metrics.pairwise_distances(\n", + " out_features.cpu(), instance_vector[start:end].cpu(), metric=\"euclidean\", n_jobs=-1)\n", + " all_dists.append(np.diagonal(dists))\n", + " all_outs.append(out.detach().cpu())\n", + " del (out)\n", + "all_outs = torch.cat(all_outs)\n", + "all_dists = np.concatenate(all_dists)\n", + "\n", + "# Order samples by distance to conditioning feature vector and select only num_samples_ranked images\n", + "selected_idxs =np.argsort(all_dists)[:num_samples_ranked]\n", + "#print('All distances re-ordered ', np.sort(all_dists))\n", + "# Create figure \n", + "row_i, col_i, i_im = 0, 0, 0\n", + "all_images_mosaic = np.zeros((3,size*(int(np.sqrt(num_samples_ranked))), size*(int(np.sqrt(num_samples_ranked)))))\n", + "for j in selected_idxs:\n", + " all_images_mosaic[:,row_i*size:row_i*size+size, col_i*size:col_i*size+size] = all_outs[j]\n", + " if row_i == int(np.sqrt(num_samples_ranked))-1:\n", + " row_i = 0\n", + " if col_i == int(np.sqrt(num_samples_ranked))-1:\n", + " col_i = 0\n", + " else:\n", + " col_i +=1\n", + " else:\n", + " row_i+=1\n", + " i_im +=1\n", + "\n", + "name = '/content/%s_seed%i.png'%(name_file,seed if seed is not None else -1)\n", + "pil_image = save(all_images_mosaic[np.newaxis,...],name, torch_format=False) \n", + "print('Displaying generated images')\n", + "display(pil_image)\n", + "\n", + "if download_file:\n", + " files.download(name)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da5ee254", + "metadata": {}, + "outputs": [], + "source": [ + "#@title Generate images with IC-GAN + CLIP!\n", + "#@markdown 1. For **prompt** OpenAI suggest to use the template \"A photo of a X.\" or \"A photo of a X, a type of Y.\" [[paper]](https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.pdf)\n", + "#@markdown 1. Select type of IC-GAN model with **gen_model**: \"icgan\" is conditioned on an instance; \"cc_icgan\" is conditioned on both instance and a class index.\n", + "#@markdown 1. Select which instance to condition on, following one of the following options:\n", + "#@markdown 1. **input_image_instance** is the path to an input image, from either the mounted Google Drive or a manually uploaded image to \"Files\" (left part of the screen).\n", + "#@markdown 1. **input_feature_index** write an integer from 0 to 1000. This will change the instance conditioning and therefore the style and semantics of the generated images. This will select one of the 1000 instance features pre-selected from ImageNet using k-means.\n", + "#@markdown 1. For **class_index** (only valid for gen_model=\"cc_icgan\") write an integer from 0 to 1000. This will change the ImageNet class to condition on. Consult [this link](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) for a correspondence between class name and indexes.\n", + "#@markdown 1. Vary **truncation** from 0 to 1 to apply the [truncation trick](https://arxiv.org/abs/1809.11096). Truncation=1 will provide more diverse but possibly poorer quality images. Trucation values between 0.7 and 0.9 seem to empirically work well.\n", + "#@markdown 4. **seed**=0 means no seed.\n", + "prompt = 'A dragon' #@param {type:'string'}\n", + "gen_model = 'icgan' #@param ['icgan', 'cc_icgan']\n", + "if gen_model == 'icgan': \n", + " experiment_name = 'icgan_biggan_imagenet_res256'\n", + "else:\n", + " experiment_name = 'cc_icgan_biggan_imagenet_res256'\n", + "#last_gen_model = experiment_name\n", + "size = '256'\n", + "input_image_instance = \"\"#@param {type:\"string\"}\n", + "\n", + "input_feature_index = 500#@param {type:'integer'}\n", + "class_index = 627 #@param {type:'integer'} (only with cc_icgan)\n", + "download_image = False #@param {type:'boolean'}\n", + "download_video = False #@param {type:'boolean'}\n", + "truncation = 0.7 #@param {type:'number'}\n", + "stochastic_truncation = False #@param {type:'boolean'}\n", + "optimizer = 'CMA-ES' #@param ['SGD','Adam','CMA-ES','CMA-ES + SGD interleaved','CMA-ES + Adam interleaved','CMA-ES + terminal SGD','CMA-ES + terminal Adam']\n", + "pop_size = 50 #@param {type:'integer'}\n", + "clip_model = 'ViT-B/32' #@param ['ViT-B/32','RN50','RN101','RN50x4']\n", + "augmentations = 64#@param {type:'integer'}\n", + "learning_rate = 0.1#@param {type:'number'}\n", + "noise_normality_loss = 0#@param {type:'number'}\n", + "minimum_entropy_loss = 0.0001 #@param {type:'number'}\n", + "total_variation_loss = 0.1 #@param {type:'number'}\n", + "iterations = 100#@param {type:'integer'}\n", + "terminal_iterations = 100#@param {type:'integer'}\n", + "show_every = 1 #@param {type:'integer'}\n", + "save_every = 1 #@param {type:'integer'}\n", + "fps = 2#@param {type:'number'}\n", + "freeze_secs = 0 #@param {type:'number'}\n", + "seed = 10#@param {type:'number'}\n", + "if seed == 0:\n", + " seed = None\n", + "\n", + "softmax_temp = 1\n", + "emb_factor = 0.067 #calculated empirically \n", + "loss_factor = 100\n", + "sigma0 = 0.5 #http://cma.gforge.inria.fr/cmaes_sourcecode_page.html#practical\n", + "cma_adapt = True\n", + "cma_diag = False\n", + "cma_active = True\n", + "cma_elitist = False\n", + "noise_size = 128\n", + "class_size = 1000\n", + "channels = 3\n", + "if gen_model == 'icgan':\n", + " class_index = None\n", + "\n", + "import numpy as np\n", + "state = None if not seed else np.random.RandomState(seed)\n", + "np.random.seed(seed)\n", + "# Load features \n", + "if input_image_instance not in ['None',\"\"]:\n", + " print('Obtaining instance features from input image!')\n", + " input_feature_index = None\n", + " feature_extractor, last_feature_extractor = load_feature_extractor(gen_model, last_feature_extractor, feature_extractor)\n", + " input_image_tensor = preprocess_input_image(input_image_instance, int(size))\n", + " input_features, _ = feature_extractor(input_image_tensor.cuda())\n", + " input_features/=torch.linalg.norm(input_features,dim=-1, keepdims=True)\n", + "elif input_feature_index is not None:\n", + " print('Selecting an instance from pre-extracted vectors!')\n", + " feature_extractor_name = 'classification' if gen_model == 'cc_icgan' else 'selfsupervised'\n", + " input_features = np.load('/content/stored_instances/imagenet_res'+str(size)+'_rn50_'+feature_extractor_name+'_kmeans_k1000_instance_features.npy', allow_pickle=True).item()[\"instance_features\"][input_feature_index:input_feature_index+1]\n", + "else:\n", + " input_features = None\n", + "\n", + "\n", + "# Load generative model\n", + "model, last_gen_model = load_generative_model(gen_model, last_gen_model, experiment_name, model)\n", + "\n", + "# Load CLIP model\n", + "if clip_model != last_clip_model:\n", + " perceptor, preprocess = clip.load(clip_model)\n", + " last_clip_model = clip_model\n", + "clip_res = perceptor.visual.input_resolution\n", + "sideX = sideY = int(size)\n", + "if sideX<=clip_res and sideY<=clip_res:\n", + " augmentations = 1\n", + "if 'CMA' not in optimizer:\n", + " pop_size = 1\n", + "\n", + "# Prepare other variables\n", + "name_file = '%s_%s_class_index%s_instance_index%s'%(gen_model, prompt, str(class_index) if class_index is not None else 'None', str(input_feature_index) if input_feature_index is not None else 'None')\n", + "requires_grad = ('SGD' in optimizer or 'Adam' in optimizer) and ('terminal' not in optimizer or terminal_iterations>0)\n", + "total_iterations = iterations + terminal_iterations*('terminal' in optimizer)\n", + "\n", + "!rm -rf /content/output\n", + "!mkdir -p /content/output\n", + "\n", + "replace_to_inplace_relu(model)\n", + "replace_to_inplace_relu(perceptor)\n", + "ind2name = {index: wn.of2ss('%08dn'%offset).lemma_names()[0] for offset, index in utils.IMAGENET.items()}\n", + "eps = 1e-8\n", + "\n", + "# Create noise and instance vector\n", + "noise_vector = truncnorm.rvs(-2*truncation, 2*truncation, size=(pop_size, noise_size), random_state=state).astype(np.float32) #see https://github.com/tensorflow/hub/issues/214\n", + "noise_vector = torch.tensor(noise_vector, requires_grad=requires_grad, device='cuda')\n", + "if input_features is not None:\n", + " instance_vector = torch.tensor(input_features, requires_grad=False, device='cuda')\n", + "else: \n", + " instance_vector = None\n", + "if class_index is not None:\n", + " print('Conditioning on class: ', ind2name[class_index])\n", + "if input_feature_index is not None:\n", + " print('Conditioning on instance with index: ', input_feature_index)\n", + "\n", + "# Prepare optimizer\n", + "if requires_grad:\n", + " params = [noise_vector]\n", + " if 'SGD' in optimizer:\n", + " optim = torch.optim.SGD(params, lr=learning_rate, momentum=0.9) \n", + " else:\n", + " optim = torch.optim.Adam(params, lr=learning_rate)\n", + "\n", + "def ascend_txt(i, grad_step=False, show_save=False):\n", + " global global_best_loss, global_best_iteration, global_best_noise_vector, global_best_class_vector\n", + " regs = []\n", + " losses = []\n", + " total_losses = []\n", + " best_loss = np.inf\n", + " global_reg = torch.tensor(0, device='cuda', dtype=torch.float32, requires_grad=grad_step)\n", + " if noise_normality_loss:\n", + " global_reg = global_reg+noise_normality_loss*normality_loss(noise_vector)\n", + " global_reg = loss_factor*global_reg \n", + " if grad_step:\n", + " global_reg.backward()\n", + " for j in range(pop_size):\n", + " p_s = []\n", + " out = get_output(noise_vector[j:j+1], [class_index] if class_index is not None else None, instance_vector)\n", + " for aug in range(augmentations):\n", + " if sideX<=clip_res and sideY<=clip_res or augmentations==1:\n", + " apper = out \n", + " else:\n", + " size = torch.randint(int(.7*sideX), int(.98*sideX), ())\n", + " offsetx = torch.randint(0, sideX - size, ())\n", + " offsety = torch.randint(0, sideX - size, ())\n", + " apper = out[:, :, offsetx:offsetx + size, offsety:offsety + size]\n", + " apper = (apper+1)/2\n", + " apper = nn.functional.interpolate(apper, clip_res, mode='bilinear')\n", + " #apper = apper.clamp(0,1)\n", + " p_s.append(apper)\n", + " into = nom(torch.cat(p_s, 0))\n", + " predict_clip = perceptor.encode_image(into)\n", + " loss = loss_factor*(1-torch.cosine_similarity(predict_clip, target_clip).mean())\n", + " total_loss = loss\n", + " regs.append(global_reg.item())\n", + "\n", + " with torch.no_grad():\n", + " losses.append(loss.item())\n", + " total_losses.append(total_loss.item()+global_reg.item())\n", + " if total_losses[-1]=iterations):\n", + " losses, best_ind = ascend_txt(i, grad_step=True, show_save='CMA' not in optimizer or i>=iterations)\n", + " assert noise_vector.requires_grad and noise_vector.is_leaf and (not optimize_class or class_vector.requires_grad and class_vector.is_leaf), (noise_vector.requires_grad, noise_vector.is_leaf, class_vector.requires_grad, class_vector.is_leaf)\n", + " if 'CMA' in optimizer and i\n", + " \n", + " \"\"\" % data_url))\n", + "\n", + " from google.colab import files, output\n", + " output.eval_js('new Audio(\"https://freesound.org/data/previews/80/80921_1022651-lq.ogg\").play()')\n", + " files.download(file_name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "accelerator": "GPU", + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/inference/sample.py b/inference/sample.py new file mode 100644 index 0000000000000000000000000000000000000000..67d812e56dbd036e21cb1896c5e3cebe1e7e3e1d --- /dev/null +++ b/inference/sample.py @@ -0,0 +1,158 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +""" BigGAN: The Authorized Unofficial PyTorch release + Code by A. Brock and A. Andonian + This code is an unofficial reimplementation of + "Large-Scale GAN Training for High Fidelity Natural Image Synthesis," + by A. Brock, J. Donahue, and K. Simonyan (arXiv 1809.11096). + + Let's go. +""" + +import os +import numpy as np +from tqdm import tqdm, trange +import json + +from imageio import imwrite as imsave + +# Import my stuff +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +import inference.utils as inference_utils +import BigGAN_PyTorch.utils as biggan_utils + + +class Tester: + def __init__(self, config): + self.config = vars(config) if not isinstance(config, dict) else config + + def __call__(self) -> float: + # Seed RNG + biggan_utils.seed_rng(self.config["seed"]) + + import torch + + # Setup cudnn.benchmark for free speed + torch.backends.cudnn.benchmark = True + + self.config = biggan_utils.update_config_roots( + self.config, change_weight_folder=False + ) + # Prepare root folders if necessary + biggan_utils.prepare_root(self.config) + + # Load model + self.G, self.config = inference_utils.load_model_inference(self.config) + biggan_utils.count_parameters(self.G) + + # Get sampling function and reference statistics for FID + print("Eval reference set is ", self.config["eval_reference_set"]) + sample, im_reference_filename = inference_utils.get_sampling_funct( + self.config, + self.G, + instance_set=self.config["eval_instance_set"], + reference_set=self.config["eval_reference_set"], + which_dataset=self.config["which_dataset"], + ) + + if config["which_dataset"] == "coco": + image_format = "jpg" + else: + image_format = "png" + if ( + self.config["eval_instance_set"] == "val" + and config["which_dataset"] == "coco" + ): + # using evaluation set + test_part = True + else: + test_part = False + path_samples = os.path.join( + self.config["samples_root"], + self.config["experiment_name"], + "%s_images_seed%i%s%s%s" + % ( + config["which_dataset"], + config["seed"], + "_test" if test_part else "", + "_hd" + str(self.config["filter_hd"]) + if self.config["filter_hd"] > -1 + else "", + "" + if self.config["kmeans_subsampled"] == -1 + else "_" + str(self.config["kmeans_subsampled"]) + "centers", + ), + ) + + print("Path samples will be ", path_samples) + if not os.path.exists(path_samples): + os.makedirs(path_samples) + + if not os.path.exists( + os.path.join(self.config["samples_root"], self.config["experiment_name"]) + ): + os.mkdir( + os.path.join( + self.config["samples_root"], self.config["experiment_name"] + ) + ) + print( + "Sampling %d images and saving them with %s format..." + % (self.config["sample_num_npz"], image_format) + ) + counter_i = 0 + for i in trange( + int( + np.ceil( + self.config["sample_num_npz"] / float(self.config["batch_size"]) + ) + ) + ): + with torch.no_grad(): + images, labels, _ = sample() + + fake_imgs = images.cpu().detach().numpy().transpose(0, 2, 3, 1) + if self.config["model_backbone"] == "biggan": + fake_imgs = fake_imgs * 0.5 + 0.5 + elif self.config["model_backbone"] == "stylegan2": + fake_imgs = np.clip((fake_imgs * 127.5 + 128), 0, 255).astype( + np.uint8 + ) + for fake_img in fake_imgs: + imsave( + "%s/%06d.%s" % (path_samples, counter_i, image_format), fake_img + ) + counter_i += 1 + if counter_i >= self.config["sample_num_npz"]: + break + + +if __name__ == "__main__": + parser = biggan_utils.prepare_parser() + parser = biggan_utils.add_sample_parser(parser) + parser = inference_utils.add_backbone_parser(parser) + config = vars(parser.parse_args()) + config["n_classes"] = 1000 + if config["json_config"] != "": + data = json.load(open(config["json_config"])) + for key in data.keys(): + if "exp_name" in key: + config["experiment_name"] = data[key] + else: + config[key] = data[key] + else: + print("No json file to load configuration from") + + tester = Tester(config) + + tester() diff --git a/inference/test.py b/inference/test.py new file mode 100644 index 0000000000000000000000000000000000000000..b2081dfdc26861078005ec21eed6f9c4c200fed4 --- /dev/null +++ b/inference/test.py @@ -0,0 +1,305 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +import os +import numpy as np +from tqdm import trange +import pickle +import json +import sys + +print(sys.path[0]) +sys.path.insert(1, os.path.join(sys.path[0], "..")) +import data_utils.inception_utils as inception_utils +import data_utils.utils as data_utils +import inference.utils as inference_utils +import BigGAN_PyTorch.utils as biggan_utils + +LOCAL = False +try: + import submitit +except: + print( + "No submitit package found! Defaulting to executing the script in the local machine" + ) + LOCAL = True + + +class Tester: + def __init__(self, config): + self.config = vars(config) if not isinstance(config, dict) else config + + def __call__(self) -> float: + # Seed RNG + biggan_utils.seed_rng(self.config["seed"]) + + import torch + + # Setup cudnn.benchmark for free speed + torch.backends.cudnn.benchmark = True + + self.config = biggan_utils.update_config_roots( + self.config, change_weight_folder=False + ) + # Prepare root folders if necessary + biggan_utils.prepare_root(self.config) + + # Load model + self.G, self.config = inference_utils.load_model_inference(self.config) + biggan_utils.count_parameters(self.G) + + # Get sampling function and reference statistics for FID + print("Eval reference set is ", self.config["eval_reference_set"]) + sample, im_reference_filename = inference_utils.get_sampling_funct( + self.config, + self.G, + instance_set=self.config["eval_instance_set"], + reference_set=self.config["eval_reference_set"], + which_dataset=self.config["which_dataset"], + ) + + # Obtain additional variables for testing + if self.config["eval_reference_set"] == "val" and self.config["longtail"]: + stratified_m = True + else: + stratified_m = False + if self.config["longtail"]: + samples_per_class = np.load( + "../BigGAN_PyTorch/imagenet_lt/imagenet_lt_samples_per_class.npy", + allow_pickle=True, + ) + else: + samples_per_class = None + + # Prepare inception metrics function + get_inception_metrics = inception_utils.prepare_inception_metrics( + im_reference_filename, + samples_per_class, + self.config["parallel"], + self.config["no_fid"], + self.config["data_root"], + prdc=self.config["eval_prdc"], + stratified_fid=stratified_m, + backbone=self.config["model_backbone"], + ) + + # If computing PRDC, we need a loader to obtain reference Inception features + if self.config["eval_prdc"]: + prdc_ref_set = data_utils.get_dataset_hdf5( + **{ + **self.config, + "data_path": self.config["data_root"], + "load_in_mem_feats": self.config["load_in_mem"], + "kmeans_subsampled": -1, + "test_part": True + if self.config["which_dataset"] == "coco" + and self.config["eval_reference_set"] == "val" + else False, + "split": self.config["eval_reference_set"], + "ddp": False, + } + ) + prdc_loader = data_utils.get_dataloader( + **{ + **self.config, + "dataset": prdc_ref_set, + "batch_size": self.config["batch_size"], + "use_checkpointable_sampler": False, + "shuffle": True, + "drop_last": False, + } + ) + else: + prdc_loader = None + + # Get metrics + eval_metrics = get_inception_metrics( + sample, + num_inception_images=self.config["num_inception_images"], + num_splits=10, + prints=False, + loader_ref=prdc_loader, + num_pr_images=self.config["num_inception_images"] + if ( + self.config["longtail"] and self.config["eval_reference_set"] == "train" + ) + else 10000, + ) + eval_metrics_dict = dict() + if self.config["eval_prdc"]: + IS_mean, IS_std, FID, stratified_FID, prdc_metrics = eval_metrics + else: + IS_mean, IS_std, FID, stratified_FID = eval_metrics + if stratified_m: + eval_metrics_dict["stratified_FID"] = stratified_FID + + eval_metrics_dict["IS_mean"] = IS_mean + eval_metrics_dict["IS_std"] = IS_std + eval_metrics_dict["FID"] = FID + print(eval_metrics_dict) + if self.config["eval_prdc"]: + eval_metrics_dict = {**prdc_metrics, **eval_metrics_dict} + + add_suffix = "" + if self.config["z_var"] != 1.0: + add_suffix = "_z_var" + str(self.config["z_var"]) + if not os.path.exists( + os.path.join(self.config["samples_root"], self.config["experiment_name"]) + ): + os.mkdir( + os.path.join( + self.config["samples_root"], self.config["experiment_name"] + ) + ) + # Save metrics in file + print('Saving metrics in ', os.path.join( + self.config["samples_root"], + self.config["experiment_name"])) + np.save( + os.path.join( + self.config["samples_root"], + self.config["experiment_name"], + "eval_metrics_reference_" + + self.config["eval_reference_set"] + + "_instances_" + + self.config["eval_instance_set"] + + "_kmeans" + + str(self.config["kmeans_subsampled"]) + + "_seed" + + str(self.config["seed"]) + + add_suffix + + ".npy", + ), + eval_metrics_dict, + ) + print("Computed metrics:") + for key, value in eval_metrics_dict.items(): + print(key, ": ", value) + + if self.config["sample_npz"]: + # Sample a number of images and save them to an NPZ, for use with TF-Inception + # Lists to hold images and labels for images + samples_path = os.path.join( + self.config["samples_root"], self.config["experiment_name"] + ) + if not os.path.exists(samples_path): + os.mkdir(samples_path) + x, y = [], [] + print( + "Sampling %d images and saving them to npz..." + % self.config["sample_num_npz"] + ) + dict_tosave = {} + for i in trange( + int( + np.ceil( + self.config["sample_num_npz"] / float(self.config["batch_size"]) + ) + ) + ): + with torch.no_grad(): + images, labels, _ = sample() + if self.config["model_backbone"] == "stylegan2": + images = torch.clamp((images * 127.5 + 128), 0, 255) + images = ((images / 255) - 0.5) * 2 + + x += [images.cpu().numpy()] + if self.config["class_cond"]: + y += [labels.cpu().numpy()] + if self.config["which_dataset"] == "imagenet": + x = np.concatenate(x, 0)[: self.config["sample_num_npz"]] + if self.config["class_cond"]: + y = np.concatenate(y, 0)[: self.config["sample_num_npz"]] + + np_filename = "%s/samples%s_seed%i.pickle" % ( + samples_path, + "_kmeans" + str(self.config["kmeans_subsampled"]) + if self.config["kmeans_subsampled"] > -1 + else "", + self.config["seed"], + ) + print("Saving npy to %s..." % np_filename) + dict_tosave["x"] = x + dict_tosave["y"] = y + file_to_store = open(np_filename, "wb") + pickle.dump(dict_tosave, file_to_store, protocol=4) + file_to_store.close() + + if ( + self.config["longtail"] + and self.config["eval_reference_set"] == "val" + ): + print("Additionally storing stratified samples") + for strat_name in ["_many", "_low", "_few"]: + np_filename = "%s/%s/samples%s_seed%i_strat%s.pickle" % ( + self.config["samples_root"], + self.config["experiment_name"], + "_kmeans" + str(self.config["kmeans_subsampled"]) + if self.config["kmeans_subsampled"] > -1 + else "", + self.config["seed"], + strat_name, + ) + print(np_filename) + if strat_name == "_many": + x_ = x[samples_per_class[y] >= 100] + y_ = y[samples_per_class[y] >= 100] + elif strat_name == "_low": + x_ = x[samples_per_class[y] < 100] + y_ = y[samples_per_class[y] < 100] + x_ = x_[samples_per_class[y_] > 20] + y_ = y_[samples_per_class[y_] > 20] + elif strat_name == "_few": + x_ = x[samples_per_class[y] <= 20] + y_ = y[samples_per_class[y] <= 20] + dict_tosave = {} + dict_tosave["x"] = x_ + dict_tosave["y"] = y_ + file_to_store = open(np_filename, "wb") + pickle.dump(dict_tosave, file_to_store, protocol=4) + file_to_store.close() + + +if __name__ == "__main__": + parser = biggan_utils.prepare_parser() + parser = biggan_utils.add_sample_parser(parser) + parser = inference_utils.add_backbone_parser(parser) + config = vars(parser.parse_args()) + config["n_classes"] = 1000 + if config["json_config"] != "": + data = json.load(open(config["json_config"])) + for key in data.keys(): + if "exp_name" in key: + config["experiment_name"] = data[key] + else: + config[key] = data[key] + else: + print("No json file to load configuration from") + + tester = Tester(config) + + if config["run_setup"] == "local_debug": # or LOCAL: + tester() + else: + executor = submitit.SlurmExecutor( + folder=config["slurm_logdir"], max_num_timeout=10 + ) + executor.update_parameters( + gpus_per_node=1, + partition=config["partition"], + cpus_per_task=8, + mem=128000, + time=30, + job_name="testing_" + config["experiment_name"], + ) + executor.submit(tester) + import time + + time.sleep(1) diff --git a/inference/utils.py b/inference/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1f9514bc2ad4b9e4378220b28d98c264d73e0529 --- /dev/null +++ b/inference/utils.py @@ -0,0 +1,423 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# All contributions by Andy Brock: +# Copyright (c) 2019 Andy Brock +# +# MIT License +import os +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) + +import numpy as np +import functools + +import torch +import torch.nn as nn + +import stylegan2_ada_pytorch.dnnlib as dnnlib +import stylegan2_ada_pytorch.legacy as legacy +import BigGAN_PyTorch.utils as biggan_utils +import BigGAN_PyTorch.BigGAN as BigGANModel +import data_utils.utils as data_utils + + +def get_sampling_funct( + config, + generator, + instance_set="train", + reference_set="train", + which_dataset="imagenet", +): + """It prepares the generation sampling function and the inception moments filename. + + Arguments + --------- + config: dict + Dictionary with configuration parameters. + generator: torch.nn.module + Generator network. + instance_set: str, optional + If `train`, build a dataset with the training split. + If `val`, build a dataset with the validation split. + reference_set: str, optional + If `train`, use training as a reference to compute metrics. + If `val`, use validation as a reference to compute metrics. + which_dataset: str, optional + Dataset name + Returns + ------- + sample_, im_filename, dataset + sample_: function + Function to sample generated images from. + im_filename: str + Filename where to find the inception moments used to compute the FID metric + (with Pytorch code). + + """ + # Class labels will follow either a long-tail + # distribution(if reference==train) or a uniform distribution + # otherwise). + if config["longtail"]: + class_probabilities = np.load( + "../BigGAN_PyTorch/imagenet_lt/imagenet_lt_class_prob.npy", + allow_pickle=True, + ) + samples_per_class = np.load( + "../BigGAN_PyTorch/imagenet_lt/imagenet_lt_samples_per_class.npy", + allow_pickle=True, + ) + else: + class_probabilities, samples_per_class = None, None + + if (reference_set == "val" and instance_set == "val") and config[ + "which_dataset" + ] == "coco": + # using evaluation set + test_part = True + else: + test_part = False + + # Prepare the noise distribution and class distribution + z_, y_ = data_utils.prepare_z_y( + config["batch_size"], + generator.dim_z if config["model_backbone"] == "biggan" else 512, + config["n_classes"], + device="cuda", + fp16=config["G_fp16"], + longtail_gen=config["longtail"] if reference_set == "train" else False, + z_var=config["z_var"], + class_probabilities=class_probabilities, + ) + + # Obtain dataset to sample instances from. + if config["instance_cond"]: + dataset = data_utils.get_dataset_hdf5( + **{ + **config, + "data_path": config["data_root"], + "batch_size": config["batch_size"], + "load_in_mem_feats": config["load_in_mem"], + "split": instance_set, + "test_part": test_part, + "augment": False, + "ddp": False, + } + ) + + else: + dataset = None + + # Weights to sample instances (+classes). By default, weights are None, + # which means no specific sampling weights will be used (uniform). + # For long-tail experiments with training as reference distribution, + # balance the sampling with a long-tail distribution. + weights_sampling = None + nn_sampling_strategy = "instance_balance" + if config["instance_cond"] and config["class_cond"] and config["longtail"]: + nn_sampling_strategy = "nnclass_balance" + if reference_set == "val": + print("Sampling classes uniformly for generator.") + # Sampling classes uniformly + weights_sampling = None + else: + print("Balancing with weights=samples_per_class (long-tailed).") + weights_sampling = samples_per_class + + # Prepare conditioning sampling function + sample_conditioning = functools.partial( + data_utils.sample_conditioning_values, + z_=z_, + y_=y_, + constant_conditioning=config["constant_conditioning"], + batch_size=config["batch_size"], + weights_sampling=weights_sampling, + dataset=dataset, + class_cond=config["class_cond"], + instance_cond=config["instance_cond"], + nn_sampling_strategy=nn_sampling_strategy, + ) + + # Prepare Sample function for use with inception metrics + sample_ = functools.partial( + sample, + generator, + sample_conditioning_func=sample_conditioning, + config=config, + class_cond=config["class_cond"], + instance_cond=config["instance_cond"], + backbone=config["model_backbone"], + truncation_value=config["z_var"], + ) + + # Get reference statistics to compute FID + im_prefix = "I" if which_dataset == "imagenet" else "COCO" + if reference_set == "train": + im_filename = "%s%i_%s" % ( + im_prefix, + config["resolution"], + "" if not config["longtail"] else "longtail", + ) + else: + im_filename = "%s%i_%s%s" % ( + im_prefix, + config["resolution"], + "_val", + "_test" if test_part else "", + ) + print("Using ", im_filename, "for Inception metrics.") + return sample_, im_filename + + +def sample( + generator, + sample_conditioning_func, + config, + class_cond=True, + instance_cond=False, + device="cuda", + backbone="biggan", + truncation_value=1.0, +): + """It samples generated images from the model, given the input noise (and conditioning). + + Arguments + --------- + generator: torch.nn.module + Generator network. + sample_conditioning_func: function + A function that samples and outputs the conditionings to be fed to the generator. + config: dict + Dictionary with configuration parameters. + class_cond: bool, optional + If True, use class labels to condition the generator. + instance_cond: bool, optional + If True, use instance features to condition the generator. + device: str, optional + Device name + backbone: str, optional + Name of the backbone architecture to use ("biggan" or "stylegan2"). + truncation_value: float, optional + Variance for the noise distribution, attributed to the truncation values in BigGAN. + Returns + ------- + gen_samples: torch.FloatTensor + Generated images. + y_: torch.Tensor + Sampled class labels. If using BigGAN backbone, y_.shape = [bs], + if using StyleGAN2 backbone, y_.shape = [bs, c_dim], where `bs` is the batch size + and `c_dim` is the dimensionality of the class embedding. + feats_: torch.Tensor + Sampled instance feature vectors, with shape [bs, h_dim], where `bs` is the batch size + and `h_dim` is the dimensionality of the instance feature vectors. + + """ + # Sample conditioning + conditioning = sample_conditioning_func() + # Send conditionings to proper devices + with torch.no_grad(): + if not class_cond and not instance_cond: + z_ = conditioning + y_, feats_ = None, None + elif class_cond and not instance_cond: + z_, y_ = conditioning + y_ = y_.long() + y_ = y_.to(device, non_blocking=True) + feats_ = None + elif instance_cond and not class_cond: + z_, feats_ = conditioning + feats_ = feats_.to(device, non_blocking=True) + y_ = None + elif instance_cond and class_cond: + z_, y_, feats_ = conditioning + y_, feats_ = ( + y_.to(device, non_blocking=True), + feats_.to(device, non_blocking=True), + ) + z_ = z_.to(device, non_blocking=True) + + if backbone == "stylegan2": + if y_ is None: + y_ = torch.empty([z_.shape[0], generator.c_dim], device=device) + else: + y_ = torch.eye(config["n_classes"], device=device)[y_] + if feats_ is None: + feats_ = torch.empty([z_.shape[0], generator.h_dim], device=device) + + # Sample images given the conditionings + if backbone == "biggan": + if config["parallel"]: + gen_samples = nn.parallel.data_parallel(generator, (z_, y_, feats_)) + else: + gen_samples = generator(z_, y_, feats_) + elif backbone == "stylegan2": + gen_samples = generator( + z=z_, + c=y_, + feats=feats_, + truncation_psi=truncation_value, + noise_mode="const", + ) + return gen_samples, y_, feats_ + + +def load_model_inference(config, device="cuda"): + """It loads the generator network to do inference with and over-rides the configuration file. + + Arguments + --------- + config: dict + Dictionary with configuration parameters. + device: str, optional + Device name + Returns + ------- + generator: torch.nn.module + Generator network. + config: dict + Overwritten configuration dictionary from the model checkpoint if it exists. + + """ + if config["model_backbone"] == "biggan": + # Select checkpoint name according to best FID in checkpoint + best_fid = 1e5 + best_name_final = "" + for name_best in ["best0", "best1"]: + try: + root = "/".join([config["weights_root"], config["experiment_name"]]) + state_dict_loaded = torch.load( + "%s/%s.pth" + % (root, biggan_utils.join_strings("_", ["state_dict", name_best])) + ) + print( + "For name best ", + name_best, + " we have an FID: ", + state_dict_loaded["best_FID"], + ) + if state_dict_loaded["best_FID"] < best_fid: + best_fid = state_dict_loaded["best_FID"] + best_name_final = name_best + except: + print("Checkpoint with name ", name_best, " not in folder.") + config["load_weights"] = best_name_final + print("Final name selected is ", best_name_final) + + # Prepare state dict, which holds things like epoch # and itr # + state_dict = { + "itr": 0, + "epoch": 0, + "save_num": 0, + "save_best_num": 0, + "best_IS": 0, + "best_FID": 999999, + "config": config, + } + # Get override some parameters from trained model in experiment config + biggan_utils.load_weights( + None, + None, + state_dict, + config["weights_root"], + config["experiment_name"], + config["load_weights"], + None, + strict=False, + load_optim=False, + eval=True, + ) + + # Ignore items which we might want to overwrite from the command line + for item in state_dict["config"]: + if item not in [ + "base_root", + "data_root", + "load_weights", + "batch_size", + "num_workers", + "weights_root", + "logs_root", + "samples_root", + "eval_reference_set", + "eval_instance_set", + "which_dataset", + "seed", + "eval_prdc", + "use_balanced_sampler", + "custom_distrib", + "longtail_temperature", + "longtail_gen", + "num_inception_images", + "sample_num_npz", + "load_in_mem", + "split", + "z_var", + "kmeans_subsampled", + "filter_hd", + "n_subsampled_data", + "feature_augmentation", + ]: + if item == "experiment_name" and config["experiment_name"] != "": + continue # Allows to overwride the name of the experiment at test time + config[item] = state_dict["config"][item] + # No data augmentation during testing + config["feature_augmentation"] = False + config["hflips"] = False + config["DA"] = False + + experiment_name = ( + config["experiment_name"] + if config["experiment_name"] + else biggan_utils.name_from_config(config) + ) + print("Experiment name is %s" % experiment_name) + + # Next, build the model + generator = BigGANModel.Generator(**config).to(device) + + # Load weights + print("Loading weights...") + + # Here is where we deal with the ema--load ema weights or load normal weights + biggan_utils.load_weights( + generator if not (config["use_ema"]) else None, + None, + state_dict, + config["weights_root"], + experiment_name, + config["load_weights"], + generator if config["ema"] and config["use_ema"] else None, + strict=False, + load_optim=False, + ) + + if config["G_eval_mode"]: + print("Putting G in eval mode..") + generator.eval() + else: + print("G is in %s mode..." % ("training" if generator.training else "eval")) + + elif config["model_backbone"] == "stylegan2": + # StyleGAN2 saves the entire network + weights in a pickle. Load it here. + network_pkl = os.path.join( + config["base_root"], config["experiment_name"], "best-network-snapshot.pkl" + ) + print('Loading networks from "%s"...' % network_pkl) + with dnnlib.util.open_url(network_pkl) as f: + generator = legacy.load_network_pkl(f)["G_ema"].to(device) # type: ignore + return generator, config + + +def add_backbone_parser(parser): + parser.add_argument( + "--model_backbone", + type=str, + default="biggan", + choices=["biggan", "stylegan2"], + help="Backbone model? " "(default: %(default)s)", + ) + return parser diff --git a/predict.py b/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..9bf62904eeafcdf8cce67666b62a32ca7f69c64d --- /dev/null +++ b/predict.py @@ -0,0 +1,307 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import sys +import os +import tempfile +import warnings +from pathlib import Path +import nltk +import torch +from torch import nn +import torchvision.transforms as transforms +import numpy as np +import imageio +from PIL import Image as Image_PIL +from scipy.stats import truncnorm +from nltk.corpus import wordnet as wn +import cma +import sklearn.metrics +import cog + +sys.path.insert(0, "stylegan2_ada_pytorch") +from pytorch_pretrained_biggan import convert_to_images, utils +import inference.utils as inference_utils +import data_utils.utils as data_utils + +NORM_MEAN = torch.Tensor([0.485, 0.456, 0.406]).view(3, 1, 1) +NORM_STD = torch.Tensor([0.229, 0.224, 0.225]).view(3, 1, 1) + +nltk.download("wordnet") +IND2NAME = { + index: wn.of2ss("%08dn" % offset).lemma_names()[0] + for offset, index in utils.IMAGENET.items() +} +NAME2IND = dict([(value, key) for key, value in IND2NAME.items()]) + +CLASS_NAMES = sorted(list(IND2NAME.values())) + + +class Predictor(cog.Predictor): + def setup(self): + torch.manual_seed(np.random.randint(sys.maxsize)) + warnings.simplefilter("ignore", cma.evolution_strategy.InjectionWarning) + self.last_gen_model = None + self.last_feature_extractor = None + self.model = None + self.feature_extractor = None + self.noise_size = 128 + self.batch_size = 4 + self.size = 256 + + @cog.input("image", type=Path, help="Input image Instance") + @cog.input("gen_model", type=str, options=["icgan", "cc_icgan"], default="icgan", + help='Select type of IC-GAN model. "icgan" is conditioned on the input image; ' + '"cc_icgan" is conditioned on both input image and a conditional_class') + @cog.input("conditional_class", type=str, default=None, options=CLASS_NAMES, + help="Choose conditional class. Only valid for gen_model=cc_icgan") + @cog.input("num_samples", type=int, default=1, options=[1, 4, 9, 16], + help="number of samples generated") + @cog.input("seed", type=int, default=0, help="seed=0 means no seed") + def predict(self, image, gen_model="icgan", conditional_class=None, num_samples=1, seed=0): + assert isinstance(seed, int), "seed should be an integer" + if gen_model == 'cc_icgan': + assert conditional_class is not None, 'please set conditional_class for cc_icgan' + num_samples_ranked = num_samples + experiment_name = ( + "icgan_biggan_imagenet_res256" + if gen_model == "icgan" + else "cc_icgan_biggan_imagenet_res256" + ) + num_samples_total = num_samples * 10 + truncation = 0.7 + if conditional_class is not None: + class_index = NAME2IND[conditional_class] + + input_image_instance = str(image) + + if gen_model == "icgan": + class_index = None + + if seed == 0: + seed = None + + state = None if not seed else np.random.RandomState(seed) + np.random.seed(seed) + + feature_extractor_name = ("classification" if gen_model == "cc_icgan" else "selfsupervised") + + # Load feature extractor (outlier filtering and optionally input image feature extraction) + self.feature_extractor, self.last_feature_extractor = load_feature_extractor( + gen_model, self.last_feature_extractor, self.feature_extractor) + # Load features + if input_image_instance not in ["None", "", None]: + print("Obtaining instance features from input image!") + input_feature_index = None + input_image_tensor = preprocess_input_image(input_image_instance, self.size) + with torch.no_grad(): + input_features, _ = self.feature_extractor(input_image_tensor.cuda()) + input_features /= torch.linalg.norm(input_features, dim=-1, keepdims=True) + elif input_feature_index is not None: + print("Selecting an instance from pre-extracted vectors!") + input_features = np.load( + "stored_instances/imagenet_res" + + str(self.size) + + "_rn50_" + + feature_extractor_name + + "_kmeans_k1000_instance_features.npy", + allow_pickle=True, + ).item()["instance_features"][input_feature_index: input_feature_index + 1] + else: + input_features = None + + # Load generative model + self.model, self.last_gen_model = load_generative_model( + gen_model, self.last_gen_model, experiment_name, self.model) + # Prepare other variables + + replace_to_inplace_relu(self.model) + + # Create noise, instance and class vector + noise_vector = truncnorm.rvs( + -2 * truncation, + 2 * truncation, + size=(num_samples_total, self.noise_size), + random_state=state, + ).astype(np.float32) + noise_vector = torch.tensor(noise_vector, requires_grad=False, device="cuda") + if input_features is not None: + instance_vector = torch.tensor( + input_features, requires_grad=False, device="cuda" + ).repeat(num_samples_total, 1) + else: + instance_vector = None + if class_index is not None: + input_label = torch.LongTensor([class_index] * num_samples_total) + else: + input_label = None + if input_feature_index is not None: + print("Conditioning on instance with index: ", input_feature_index) + + all_outs, all_dists = [], [] + for i_bs in range(num_samples_total // self.batch_size + 1): + start = i_bs * self.batch_size + end = min(start + self.batch_size, num_samples_total) + if start == end: + break + out = get_output( + noise_vector[start:end], + input_label[start:end] if input_label is not None else None, + instance_vector[start:end] if instance_vector is not None else None, + self.model, + truncation, + channels=3, + ) + + if instance_vector is not None: + # Get features from generated images + feature extractor + out_ = preprocess_generated_image(out) + with torch.no_grad(): + out_features, _ = self.feature_extractor(out_.cuda()) + out_features /= torch.linalg.norm(out_features, dim=-1, keepdims=True) + dists = sklearn.metrics.pairwise_distances( + out_features.cpu(), + instance_vector[start:end].cpu(), + metric="euclidean", + n_jobs=-1, + ) + all_dists.append(np.diagonal(dists)) + all_outs.append(out.detach().cpu()) + del out + all_outs = torch.cat(all_outs) + all_dists = np.concatenate(all_dists) + + # Order samples by distance to conditioning feature vector and select only num_samples_ranked images + selected_idxs = np.argsort(all_dists)[:num_samples_ranked] + # Create figure + row_i, col_i, i_im = 0, 0, 0 + all_images_mosaic = np.zeros( + ( + 3, + self.size * (int(np.sqrt(num_samples_ranked))), + self.size * (int(np.sqrt(num_samples_ranked))), + ) + ) + for j in selected_idxs: + all_images_mosaic[ + :, + row_i * self.size: row_i * self.size + self.size, + col_i * self.size: col_i * self.size + self.size, + ] = all_outs[j] + if row_i == int(np.sqrt(num_samples_ranked)) - 1: + row_i = 0 + if col_i == int(np.sqrt(num_samples_ranked)) - 1: + col_i = 0 + else: + col_i += 1 + else: + row_i += 1 + i_im += 1 + + out_path = Path(tempfile.mkdtemp()) / "out.png" + save(all_images_mosaic[np.newaxis, ...], str(out_path), torch_format=False) + return out_path + + +def replace_to_inplace_relu(model): + for child_name, child in model.named_children(): + if isinstance(child, nn.ReLU): + setattr(model, child_name, nn.ReLU(inplace=False)) + else: + replace_to_inplace_relu(child) + + +def save(out, name=None, torch_format=True): + if torch_format: + with torch.no_grad(): + out = out.cpu().numpy() + img = convert_to_images(out)[0] + if name: + imageio.imwrite(name, np.asarray(img)) + return img + + +def load_icgan(experiment_name, root_=""): + root = os.path.join(root_, experiment_name) + config = torch.load("%s/%s.pth" % (root, "state_dict_best0"))["config"] + + config["weights_root"] = root_ + config["model_backbone"] = "biggan" + config["experiment_name"] = experiment_name + G, config = inference_utils.load_model_inference(config) + G.cuda() + G.eval() + return G + + +def get_output(noise_vector, input_label, input_features, model, truncation, channels): + # stochastic_truncation = False as how it is set in colab + noise_vector = noise_vector.clamp(-2 * truncation, 2 * truncation) + if input_label is not None: + input_label = torch.LongTensor(input_label) + else: + input_label = None + + out = model( + noise_vector, + input_label.cuda() if input_label is not None else None, + input_features.cuda() if input_features is not None else None, + ) + + if channels == 1: + out = out.mean(dim=1, keepdim=True) + out = out.repeat(1, 3, 1, 1) + return out + + +def load_generative_model(gen_model, last_gen_model, experiment_name, model): + # Load generative model + if gen_model != last_gen_model: + model = load_icgan(experiment_name, root_="./") + last_gen_model = gen_model + return model, last_gen_model + + +def load_feature_extractor(gen_model, last_feature_extractor, feature_extractor): + # Load feature extractor to obtain instance features + feat_ext_name = "classification" if gen_model == "cc_icgan" else "selfsupervised" + if last_feature_extractor != feat_ext_name: + if feat_ext_name == "classification": + feat_ext_path = "" + else: + feat_ext_path = "swav_pretrained.pth.tar" + last_feature_extractor = feat_ext_name + feature_extractor = data_utils.load_pretrained_feature_extractor( + feat_ext_path, feature_extractor=feat_ext_name + ) + feature_extractor.eval() + return feature_extractor, last_feature_extractor + + +def preprocess_input_image(input_image_path, size): + pil_image = Image_PIL.open(input_image_path).convert("RGB") + transform_list = transforms.Compose( + [ + data_utils.CenterCropLongEdge(), + transforms.Resize((size, size)), + transforms.ToTensor(), + transforms.Normalize(NORM_MEAN, NORM_STD), + ] + ) + tensor_image = transform_list(pil_image) + tensor_image = torch.nn.functional.interpolate( + tensor_image.unsqueeze(0), 224, mode="bicubic", align_corners=True + ) + return tensor_image + + +def preprocess_generated_image(image): + transform_list = transforms.Normalize(NORM_MEAN, NORM_STD) + image = transform_list(image * 0.5 + 0.5) + image = torch.nn.functional.interpolate( + image, 224, mode="bicubic", align_corners=True + ) + return image diff --git a/stylegan2_ada_pytorch/.github/ISSUE_TEMPLATE/bug_report.md b/stylegan2_ada_pytorch/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000000000000000000000000000000000000..d79b107957f1012926ccf77c141767e1ff47affe --- /dev/null +++ b/stylegan2_ada_pytorch/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,35 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. In '...' directory, run command '...' +2. See error (copy&paste full log, including exceptions and **stacktraces**). + +Please copy&paste text instead of screenshots for better searchability. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. Linux Ubuntu 20.04, Windows 10] + - PyTorch version (e.g., pytorch 1.7.1) + - CUDA toolkit version (e.g., CUDA 11.0) + - NVIDIA driver version + - GPU [e.g., Titan V, RTX 3090] + - Docker: did you use Docker? If yes, specify docker image URL (e.g., nvcr.io/nvidia/pytorch:20.12-py3) + +**Additional context** +Add any other context about the problem here. diff --git a/stylegan2_ada_pytorch/.gitignore b/stylegan2_ada_pytorch/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..308af3ccf599cefeb9fbb2a31a788c5f9ed3c90e --- /dev/null +++ b/stylegan2_ada_pytorch/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +.cache/ diff --git a/stylegan2_ada_pytorch/Dockerfile b/stylegan2_ada_pytorch/Dockerfile new file mode 100755 index 0000000000000000000000000000000000000000..6e391f4d8c4b619e1381594248565f63492daf19 --- /dev/null +++ b/stylegan2_ada_pytorch/Dockerfile @@ -0,0 +1,22 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +FROM nvcr.io/nvidia/pytorch:20.12-py3 + +ENV PYTHONDONTWRITEBYTECODE 1 +ENV PYTHONUNBUFFERED 1 + +RUN pip install imageio-ffmpeg==0.4.3 pyspng==0.1.0 + +WORKDIR /workspace + +# Unset TORCH_CUDA_ARCH_LIST and exec. This makes pytorch run-time +# extension builds significantly faster as we only compile for the +# currently active GPU configuration. +RUN (printf '#!/bin/bash\nunset TORCH_CUDA_ARCH_LIST\nexec \"$@\"\n' >> /entry.sh) && chmod a+x /entry.sh +ENTRYPOINT ["/entry.sh"] diff --git a/stylegan2_ada_pytorch/LICENSE.txt b/stylegan2_ada_pytorch/LICENSE.txt new file mode 100755 index 0000000000000000000000000000000000000000..48e03de7fd9ce9dda1a8f2c5b0f8cbca8f6b24e6 --- /dev/null +++ b/stylegan2_ada_pytorch/LICENSE.txt @@ -0,0 +1,97 @@ +Copyright (c) 2021, NVIDIA Corporation. All rights reserved. + + +NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator Augmentation (ADA) + + +======================================================================= + +1. Definitions + +"Licensor" means any person or entity that distributes its Work. + +"Software" means the original work of authorship made available under +this License. + +"Work" means the Software and any additions to or derivative works of +the Software that are made available under this License. + +The terms "reproduce," "reproduction," "derivative works," and +"distribution" have the meaning as provided under U.S. copyright law; +provided, however, that for the purposes of this License, derivative +works shall not include works that remain separable from, or merely +link (or bind by name) to the interfaces of, the Work. + +Works, including the Software, are "made available" under this License +by including in or with the Work either (a) a copyright notice +referencing the applicability of this License to the Work, or (b) a +copy of this License. + +2. License Grants + + 2.1 Copyright Grant. Subject to the terms and conditions of this + License, each Licensor grants to you a perpetual, worldwide, + non-exclusive, royalty-free, copyright license to reproduce, + prepare derivative works of, publicly display, publicly perform, + sublicense and distribute its Work and any resulting derivative + works in any form. + +3. Limitations + + 3.1 Redistribution. You may reproduce or distribute the Work only + if (a) you do so under this License, (b) you include a complete + copy of this License with your distribution, and (c) you retain + without modification any copyright, patent, trademark, or + attribution notices that are present in the Work. + + 3.2 Derivative Works. You may specify that additional or different + terms apply to the use, reproduction, and distribution of your + derivative works of the Work ("Your Terms") only if (a) Your Terms + provide that the use limitation in Section 3.3 applies to your + derivative works, and (b) you identify the specific derivative + works that are subject to Your Terms. Notwithstanding Your Terms, + this License (including the redistribution requirements in Section + 3.1) will continue to apply to the Work itself. + + 3.3 Use Limitation. The Work and any derivative works thereof only + may be used or intended for use non-commercially. Notwithstanding + the foregoing, NVIDIA and its affiliates may use the Work and any + derivative works commercially. As used herein, "non-commercially" + means for research or evaluation purposes only. + + 3.4 Patent Claims. If you bring or threaten to bring a patent claim + against any Licensor (including any claim, cross-claim or + counterclaim in a lawsuit) to enforce any patents that you allege + are infringed by any Work, then your rights under this License from + such Licensor (including the grant in Section 2.1) will terminate + immediately. + + 3.5 Trademarks. This License does not grant any rights to use any + Licensor’s or its affiliates’ names, logos, or trademarks, except + as necessary to reproduce the notices described in this License. + + 3.6 Termination. If you violate any term of this License, then your + rights under this License (including the grant in Section 2.1) will + terminate immediately. + +4. Disclaimer of Warranty. + +THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR +NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER +THIS LICENSE. + +5. Limitation of Liability. + +EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL +THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE +SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, +INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF +OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK +(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, +LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER +COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF +THE POSSIBILITY OF SUCH DAMAGES. + +======================================================================= diff --git a/stylegan2_ada_pytorch/README.md b/stylegan2_ada_pytorch/README.md new file mode 100755 index 0000000000000000000000000000000000000000..092b52bb4cdf71264119ffd8511ecbb74719e85a --- /dev/null +++ b/stylegan2_ada_pytorch/README.md @@ -0,0 +1,378 @@ +## StyleGAN2-ADA — Official PyTorch implementation + +![Teaser image](./docs/stylegan2-ada-teaser-1024x252.png) + +**Training Generative Adversarial Networks with Limited Data**
+Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila
+https://arxiv.org/abs/2006.06676
+ +Abstract: *Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset. We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images. We expect this to open up new application domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42.* + +For business inquiries, please contact [researchinquiries@nvidia.com](mailto:researchinquiries@nvidia.com)
+For press and other inquiries, please contact Hector Marinez at [hmarinez@nvidia.com](mailto:hmarinez@nvidia.com)
+ +## Release notes + +This repository is a faithful reimplementation of [StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada/) in PyTorch, focusing on correctness, performance, and compatibility. + +**Correctness** +* Full support for all primary training configurations. +* Extensive verification of image quality, training curves, and quality metrics against the TensorFlow version. +* Results are expected to match in all cases, excluding the effects of pseudo-random numbers and floating-point arithmetic. + +**Performance** +* Training is typically 5%–30% faster compared to the TensorFlow version on NVIDIA Tesla V100 GPUs. +* Inference is up to 35% faster in high resolutions, but it may be slightly slower in low resolutions. +* GPU memory usage is comparable to the TensorFlow version. +* Faster startup time when training new networks (<50s), and also when using pre-trained networks (<4s). +* New command line options for tweaking the training performance. + +**Compatibility** +* Compatible with old network pickles created using the TensorFlow version. +* New ZIP/PNG based dataset format for maximal interoperability with existing 3rd party tools. +* TFRecords datasets are no longer supported — they need to be converted to the new format. +* New JSON-based format for logs, metrics, and training curves. +* Training curves are also exported in the old TFEvents format if TensorBoard is installed. +* Command line syntax is mostly unchanged, with a few exceptions (e.g., `dataset_tool.py`). +* Comparison methods are not supported (`--cmethod`, `--dcap`, `--cfg=cifarbaseline`, `--aug=adarv`) +* **Truncation is now disabled by default.** + +## Data repository + +| Path | Description +| :--- | :---------- +| [stylegan2-ada-pytorch](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/) | Main directory hosted on Amazon S3 +|   ├  [ada-paper.pdf](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/ada-paper.pdf) | Paper PDF +|   ├  [images](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/images/) | Curated example images produced using the pre-trained models +|   ├  [videos](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/videos/) | Curated example interpolation videos +|   └  [pretrained](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/) | Pre-trained models +|     ├  ffhq.pkl | FFHQ at 1024x1024, trained using original StyleGAN2 +|     ├  metfaces.pkl | MetFaces at 1024x1024, transfer learning from FFHQ using ADA +|     ├  afhqcat.pkl | AFHQ Cat at 512x512, trained from scratch using ADA +|     ├  afhqdog.pkl | AFHQ Dog at 512x512, trained from scratch using ADA +|     ├  afhqwild.pkl | AFHQ Wild at 512x512, trained from scratch using ADA +|     ├  cifar10.pkl | Class-conditional CIFAR-10 at 32x32 +|     ├  brecahad.pkl | BreCaHAD at 512x512, trained from scratch using ADA +|     ├  [paper-fig7c-training-set-sweeps](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/paper-fig7c-training-set-sweeps/) | Models used in Fig.7c (sweep over training set size) +|     ├  [paper-fig11a-small-datasets](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/paper-fig11a-small-datasets/) | Models used in Fig.11a (small datasets & transfer learning) +|     ├  [paper-fig11b-cifar10](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/paper-fig11b-cifar10/) | Models used in Fig.11b (CIFAR-10) +|     ├  [transfer-learning-source-nets](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/) | Models used as starting point for transfer learning +|     └  [metrics](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/) | Feature detectors used by the quality metrics + +## Requirements + +* Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons. +* 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using NVIDIA DGX-1 with 8 Tesla V100 GPUs. +* 64-bit Python 3.7 and PyTorch 1.7.1. See [https://pytorch.org/](https://pytorch.org/) for PyTorch install instructions. +* CUDA toolkit 11.0 or later. Use at least version 11.1 if running on RTX 3090. (Why is a separate CUDA toolkit installation required? See comments in [#2](https://github.com/NVlabs/stylegan2-ada-pytorch/issues/2#issuecomment-779457121).) +* Python libraries: `pip install click requests tqdm pyspng ninja imageio-ffmpeg==0.4.3`. We use the Anaconda3 2020.11 distribution which installs most of these by default. +* Docker users: use the [provided Dockerfile](./Dockerfile) to build an image with the required library dependencies. + +The code relies heavily on custom PyTorch extensions that are compiled on the fly using NVCC. On Windows, the compilation requires Microsoft Visual Studio. We recommend installing [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/) and adding it into `PATH` using `"C:\Program Files (x86)\Microsoft Visual Studio\\Community\VC\Auxiliary\Build\vcvars64.bat"`. + +## Getting started + +Pre-trained networks are stored as `*.pkl` files that can be referenced using local filenames or URLs: + +```.bash +# Generate curated MetFaces images without truncation (Fig.10 left) +python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl + +# Generate uncurated MetFaces images with truncation (Fig.12 upper left) +python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl + +# Generate class conditional CIFAR-10 images (Fig.17 left, Car) +python generate.py --outdir=out --seeds=0-35 --class=1 \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/cifar10.pkl + +# Style mixing example +python style_mixing.py --outdir=out --rows=85,100,75,458,1500 --cols=55,821,1789,293 \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl +``` + +Outputs from the above commands are placed under `out/*.png`, controlled by `--outdir`. Downloaded network pickles are cached under `$HOME/.cache/dnnlib`, which can be overridden by setting the `DNNLIB_CACHE_DIR` environment variable. The default PyTorch extension build directory is `$HOME/.cache/torch_extensions`, which can be overridden by setting `TORCH_EXTENSIONS_DIR`. + +**Docker**: You can run the above curated image example using Docker as follows: + +```.bash +docker build --tag sg2ada:latest . +./docker_run.sh python3 generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl +``` + +Note: The Docker image requires NVIDIA driver release `r455.23` or later. + +**Legacy networks**: The above commands can load most of the network pickles created using the previous TensorFlow versions of StyleGAN2 and StyleGAN2-ADA. However, for future compatibility, we recommend converting such legacy pickles into the new format used by the PyTorch version: + +```.bash +python legacy.py \ + --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \ + --dest=stylegan2-cat-config-f.pkl +``` + +## Projecting images to latent space + +To find the matching latent vector for a given image file, run: + +```.bash +python projector.py --outdir=out --target=~/mytargetimg.png \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl +``` + +For optimal results, the target image should be cropped and aligned similar to the [FFHQ dataset](https://github.com/NVlabs/ffhq-dataset). The above command saves the projection target `out/target.png`, result `out/proj.png`, latent vector `out/projected_w.npz`, and progression video `out/proj.mp4`. You can render the resulting latent vector by specifying `--projected_w` for `generate.py`: + +```.bash +python generate.py --outdir=out --projected_w=out/projected_w.npz \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl +``` + +## Using networks from Python + +You can use pre-trained networks in your own Python code as follows: + +```.python +with open('ffhq.pkl', 'rb') as f: + G = pickle.load(f)['G_ema'].cuda() # torch.nn.Module +z = torch.randn([1, G.z_dim]).cuda() # latent codes +c = None # class labels (not used in this example) +img = G(z, c) # NCHW, float32, dynamic range [-1, +1] +``` + +The above code requires `torch_utils` and `dnnlib` to be accessible via `PYTHONPATH`. It does not need source code for the networks themselves — their class definitions are loaded from the pickle via `torch_utils.persistence`. + +The pickle contains three networks. `'G'` and `'D'` are instantaneous snapshots taken during training, and `'G_ema'` represents a moving average of the generator weights over several training steps. The networks are regular instances of `torch.nn.Module`, with all of their parameters and buffers placed on the CPU at import and gradient computation disabled by default. + +The generator consists of two submodules, `G.mapping` and `G.synthesis`, that can be executed separately. They also support various additional options: + +```.python +w = G.mapping(z, c, truncation_psi=0.5, truncation_cutoff=8) +img = G.synthesis(w, noise_mode='const', force_fp32=True) +``` + +Please refer to [`generate.py`](./generate.py), [`style_mixing.py`](./style_mixing.py), and [`projector.py`](./projector.py) for further examples. + +## Preparing datasets + +Datasets are stored as uncompressed ZIP archives containing uncompressed PNG files and a metadata file `dataset.json` for labels. + +Custom datasets can be created from a folder containing images; see [`python dataset_tool.py --help`](./docs/dataset-tool-help.txt) for more information. Alternatively, the folder can also be used directly as a dataset, without running it through `dataset_tool.py` first, but doing so may lead to suboptimal performance. + +Legacy TFRecords datasets are not supported — see below for instructions on how to convert them. + +**FFHQ**: + +Step 1: Download the [Flickr-Faces-HQ dataset](https://github.com/NVlabs/ffhq-dataset) as TFRecords. + +Step 2: Extract images from TFRecords using `dataset_tool.py` from the [TensorFlow version of StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada/): + +```.bash +# Using dataset_tool.py from TensorFlow version at +# https://github.com/NVlabs/stylegan2-ada/ +python ../stylegan2-ada/dataset_tool.py unpack \ + --tfrecord_dir=~/ffhq-dataset/tfrecords/ffhq --output_dir=/tmp/ffhq-unpacked +``` + +Step 3: Create ZIP archive using `dataset_tool.py` from this repository: + +```.bash +# Original 1024x1024 resolution. +python dataset_tool.py --source=/tmp/ffhq-unpacked --dest=~/datasets/ffhq.zip + +# Scaled down 256x256 resolution. +python dataset_tool.py --source=/tmp/ffhq-unpacked --dest=~/datasets/ffhq256x256.zip \ + --width=256 --height=256 +``` + +**MetFaces**: Download the [MetFaces dataset](https://github.com/NVlabs/metfaces-dataset) and create ZIP archive: + +```.bash +python dataset_tool.py --source=~/downloads/metfaces/images --dest=~/datasets/metfaces.zip +``` + +**AFHQ**: Download the [AFHQ dataset](https://github.com/clovaai/stargan-v2/blob/master/README.md#animal-faces-hq-dataset-afhq) and create ZIP archive: + +```.bash +python dataset_tool.py --source=~/downloads/afhq/train/cat --dest=~/datasets/afhqcat.zip +python dataset_tool.py --source=~/downloads/afhq/train/dog --dest=~/datasets/afhqdog.zip +python dataset_tool.py --source=~/downloads/afhq/train/wild --dest=~/datasets/afhqwild.zip +``` + +**CIFAR-10**: Download the [CIFAR-10 python version](https://www.cs.toronto.edu/~kriz/cifar.html) and convert to ZIP archive: + +```.bash +python dataset_tool.py --source=~/downloads/cifar-10-python.tar.gz --dest=~/datasets/cifar10.zip +``` + +**LSUN**: Download the desired categories from the [LSUN project page](https://www.yf.io/p/lsun/) and convert to ZIP archive: + +```.bash +python dataset_tool.py --source=~/downloads/lsun/raw/cat_lmdb --dest=~/datasets/lsuncat200k.zip \ + --transform=center-crop --width=256 --height=256 --max_images=200000 + +python dataset_tool.py --source=~/downloads/lsun/raw/car_lmdb --dest=~/datasets/lsuncar200k.zip \ + --transform=center-crop-wide --width=512 --height=384 --max_images=200000 +``` + +**BreCaHAD**: + +Step 1: Download the [BreCaHAD dataset](https://figshare.com/articles/BreCaHAD_A_Dataset_for_Breast_Cancer_Histopathological_Annotation_and_Diagnosis/7379186). + +Step 2: Extract 512x512 resolution crops using `dataset_tool.py` from the [TensorFlow version of StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada/): + +```.bash +# Using dataset_tool.py from TensorFlow version at +# https://github.com/NVlabs/stylegan2-ada/ +python dataset_tool.py extract_brecahad_crops --cropsize=512 \ + --output_dir=/tmp/brecahad-crops --brecahad_dir=~/downloads/brecahad/images +``` + +Step 3: Create ZIP archive using `dataset_tool.py` from this repository: + +```.bash +python dataset_tool.py --source=/tmp/brecahad-crops --dest=~/datasets/brecahad.zip +``` + +## Training new networks + +In its most basic form, training new networks boils down to: + +```.bash +python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1 --dry-run +python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1 +``` + +The first command is optional; it validates the arguments, prints out the training configuration, and exits. The second command kicks off the actual training. + +In this example, the results are saved to a newly created directory `~/training-runs/-mydataset-auto1`, controlled by `--outdir`. The training exports network pickles (`network-snapshot-.pkl`) and example images (`fakes.png`) at regular intervals (controlled by `--snap`). For each pickle, it also evaluates FID (controlled by `--metrics`) and logs the resulting scores in `metric-fid50k_full.jsonl` (as well as TFEvents if TensorBoard is installed). + +The name of the output directory reflects the training configuration. For example, `00000-mydataset-auto1` indicates that the *base configuration* was `auto1`, meaning that the hyperparameters were selected automatically for training on one GPU. The base configuration is controlled by `--cfg`: + +| Base config | Description +| :-------------------- | :---------- +| `auto` (default) | Automatically select reasonable defaults based on resolution and GPU count. Serves as a good starting point for new datasets but does not necessarily lead to optimal results. +| `stylegan2` | Reproduce results for StyleGAN2 config F at 1024x1024 using 1, 2, 4, or 8 GPUs. +| `paper256` | Reproduce results for FFHQ and LSUN Cat at 256x256 using 1, 2, 4, or 8 GPUs. +| `paper512` | Reproduce results for BreCaHAD and AFHQ at 512x512 using 1, 2, 4, or 8 GPUs. +| `paper1024` | Reproduce results for MetFaces at 1024x1024 using 1, 2, 4, or 8 GPUs. +| `cifar` | Reproduce results for CIFAR-10 (tuned configuration) using 1 or 2 GPUs. + +The training configuration can be further customized with additional command line options: + +* `--aug=noaug` disables ADA. +* `--cond=1` enables class-conditional training (requires a dataset with labels). +* `--mirror=1` amplifies the dataset with x-flips. Often beneficial, even with ADA. +* `--resume=ffhq1024 --snap=10` performs transfer learning from FFHQ trained at 1024x1024. +* `--resume=~/training-runs//network-snapshot-.pkl` resumes a previous training run. +* `--gamma=10` overrides R1 gamma. We recommend trying a couple of different values for each new dataset. +* `--aug=ada --target=0.7` adjusts ADA target value (default: 0.6). +* `--augpipe=blit` enables pixel blitting but disables all other augmentations. +* `--augpipe=bgcfnc` enables all available augmentations (blit, geom, color, filter, noise, cutout). + +Please refer to [`python train.py --help`](./docs/train-help.txt) for the full list. + +## Expected training time + +The total training time depends heavily on resolution, number of GPUs, dataset, desired quality, and hyperparameters. The following table lists expected wallclock times to reach different points in the training, measured in thousands of real images shown to the discriminator ("kimg"): + +| Resolution | GPUs | 1000 kimg | 25000 kimg | sec/kimg | GPU mem | CPU mem +| :--------: | :--: | :-------: | :--------: | :---------------: | :-----: | :-----: +| 128x128 | 1 | 4h 05m | 4d 06h | 12.8–13.7 | 7.2 GB | 3.9 GB +| 128x128 | 2 | 2h 06m | 2d 04h | 6.5–6.8 | 7.4 GB | 7.9 GB +| 128x128 | 4 | 1h 20m | 1d 09h | 4.1–4.6 | 4.2 GB | 16.3 GB +| 128x128 | 8 | 1h 13m | 1d 06h | 3.9–4.9 | 2.6 GB | 31.9 GB +| 256x256 | 1 | 6h 36m | 6d 21h | 21.6–24.2 | 5.0 GB | 4.5 GB +| 256x256 | 2 | 3h 27m | 3d 14h | 11.2–11.8 | 5.2 GB | 9.0 GB +| 256x256 | 4 | 1h 45m | 1d 20h | 5.6–5.9 | 5.2 GB | 17.8 GB +| 256x256 | 8 | 1h 24m | 1d 11h | 4.4–5.5 | 3.2 GB | 34.7 GB +| 512x512 | 1 | 21h 03m | 21d 22h | 72.5–74.9 | 7.6 GB | 5.0 GB +| 512x512 | 2 | 10h 59m | 11d 10h | 37.7–40.0 | 7.8 GB | 9.8 GB +| 512x512 | 4 | 5h 29m | 5d 17h | 18.7–19.1 | 7.9 GB | 17.7 GB +| 512x512 | 8 | 2h 48m | 2d 22h | 9.5–9.7 | 7.8 GB | 38.2 GB +| 1024x1024 | 1 | 1d 20h | 46d 03h | 154.3–161.6 | 8.1 GB | 5.3 GB +| 1024x1024 | 2 | 23h 09m | 24d 02h | 80.6–86.2 | 8.6 GB | 11.9 GB +| 1024x1024 | 4 | 11h 36m | 12d 02h | 40.1–40.8 | 8.4 GB | 21.9 GB +| 1024x1024 | 8 | 5h 54m | 6d 03h | 20.2–20.6 | 8.3 GB | 44.7 GB + +The above measurements were done using NVIDIA Tesla V100 GPUs with default settings (`--cfg=auto --aug=ada --metrics=fid50k_full`). "sec/kimg" shows the expected range of variation in raw training performance, as reported in `log.txt`. "GPU mem" and "CPU mem" show the highest observed memory consumption, excluding the peak at the beginning caused by `torch.backends.cudnn.benchmark`. + +In typical cases, 25000 kimg or more is needed to reach convergence, but the results are already quite reasonable around 5000 kimg. 1000 kimg is often enough for transfer learning, which tends to converge significantly faster. The following figure shows example convergence curves for different datasets as a function of wallclock time, using the same settings as above: + +![Training curves](./docs/stylegan2-ada-training-curves.png) + +Note: `--cfg=auto` serves as a reasonable first guess for the hyperparameters but it does not necessarily lead to optimal results for a given dataset. For example, `--cfg=stylegan2` yields considerably better FID for FFHQ-140k at 1024x1024 than illustrated above. We recommend trying out at least a few different values of `--gamma` for each new dataset. + +## Quality metrics + +By default, `train.py` automatically computes FID for each network pickle exported during training. We recommend inspecting `metric-fid50k_full.jsonl` (or TensorBoard) at regular intervals to monitor the training progress. When desired, the automatic computation can be disabled with `--metrics=none` to speed up the training slightly (3%–9%). + +Additional quality metrics can also be computed after the training: + +```.bash +# Previous training run: look up options automatically, save result to JSONL file. +python calc_metrics.py --metrics=pr50k3_full \ + --network=~/training-runs/00000-ffhq10k-res64-auto1/network-snapshot-000000.pkl + +# Pre-trained network pickle: specify dataset explicitly, print result to stdout. +python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq.zip --mirror=1 \ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl +``` + +The first example looks up the training configuration and performs the same operation as if `--metrics=pr50k3_full` had been specified during training. The second example downloads a pre-trained network pickle, in which case the values of `--mirror` and `--data` must be specified explicitly. + +Note that many of the metrics have a significant one-off cost when calculating them for the first time for a new dataset (up to 30min). Also note that the evaluation is done using a different random seed each time, so the results will vary if the same metric is computed multiple times. + +We employ the following metrics in the ADA paper. Execution time and GPU memory usage is reported for one NVIDIA Tesla V100 GPU at 1024x1024 resolution: + +| Metric | Time | GPU mem | Description | +| :----- | :----: | :-----: | :---------- | +| `fid50k_full` | 13 min | 1.8 GB | Fréchet inception distance[1] against the full dataset +| `kid50k_full` | 13 min | 1.8 GB | Kernel inception distance[2] against the full dataset +| `pr50k3_full` | 13 min | 4.1 GB | Precision and recall[3] againt the full dataset +| `is50k` | 13 min | 1.8 GB | Inception score[4] for CIFAR-10 + +In addition, the following metrics from the [StyleGAN](https://github.com/NVlabs/stylegan) and [StyleGAN2](https://github.com/NVlabs/stylegan2) papers are also supported: + +| Metric | Time | GPU mem | Description | +| :------------ | :----: | :-----: | :---------- | +| `fid50k` | 13 min | 1.8 GB | Fréchet inception distance against 50k real images +| `kid50k` | 13 min | 1.8 GB | Kernel inception distance against 50k real images +| `pr50k3` | 13 min | 4.1 GB | Precision and recall against 50k real images +| `ppl2_wend` | 36 min | 2.4 GB | Perceptual path length[5] in W, endpoints, full image +| `ppl_zfull` | 36 min | 2.4 GB | Perceptual path length in Z, full paths, cropped image +| `ppl_wfull` | 36 min | 2.4 GB | Perceptual path length in W, full paths, cropped image +| `ppl_zend` | 36 min | 2.4 GB | Perceptual path length in Z, endpoints, cropped image +| `ppl_wend` | 36 min | 2.4 GB | Perceptual path length in W, endpoints, cropped image + +References: +1. [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500), Heusel et al. 2017 +2. [Demystifying MMD GANs](https://arxiv.org/abs/1801.01401), Bińkowski et al. 2018 +3. [Improved Precision and Recall Metric for Assessing Generative Models](https://arxiv.org/abs/1904.06991), Kynkäänniemi et al. 2019 +4. [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498), Salimans et al. 2016 +5. [A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/abs/1812.04948), Karras et al. 2018 + +## License + +Copyright © 2021, NVIDIA Corporation. All rights reserved. + +This work is made available under the [Nvidia Source Code License](https://nvlabs.github.io/stylegan2-ada-pytorch/license.html). + +## Citation + +``` +@inproceedings{Karras2020ada, + title = {Training Generative Adversarial Networks with Limited Data}, + author = {Tero Karras and Miika Aittala and Janne Hellsten and Samuli Laine and Jaakko Lehtinen and Timo Aila}, + booktitle = {Proc. NeurIPS}, + year = {2020} +} +``` + +## Development + +This is a research reference implementation and is treated as a one-time code drop. As such, we do not accept outside code contributions in the form of pull requests. + +## Acknowledgements + +We thank David Luebke for helpful comments; Tero Kuosmanen and Sabu Nadarajan for their support with compute infrastructure; and Edgar Schönfeld for guidance on setting up unconditional BigGAN. diff --git a/stylegan2_ada_pytorch/calc_metrics.py b/stylegan2_ada_pytorch/calc_metrics.py new file mode 100755 index 0000000000000000000000000000000000000000..73877620a5bbf68693f6d6cae8cce0a02710edf8 --- /dev/null +++ b/stylegan2_ada_pytorch/calc_metrics.py @@ -0,0 +1,265 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Calculate quality metrics for previous training run or pretrained network pickle.""" + +import os +import click +import json +import tempfile +import copy +import torch +import dnnlib + +import legacy +from metrics import metric_main +from metrics import metric_utils +from torch_utils import training_stats +from torch_utils import custom_ops +from torch_utils import misc + +# ---------------------------------------------------------------------------- + + +def subprocess_fn(rank, args, temp_dir): + dnnlib.util.Logger(should_flush=True) + + # Init torch.distributed. + if args.num_gpus > 1: + init_file = os.path.abspath(os.path.join(temp_dir, ".torch_distributed_init")) + if os.name == "nt": + init_method = "file:///" + init_file.replace("\\", "/") + torch.distributed.init_process_group( + backend="gloo", + init_method=init_method, + rank=rank, + world_size=args.num_gpus, + ) + else: + init_method = f"file://{init_file}" + torch.distributed.init_process_group( + backend="nccl", + init_method=init_method, + rank=rank, + world_size=args.num_gpus, + ) + + # Init torch_utils. + sync_device = torch.device("cuda", rank) if args.num_gpus > 1 else None + training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) + if rank != 0 or not args.verbose: + custom_ops.verbosity = "none" + + # Print network summary. + device = torch.device("cuda", rank) + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + G = copy.deepcopy(args.G).eval().requires_grad_(False).to(device) + if rank == 0 and args.verbose: + z = torch.empty([1, G.z_dim], device=device) + c = torch.empty([1, G.c_dim], device=device) + misc.print_module_summary(G, [z, c]) + + # Calculate each metric. + for metric in args.metrics: + if rank == 0 and args.verbose: + print(f"Calculating {metric}...") + progress = metric_utils.ProgressMonitor(verbose=args.verbose) + result_dict = metric_main.calc_metric( + metric=metric, + G=G, + dataset_kwargs=args.dataset_kwargs, + num_gpus=args.num_gpus, + rank=rank, + device=device, + progress=progress, + ) + if rank == 0: + metric_main.report_metric( + result_dict, run_dir=args.run_dir, snapshot_pkl=args.network_pkl + ) + if rank == 0 and args.verbose: + print() + + # Done. + if rank == 0 and args.verbose: + print("Exiting...") + + +# ---------------------------------------------------------------------------- + + +class CommaSeparatedList(click.ParamType): + name = "list" + + def convert(self, value, param, ctx): + _ = param, ctx + if value is None or value.lower() == "none" or value == "": + return [] + return value.split(",") + + +# ---------------------------------------------------------------------------- + + +@click.command() +@click.pass_context +@click.option( + "network_pkl", + "--network", + help="Network pickle filename or URL", + metavar="PATH", + required=True, +) +@click.option( + "--metrics", + help='Comma-separated list or "none"', + type=CommaSeparatedList(), + default="fid50k_full", + show_default=True, +) +@click.option( + "--data", + help="Dataset to evaluate metrics against (directory or zip) [default: same as training data]", + metavar="PATH", +) +@click.option( + "--mirror", + help="Whether the dataset was augmented with x-flips during training [default: look up]", + type=bool, + metavar="BOOL", +) +@click.option( + "--gpus", + help="Number of GPUs to use", + type=int, + default=1, + metavar="INT", + show_default=True, +) +@click.option( + "--verbose", + help="Print optional information", + type=bool, + default=True, + metavar="BOOL", + show_default=True, +) +def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose): + """Calculate quality metrics for previous training run or pretrained network pickle. + + Examples: + + \b + # Previous training run: look up options automatically, save result to JSONL file. + python calc_metrics.py --metrics=pr50k3_full \\ + --network=~/training-runs/00000-ffhq10k-res64-auto1/network-snapshot-000000.pkl + + \b + # Pre-trained network pickle: specify dataset explicitly, print result to stdout. + python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq.zip --mirror=1 \\ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl + + Available metrics: + + \b + ADA paper: + fid50k_full Frechet inception distance against the full dataset. + kid50k_full Kernel inception distance against the full dataset. + pr50k3_full Precision and recall againt the full dataset. + is50k Inception score for CIFAR-10. + + \b + StyleGAN and StyleGAN2 papers: + fid50k Frechet inception distance against 50k real images. + kid50k Kernel inception distance against 50k real images. + pr50k3 Precision and recall against 50k real images. + ppl2_wend Perceptual path length in W at path endpoints against full image. + ppl_zfull Perceptual path length in Z for full paths against cropped image. + ppl_wfull Perceptual path length in W for full paths against cropped image. + ppl_zend Perceptual path length in Z at path endpoints against cropped image. + ppl_wend Perceptual path length in W at path endpoints against cropped image. + """ + dnnlib.util.Logger(should_flush=True) + + # Validate arguments. + args = dnnlib.EasyDict( + metrics=metrics, num_gpus=gpus, network_pkl=network_pkl, verbose=verbose + ) + if not all(metric_main.is_valid_metric(metric) for metric in args.metrics): + ctx.fail( + "\n".join( + ["--metrics can only contain the following values:"] + + metric_main.list_valid_metrics() + ) + ) + if not args.num_gpus >= 1: + ctx.fail("--gpus must be at least 1") + + # Load network. + if not dnnlib.util.is_url(network_pkl, allow_file_urls=True) and not os.path.isfile( + network_pkl + ): + ctx.fail("--network must point to a file or URL") + if args.verbose: + print(f'Loading network from "{network_pkl}"...') + with dnnlib.util.open_url(network_pkl, verbose=args.verbose) as f: + network_dict = legacy.load_network_pkl(f) + args.G = network_dict["G_ema"] # subclass of torch.nn.Module + + # Initialize dataset options. + if data is not None: + args.dataset_kwargs = dnnlib.EasyDict( + class_name="training.dataset.ImageFolderDataset", path=data + ) + elif network_dict["training_set_kwargs"] is not None: + args.dataset_kwargs = dnnlib.EasyDict(network_dict["training_set_kwargs"]) + else: + ctx.fail("Could not look up dataset options; please specify --data") + + # Finalize dataset options. + args.dataset_kwargs.resolution = args.G.img_resolution + args.dataset_kwargs.use_labels = args.G.c_dim != 0 + if mirror is not None: + args.dataset_kwargs.xflip = mirror + + # Print dataset options. + if args.verbose: + print("Dataset options:") + print(json.dumps(args.dataset_kwargs, indent=2)) + + # Locate run dir. + args.run_dir = None + if os.path.isfile(network_pkl): + pkl_dir = os.path.dirname(network_pkl) + if os.path.isfile(os.path.join(pkl_dir, "training_options.json")): + args.run_dir = pkl_dir + + # Launch processes. + if args.verbose: + print("Launching processes...") + torch.multiprocessing.set_start_method("spawn") + with tempfile.TemporaryDirectory() as temp_dir: + if args.num_gpus == 1: + subprocess_fn(rank=0, args=args, temp_dir=temp_dir) + else: + torch.multiprocessing.spawn( + fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus + ) + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + calc_metrics() # pylint: disable=no-value-for-parameter + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/config_files/COCO_Stuff/IC-GAN/icgan_stylegan_res128.json b/stylegan2_ada_pytorch/config_files/COCO_Stuff/IC-GAN/icgan_stylegan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..2a75530e9955ecb16a0a9f20a82ee3bb1e46c27b --- /dev/null +++ b/stylegan2_ada_pytorch/config_files/COCO_Stuff/IC-GAN/icgan_stylegan_res128.json @@ -0,0 +1,20 @@ +{ + "exp_name": "icgan_stylegan2_coco_res128", + "data": "COCO128_xy.hdf5", + "root_feats": "COCO128_feats_selfsupervised_resnet50.hdf5", + "root_nns": "COCO128_feats_selfsupervised_resnet50_nn_k5.hdf5", + "gpus": 2, + "slurm": false, + "aug": "noaug", + "lrate": 0.0025, + "gamma": 0.05, + "kimg": 100000, + "es_patience": 3738850, + "instance_cond": true, + "mirror": true, + "resolution": 128, + "feature_augmentation": true, + "feature_extractor": "selfsupervised", + "k_nn": 5, + "run_setup": "local_debug" +} \ No newline at end of file diff --git a/stylegan2_ada_pytorch/config_files/COCO_Stuff/IC-GAN/icgan_stylegan_res256.json b/stylegan2_ada_pytorch/config_files/COCO_Stuff/IC-GAN/icgan_stylegan_res256.json new file mode 100644 index 0000000000000000000000000000000000000000..23a84402c0b2400c63b1e2994b06a05fee969013 --- /dev/null +++ b/stylegan2_ada_pytorch/config_files/COCO_Stuff/IC-GAN/icgan_stylegan_res256.json @@ -0,0 +1,20 @@ +{ + "exp_name": "icgan_stylegan2_coco_res256", + "data": "COCO256_xy.hdf5", + "root_feats": "COCO256_feats_selfsupervised_resnet50.hdf5", + "root_nns": "COCO256_feats_selfsupervised_resnet50_nn_k5.hdf5", + "gpus": 2, + "slurm": true, + "aug": "noaug", + "lrate": 0.003, + "gamma": 0.5, + "kimg": 100000, + "es_patience": 3738850, + "instance_cond": true, + "mirror": true, + "resolution": 256, + "feature_augmentation": true, + "feature_extractor": "selfsupervised", + "k_nn": 5, + "run_setup": "local_debug" +} \ No newline at end of file diff --git a/stylegan2_ada_pytorch/config_files/COCO_Stuff/StyleGAN2/unconditional_stylegan_res128.json b/stylegan2_ada_pytorch/config_files/COCO_Stuff/StyleGAN2/unconditional_stylegan_res128.json new file mode 100644 index 0000000000000000000000000000000000000000..a15d469be9472e5b7a7b33558f573cd05a936249 --- /dev/null +++ b/stylegan2_ada_pytorch/config_files/COCO_Stuff/StyleGAN2/unconditional_stylegan_res128.json @@ -0,0 +1,16 @@ +{ + "exp_name": "unconditional_stylegan2_coco_res128", + "data": "COCO128_xy.hdf5", + "gpus": 2, + "slurm": true, + "aug": "noaug", + "lrate": 0.0025, + "gamma": 0.05, + "kimg": 100000, + "es_patience": 3738850, + "mirror": true, + "resolution": 128, + "class_cond": false, + "instance_cond": false, + "run_setup": "local_debug" +} \ No newline at end of file diff --git a/stylegan2_ada_pytorch/config_files/COCO_Stuff/StyleGAN2/unconditional_stylegan_res256.json b/stylegan2_ada_pytorch/config_files/COCO_Stuff/StyleGAN2/unconditional_stylegan_res256.json new file mode 100644 index 0000000000000000000000000000000000000000..57910afcaf1c89b38d69899a603e8d4402108840 --- /dev/null +++ b/stylegan2_ada_pytorch/config_files/COCO_Stuff/StyleGAN2/unconditional_stylegan_res256.json @@ -0,0 +1,16 @@ +{ + "exp_name": "unconditional_stylegan2_coco_res256", + "data": "COCO256_xy.hdf5", + "gpus": 2, + "slurm": true, + "aug": "noaug", + "lrate": 0.002, + "gamma": 0.2, + "kimg": 100000, + "es_patience": 3738850, + "mirror": true, + "resolution": 256, + "class_cond": false, + "instance_cond": false, + "run_setup": "local_debug" +} \ No newline at end of file diff --git a/stylegan2_ada_pytorch/dataset_tool.py b/stylegan2_ada_pytorch/dataset_tool.py new file mode 100755 index 0000000000000000000000000000000000000000..a4695115d4bf3478bd67e9758048ab03a19cbe4c --- /dev/null +++ b/stylegan2_ada_pytorch/dataset_tool.py @@ -0,0 +1,559 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# All contributions by NVIDIA CORPORATION: +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import functools +import io +import json +import os +import pickle +import sys +import tarfile +import gzip +import zipfile +from pathlib import Path +from typing import Callable, Optional, Tuple, Union + +import click +import numpy as np +import PIL.Image +from tqdm import tqdm +import h5py as h5 + +# ---------------------------------------------------------------------------- + + +def error(msg): + print("Error: " + msg) + sys.exit(1) + + +# ---------------------------------------------------------------------------- + + +def maybe_min(a: int, b: Optional[int]) -> int: + if b is not None: + return min(a, b) + return a + + +# ---------------------------------------------------------------------------- + + +def file_ext(name: Union[str, Path]) -> str: + return str(name).split(".")[-1] + + +# ---------------------------------------------------------------------------- + + +def is_image_ext(fname: Union[str, Path]) -> bool: + ext = file_ext(fname).lower() + return f".{ext}" in PIL.Image.EXTENSION # type: ignore + + +# ---------------------------------------------------------------------------- + + +def open_image_folder(source_dir, *, max_images: Optional[int]): + input_images = [ + str(f) + for f in sorted(Path(source_dir).rglob("*")) + if is_image_ext(f) and os.path.isfile(f) + ] + + # Load labels. + labels = {} + meta_fname = os.path.join(source_dir, "dataset.json") + if os.path.isfile(meta_fname): + with open(meta_fname, "r") as file: + labels = json.load(file)["labels"] + if labels is not None: + labels = {x[0]: x[1] for x in labels} + else: + labels = {} + + max_idx = maybe_min(len(input_images), max_images) + + def iterate_images(): + for idx, fname in enumerate(input_images): + arch_fname = os.path.relpath(fname, source_dir) + arch_fname = arch_fname.replace("\\", "/") + img = np.array(PIL.Image.open(fname).convert("RGB")) + yield dict(img=img, label=labels.get(arch_fname)) + if idx >= max_idx - 1: + break + + return max_idx, iterate_images() + + +# ---------------------------------------------------------------------------- + + +def open_image_zip(source, *, max_images: Optional[int]): + with zipfile.ZipFile(source, mode="r") as z: + input_images = [str(f) for f in sorted(z.namelist()) if is_image_ext(f)] + + # Load labels. + labels = {} + if "dataset.json" in z.namelist(): + with z.open("dataset.json", "r") as file: + labels = json.load(file)["labels"] + if labels is not None: + labels = {x[0]: x[1] for x in labels} + else: + labels = {} + + max_idx = maybe_min(len(input_images), max_images) + + def iterate_images(): + with zipfile.ZipFile(source, mode="r") as z: + for idx, fname in enumerate(input_images): + with z.open(fname, "r") as file: + img = PIL.Image.open(file) # type: ignore + img = np.array(img) + yield dict(img=img, label=labels.get(fname)) + if idx >= max_idx - 1: + break + + return max_idx, iterate_images() + + +# ---------------------------------------------------------------------------- + + +def open_image_hdf5(source, *, max_images: Optional[int]): + with h5.File(source, "r") as f: + all_imgs = f["imgs"][:] + all_imgs = all_imgs.transpose(0, 2, 3, 1) + all_labels = f["labels"][:] + all_labels = all_labels.astype("int32") + + max_idx = len(all_imgs) + print("max images is ", max_idx) + + def iterate_images(): + for idx, img in enumerate(all_imgs): + yield dict(img=img, label=all_labels[idx]) + if idx >= max_idx - 1: + break + + return max_idx, iterate_images() + + +# ---------------------------------------------------------------------------- + + +def open_lmdb(lmdb_dir: str, *, max_images: Optional[int]): + import cv2 # pip install opencv-python + import lmdb # pip install lmdb # pylint: disable=import-error + + with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn: + max_idx = maybe_min(txn.stat()["entries"], max_images) + + def iterate_images(): + with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn: + for idx, (_key, value) in enumerate(txn.cursor()): + try: + try: + img = cv2.imdecode(np.frombuffer(value, dtype=np.uint8), 1) + if img is None: + raise IOError("cv2.imdecode failed") + img = img[:, :, ::-1] # BGR => RGB + except IOError: + img = np.array(PIL.Image.open(io.BytesIO(value))) + yield dict(img=img, label=None) + if idx >= max_idx - 1: + break + except: + print(sys.exc_info()[1]) + + return max_idx, iterate_images() + + +# ---------------------------------------------------------------------------- + + +def open_cifar10(tarball: str, *, max_images: Optional[int]): + images = [] + labels = [] + + with tarfile.open(tarball, "r:gz") as tar: + for batch in range(1, 6): + member = tar.getmember(f"cifar-10-batches-py/data_batch_{batch}") + with tar.extractfile(member) as file: + data = pickle.load(file, encoding="latin1") + images.append(data["data"].reshape(-1, 3, 32, 32)) + labels.append(data["labels"]) + + images = np.concatenate(images) + labels = np.concatenate(labels) + images = images.transpose([0, 2, 3, 1]) # NCHW -> NHWC + assert images.shape == (50000, 32, 32, 3) and images.dtype == np.uint8 + assert labels.shape == (50000,) and labels.dtype in [np.int32, np.int64] + assert np.min(images) == 0 and np.max(images) == 255 + assert np.min(labels) == 0 and np.max(labels) == 9 + + max_idx = maybe_min(len(images), max_images) + + def iterate_images(): + for idx, img in enumerate(images): + yield dict(img=img, label=int(labels[idx])) + if idx >= max_idx - 1: + break + + return max_idx, iterate_images() + + +# ---------------------------------------------------------------------------- + + +def open_mnist(images_gz: str, *, max_images: Optional[int]): + labels_gz = images_gz.replace("-images-idx3-ubyte.gz", "-labels-idx1-ubyte.gz") + assert labels_gz != images_gz + images = [] + labels = [] + + with gzip.open(images_gz, "rb") as f: + images = np.frombuffer(f.read(), np.uint8, offset=16) + with gzip.open(labels_gz, "rb") as f: + labels = np.frombuffer(f.read(), np.uint8, offset=8) + + images = images.reshape(-1, 28, 28) + images = np.pad(images, [(0, 0), (2, 2), (2, 2)], "constant", constant_values=0) + assert images.shape == (60000, 32, 32) and images.dtype == np.uint8 + assert labels.shape == (60000,) and labels.dtype == np.uint8 + assert np.min(images) == 0 and np.max(images) == 255 + assert np.min(labels) == 0 and np.max(labels) == 9 + + max_idx = maybe_min(len(images), max_images) + + def iterate_images(): + for idx, img in enumerate(images): + yield dict(img=img, label=int(labels[idx])) + if idx >= max_idx - 1: + break + + return max_idx, iterate_images() + + +# ---------------------------------------------------------------------------- + + +def make_transform( + transform: Optional[str], + output_width: Optional[int], + output_height: Optional[int], + resize_filter: str, +) -> Callable[[np.ndarray], Optional[np.ndarray]]: + resample = {"box": PIL.Image.BOX, "lanczos": PIL.Image.LANCZOS}[resize_filter] + + def scale(width, height, img): + w = img.shape[1] + h = img.shape[0] + if width == w and height == h: + return img + img = PIL.Image.fromarray(img) + ww = width if width is not None else w + hh = height if height is not None else h + img = img.resize((ww, hh), resample) + return np.array(img) + + def center_crop(width, height, img): + crop = np.min(img.shape[:2]) + img = img[ + (img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, + (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2, + ] + img = PIL.Image.fromarray(img, "RGB") + img = img.resize((width, height), resample) + return np.array(img) + + def center_crop_wide(width, height, img): + ch = int(np.round(width * img.shape[0] / img.shape[1])) + if img.shape[1] < width or ch < height: + return None + + img = img[(img.shape[0] - ch) // 2 : (img.shape[0] + ch) // 2] + img = PIL.Image.fromarray(img, "RGB") + img = img.resize((width, height), resample) + img = np.array(img) + + canvas = np.zeros([width, width, 3], dtype=np.uint8) + canvas[(width - height) // 2 : (width + height) // 2, :] = img + return canvas + + if transform is None: + return functools.partial(scale, output_width, output_height) + if transform == "center-crop": + if (output_width is None) or (output_height is None): + error( + "must specify --width and --height when using " + + transform + + "transform" + ) + return functools.partial(center_crop, output_width, output_height) + if transform == "center-crop-wide": + if (output_width is None) or (output_height is None): + error( + "must specify --width and --height when using " + + transform + + " transform" + ) + return functools.partial(center_crop_wide, output_width, output_height) + assert False, "unknown transform" + + +# ---------------------------------------------------------------------------- + + +def open_dataset(source, *, max_images: Optional[int]): + if os.path.isdir(source): + if source.rstrip("/").endswith("_lmdb"): + return open_lmdb(source, max_images=max_images) + else: + return open_image_folder(source, max_images=max_images) + elif os.path.isfile(source): + if source.rstrip("/").endswith(".hdf5"): + return open_image_hdf5(source, max_images=max_images) + elif os.path.basename(source) == "cifar-10-python.tar.gz": + return open_cifar10(source, max_images=max_images) + elif os.path.basename(source) == "train-images-idx3-ubyte.gz": + return open_mnist(source, max_images=max_images) + elif file_ext(source) == "zip": + return open_image_zip(source, max_images=max_images) + else: + assert False, "unknown archive type" + else: + error(f"Missing input file or directory: {source}") + + +# ---------------------------------------------------------------------------- + + +def open_dest( + dest: str +) -> Tuple[str, Callable[[str, Union[bytes, str]], None], Callable[[], None]]: + dest_ext = file_ext(dest) + + if dest_ext == "zip": + if os.path.dirname(dest) != "": + os.makedirs(os.path.dirname(dest), exist_ok=True) + zf = zipfile.ZipFile(file=dest, mode="w", compression=zipfile.ZIP_STORED) + + def zip_write_bytes(fname: str, data: Union[bytes, str]): + zf.writestr(fname, data) + + return "", zip_write_bytes, zf.close + else: + # If the output folder already exists, check that is is + # empty. + # + # Note: creating the output directory is not strictly + # necessary as folder_write_bytes() also mkdirs, but it's better + # to give an error message earlier in case the dest folder + # somehow cannot be created. + if os.path.isdir(dest) and len(os.listdir(dest)) != 0: + error("--dest folder must be empty") + os.makedirs(dest, exist_ok=True) + + def folder_write_bytes(fname: str, data: Union[bytes, str]): + os.makedirs(os.path.dirname(fname), exist_ok=True) + with open(fname, "wb") as fout: + if isinstance(data, str): + data = data.encode("utf8") + fout.write(data) + + return dest, folder_write_bytes, lambda: None + + +# ---------------------------------------------------------------------------- + + +@click.command() +@click.pass_context +@click.option( + "--source", + help="Directory or archive name for input dataset", + required=True, + metavar="PATH", +) +@click.option( + "--dest", + help="Output directory or archive name for output dataset", + required=True, + metavar="PATH", +) +@click.option( + "--max-images", help="Output only up to `max-images` images", type=int, default=None +) +@click.option( + "--resize-filter", + help="Filter to use when resizing images for output resolution", + type=click.Choice(["box", "lanczos"]), + default="lanczos", + show_default=True, +) +@click.option( + "--transform", + help="Input crop/resize mode", + type=click.Choice(["center-crop", "center-crop-wide"]), +) +@click.option("--width", help="Output width", type=int) +@click.option("--height", help="Output height", type=int) +def convert_dataset( + ctx: click.Context, + source: str, + dest: str, + max_images: Optional[int], + transform: Optional[str], + resize_filter: str, + width: Optional[int], + height: Optional[int], +): + """Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch. + + The input dataset format is guessed from the --source argument: + + \b + --source *_lmdb/ Load LSUN dataset + --source cifar-10-python.tar.gz Load CIFAR-10 dataset + --source train-images-idx3-ubyte.gz Load MNIST dataset + --source path/ Recursively load all images from path/ + --source dataset.zip Recursively load all images from dataset.zip + + Specifying the output format and path: + + \b + --dest /path/to/dir Save output files under /path/to/dir + --dest /path/to/dataset.zip Save output files into /path/to/dataset.zip + + The output dataset format can be either an image folder or an uncompressed zip archive. + Zip archives makes it easier to move datasets around file servers and clusters, and may + offer better training performance on network file systems. + + Images within the dataset archive will be stored as uncompressed PNG. + Uncompresed PNGs can be efficiently decoded in the training loop. + + Class labels are stored in a file called 'dataset.json' that is stored at the + dataset root folder. This file has the following structure: + + \b + { + "labels": [ + ["00000/img00000000.png",6], + ["00000/img00000001.png",9], + ... repeated for every image in the datase + ["00049/img00049999.png",1] + ] + } + + If the 'dataset.json' file cannot be found, the dataset is interpreted as + not containing class labels. + + Image scale/crop and resolution requirements: + + Output images must be square-shaped and they must all have the same power-of-two + dimensions. + + To scale arbitrary input image size to a specific width and height, use the + --width and --height options. Output resolution will be either the original + input resolution (if --width/--height was not specified) or the one specified with + --width/height. + + Use the --transform=center-crop or --transform=center-crop-wide options to apply a + center crop transform on the input image. These options should be used with the + --width and --height options. For example: + + \b + python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \\ + --transform=center-crop-wide --width 512 --height=384 + """ + + PIL.Image.init() # type: ignore + + if dest == "": + ctx.fail("--dest output filename or directory must not be an empty string") + + num_files, input_iter = open_dataset(source, max_images=max_images) + archive_root_dir, save_bytes, close_dest = open_dest(dest) + + transform_image = make_transform(transform, width, height, resize_filter) + + dataset_attrs = None + + labels = [] + for idx, image in tqdm(enumerate(input_iter), total=num_files): + idx_str = f"{idx:08d}" + archive_fname = f"{idx_str[:5]}/img{idx_str}.png" + + # Apply crop and resize. + img = transform_image(image["img"]) + + # Transform may drop images. + if img is None: + continue + + # Error check to require uniform image attributes across + # the whole dataset. + channels = img.shape[2] if img.ndim == 3 else 1 + cur_image_attrs = { + "width": img.shape[1], + "height": img.shape[0], + "channels": channels, + } + if dataset_attrs is None: + dataset_attrs = cur_image_attrs + width = dataset_attrs["width"] + height = dataset_attrs["height"] + if width != height: + error( + f"Image dimensions after scale and crop are required to be square. Got {width}x{height}" + ) + if dataset_attrs["channels"] not in [1, 3]: + error("Input images must be stored as RGB or grayscale") + if width != 2 ** int(np.floor(np.log2(width))): + error( + "Image width/height after scale and crop are required to be power-of-two" + ) + elif dataset_attrs != cur_image_attrs: + err = [ + f" dataset {k}/cur image {k}: {dataset_attrs[k]}/{cur_image_attrs[k]}" + for k in dataset_attrs.keys() + ] + error( + f"Image {archive_fname} attributes must be equal across all images of the dataset. Got:\n" + + "\n".join(err) + ) + + # Save the image as an uncompressed PNG. + img = PIL.Image.fromarray(img, {1: "L", 3: "RGB"}[channels]) + image_bits = io.BytesIO() + img.save(image_bits, format="png", compress_level=0, optimize=False) + save_bytes( + os.path.join(archive_root_dir, archive_fname), image_bits.getbuffer() + ) + labels.append( + [archive_fname, image["label"]] if image["label"] is not None else None + ) + + metadata = {"labels": labels if all(x is not None for x in labels) else None} + save_bytes(os.path.join(archive_root_dir, "dataset.json"), json.dumps(metadata)) + close_dest() + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + convert_dataset() # pylint: disable=no-value-for-parameter diff --git a/stylegan2_ada_pytorch/dnnlib/__init__.py b/stylegan2_ada_pytorch/dnnlib/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..2f08cf36f11f9b0fd94c1b7caeadf69b98375b04 --- /dev/null +++ b/stylegan2_ada_pytorch/dnnlib/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +from .util import EasyDict, make_cache_dir_path diff --git a/stylegan2_ada_pytorch/dnnlib/util.py b/stylegan2_ada_pytorch/dnnlib/util.py new file mode 100755 index 0000000000000000000000000000000000000000..a554e94384176f0f1e33dd0bd15d3813feeabd8a --- /dev/null +++ b/stylegan2_ada_pytorch/dnnlib/util.py @@ -0,0 +1,516 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Miscellaneous utility classes and functions.""" + +import ctypes +import fnmatch +import importlib +import inspect +import numpy as np +import os +import shutil +import sys +import types +import io +import pickle +import re +import requests +import html +import hashlib +import glob +import tempfile +import urllib +import urllib.request +import uuid + +from distutils.util import strtobool +from typing import Any, List, Tuple, Union + + +# Util classes +# ------------------------------------------------------------------------------------------ + + +class EasyDict(dict): + """Convenience class that behaves like a dict but allows access with the attribute syntax.""" + + def __getattr__(self, name: str) -> Any: + try: + return self[name] + except KeyError: + raise AttributeError(name) + + def __setattr__(self, name: str, value: Any) -> None: + self[name] = value + + def __delattr__(self, name: str) -> None: + del self[name] + + +class Logger(object): + """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" + + def __init__( + self, file_name: str = None, file_mode: str = "w", should_flush: bool = True + ): + self.file = None + + if file_name is not None: + self.file = open(file_name, file_mode) + + self.should_flush = should_flush + self.stdout = sys.stdout + self.stderr = sys.stderr + + sys.stdout = self + sys.stderr = self + + def __enter__(self) -> "Logger": + return self + + def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: + self.close() + + def write(self, text: Union[str, bytes]) -> None: + """Write text to stdout (and a file) and optionally flush.""" + if isinstance(text, bytes): + text = text.decode() + if ( + len(text) == 0 + ): # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash + return + + if self.file is not None: + self.file.write(text) + + self.stdout.write(text) + + if self.should_flush: + self.flush() + + def flush(self) -> None: + """Flush written text to both stdout and a file, if open.""" + if self.file is not None: + self.file.flush() + + self.stdout.flush() + + def close(self) -> None: + """Flush, close possible files, and remove stdout/stderr mirroring.""" + self.flush() + + # if using multiple loggers, prevent closing in wrong order + if sys.stdout is self: + sys.stdout = self.stdout + if sys.stderr is self: + sys.stderr = self.stderr + + if self.file is not None: + self.file.close() + self.file = None + + +# Cache directories +# ------------------------------------------------------------------------------------------ + +_dnnlib_cache_dir = None + + +def set_cache_dir(path: str) -> None: + global _dnnlib_cache_dir + _dnnlib_cache_dir = path + + +def make_cache_dir_path(*paths: str) -> str: + if _dnnlib_cache_dir is not None: + return os.path.join(_dnnlib_cache_dir, *paths) + if "DNNLIB_CACHE_DIR" in os.environ: + return os.path.join(os.environ["DNNLIB_CACHE_DIR"], *paths) + if "HOME" in os.environ: + return os.path.join(os.environ["HOME"], ".cache", "dnnlib", *paths) + if "USERPROFILE" in os.environ: + return os.path.join(os.environ["USERPROFILE"], ".cache", "dnnlib", *paths) + return os.path.join(tempfile.gettempdir(), ".cache", "dnnlib", *paths) + + +# Small util functions +# ------------------------------------------------------------------------------------------ + + +def format_time(seconds: Union[int, float]) -> str: + """Convert the seconds to human readable string with days, hours, minutes and seconds.""" + s = int(np.rint(seconds)) + + if s < 60: + return "{0}s".format(s) + elif s < 60 * 60: + return "{0}m {1:02}s".format(s // 60, s % 60) + elif s < 24 * 60 * 60: + return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) + else: + return "{0}d {1:02}h {2:02}m".format( + s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60 + ) + + +def ask_yes_no(question: str) -> bool: + """Ask the user the question until the user inputs a valid answer.""" + while True: + try: + print("{0} [y/n]".format(question)) + return strtobool(input().lower()) + except ValueError: + pass + + +def tuple_product(t: Tuple) -> Any: + """Calculate the product of the tuple elements.""" + result = 1 + + for v in t: + result *= v + + return result + + +_str_to_ctype = { + "uint8": ctypes.c_ubyte, + "uint16": ctypes.c_uint16, + "uint32": ctypes.c_uint32, + "uint64": ctypes.c_uint64, + "int8": ctypes.c_byte, + "int16": ctypes.c_int16, + "int32": ctypes.c_int32, + "int64": ctypes.c_int64, + "float32": ctypes.c_float, + "float64": ctypes.c_double, +} + + +def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: + """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" + type_str = None + + if isinstance(type_obj, str): + type_str = type_obj + elif hasattr(type_obj, "__name__"): + type_str = type_obj.__name__ + elif hasattr(type_obj, "name"): + type_str = type_obj.name + else: + raise RuntimeError("Cannot infer type name from input") + + assert type_str in _str_to_ctype.keys() + + my_dtype = np.dtype(type_str) + my_ctype = _str_to_ctype[type_str] + + assert my_dtype.itemsize == ctypes.sizeof(my_ctype) + + return my_dtype, my_ctype + + +def is_pickleable(obj: Any) -> bool: + try: + with io.BytesIO() as stream: + pickle.dump(obj, stream) + return True + except: + return False + + +# Functionality to import modules/objects by name, and call functions by name +# ------------------------------------------------------------------------------------------ + + +def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: + """Searches for the underlying module behind the name to some python object. + Returns the module and the object name (original name with module part removed).""" + + # allow convenience shorthands, substitute them by full names + obj_name = re.sub("^np.", "numpy.", obj_name) + obj_name = re.sub("^tf.", "tensorflow.", obj_name) + + # list alternatives for (module_name, local_obj_name) + parts = obj_name.split(".") + name_pairs = [ + (".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1) + ] + + # try each alternative in turn + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + return module, local_obj_name + except: + pass + + # maybe some of the modules themselves contain errors? + for module_name, _local_obj_name in name_pairs: + try: + importlib.import_module(module_name) # may raise ImportError + except ImportError: + if not str(sys.exc_info()[1]).startswith( + "No module named '" + module_name + "'" + ): + raise + + # maybe the requested attribute is missing? + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + except ImportError: + pass + + # we are out of luck, but we have no idea why + raise ImportError(obj_name) + + +def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: + """Traverses the object name and returns the last (rightmost) python object.""" + if obj_name == "": + return module + obj = module + for part in obj_name.split("."): + obj = getattr(obj, part) + return obj + + +def get_obj_by_name(name: str) -> Any: + """Finds the python object with the given name.""" + module, obj_name = get_module_from_obj_name(name) + return get_obj_from_module(module, obj_name) + + +def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: + """Finds the python object with the given name and calls it as a function.""" + assert func_name is not None + func_obj = get_obj_by_name(func_name) + assert callable(func_obj) + return func_obj(*args, **kwargs) + + +def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: + """Finds the python class with the given name and constructs it with the given arguments.""" + return call_func_by_name(*args, func_name=class_name, **kwargs) + + +def get_module_dir_by_obj_name(obj_name: str) -> str: + """Get the directory path of the module containing the given object name.""" + module, _ = get_module_from_obj_name(obj_name) + return os.path.dirname(inspect.getfile(module)) + + +def is_top_level_function(obj: Any) -> bool: + """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" + return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ + + +def get_top_level_function_name(obj: Any) -> str: + """Return the fully-qualified name of a top-level function.""" + assert is_top_level_function(obj) + module = obj.__module__ + if module == "__main__": + module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] + return module + "." + obj.__name__ + + +# File system helpers +# ------------------------------------------------------------------------------------------ + + +def list_dir_recursively_with_ignore( + dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False +) -> List[Tuple[str, str]]: + """List all files recursively in a given directory while ignoring given file and directory names. + Returns list of tuples containing both absolute and relative paths.""" + assert os.path.isdir(dir_path) + base_name = os.path.basename(os.path.normpath(dir_path)) + + if ignores is None: + ignores = [] + + result = [] + + for root, dirs, files in os.walk(dir_path, topdown=True): + for ignore_ in ignores: + dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] + + # dirs need to be edited in-place + for d in dirs_to_remove: + dirs.remove(d) + + files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] + + absolute_paths = [os.path.join(root, f) for f in files] + relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] + + if add_base_to_relative: + relative_paths = [os.path.join(base_name, p) for p in relative_paths] + + assert len(absolute_paths) == len(relative_paths) + result += zip(absolute_paths, relative_paths) + + return result + + +def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: + """Takes in a list of tuples of (src, dst) paths and copies files. + Will create all necessary directories.""" + for file in files: + target_dir_name = os.path.dirname(file[1]) + + # will create all intermediate-level directories + if not os.path.exists(target_dir_name): + os.makedirs(target_dir_name) + + shutil.copyfile(file[0], file[1]) + + +# URL helpers +# ------------------------------------------------------------------------------------------ + + +def is_url(obj: Any, allow_file_urls: bool = False) -> bool: + """Determine whether the given object is a valid URL string.""" + if not isinstance(obj, str) or not "://" in obj: + return False + if allow_file_urls and obj.startswith("file://"): + return True + try: + res = requests.compat.urlparse(obj) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + except: + return False + return True + + +def open_url( + url: str, + cache_dir: str = None, + num_attempts: int = 10, + verbose: bool = True, + return_filename: bool = False, + cache: bool = True, +) -> Any: + """Download the given URL and return a binary-mode file object to access the data.""" + assert num_attempts >= 1 + assert not (return_filename and (not cache)) + + # Doesn't look like an URL scheme so interpret it as a local filename. + if not re.match("^[a-z]+://", url): + return url if return_filename else open(url, "rb") + + # Handle file URLs. This code handles unusual file:// patterns that + # arise on Windows: + # + # file:///c:/foo.txt + # + # which would translate to a local '/c:/foo.txt' filename that's + # invalid. Drop the forward slash for such pathnames. + # + # If you touch this code path, you should test it on both Linux and + # Windows. + # + # Some internet resources suggest using urllib.request.url2pathname() but + # but that converts forward slashes to backslashes and this causes + # its own set of problems. + if url.startswith("file://"): + filename = urllib.parse.urlparse(url).path + if re.match(r"^/[a-zA-Z]:", filename): + filename = filename[1:] + return filename if return_filename else open(filename, "rb") + + assert is_url(url) + + # Lookup from cache. + if cache_dir is None: + cache_dir = make_cache_dir_path("downloads") + + url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() + if cache: + cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) + if len(cache_files) == 1: + filename = cache_files[0] + return filename if return_filename else open(filename, "rb") + + # Download. + url_name = None + url_data = None + with requests.Session() as session: + if verbose: + print("Downloading %s ..." % url, end="", flush=True) + for attempts_left in reversed(range(num_attempts)): + try: + with session.get(url) as res: + res.raise_for_status() + if len(res.content) == 0: + raise IOError("No data received") + + if len(res.content) < 8192: + content_str = res.content.decode("utf-8") + if "download_warning" in res.headers.get("Set-Cookie", ""): + links = [ + html.unescape(link) + for link in content_str.split('"') + if "export=download" in link + ] + if len(links) == 1: + url = requests.compat.urljoin(url, links[0]) + raise IOError("Google Drive virus checker nag") + if "Google Drive - Quota exceeded" in content_str: + raise IOError( + "Google Drive download quota exceeded -- please try again later" + ) + + match = re.search( + r'filename="([^"]*)"', + res.headers.get("Content-Disposition", ""), + ) + url_name = match[1] if match else url + url_data = res.content + if verbose: + print(" done") + break + except KeyboardInterrupt: + raise + except: + if not attempts_left: + if verbose: + print(" failed") + raise + if verbose: + print(".", end="", flush=True) + + # Save to cache. + if cache: + safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) + cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) + temp_file = os.path.join( + cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name + ) + os.makedirs(cache_dir, exist_ok=True) + with open(temp_file, "wb") as f: + f.write(url_data) + os.replace(temp_file, cache_file) # atomic + if return_filename: + return cache_file + + # Return data as file object. + assert not return_filename + return io.BytesIO(url_data) diff --git a/stylegan2_ada_pytorch/docker_run.sh b/stylegan2_ada_pytorch/docker_run.sh new file mode 100755 index 0000000000000000000000000000000000000000..4018ca24d57388b9481ab0953df306814879c2ae --- /dev/null +++ b/stylegan2_ada_pytorch/docker_run.sh @@ -0,0 +1,41 @@ +#!/bin/bash + +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +set -e + +# Wrapper script for setting up `docker run` to properly +# cache downloaded files, custom extension builds and +# mount the source directory into the container and make it +# run as non-root user. +# +# Use it like: +# +# ./docker_run.sh python generate.py --help +# +# To override the default `stylegan2ada:latest` image, run: +# +# IMAGE=my_image:v1.0 ./docker_run.sh python generate.py --help +# + +rest=$@ + +IMAGE="${IMAGE:-sg2ada:latest}" + +CONTAINER_ID=$(docker inspect --format="{{.Id}}" ${IMAGE} 2> /dev/null) +if [[ "${CONTAINER_ID}" ]]; then + docker run --shm-size=2g --gpus all -it --rm -v `pwd`:/scratch --user $(id -u):$(id -g) \ + --workdir=/scratch -e HOME=/scratch $IMAGE $@ +else + echo "Unknown container image: ${IMAGE}" + exit 1 +fi diff --git a/stylegan2_ada_pytorch/docs/dataset-tool-help.txt b/stylegan2_ada_pytorch/docs/dataset-tool-help.txt new file mode 100755 index 0000000000000000000000000000000000000000..cf58a7c76def9b104caf9a4e6a2a018dd7a93bc2 --- /dev/null +++ b/stylegan2_ada_pytorch/docs/dataset-tool-help.txt @@ -0,0 +1,50 @@ +Usage: dataset_tool.py [OPTIONS] + + Convert an image dataset into a dataset archive usable with StyleGAN2 ADA + PyTorch. + + The input dataset format is guessed from the --source argument: + + --source *_lmdb/ - Load LSUN dataset + --source cifar-10-python.tar.gz - Load CIFAR-10 dataset + --source path/ - Recursively load all images from path/ + --source dataset.zip - Recursively load all images from dataset.zip + + The output dataset format can be either an image folder or a zip archive. + Specifying the output format and path: + + --dest /path/to/dir - Save output files under /path/to/dir + --dest /path/to/dataset.zip - Save output files into /path/to/dataset.zip archive + + Images within the dataset archive will be stored as uncompressed PNG. + + Image scale/crop and resolution requirements: + + Output images must be square-shaped and they must all have the same power- + of-two dimensions. + + To scale arbitrary input image size to a specific width and height, use + the --width and --height options. Output resolution will be either the + original input resolution (if --width/--height was not specified) or the + one specified with --width/height. + + Use the --transform=center-crop or --transform=center-crop-wide options to + apply a center crop transform on the input image. These options should be + used with the --width and --height options. For example: + + python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \ + --transform=center-crop-wide --width 512 --height=384 + +Options: + --source PATH Directory or archive name for input dataset + [required] + --dest PATH Output directory or archive name for output + dataset [required] + --max-images INTEGER Output only up to `max-images` images + --resize-filter [box|lanczos] Filter to use when resizing images for + output resolution [default: lanczos] + --transform [center-crop|center-crop-wide] + Input crop/resize mode + --width INTEGER Output width + --height INTEGER Output height + --help Show this message and exit. diff --git a/stylegan2_ada_pytorch/docs/license.html b/stylegan2_ada_pytorch/docs/license.html new file mode 100755 index 0000000000000000000000000000000000000000..ebe83a9ae3ad92ced238fc2108c14322d51db2e1 --- /dev/null +++ b/stylegan2_ada_pytorch/docs/license.html @@ -0,0 +1,153 @@ + + + + + + Nvidia Source Code License-NC + + + + + +

NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator Augmentation (ADA)

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+ +
+
+ + + diff --git a/stylegan2_ada_pytorch/docs/stylegan2-ada-teaser-1024x252.png b/stylegan2_ada_pytorch/docs/stylegan2-ada-teaser-1024x252.png new file mode 100755 index 0000000000000000000000000000000000000000..14eb641b599d27a06b463173c1f46f8d496d103d Binary files /dev/null and b/stylegan2_ada_pytorch/docs/stylegan2-ada-teaser-1024x252.png differ diff --git a/stylegan2_ada_pytorch/docs/stylegan2-ada-training-curves.png b/stylegan2_ada_pytorch/docs/stylegan2-ada-training-curves.png new file mode 100755 index 0000000000000000000000000000000000000000..4d9d379c4fb0746d01f88ba4c3eb2c9fb96b86d4 Binary files /dev/null and b/stylegan2_ada_pytorch/docs/stylegan2-ada-training-curves.png differ diff --git a/stylegan2_ada_pytorch/docs/train-help.txt b/stylegan2_ada_pytorch/docs/train-help.txt new file mode 100755 index 0000000000000000000000000000000000000000..31f38f9fe6a57931fea09759f7ff2e94154ba196 --- /dev/null +++ b/stylegan2_ada_pytorch/docs/train-help.txt @@ -0,0 +1,70 @@ +Usage: train.py [OPTIONS] + + Train a GAN using the techniques described in the paper "Training + Generative Adversarial Networks with Limited Data". + + Examples: + + # Train with custom images using 1 GPU. + python train.py --outdir=~/training-runs --data=~/my-image-folder + + # Train class-conditional CIFAR-10 using 2 GPUs. + python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \ + --gpus=2 --cfg=cifar --cond=1 + + # Transfer learn MetFaces from FFHQ using 4 GPUs. + python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \ + --gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10 + + # Reproduce original StyleGAN2 config F. + python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \ + --gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug + + Base configs (--cfg): + auto Automatically select reasonable defaults based on resolution + and GPU count. Good starting point for new datasets. + stylegan2 Reproduce results for StyleGAN2 config F at 1024x1024. + paper256 Reproduce results for FFHQ and LSUN Cat at 256x256. + paper512 Reproduce results for BreCaHAD and AFHQ at 512x512. + paper1024 Reproduce results for MetFaces at 1024x1024. + cifar Reproduce results for CIFAR-10 at 32x32. + + Transfer learning source networks (--resume): + ffhq256 FFHQ trained at 256x256 resolution. + ffhq512 FFHQ trained at 512x512 resolution. + ffhq1024 FFHQ trained at 1024x1024 resolution. + celebahq256 CelebA-HQ trained at 256x256 resolution. + lsundog256 LSUN Dog trained at 256x256 resolution. + Custom network pickle. + +Options: + --outdir DIR Where to save the results [required] + --gpus INT Number of GPUs to use [default: 1] + --snap INT Snapshot interval [default: 50 ticks] + --metrics LIST Comma-separated list or "none" [default: + fid50k_full] + --seed INT Random seed [default: 0] + -n, --dry-run Print training options and exit + --data PATH Training data (directory or zip) [required] + --cond BOOL Train conditional model based on dataset + labels [default: false] + --subset INT Train with only N images [default: all] + --mirror BOOL Enable dataset x-flips [default: false] + --cfg [auto|stylegan2|paper256|paper512|paper1024|cifar] + Base config [default: auto] + --gamma FLOAT Override R1 gamma + --kimg INT Override training duration + --batch INT Override batch size + --aug [noaug|ada|fixed] Augmentation mode [default: ada] + --p FLOAT Augmentation probability for --aug=fixed + --target FLOAT ADA target value for --aug=ada + --augpipe [blit|geom|color|filter|noise|cutout|bg|bgc|bgcf|bgcfn|bgcfnc] + Augmentation pipeline [default: bgc] + --resume PKL Resume training [default: noresume] + --freezed INT Freeze-D [default: 0 layers] + --fp32 BOOL Disable mixed-precision training + --nhwc BOOL Use NHWC memory format with FP16 + --nobench BOOL Disable cuDNN benchmarking + --allow-tf32 BOOL Allow PyTorch to use TF32 internally + --workers INT Override number of DataLoader workers + --help Show this message and exit. diff --git a/stylegan2_ada_pytorch/generate.py b/stylegan2_ada_pytorch/generate.py new file mode 100755 index 0000000000000000000000000000000000000000..bdc5fe6dfe0609cbb68abd2a50fa575764e79a18 --- /dev/null +++ b/stylegan2_ada_pytorch/generate.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Generate images using pretrained network pickle.""" + +import os +import re +from typing import List, Optional + +import click +import dnnlib +import numpy as np +import PIL.Image +import torch + +import legacy + +# ---------------------------------------------------------------------------- + + +def num_range(s: str) -> List[int]: + """Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.""" + + range_re = re.compile(r"^(\d+)-(\d+)$") + m = range_re.match(s) + if m: + return list(range(int(m.group(1)), int(m.group(2)) + 1)) + vals = s.split(",") + return [int(x) for x in vals] + + +# ---------------------------------------------------------------------------- + + +@click.command() +@click.pass_context +@click.option("--network", "network_pkl", help="Network pickle filename", required=True) +@click.option("--seeds", type=num_range, help="List of random seeds") +@click.option( + "--trunc", + "truncation_psi", + type=float, + help="Truncation psi", + default=1, + show_default=True, +) +@click.option( + "--class", + "class_idx", + type=int, + help="Class label (unconditional if not specified)", +) +@click.option( + "--noise-mode", + help="Noise mode", + type=click.Choice(["const", "random", "none"]), + default="const", + show_default=True, +) +@click.option("--projected-w", help="Projection result file", type=str, metavar="FILE") +@click.option( + "--outdir", + help="Where to save the output images", + type=str, + required=True, + metavar="DIR", +) +def generate_images( + ctx: click.Context, + network_pkl: str, + seeds: Optional[List[int]], + truncation_psi: float, + noise_mode: str, + outdir: str, + class_idx: Optional[int], + projected_w: Optional[str], +): + """Generate images using pretrained network pickle. + + Examples: + + \b + # Generate curated MetFaces images without truncation (Fig.10 left) + python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \\ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl + + \b + # Generate uncurated MetFaces images with truncation (Fig.12 upper left) + python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl + + \b + # Generate class conditional CIFAR-10 images (Fig.17 left, Car) + python generate.py --outdir=out --seeds=0-35 --class=1 \\ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/cifar10.pkl + + \b + # Render an image from projected W + python generate.py --outdir=out --projected_w=projected_w.npz \\ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl + """ + + print('Loading networks from "%s"...' % network_pkl) + device = torch.device("cuda") + with dnnlib.util.open_url(network_pkl) as f: + G = legacy.load_network_pkl(f)["G_ema"].to(device) # type: ignore + + os.makedirs(outdir, exist_ok=True) + + # Synthesize the result of a W projection. + if projected_w is not None: + if seeds is not None: + print("warn: --seeds is ignored when using --projected-w") + print(f'Generating images from projected W "{projected_w}"') + ws = np.load(projected_w)["w"] + ws = torch.tensor(ws, device=device) # pylint: disable=not-callable + assert ws.shape[1:] == (G.num_ws, G.w_dim) + for idx, w in enumerate(ws): + img = G.synthesis(w.unsqueeze(0), noise_mode=noise_mode) + img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) + img = PIL.Image.fromarray(img[0].cpu().numpy(), "RGB").save( + f"{outdir}/proj{idx:02d}.png" + ) + return + + if seeds is None: + ctx.fail("--seeds option is required when not using --projected-w") + + # Labels. + label = torch.zeros([1, G.c_dim], device=device) + if G.c_dim != 0: + if class_idx is None: + ctx.fail( + "Must specify class label with --class when using a conditional network" + ) + label[:, class_idx] = 1 + else: + if class_idx is not None: + print("warn: --class=lbl ignored when running on an unconditional network") + + # Generate images. + for seed_idx, seed in enumerate(seeds): + print("Generating image for seed %d (%d/%d) ..." % (seed, seed_idx, len(seeds))) + z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device) + img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode) + img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) + PIL.Image.fromarray(img[0].cpu().numpy(), "RGB").save( + f"{outdir}/seed{seed:04d}.png" + ) + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + generate_images() # pylint: disable=no-value-for-parameter + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/legacy.py b/stylegan2_ada_pytorch/legacy.py new file mode 100755 index 0000000000000000000000000000000000000000..5d1e0c2401b1a3d914f110e2ef22dbbde144b82a --- /dev/null +++ b/stylegan2_ada_pytorch/legacy.py @@ -0,0 +1,415 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import click +import pickle +import re +import copy +import numpy as np +import torch +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "../stylegan2_ada_pytorch")) +import dnnlib +from torch_utils import misc + +# ---------------------------------------------------------------------------- + + +def load_network_pkl(f, force_fp16=False): + data = _LegacyUnpickler(f).load() + + # Legacy TensorFlow pickle => convert. + if ( + isinstance(data, tuple) + and len(data) == 3 + and all(isinstance(net, _TFNetworkStub) for net in data) + ): + tf_G, tf_D, tf_Gs = data + G = convert_tf_generator(tf_G) + D = convert_tf_discriminator(tf_D) + G_ema = convert_tf_generator(tf_Gs) + data = dict(G=G, D=D, G_ema=G_ema) + + # Add missing fields. + if "training_set_kwargs" not in data: + data["training_set_kwargs"] = None + if "augment_pipe" not in data: + data["augment_pipe"] = None + + # Validate contents. + # assert isinstance(data['G'], torch.nn.Module) + # assert isinstance(data['D'], torch.nn.Module) + assert isinstance(data["G_ema"], torch.nn.Module) + # assert isinstance(data['training_set_kwargs'], (dict, type(None))) + # assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None))) + + # Force FP16. + if force_fp16: + for key in ["G", "D", "G_ema"]: + old = data[key] + kwargs = copy.deepcopy(old.init_kwargs) + if key.startswith("G"): + kwargs.synthesis_kwargs = dnnlib.EasyDict( + kwargs.get("synthesis_kwargs", {}) + ) + kwargs.synthesis_kwargs.num_fp16_res = 4 + kwargs.synthesis_kwargs.conv_clamp = 256 + if key.startswith("D"): + kwargs.num_fp16_res = 4 + kwargs.conv_clamp = 256 + if kwargs != old.init_kwargs: + new = type(old)(**kwargs).eval().requires_grad_(False) + misc.copy_params_and_buffers(old, new, require_all=True) + data[key] = new + return data + + +# ---------------------------------------------------------------------------- + + +class _TFNetworkStub(dnnlib.EasyDict): + pass + + +class _LegacyUnpickler(pickle.Unpickler): + def find_class(self, module, name): + if module == "dnnlib.tflib.network" and name == "Network": + return _TFNetworkStub + return super().find_class(module, name) + + +# ---------------------------------------------------------------------------- + + +def _collect_tf_params(tf_net): + # pylint: disable=protected-access + tf_params = dict() + + def recurse(prefix, tf_net): + for name, value in tf_net.variables: + tf_params[prefix + name] = value + for name, comp in tf_net.components.items(): + recurse(prefix + name + "/", comp) + + recurse("", tf_net) + return tf_params + + +# ---------------------------------------------------------------------------- + + +def _populate_module_params(module, *patterns): + for name, tensor in misc.named_params_and_buffers(module): + found = False + value = None + for pattern, value_fn in zip(patterns[0::2], patterns[1::2]): + match = re.fullmatch(pattern, name) + if match: + found = True + if value_fn is not None: + value = value_fn(*match.groups()) + break + try: + assert found + if value is not None: + tensor.copy_(torch.from_numpy(np.array(value))) + except: + print(name, list(tensor.shape)) + raise + + +# ---------------------------------------------------------------------------- + + +def convert_tf_generator(tf_G): + if tf_G.version < 4: + raise ValueError("TensorFlow pickle version too low") + + # Collect kwargs. + tf_kwargs = tf_G.static_kwargs + known_kwargs = set() + + def kwarg(tf_name, default=None, none=None): + known_kwargs.add(tf_name) + val = tf_kwargs.get(tf_name, default) + return val if val is not None else none + + # Convert kwargs. + kwargs = dnnlib.EasyDict( + z_dim=kwarg("latent_size", 512), + c_dim=kwarg("label_size", 0), + w_dim=kwarg("dlatent_size", 512), + img_resolution=kwarg("resolution", 1024), + img_channels=kwarg("num_channels", 3), + mapping_kwargs=dnnlib.EasyDict( + num_layers=kwarg("mapping_layers", 8), + embed_features=kwarg("label_fmaps", None), + layer_features=kwarg("mapping_fmaps", None), + activation=kwarg("mapping_nonlinearity", "lrelu"), + lr_multiplier=kwarg("mapping_lrmul", 0.01), + w_avg_beta=kwarg("w_avg_beta", 0.995, none=1), + ), + synthesis_kwargs=dnnlib.EasyDict( + channel_base=kwarg("fmap_base", 16384) * 2, + channel_max=kwarg("fmap_max", 512), + num_fp16_res=kwarg("num_fp16_res", 0), + conv_clamp=kwarg("conv_clamp", None), + architecture=kwarg("architecture", "skip"), + resample_filter=kwarg("resample_kernel", [1, 3, 3, 1]), + use_noise=kwarg("use_noise", True), + activation=kwarg("nonlinearity", "lrelu"), + ), + ) + + # Check for unknown kwargs. + kwarg("truncation_psi") + kwarg("truncation_cutoff") + kwarg("style_mixing_prob") + kwarg("structure") + unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs) + if len(unknown_kwargs) > 0: + raise ValueError("Unknown TensorFlow kwarg", unknown_kwargs[0]) + + # Collect params. + tf_params = _collect_tf_params(tf_G) + for name, value in list(tf_params.items()): + match = re.fullmatch(r"ToRGB_lod(\d+)/(.*)", name) + if match: + r = kwargs.img_resolution // (2 ** int(match.group(1))) + tf_params[f"{r}x{r}/ToRGB/{match.group(2)}"] = value + kwargs.synthesis.kwargs.architecture = "orig" + # for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') + + # Convert params. + from training import networks + + G = networks.Generator(**kwargs).eval().requires_grad_(False) + # pylint: disable=unnecessary-lambda + _populate_module_params( + G, + r"mapping\.w_avg", + lambda: tf_params[f"dlatent_avg"], + r"mapping\.embed\.weight", + lambda: tf_params[f"mapping/LabelEmbed/weight"].transpose(), + r"mapping\.embed\.bias", + lambda: tf_params[f"mapping/LabelEmbed/bias"], + r"mapping\.fc(\d+)\.weight", + lambda i: tf_params[f"mapping/Dense{i}/weight"].transpose(), + r"mapping\.fc(\d+)\.bias", + lambda i: tf_params[f"mapping/Dense{i}/bias"], + r"synthesis\.b4\.const", + lambda: tf_params[f"synthesis/4x4/Const/const"][0], + r"synthesis\.b4\.conv1\.weight", + lambda: tf_params[f"synthesis/4x4/Conv/weight"].transpose(3, 2, 0, 1), + r"synthesis\.b4\.conv1\.bias", + lambda: tf_params[f"synthesis/4x4/Conv/bias"], + r"synthesis\.b4\.conv1\.noise_const", + lambda: tf_params[f"synthesis/noise0"][0, 0], + r"synthesis\.b4\.conv1\.noise_strength", + lambda: tf_params[f"synthesis/4x4/Conv/noise_strength"], + r"synthesis\.b4\.conv1\.affine\.weight", + lambda: tf_params[f"synthesis/4x4/Conv/mod_weight"].transpose(), + r"synthesis\.b4\.conv1\.affine\.bias", + lambda: tf_params[f"synthesis/4x4/Conv/mod_bias"] + 1, + r"synthesis\.b(\d+)\.conv0\.weight", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv0_up/weight"][::-1, ::-1].transpose( + 3, 2, 0, 1 + ), + r"synthesis\.b(\d+)\.conv0\.bias", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv0_up/bias"], + r"synthesis\.b(\d+)\.conv0\.noise_const", + lambda r: tf_params[f"synthesis/noise{int(np.log2(int(r)))*2-5}"][0, 0], + r"synthesis\.b(\d+)\.conv0\.noise_strength", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv0_up/noise_strength"], + r"synthesis\.b(\d+)\.conv0\.affine\.weight", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv0_up/mod_weight"].transpose(), + r"synthesis\.b(\d+)\.conv0\.affine\.bias", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv0_up/mod_bias"] + 1, + r"synthesis\.b(\d+)\.conv1\.weight", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv1/weight"].transpose(3, 2, 0, 1), + r"synthesis\.b(\d+)\.conv1\.bias", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv1/bias"], + r"synthesis\.b(\d+)\.conv1\.noise_const", + lambda r: tf_params[f"synthesis/noise{int(np.log2(int(r)))*2-4}"][0, 0], + r"synthesis\.b(\d+)\.conv1\.noise_strength", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv1/noise_strength"], + r"synthesis\.b(\d+)\.conv1\.affine\.weight", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv1/mod_weight"].transpose(), + r"synthesis\.b(\d+)\.conv1\.affine\.bias", + lambda r: tf_params[f"synthesis/{r}x{r}/Conv1/mod_bias"] + 1, + r"synthesis\.b(\d+)\.torgb\.weight", + lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/weight"].transpose(3, 2, 0, 1), + r"synthesis\.b(\d+)\.torgb\.bias", + lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/bias"], + r"synthesis\.b(\d+)\.torgb\.affine\.weight", + lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/mod_weight"].transpose(), + r"synthesis\.b(\d+)\.torgb\.affine\.bias", + lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/mod_bias"] + 1, + r"synthesis\.b(\d+)\.skip\.weight", + lambda r: tf_params[f"synthesis/{r}x{r}/Skip/weight"][::-1, ::-1].transpose( + 3, 2, 0, 1 + ), + r".*\.resample_filter", + None, + ) + return G + + +# ---------------------------------------------------------------------------- + + +def convert_tf_discriminator(tf_D): + if tf_D.version < 4: + raise ValueError("TensorFlow pickle version too low") + + # Collect kwargs. + tf_kwargs = tf_D.static_kwargs + known_kwargs = set() + + def kwarg(tf_name, default=None): + known_kwargs.add(tf_name) + return tf_kwargs.get(tf_name, default) + + # Convert kwargs. + kwargs = dnnlib.EasyDict( + c_dim=kwarg("label_size", 0), + img_resolution=kwarg("resolution", 1024), + img_channels=kwarg("num_channels", 3), + architecture=kwarg("architecture", "resnet"), + channel_base=kwarg("fmap_base", 16384) * 2, + channel_max=kwarg("fmap_max", 512), + num_fp16_res=kwarg("num_fp16_res", 0), + conv_clamp=kwarg("conv_clamp", None), + cmap_dim=kwarg("mapping_fmaps", None), + block_kwargs=dnnlib.EasyDict( + activation=kwarg("nonlinearity", "lrelu"), + resample_filter=kwarg("resample_kernel", [1, 3, 3, 1]), + freeze_layers=kwarg("freeze_layers", 0), + ), + mapping_kwargs=dnnlib.EasyDict( + num_layers=kwarg("mapping_layers", 0), + embed_features=kwarg("mapping_fmaps", None), + layer_features=kwarg("mapping_fmaps", None), + activation=kwarg("nonlinearity", "lrelu"), + lr_multiplier=kwarg("mapping_lrmul", 0.1), + ), + epilogue_kwargs=dnnlib.EasyDict( + mbstd_group_size=kwarg("mbstd_group_size", None), + mbstd_num_channels=kwarg("mbstd_num_features", 1), + activation=kwarg("nonlinearity", "lrelu"), + ), + ) + + # Check for unknown kwargs. + kwarg("structure") + unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs) + if len(unknown_kwargs) > 0: + raise ValueError("Unknown TensorFlow kwarg", unknown_kwargs[0]) + + # Collect params. + tf_params = _collect_tf_params(tf_D) + for name, value in list(tf_params.items()): + match = re.fullmatch(r"FromRGB_lod(\d+)/(.*)", name) + if match: + r = kwargs.img_resolution // (2 ** int(match.group(1))) + tf_params[f"{r}x{r}/FromRGB/{match.group(2)}"] = value + kwargs.architecture = "orig" + # for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') + + # Convert params. + from training import networks + + D = networks.Discriminator(**kwargs).eval().requires_grad_(False) + # pylint: disable=unnecessary-lambda + _populate_module_params( + D, + r"b(\d+)\.fromrgb\.weight", + lambda r: tf_params[f"{r}x{r}/FromRGB/weight"].transpose(3, 2, 0, 1), + r"b(\d+)\.fromrgb\.bias", + lambda r: tf_params[f"{r}x{r}/FromRGB/bias"], + r"b(\d+)\.conv(\d+)\.weight", + lambda r, i: tf_params[ + f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight' + ].transpose(3, 2, 0, 1), + r"b(\d+)\.conv(\d+)\.bias", + lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'], + r"b(\d+)\.skip\.weight", + lambda r: tf_params[f"{r}x{r}/Skip/weight"].transpose(3, 2, 0, 1), + r"mapping\.embed\.weight", + lambda: tf_params[f"LabelEmbed/weight"].transpose(), + r"mapping\.embed\.bias", + lambda: tf_params[f"LabelEmbed/bias"], + r"mapping\.fc(\d+)\.weight", + lambda i: tf_params[f"Mapping{i}/weight"].transpose(), + r"mapping\.fc(\d+)\.bias", + lambda i: tf_params[f"Mapping{i}/bias"], + r"b4\.conv\.weight", + lambda: tf_params[f"4x4/Conv/weight"].transpose(3, 2, 0, 1), + r"b4\.conv\.bias", + lambda: tf_params[f"4x4/Conv/bias"], + r"b4\.fc\.weight", + lambda: tf_params[f"4x4/Dense0/weight"].transpose(), + r"b4\.fc\.bias", + lambda: tf_params[f"4x4/Dense0/bias"], + r"b4\.out\.weight", + lambda: tf_params[f"Output/weight"].transpose(), + r"b4\.out\.bias", + lambda: tf_params[f"Output/bias"], + r".*\.resample_filter", + None, + ) + return D + + +# ---------------------------------------------------------------------------- + + +@click.command() +@click.option("--source", help="Input pickle", required=True, metavar="PATH") +@click.option("--dest", help="Output pickle", required=True, metavar="PATH") +@click.option( + "--force-fp16", + help="Force the networks to use FP16", + type=bool, + default=False, + metavar="BOOL", + show_default=True, +) +def convert_network_pickle(source, dest, force_fp16): + """Convert legacy network pickle into the native PyTorch format. + + The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA. + It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks. + + Example: + + \b + python legacy.py \\ + --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\ + --dest=stylegan2-cat-config-f.pkl + """ + print(f'Loading "{source}"...') + with dnnlib.util.open_url(source) as f: + data = load_network_pkl(f, force_fp16=force_fp16) + print(f'Saving "{dest}"...') + with open(dest, "wb") as f: + pickle.dump(data, f) + print("Done.") + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + convert_network_pickle() # pylint: disable=no-value-for-parameter + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/metrics/__init__.py b/stylegan2_ada_pytorch/metrics/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..e1e1a5ba99e56a56ecaa14f7d4fa41777789c0cf --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/stylegan2_ada_pytorch/metrics/frechet_inception_distance.py b/stylegan2_ada_pytorch/metrics/frechet_inception_distance.py new file mode 100755 index 0000000000000000000000000000000000000000..9eba270033f19d9d071adb4a6493dad423801e20 --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/frechet_inception_distance.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Frechet Inception Distance (FID) from the paper +"GANs trained by a two time-scale update rule converge to a local Nash +equilibrium". Matches the original implementation by Heusel et al. at +https://github.com/bioinf-jku/TTUR/blob/master/fid.py""" + +import numpy as np +import scipy.linalg +from . import metric_utils + +# ---------------------------------------------------------------------------- + + +def compute_fid(opts, max_real, num_gen): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt" + detector_kwargs = dict( + return_features=True + ) # Return raw features before the softmax layer. + + mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset( + opts=opts, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + rel_lo=0, + rel_hi=0, + capture_mean_cov=True, + max_items=max_real, + ).get_mean_cov() + + mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator( + opts=opts, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + rel_lo=0, + rel_hi=1, + capture_mean_cov=True, + max_items=num_gen, + ).get_mean_cov() + + if opts.rank != 0: + return float("nan") + + m = np.square(mu_gen - mu_real).sum() + s, _ = scipy.linalg.sqrtm( + np.dot(sigma_gen, sigma_real), disp=False + ) # pylint: disable=no-member + fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) + return float(fid) + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/metrics/inception_score.py b/stylegan2_ada_pytorch/metrics/inception_score.py new file mode 100755 index 0000000000000000000000000000000000000000..03697584f775fb65abbb3eb881879fcf028e8167 --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/inception_score.py @@ -0,0 +1,49 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Inception Score (IS) from the paper "Improved techniques for training +GANs". Matches the original implementation by Salimans et al. at +https://github.com/openai/improved-gan/blob/master/inception_score/model.py""" + +import numpy as np +from . import metric_utils + +# ---------------------------------------------------------------------------- + + +def compute_is(opts, num_gen, num_splits): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt" + detector_kwargs = dict( + no_output_bias=True + ) # Match the original implementation by not applying bias in the softmax layer. + + gen_probs = metric_utils.compute_feature_stats_for_generator( + opts=opts, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + capture_all=True, + max_items=num_gen, + ).get_all() + + if opts.rank != 0: + return float("nan"), float("nan") + + scores = [] + for i in range(num_splits): + part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits] + kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True))) + kl = np.mean(np.sum(kl, axis=1)) + scores.append(np.exp(kl)) + return float(np.mean(scores)), float(np.std(scores)) + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/metrics/kernel_inception_distance.py b/stylegan2_ada_pytorch/metrics/kernel_inception_distance.py new file mode 100755 index 0000000000000000000000000000000000000000..bf96197bda3776eb753a96d69a9e289d15c0d829 --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/kernel_inception_distance.py @@ -0,0 +1,65 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Kernel Inception Distance (KID) from the paper "Demystifying MMD +GANs". Matches the original implementation by Binkowski et al. at +https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py""" + +import numpy as np +from . import metric_utils + +# ---------------------------------------------------------------------------- + + +def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt" + detector_kwargs = dict( + return_features=True + ) # Return raw features before the softmax layer. + + real_features = metric_utils.compute_feature_stats_for_dataset( + opts=opts, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + rel_lo=0, + rel_hi=0, + capture_all=True, + max_items=max_real, + ).get_all() + + gen_features = metric_utils.compute_feature_stats_for_generator( + opts=opts, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + rel_lo=0, + rel_hi=1, + capture_all=True, + max_items=num_gen, + ).get_all() + + if opts.rank != 0: + return float("nan") + + n = real_features.shape[1] + m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size) + t = 0 + for _subset_idx in range(num_subsets): + x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)] + y = real_features[np.random.choice(real_features.shape[0], m, replace=False)] + a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3 + b = (x @ y.T / n + 1) ** 3 + t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m + kid = t / num_subsets / m + return float(kid) + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/metrics/metric_main.py b/stylegan2_ada_pytorch/metrics/metric_main.py new file mode 100755 index 0000000000000000000000000000000000000000..5834c5b11aa9a91d393253f27293329e7f283fa8 --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/metric_main.py @@ -0,0 +1,241 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import time +import json +import torch +import dnnlib + +from . import metric_utils +from . import frechet_inception_distance +from . import kernel_inception_distance +from . import precision_recall +from . import perceptual_path_length +from . import inception_score + +# ---------------------------------------------------------------------------- + +_metric_dict = dict() # name => fn + + +def register_metric(fn): + assert callable(fn) + _metric_dict[fn.__name__] = fn + return fn + + +def is_valid_metric(metric): + return metric in _metric_dict + + +def list_valid_metrics(): + return list(_metric_dict.keys()) + + +# ---------------------------------------------------------------------------- + + +def calc_metric( + metric, **kwargs +): # See metric_utils.MetricOptions for the full list of arguments. + assert is_valid_metric(metric) + opts = metric_utils.MetricOptions(**kwargs) + + # Calculate. + start_time = time.time() + results = _metric_dict[metric](opts) + total_time = time.time() - start_time + + # Broadcast results. + for key, value in list(results.items()): + if opts.num_gpus > 1: + value = torch.as_tensor(value, dtype=torch.float64, device=opts.device) + torch.distributed.broadcast(tensor=value, src=0) + value = float(value.cpu()) + results[key] = value + + # Decorate with metadata. + return dnnlib.EasyDict( + results=dnnlib.EasyDict(results), + metric=metric, + total_time=total_time, + total_time_str=dnnlib.util.format_time(total_time), + num_gpus=opts.num_gpus, + ) + + +# ---------------------------------------------------------------------------- + + +def report_metric(result_dict, run_dir=None, snapshot_pkl=None): + metric = result_dict["metric"] + assert is_valid_metric(metric) + if run_dir is not None and snapshot_pkl is not None: + snapshot_pkl = os.path.relpath(snapshot_pkl, run_dir) + + jsonl_line = json.dumps( + dict(result_dict, snapshot_pkl=snapshot_pkl, timestamp=time.time()) + ) + print(jsonl_line) + if run_dir is not None and os.path.isdir(run_dir): + with open(os.path.join(run_dir, f"metric-{metric}.jsonl"), "at") as f: + f.write(jsonl_line + "\n") + + +# ---------------------------------------------------------------------------- +# Primary metrics. + + +@register_metric +def fid50k_full(opts): + opts.dataset_kwargs.update( + max_size=None, xflip=False, apply_norm=False, label_onehot=True + ) + fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=50000) + return dict(fid50k_full=fid) + + +@register_metric +def kid50k_full(opts): + opts.dataset_kwargs.update(max_size=None, xflip=False) + kid = kernel_inception_distance.compute_kid( + opts, max_real=1000000, num_gen=50000, num_subsets=100, max_subset_size=1000 + ) + return dict(kid50k_full=kid) + + +@register_metric +def pr50k3_full(opts): + opts.dataset_kwargs.update(max_size=None, xflip=False) + precision, recall = precision_recall.compute_pr( + opts, + max_real=200000, + num_gen=50000, + nhood_size=3, + row_batch_size=10000, + col_batch_size=10000, + ) + return dict(pr50k3_full_precision=precision, pr50k3_full_recall=recall) + + +@register_metric +def ppl2_wend(opts): + ppl = perceptual_path_length.compute_ppl( + opts, + num_samples=50000, + epsilon=1e-4, + space="w", + sampling="end", + crop=False, + batch_size=2, + ) + return dict(ppl2_wend=ppl) + + +@register_metric +def is50k(opts): + opts.dataset_kwargs.update(max_size=None, xflip=False) + mean, std = inception_score.compute_is(opts, num_gen=50000, num_splits=10) + return dict(is50k_mean=mean, is50k_std=std) + + +# ---------------------------------------------------------------------------- +# Legacy metrics. + + +@register_metric +def fid50k(opts): + opts.dataset_kwargs.update(max_size=None) + fid = frechet_inception_distance.compute_fid(opts, max_real=50000, num_gen=50000) + return dict(fid50k=fid) + + +@register_metric +def kid50k(opts): + opts.dataset_kwargs.update(max_size=None) + kid = kernel_inception_distance.compute_kid( + opts, max_real=50000, num_gen=50000, num_subsets=100, max_subset_size=1000 + ) + return dict(kid50k=kid) + + +@register_metric +def pr50k3(opts): + opts.dataset_kwargs.update(max_size=None) + precision, recall = precision_recall.compute_pr( + opts, + max_real=50000, + num_gen=50000, + nhood_size=3, + row_batch_size=10000, + col_batch_size=10000, + ) + return dict(pr50k3_precision=precision, pr50k3_recall=recall) + + +@register_metric +def ppl_zfull(opts): + ppl = perceptual_path_length.compute_ppl( + opts, + num_samples=50000, + epsilon=1e-4, + space="z", + sampling="full", + crop=True, + batch_size=2, + ) + return dict(ppl_zfull=ppl) + + +@register_metric +def ppl_wfull(opts): + ppl = perceptual_path_length.compute_ppl( + opts, + num_samples=50000, + epsilon=1e-4, + space="w", + sampling="full", + crop=True, + batch_size=2, + ) + return dict(ppl_wfull=ppl) + + +@register_metric +def ppl_zend(opts): + ppl = perceptual_path_length.compute_ppl( + opts, + num_samples=50000, + epsilon=1e-4, + space="z", + sampling="end", + crop=True, + batch_size=2, + ) + return dict(ppl_zend=ppl) + + +@register_metric +def ppl_wend(opts): + ppl = perceptual_path_length.compute_ppl( + opts, + num_samples=50000, + epsilon=1e-4, + space="w", + sampling="end", + crop=True, + batch_size=2, + ) + return dict(ppl_wend=ppl) + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/metrics/metric_utils.py b/stylegan2_ada_pytorch/metrics/metric_utils.py new file mode 100755 index 0000000000000000000000000000000000000000..2d56da1bb696b086e3ac28f87828743bcb6710e0 --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/metric_utils.py @@ -0,0 +1,392 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import time +import hashlib +import pickle +import copy +import uuid +import numpy as np +import torch +import dnnlib + +# ---------------------------------------------------------------------------- + + +class MetricOptions: + def __init__( + self, + G=None, + G_kwargs={}, + dataset_kwargs={}, + num_gpus=1, + rank=0, + device=None, + progress=None, + cache=True, + ): + assert 0 <= rank < num_gpus + self.G = G + self.G_kwargs = dnnlib.EasyDict(G_kwargs) + self.dataset_kwargs = dnnlib.EasyDict(dataset_kwargs) + self.num_gpus = num_gpus + self.rank = rank + self.device = device if device is not None else torch.device("cuda", rank) + self.progress = ( + progress.sub() if progress is not None and rank == 0 else ProgressMonitor() + ) + self.cache = cache + + +# ---------------------------------------------------------------------------- + +_feature_detector_cache = dict() + + +def get_feature_detector_name(url): + return os.path.splitext(url.split("/")[-1])[0] + + +def get_feature_detector( + url, device=torch.device("cpu"), num_gpus=1, rank=0, verbose=False +): + assert 0 <= rank < num_gpus + key = (url, device) + if key not in _feature_detector_cache: + is_leader = rank == 0 + if not is_leader and num_gpus > 1: + torch.distributed.barrier() # leader goes first + with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f: + _feature_detector_cache[key] = torch.jit.load(f).eval().to(device) + if is_leader and num_gpus > 1: + torch.distributed.barrier() # others follow + return _feature_detector_cache[key] + + +# ---------------------------------------------------------------------------- + + +class FeatureStats: + def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None): + self.capture_all = capture_all + self.capture_mean_cov = capture_mean_cov + self.max_items = max_items + self.num_items = 0 + self.num_features = None + self.all_features = None + self.raw_mean = None + self.raw_cov = None + + def set_num_features(self, num_features): + if self.num_features is not None: + assert num_features == self.num_features + else: + self.num_features = num_features + self.all_features = [] + self.raw_mean = np.zeros([num_features], dtype=np.float64) + self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64) + + def is_full(self): + return (self.max_items is not None) and (self.num_items >= self.max_items) + + def append(self, x): + x = np.asarray(x, dtype=np.float32) + assert x.ndim == 2 + if (self.max_items is not None) and ( + self.num_items + x.shape[0] > self.max_items + ): + if self.num_items >= self.max_items: + return + x = x[: self.max_items - self.num_items] + + self.set_num_features(x.shape[1]) + self.num_items += x.shape[0] + if self.capture_all: + self.all_features.append(x) + if self.capture_mean_cov: + x64 = x.astype(np.float64) + self.raw_mean += x64.sum(axis=0) + self.raw_cov += x64.T @ x64 + + def append_torch(self, x, num_gpus=1, rank=0): + assert isinstance(x, torch.Tensor) and x.ndim == 2 + assert 0 <= rank < num_gpus + if num_gpus > 1: + ys = [] + for src in range(num_gpus): + y = x.clone() + torch.distributed.broadcast(y, src=src) + ys.append(y) + x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples + self.append(x.cpu().numpy()) + + def get_all(self): + assert self.capture_all + return np.concatenate(self.all_features, axis=0) + + def get_all_torch(self): + return torch.from_numpy(self.get_all()) + + def get_mean_cov(self): + assert self.capture_mean_cov + mean = self.raw_mean / self.num_items + cov = self.raw_cov / self.num_items + cov = cov - np.outer(mean, mean) + return mean, cov + + def save(self, pkl_file): + with open(pkl_file, "wb") as f: + pickle.dump(self.__dict__, f) + + @staticmethod + def load(pkl_file): + with open(pkl_file, "rb") as f: + s = dnnlib.EasyDict(pickle.load(f)) + obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items) + obj.__dict__.update(s) + return obj + + +# ---------------------------------------------------------------------------- + + +class ProgressMonitor: + def __init__( + self, + tag=None, + num_items=None, + flush_interval=1000, + verbose=False, + progress_fn=None, + pfn_lo=0, + pfn_hi=1000, + pfn_total=1000, + ): + self.tag = tag + self.num_items = num_items + self.verbose = verbose + self.flush_interval = flush_interval + self.progress_fn = progress_fn + self.pfn_lo = pfn_lo + self.pfn_hi = pfn_hi + self.pfn_total = pfn_total + self.start_time = time.time() + self.batch_time = self.start_time + self.batch_items = 0 + if self.progress_fn is not None: + self.progress_fn(self.pfn_lo, self.pfn_total) + + def update(self, cur_items): + assert (self.num_items is None) or (cur_items <= self.num_items) + if (cur_items < self.batch_items + self.flush_interval) and ( + self.num_items is None or cur_items < self.num_items + ): + return + cur_time = time.time() + total_time = cur_time - self.start_time + time_per_item = (cur_time - self.batch_time) / max( + cur_items - self.batch_items, 1 + ) + if (self.verbose) and (self.tag is not None): + print( + f"{self.tag:<19s} items {cur_items:<7d} time {dnnlib.util.format_time(total_time):<12s} ms/item {time_per_item*1e3:.2f}" + ) + self.batch_time = cur_time + self.batch_items = cur_items + + if (self.progress_fn is not None) and (self.num_items is not None): + self.progress_fn( + self.pfn_lo + + (self.pfn_hi - self.pfn_lo) * (cur_items / self.num_items), + self.pfn_total, + ) + + def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1): + return ProgressMonitor( + tag=tag, + num_items=num_items, + flush_interval=flush_interval, + verbose=self.verbose, + progress_fn=self.progress_fn, + pfn_lo=self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_lo, + pfn_hi=self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_hi, + pfn_total=self.pfn_total, + ) + + +# ---------------------------------------------------------------------------- + + +def compute_feature_stats_for_dataset( + opts, + detector_url, + detector_kwargs, + rel_lo=0, + rel_hi=1, + batch_size=64, + data_loader_kwargs=None, + max_items=None, + **stats_kwargs, +): + dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) + + if data_loader_kwargs is None: + data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2) + + # Try to lookup from cache. + cache_file = None + if opts.cache: + # Choose cache file name. + args = dict( + dataset_kwargs=opts.dataset_kwargs, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + stats_kwargs=stats_kwargs, + ) + md5 = hashlib.md5(repr(sorted(args.items())).encode("utf-8")) + cache_tag = f"{os.path.splitext(os.path.basename(dataset.root))[0]}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}" + cache_file = dnnlib.make_cache_dir_path("gan-metrics", cache_tag + ".pkl") + + # Check if the file exists (all processes must agree). + flag = os.path.isfile(cache_file) if opts.rank == 0 else False + if opts.num_gpus > 1: + flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device) + torch.distributed.broadcast(tensor=flag, src=0) + flag = float(flag.cpu()) != 0 + + # Load. + if flag: + return FeatureStats.load(cache_file) + + # Initialize. + num_items = len(dataset) + if max_items is not None: + num_items = min(num_items, max_items) + stats = FeatureStats(max_items=num_items, **stats_kwargs) + progress = opts.progress.sub( + tag="dataset features", num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi + ) + detector = get_feature_detector( + url=detector_url, + device=opts.device, + num_gpus=opts.num_gpus, + rank=opts.rank, + verbose=progress.verbose, + ) + + # Main loop. + item_subset = [ + (i * opts.num_gpus + opts.rank) % num_items + for i in range((num_items - 1) // opts.num_gpus + 1) + ] + + for data in torch.utils.data.DataLoader( + dataset=dataset, + sampler=item_subset, + batch_size=batch_size, + **data_loader_kwargs, + ): + if isinstance(data, list): + images = data[0] + else: + images = data # [0] + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images.to(opts.device), **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + + # Save to cache. + if cache_file is not None and opts.rank == 0: + os.makedirs(os.path.dirname(cache_file), exist_ok=True) + temp_file = cache_file + "." + uuid.uuid4().hex + stats.save(temp_file) + os.replace(temp_file, cache_file) # atomic + return stats + + +# ---------------------------------------------------------------------------- + + +def compute_feature_stats_for_generator( + opts, + detector_url, + detector_kwargs, + rel_lo=0, + rel_hi=1, + batch_size=64, + batch_gen=None, + jit=False, + **stats_kwargs, +): + if batch_gen is None: + batch_gen = min(batch_size, 4) + assert batch_size % batch_gen == 0 + + # Setup generator and load labels. + G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) + dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) + + # Image generation func. + def run_generator(z, c, feats): + img = G(z=z, c=c, feats=feats, **opts.G_kwargs) + img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) + return img + + # JIT. + if jit: + z = torch.zeros([batch_gen, G.z_dim], device=opts.device) + c = torch.zeros([batch_gen, G.c_dim], device=opts.device) + run_generator = torch.jit.trace(run_generator, [z, c], check_trace=False) + + # Initialize. + stats = FeatureStats(**stats_kwargs) + assert stats.max_items is not None + progress = opts.progress.sub( + tag="generator features", + num_items=stats.max_items, + rel_lo=rel_lo, + rel_hi=rel_hi, + ) + detector = get_feature_detector( + url=detector_url, + device=opts.device, + num_gpus=opts.num_gpus, + rank=opts.rank, + verbose=progress.verbose, + ) + # Main loop. + while not stats.is_full(): + images = [] + for _i in range(batch_size // batch_gen): + z = torch.randn([batch_gen, G.z_dim], device=opts.device) + c = [ + dataset.get_label(np.random.randint(len(dataset))) + for _i in range(batch_gen) + ] + c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device) + h = [ + dataset.get_instance_features(np.random.randint(len(dataset))) + for _i in range(batch_gen) + ] + h = torch.from_numpy(np.stack(h)).pin_memory().to(opts.device) + images.append(run_generator(z, c, h)) + images = torch.cat(images) + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images, **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + return stats + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/metrics/perceptual_path_length.py b/stylegan2_ada_pytorch/metrics/perceptual_path_length.py new file mode 100755 index 0000000000000000000000000000000000000000..06c1c8b116b235844fd7e26b97841a8e00fac74c --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/perceptual_path_length.py @@ -0,0 +1,174 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Perceptual Path Length (PPL) from the paper "A Style-Based Generator +Architecture for Generative Adversarial Networks". Matches the original +implementation by Karras et al. at +https://github.com/NVlabs/stylegan/blob/master/metrics/perceptual_path_length.py""" + +import copy +import numpy as np +import torch +import dnnlib +from . import metric_utils + +# ---------------------------------------------------------------------------- + +# Spherical interpolation of a batch of vectors. +def slerp(a, b, t): + a = a / a.norm(dim=-1, keepdim=True) + b = b / b.norm(dim=-1, keepdim=True) + d = (a * b).sum(dim=-1, keepdim=True) + p = t * torch.acos(d) + c = b - d * a + c = c / c.norm(dim=-1, keepdim=True) + d = a * torch.cos(p) + c * torch.sin(p) + d = d / d.norm(dim=-1, keepdim=True) + return d + + +# ---------------------------------------------------------------------------- + + +class PPLSampler(torch.nn.Module): + def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16): + assert space in ["z", "w"] + assert sampling in ["full", "end"] + super().__init__() + self.G = copy.deepcopy(G) + self.G_kwargs = G_kwargs + self.epsilon = epsilon + self.space = space + self.sampling = sampling + self.crop = crop + self.vgg16 = copy.deepcopy(vgg16) + + def forward(self, c): + # Generate random latents and interpolation t-values. + t = torch.rand([c.shape[0]], device=c.device) * ( + 1 if self.sampling == "full" else 0 + ) + z0, z1 = torch.randn([c.shape[0] * 2, self.G.z_dim], device=c.device).chunk(2) + + # Interpolate in W or Z. + if self.space == "w": + w0, w1 = self.G.mapping(z=torch.cat([z0, z1]), c=torch.cat([c, c])).chunk(2) + wt0 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2)) + wt1 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2) + self.epsilon) + else: # space == 'z' + zt0 = slerp(z0, z1, t.unsqueeze(1)) + zt1 = slerp(z0, z1, t.unsqueeze(1) + self.epsilon) + wt0, wt1 = self.G.mapping( + z=torch.cat([zt0, zt1]), c=torch.cat([c, c]) + ).chunk(2) + + # Randomize noise buffers. + for name, buf in self.G.named_buffers(): + if name.endswith(".noise_const"): + buf.copy_(torch.randn_like(buf)) + + # Generate images. + img = self.G.synthesis( + ws=torch.cat([wt0, wt1]), + noise_mode="const", + force_fp32=True, + **self.G_kwargs + ) + + # Center crop. + if self.crop: + assert img.shape[2] == img.shape[3] + c = img.shape[2] // 8 + img = img[:, :, c * 3 : c * 7, c * 2 : c * 6] + + # Downsample to 256x256. + factor = self.G.img_resolution // 256 + if factor > 1: + img = img.reshape( + [ + -1, + img.shape[1], + img.shape[2] // factor, + factor, + img.shape[3] // factor, + factor, + ] + ).mean([3, 5]) + + # Scale dynamic range from [-1,1] to [0,255]. + img = (img + 1) * (255 / 2) + if self.G.img_channels == 1: + img = img.repeat([1, 3, 1, 1]) + + # Evaluate differential LPIPS. + lpips_t0, lpips_t1 = self.vgg16( + img, resize_images=False, return_lpips=True + ).chunk(2) + dist = (lpips_t0 - lpips_t1).square().sum(1) / self.epsilon ** 2 + return dist + + +# ---------------------------------------------------------------------------- + + +def compute_ppl( + opts, num_samples, epsilon, space, sampling, crop, batch_size, jit=False +): + dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) + vgg16_url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt" + vgg16 = metric_utils.get_feature_detector( + vgg16_url, num_gpus=opts.num_gpus, rank=opts.rank, verbose=opts.progress.verbose + ) + + # Setup sampler. + sampler = PPLSampler( + G=opts.G, + G_kwargs=opts.G_kwargs, + epsilon=epsilon, + space=space, + sampling=sampling, + crop=crop, + vgg16=vgg16, + ) + sampler.eval().requires_grad_(False).to(opts.device) + if jit: + c = torch.zeros([batch_size, opts.G.c_dim], device=opts.device) + sampler = torch.jit.trace(sampler, [c], check_trace=False) + + # Sampling loop. + dist = [] + progress = opts.progress.sub(tag="ppl sampling", num_items=num_samples) + for batch_start in range(0, num_samples, batch_size * opts.num_gpus): + progress.update(batch_start) + c = [ + dataset.get_label(np.random.randint(len(dataset))) + for _i in range(batch_size) + ] + c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device) + x = sampler(c) + for src in range(opts.num_gpus): + y = x.clone() + if opts.num_gpus > 1: + torch.distributed.broadcast(y, src=src) + dist.append(y) + progress.update(num_samples) + + # Compute PPL. + if opts.rank != 0: + return float("nan") + dist = torch.cat(dist)[:num_samples].cpu().numpy() + lo = np.percentile(dist, 1, interpolation="lower") + hi = np.percentile(dist, 99, interpolation="higher") + ppl = np.extract(np.logical_and(dist >= lo, dist <= hi), dist).mean() + return float(ppl) + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/metrics/precision_recall.py b/stylegan2_ada_pytorch/metrics/precision_recall.py new file mode 100755 index 0000000000000000000000000000000000000000..51ec37dccb6269d12ec04407081f15bdac73b0c1 --- /dev/null +++ b/stylegan2_ada_pytorch/metrics/precision_recall.py @@ -0,0 +1,114 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Precision/Recall (PR) from the paper "Improved Precision and Recall +Metric for Assessing Generative Models". Matches the original implementation +by Kynkaanniemi et al. at +https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py""" + +import torch +from . import metric_utils + +# ---------------------------------------------------------------------------- + + +def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size): + assert 0 <= rank < num_gpus + num_cols = col_features.shape[0] + num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus + col_batches = torch.nn.functional.pad( + col_features, [0, 0, 0, -num_cols % num_batches] + ).chunk(num_batches) + dist_batches = [] + for col_batch in col_batches[rank::num_gpus]: + dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0] + for src in range(num_gpus): + dist_broadcast = dist_batch.clone() + if num_gpus > 1: + torch.distributed.broadcast(dist_broadcast, src=src) + dist_batches.append(dist_broadcast.cpu() if rank == 0 else None) + return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None + + +# ---------------------------------------------------------------------------- + + +def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size): + detector_url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt" + detector_kwargs = dict(return_features=True) + + real_features = ( + metric_utils.compute_feature_stats_for_dataset( + opts=opts, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + rel_lo=0, + rel_hi=0, + capture_all=True, + max_items=max_real, + ) + .get_all_torch() + .to(torch.float16) + .to(opts.device) + ) + + gen_features = ( + metric_utils.compute_feature_stats_for_generator( + opts=opts, + detector_url=detector_url, + detector_kwargs=detector_kwargs, + rel_lo=0, + rel_hi=1, + capture_all=True, + max_items=num_gen, + ) + .get_all_torch() + .to(torch.float16) + .to(opts.device) + ) + + results = dict() + for name, manifold, probes in [ + ("precision", real_features, gen_features), + ("recall", gen_features, real_features), + ]: + kth = [] + for manifold_batch in manifold.split(row_batch_size): + dist = compute_distances( + row_features=manifold_batch, + col_features=manifold, + num_gpus=opts.num_gpus, + rank=opts.rank, + col_batch_size=col_batch_size, + ) + kth.append( + dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) + if opts.rank == 0 + else None + ) + kth = torch.cat(kth) if opts.rank == 0 else None + pred = [] + for probes_batch in probes.split(row_batch_size): + dist = compute_distances( + row_features=probes_batch, + col_features=manifold, + num_gpus=opts.num_gpus, + rank=opts.rank, + col_batch_size=col_batch_size, + ) + pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None) + results[name] = float( + torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else "nan" + ) + return results["precision"], results["recall"] + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/parser.py b/stylegan2_ada_pytorch/parser.py new file mode 100644 index 0000000000000000000000000000000000000000..614dd24db786ca24206c339428014220b2989c31 --- /dev/null +++ b/stylegan2_ada_pytorch/parser.py @@ -0,0 +1,257 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from argparse import ArgumentParser + +def get_parser(): + parser = ArgumentParser(description="StyleGANv2") + # General options. + parser.add_argument( + "--json_config", + type=str, + default="", + help="Json config from where to load the configuration parameters.", + ) + + parser.add_argument( + "--exp_name", help="Experiment name", required=False, default="default_name" + ) + parser.add_argument( + "--base_root", + help="Where to save the results", + required=False, + default="training-runs", + metavar="DIR", + ) + parser.add_argument( + "--slurm_logdir", + help="Where to save the logs from SLURM", + required=False, + default="training-runs", + metavar="DIR", + ) + parser.add_argument( + "--partition", + help="Partition name for SLURM", + required=False, + default="learnlab", + ) + parser.add_argument( + "--slurm_time", + help="Time in minutes that an experiment runs in SLURM", + default=3200, + type=int, + metavar="INT", + ) + parser.add_argument( + "--gpus", help="Number of GPUs to use [default: 1]", type=int, metavar="INT" + ) + parser.add_argument( + "--nodes", + help="Number of nodes to use [default: 1]", + type=int, + metavar="INT", + default=1, + ) + parser.add_argument( + "--snap", help="Snapshot interval [default: 50 ticks]", type=int, metavar="INT" + ) + parser.add_argument( + "--seed", help="Random seed [default: 0]", type=int, metavar="INT" + ) + parser.add_argument( + "--port", + help="Port number for DDP connection [default: 40000]", + type=int, + default=40000, + metavar="INT", + ) + parser.add_argument( + "--dry-run", help="Print training options and exit", type=bool, metavar="BOOL" + ) + + # Dataset. + parser.add_argument( + "--data_root", + help="Path where to find the data", + metavar="PATH", + required=False, + default=None, + ) + parser.add_argument( + "--data", + help="Training data (directory or zip)", + metavar="PATH", + required=False, + default="datasets/cocostuff_128.zip", + ) + # parser.add_argument('--cond', + # help='Train conditional model based on dataset labels [default: false]', + # type=bool, metavar='BOOL') + parser.add_argument( + "--class_cond", + help="Use class labels to condition model [default: false]", + type=bool, + metavar="BOOL", + ) + parser.add_argument( + "--subset", + help="Train with only N images [default: all]", + type=int, + metavar="INT", + ) + parser.add_argument( + "--mirror", + help="Enable dataset x-flips [default: false]", + type=bool, + metavar="BOOL", + ) + parser.add_argument( + "--label_dim", + help="nb of classes when using class conditioning", + default=1000, + type=int, + metavar="INT", + ) + + # IC-GAN options for Dataset. + parser.add_argument( + "--root_feats", + help="Training data features as instance conditioning (hdf5 file)", + metavar="PATH", + required=False, + default="", + ) + parser.add_argument( + "--root_nns", + help="NN Training data for each instance conditioning (hdf5 file)", + metavar="PATH", + required=False, + default="", + ) + parser.add_argument( + "--instance_cond", + help="Use instance features to condition model [default: false]", + type=bool, + metavar="BOOL", + ) + parser.add_argument( + "--feature_augmentation", + help="Use horizontal flips in instances to obtain instance features [default: false]", + type=bool, + metavar="BOOL", + ) + + # Base config. + parser.add_argument( + "--cfg", + help="Base config [default: auto]", + choices=["auto", "stylegan2", "paper256", "paper512", "paper1024", "cifar"], + ) + parser.add_argument("--gamma", help="Override R1 gamma", type=float) + parser.add_argument( + "--kimg", help="Override training duration", type=int, metavar="INT" + ) + parser.add_argument("--batch", help="Override batch size", type=int, metavar="INT") + parser.add_argument("--lrate", help="Override lrate", type=float) + parser.add_argument("--num_channel_g", help="Override width of generator", type=int) + parser.add_argument( + "--num_channel_d", help="Override width of discriminator", type=int + ) + parser.add_argument( + "--channel_max_g", help="Override max width of generator", type=int + ) + parser.add_argument( + "--channel_max_d", help="Override max width of discriminator", type=int + ) + parser.add_argument( + "--hidden_dim_c", + help="Override embedding size in maping network for class conditioning", + type=int, + ) + parser.add_argument( + "--hidden_dim_h", + help="Override embedding size in maping network for class conditioning", + type=int, + ) + parser.add_argument( + "--es_patience", + help="Early stopping patience", + type=int, + default=100000000, + metavar="INT", + ) + + # Discriminator augmentation. + parser.add_argument( + "--aug", + help="Augmentation mode [default: ada]", + choices=["noaug", "ada", "fixed"], + ) + parser.add_argument( + "--p", help="Augmentation probability for --aug=fixed", type=float + ) + parser.add_argument("--target", help="ADA target value for --aug=ada", type=float) + parser.add_argument( + "--augpipe", + help="Augmentation pipeline [default: bgc]", + choices=[ + "blit", + "geom", + "color", + "filter", + "noise", + "cutout", + "bg", + "bgc", + "bgcf", + "bgcfn", + "bgcfnc", + ], + ) + + # Transfer learning. + parser.add_argument( + "--resume", help="Resume training [default: noresume]", metavar="PKL" + ) + parser.add_argument( + "--freezed", help="Freeze-D [default: 0 layers]", type=int, metavar="INT" + ) + + # Performance options. + parser.add_argument( + "--fp32", help="Disable mixed-precision training", type=bool, metavar="BOOL" + ) + parser.add_argument( + "--nhwc", help="Use NHWC memory format with FP16", type=bool, metavar="BOOL" + ) + parser.add_argument( + "--nobench", help="Disable cuDNN benchmarking", type=bool, metavar="BOOL" + ) + parser.add_argument( + "--allow-tf32", + help="Allow PyTorch to use TF32 internally", + type=bool, + metavar="BOOL", + ) + parser.add_argument( + "--workers", + help="Override number of DataLoader workers", + type=int, + metavar="INT", + ) + + ## Experiment setup + parser.add_argument( + "--slurm", + help="Using SLURM to launch the experiment in a cluster", + type=bool, + metavar="BOOL", + ) + return parser + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/projector.py b/stylegan2_ada_pytorch/projector.py new file mode 100755 index 0000000000000000000000000000000000000000..b6a8e20fa0a532eb63cef76111b4459522867870 --- /dev/null +++ b/stylegan2_ada_pytorch/projector.py @@ -0,0 +1,273 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Project given image to the latent space of pretrained network pickle.""" + +import copy +import os +from time import perf_counter + +import click +import imageio +import numpy as np +import PIL.Image +import torch +import torch.nn.functional as F + +import dnnlib +import legacy + + +def project( + G, + target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution + *, + num_steps=1000, + w_avg_samples=10000, + initial_learning_rate=0.1, + initial_noise_factor=0.05, + lr_rampdown_length=0.25, + lr_rampup_length=0.05, + noise_ramp_length=0.75, + regularize_noise_weight=1e5, + verbose=False, + device: torch.device, +): + assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution) + + def logprint(*args): + if verbose: + print(*args) + + G = copy.deepcopy(G).eval().requires_grad_(False).to(device) # type: ignore + + # Compute w stats. + logprint(f"Computing W midpoint and stddev using {w_avg_samples} samples...") + z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) + w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C] + w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C] + w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C] + w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 + + # Setup noise inputs. + noise_bufs = { + name: buf + for (name, buf) in G.synthesis.named_buffers() + if "noise_const" in name + } + + # Load VGG16 feature detector. + url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt" + with dnnlib.util.open_url(url) as f: + vgg16 = torch.jit.load(f).eval().to(device) + + # Features for target image. + target_images = target.unsqueeze(0).to(device).to(torch.float32) + if target_images.shape[2] > 256: + target_images = F.interpolate(target_images, size=(256, 256), mode="area") + target_features = vgg16(target_images, resize_images=False, return_lpips=True) + + w_opt = torch.tensor( + w_avg, dtype=torch.float32, device=device, requires_grad=True + ) # pylint: disable=not-callable + w_out = torch.zeros( + [num_steps] + list(w_opt.shape[1:]), dtype=torch.float32, device=device + ) + optimizer = torch.optim.Adam( + [w_opt] + list(noise_bufs.values()), + betas=(0.9, 0.999), + lr=initial_learning_rate, + ) + + # Init noise. + for buf in noise_bufs.values(): + buf[:] = torch.randn_like(buf) + buf.requires_grad = True + + for step in range(num_steps): + # Learning rate schedule. + t = step / num_steps + w_noise_scale = ( + w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2 + ) + lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length) + lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi) + lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length) + lr = initial_learning_rate * lr_ramp + for param_group in optimizer.param_groups: + param_group["lr"] = lr + + # Synth images from opt_w. + w_noise = torch.randn_like(w_opt) * w_noise_scale + ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1]) + synth_images = G.synthesis(ws, noise_mode="const") + + # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. + synth_images = (synth_images + 1) * (255 / 2) + if synth_images.shape[2] > 256: + synth_images = F.interpolate(synth_images, size=(256, 256), mode="area") + + # Features for synth images. + synth_features = vgg16(synth_images, resize_images=False, return_lpips=True) + dist = (target_features - synth_features).square().sum() + + # Noise regularization. + reg_loss = 0.0 + for v in noise_bufs.values(): + noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d() + while True: + reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2 + reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2 + if noise.shape[2] <= 8: + break + noise = F.avg_pool2d(noise, kernel_size=2) + loss = dist + reg_loss * regularize_noise_weight + + # Step + optimizer.zero_grad(set_to_none=True) + loss.backward() + optimizer.step() + logprint( + f"step {step+1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}" + ) + + # Save projected W for each optimization step. + w_out[step] = w_opt.detach()[0] + + # Normalize noise. + with torch.no_grad(): + for buf in noise_bufs.values(): + buf -= buf.mean() + buf *= buf.square().mean().rsqrt() + + return w_out.repeat([1, G.mapping.num_ws, 1]) + + +# ---------------------------------------------------------------------------- + + +@click.command() +@click.option("--network", "network_pkl", help="Network pickle filename", required=True) +@click.option( + "--target", + "target_fname", + help="Target image file to project to", + required=True, + metavar="FILE", +) +@click.option( + "--num-steps", + help="Number of optimization steps", + type=int, + default=1000, + show_default=True, +) +@click.option("--seed", help="Random seed", type=int, default=303, show_default=True) +@click.option( + "--save-video", + help="Save an mp4 video of optimization progress", + type=bool, + default=True, + show_default=True, +) +@click.option( + "--outdir", help="Where to save the output images", required=True, metavar="DIR" +) +def run_projection( + network_pkl: str, + target_fname: str, + outdir: str, + save_video: bool, + seed: int, + num_steps: int, +): + """Project given image to the latent space of pretrained network pickle. + + Examples: + + \b + python projector.py --outdir=out --target=~/mytargetimg.png \\ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl + """ + np.random.seed(seed) + torch.manual_seed(seed) + + # Load networks. + print('Loading networks from "%s"...' % network_pkl) + device = torch.device("cuda") + with dnnlib.util.open_url(network_pkl) as fp: + G = ( + legacy.load_network_pkl(fp)["G_ema"].requires_grad_(False).to(device) + ) # type: ignore + + # Load target image. + target_pil = PIL.Image.open(target_fname).convert("RGB") + w, h = target_pil.size + s = min(w, h) + target_pil = target_pil.crop( + ((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2) + ) + target_pil = target_pil.resize( + (G.img_resolution, G.img_resolution), PIL.Image.LANCZOS + ) + target_uint8 = np.array(target_pil, dtype=np.uint8) + + # Optimize projection. + start_time = perf_counter() + projected_w_steps = project( + G, + target=torch.tensor( + target_uint8.transpose([2, 0, 1]), device=device + ), # pylint: disable=not-callable + num_steps=num_steps, + device=device, + verbose=True, + ) + print(f"Elapsed: {(perf_counter()-start_time):.1f} s") + + # Render debug output: optional video and projected image and W vector. + os.makedirs(outdir, exist_ok=True) + if save_video: + video = imageio.get_writer( + f"{outdir}/proj.mp4", mode="I", fps=10, codec="libx264", bitrate="16M" + ) + print(f'Saving optimization progress video "{outdir}/proj.mp4"') + for projected_w in projected_w_steps: + synth_image = G.synthesis(projected_w.unsqueeze(0), noise_mode="const") + synth_image = (synth_image + 1) * (255 / 2) + synth_image = ( + synth_image.permute(0, 2, 3, 1) + .clamp(0, 255) + .to(torch.uint8)[0] + .cpu() + .numpy() + ) + video.append_data(np.concatenate([target_uint8, synth_image], axis=1)) + video.close() + + # Save final projected frame and W vector. + target_pil.save(f"{outdir}/target.png") + projected_w = projected_w_steps[-1] + synth_image = G.synthesis(projected_w.unsqueeze(0), noise_mode="const") + synth_image = (synth_image + 1) * (255 / 2) + synth_image = ( + synth_image.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy() + ) + PIL.Image.fromarray(synth_image, "RGB").save(f"{outdir}/proj.png") + np.savez(f"{outdir}/projected_w.npz", w=projected_w.unsqueeze(0).cpu().numpy()) + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + run_projection() # pylint: disable=no-value-for-parameter + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/run.py b/stylegan2_ada_pytorch/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3b67ca6f775bec0e7f5c4626e13760447b30b646 --- /dev/null +++ b/stylegan2_ada_pytorch/run.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +from train import main +from submitit.helpers import Checkpointable + +LOCAL = False +try: + import submitit +except: + print( + "No submitit package found! Defaulting to executing the script in the local machine" + ) + LOCAL = True +import parser +import json + + +class Trainer(Checkpointable): + def __call__(self, args, slurm=False): + if slurm and not LOCAL: + main( + args, + args.outdir, + master_node=submitit.JobEnvironment().hostnames[0], + port=args.port, + ) + else: + main(args, args.outdir, master_node="", dry_run=args.dry_run) + + +if __name__ == "__main__": + parser_ = parser.get_parser() + args = parser_.parse_args() + + if args.json_config != "": + data = json.load(open(args.json_config)) + for key in data.keys(): + setattr(args, key, data[key]) + else: + print("Not using JSON configuration file!") + if args.data_root is not None: + print("Appending data_root to paths") + args.data = os.path.join(args.data_root, args.data) + args.root_feats = os.path.join(args.data_root, args.root_feats) + args.root_nns = os.path.join(args.data_root, args.root_nns) + args.outdir = args.base_root + + trainer = Trainer() + if not args.slurm or LOCAL: + trainer(args) + else: + + executor = submitit.SlurmExecutor(folder=args.slurm_logdir, max_num_timeout=60) + print(args.gpus) + executor.update_parameters( + gpus_per_node=args.gpus, + partition=args.partition, + constraint="volta32gb", + nodes=args.nodes, + ntasks_per_node=args.gpus, + cpus_per_task=10, + mem=256000, + time=args.slurm_time, + job_name=args.exp_name, + exclusive=True if args.gpus == 8 else False, + ) + + job = executor.submit(trainer, args, slurm=True) + print(job.job_id) + + import time + + time.sleep(1) diff --git a/stylegan2_ada_pytorch/style_mixing.py b/stylegan2_ada_pytorch/style_mixing.py new file mode 100755 index 0000000000000000000000000000000000000000..a279d98591eed36bbb3c873cfe44a3686aa5507b --- /dev/null +++ b/stylegan2_ada_pytorch/style_mixing.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Generate style mixing image matrix using pretrained network pickle.""" + +import os +import re +from typing import List + +import click +import dnnlib +import numpy as np +import PIL.Image +import torch + +import legacy + +# ---------------------------------------------------------------------------- + + +def num_range(s: str) -> List[int]: + """Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.""" + + range_re = re.compile(r"^(\d+)-(\d+)$") + m = range_re.match(s) + if m: + return list(range(int(m.group(1)), int(m.group(2)) + 1)) + vals = s.split(",") + return [int(x) for x in vals] + + +# ---------------------------------------------------------------------------- + + +@click.command() +@click.option("--network", "network_pkl", help="Network pickle filename", required=True) +@click.option( + "--rows", + "row_seeds", + type=num_range, + help="Random seeds to use for image rows", + required=True, +) +@click.option( + "--cols", + "col_seeds", + type=num_range, + help="Random seeds to use for image columns", + required=True, +) +@click.option( + "--styles", + "col_styles", + type=num_range, + help="Style layer range", + default="0-6", + show_default=True, +) +@click.option( + "--trunc", + "truncation_psi", + type=float, + help="Truncation psi", + default=1, + show_default=True, +) +@click.option( + "--noise-mode", + help="Noise mode", + type=click.Choice(["const", "random", "none"]), + default="const", + show_default=True, +) +@click.option("--outdir", type=str, required=True) +def generate_style_mix( + network_pkl: str, + row_seeds: List[int], + col_seeds: List[int], + col_styles: List[int], + truncation_psi: float, + noise_mode: str, + outdir: str, +): + """Generate images using pretrained network pickle. + + Examples: + + \b + python style_mixing.py --outdir=out --rows=85,100,75,458,1500 --cols=55,821,1789,293 \\ + --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl + """ + print('Loading networks from "%s"...' % network_pkl) + device = torch.device("cuda") + with dnnlib.util.open_url(network_pkl) as f: + G = legacy.load_network_pkl(f)["G_ema"].to(device) # type: ignore + + os.makedirs(outdir, exist_ok=True) + + print("Generating W vectors...") + all_seeds = list(set(row_seeds + col_seeds)) + all_z = np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds]) + all_w = G.mapping(torch.from_numpy(all_z).to(device), None) + w_avg = G.mapping.w_avg + all_w = w_avg + (all_w - w_avg) * truncation_psi + w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))} + + print("Generating images...") + all_images = G.synthesis(all_w, noise_mode=noise_mode) + all_images = ( + (all_images.permute(0, 2, 3, 1) * 127.5 + 128) + .clamp(0, 255) + .to(torch.uint8) + .cpu() + .numpy() + ) + image_dict = { + (seed, seed): image for seed, image in zip(all_seeds, list(all_images)) + } + + print("Generating style-mixed images...") + for row_seed in row_seeds: + for col_seed in col_seeds: + w = w_dict[row_seed].clone() + w[col_styles] = w_dict[col_seed][col_styles] + image = G.synthesis(w[np.newaxis], noise_mode=noise_mode) + image = ( + (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) + ) + image_dict[(row_seed, col_seed)] = image[0].cpu().numpy() + + print("Saving images...") + os.makedirs(outdir, exist_ok=True) + for (row_seed, col_seed), image in image_dict.items(): + PIL.Image.fromarray(image, "RGB").save(f"{outdir}/{row_seed}-{col_seed}.png") + + print("Saving image grid...") + W = G.img_resolution + H = G.img_resolution + canvas = PIL.Image.new( + "RGB", (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), "black" + ) + for row_idx, row_seed in enumerate([0] + row_seeds): + for col_idx, col_seed in enumerate([0] + col_seeds): + if row_idx == 0 and col_idx == 0: + continue + key = (row_seed, col_seed) + if row_idx == 0: + key = (col_seed, col_seed) + if col_idx == 0: + key = (row_seed, row_seed) + canvas.paste( + PIL.Image.fromarray(image_dict[key], "RGB"), (W * col_idx, H * row_idx) + ) + canvas.save(f"{outdir}/grid.png") + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + generate_style_mix() # pylint: disable=no-value-for-parameter + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/__init__.py b/stylegan2_ada_pytorch/torch_utils/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..ece0ea08fe2e939cc260a1dafc0ab5b391b773d9 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/stylegan2_ada_pytorch/torch_utils/custom_ops.py b/stylegan2_ada_pytorch/torch_utils/custom_ops.py new file mode 100755 index 0000000000000000000000000000000000000000..b0cd3afa4659002aebdc76978cbc4dec0c37f6df --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/custom_ops.py @@ -0,0 +1,151 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import glob +import torch +import torch.utils.cpp_extension +import importlib +import hashlib +import shutil +from pathlib import Path + +from torch.utils.file_baton import FileBaton + +# ---------------------------------------------------------------------------- +# Global options. + +verbosity = "brief" # Verbosity level: 'none', 'brief', 'full' + +# ---------------------------------------------------------------------------- +# Internal helper funcs. + + +def _find_compiler_bindir(): + patterns = [ + "C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64", + "C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64", + "C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64", + "C:/Program Files (x86)/Microsoft Visual Studio */vc/bin", + ] + for pattern in patterns: + matches = sorted(glob.glob(pattern)) + if len(matches): + return matches[-1] + return None + + +# ---------------------------------------------------------------------------- +# Main entry point for compiling and loading C++/CUDA plugins. + +_cached_plugins = dict() + + +def get_plugin(module_name, sources, **build_kwargs): + assert verbosity in ["none", "brief", "full"] + + # Already cached? + if module_name in _cached_plugins: + return _cached_plugins[module_name] + + # Print status. + if verbosity == "full": + print(f'Setting up PyTorch plugin "{module_name}"...') + elif verbosity == "brief": + print(f'Setting up PyTorch plugin "{module_name}"... ', end="", flush=True) + + try: # pylint: disable=too-many-nested-blocks + # Make sure we can find the necessary compiler binaries. + if os.name == "nt" and os.system("where cl.exe >nul 2>nul") != 0: + compiler_bindir = _find_compiler_bindir() + if compiler_bindir is None: + raise RuntimeError( + f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".' + ) + os.environ["PATH"] += ";" + compiler_bindir + + # Compile and load. + verbose_build = verbosity == "full" + + # Incremental build md5sum trickery. Copies all the input source files + # into a cached build directory under a combined md5 digest of the input + # source files. Copying is done only if the combined digest has changed. + # This keeps input file timestamps and filenames the same as in previous + # extension builds, allowing for fast incremental rebuilds. + # + # This optimization is done only in case all the source files reside in + # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR + # environment variable is set (we take this as a signal that the user + # actually cares about this.) + source_dirs_set = set(os.path.dirname(source) for source in sources) + if len(source_dirs_set) == 1 and ("TORCH_EXTENSIONS_DIR" in os.environ): + all_source_files = sorted( + list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()) + ) + + # Compute a combined hash digest for all source files in the same + # custom op directory (usually .cu, .cpp, .py and .h files). + hash_md5 = hashlib.md5() + for src in all_source_files: + with open(src, "rb") as f: + hash_md5.update(f.read()) + build_dir = torch.utils.cpp_extension._get_build_directory( + module_name, verbose=verbose_build + ) # pylint: disable=protected-access + digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest()) + + if not os.path.isdir(digest_build_dir): + os.makedirs(digest_build_dir, exist_ok=True) + baton = FileBaton(os.path.join(digest_build_dir, "lock")) + if baton.try_acquire(): + try: + for src in all_source_files: + shutil.copyfile( + src, + os.path.join(digest_build_dir, os.path.basename(src)), + ) + finally: + baton.release() + else: + # Someone else is copying source files under the digest dir, + # wait until done and continue. + baton.wait() + digest_sources = [ + os.path.join(digest_build_dir, os.path.basename(x)) for x in sources + ] + torch.utils.cpp_extension.load( + name=module_name, + build_directory=build_dir, + verbose=verbose_build, + sources=digest_sources, + **build_kwargs, + ) + else: + torch.utils.cpp_extension.load( + name=module_name, verbose=verbose_build, sources=sources, **build_kwargs + ) + module = importlib.import_module(module_name) + + except: + if verbosity == "brief": + print("Failed!") + raise + + # Print status and add to cache. + if verbosity == "full": + print(f'Done setting up PyTorch plugin "{module_name}".') + elif verbosity == "brief": + print("Done.") + _cached_plugins[module_name] = module + return module + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/misc.py b/stylegan2_ada_pytorch/torch_utils/misc.py new file mode 100755 index 0000000000000000000000000000000000000000..fc80d0e967b7be7ed1c9b1f92d0f264e202ca997 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/misc.py @@ -0,0 +1,339 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import re +import contextlib +import numpy as np +import torch +import warnings +import sys + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +import stylegan2_ada_pytorch.dnnlib as dnnlib + +# ---------------------------------------------------------------------------- +# Cached construction of constant tensors. Avoids CPU=>GPU copy when the +# same constant is used multiple times. + +_constant_cache = dict() + + +def constant(value, shape=None, dtype=None, device=None, memory_format=None): + value = np.asarray(value) + if shape is not None: + shape = tuple(shape) + if dtype is None: + dtype = torch.get_default_dtype() + if device is None: + device = torch.device("cpu") + if memory_format is None: + memory_format = torch.contiguous_format + + key = ( + value.shape, + value.dtype, + value.tobytes(), + shape, + dtype, + device, + memory_format, + ) + tensor = _constant_cache.get(key, None) + if tensor is None: + tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) + if shape is not None: + tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) + tensor = tensor.contiguous(memory_format=memory_format) + _constant_cache[key] = tensor + return tensor + + +# ---------------------------------------------------------------------------- +# Replace NaN/Inf with specified numerical values. + +try: + nan_to_num = torch.nan_to_num # 1.8.0a0 +except AttributeError: + + def nan_to_num( + input, nan=0.0, posinf=None, neginf=None, *, out=None + ): # pylint: disable=redefined-builtin + assert isinstance(input, torch.Tensor) + if posinf is None: + posinf = torch.finfo(input.dtype).max + if neginf is None: + neginf = torch.finfo(input.dtype).min + assert nan == 0 + return torch.clamp( + input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out + ) + + +# ---------------------------------------------------------------------------- +# Symbolic assert. + +try: + symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access +except AttributeError: + symbolic_assert = torch.Assert # 1.7.0 + +# ---------------------------------------------------------------------------- +# Context manager to suppress known warnings in torch.jit.trace(). + + +class suppress_tracer_warnings(warnings.catch_warnings): + def __enter__(self): + super().__enter__() + warnings.simplefilter("ignore", category=torch.jit.TracerWarning) + return self + + +# ---------------------------------------------------------------------------- +# Assert that the shape of a tensor matches the given list of integers. +# None indicates that the size of a dimension is allowed to vary. +# Performs symbolic assertion when used in torch.jit.trace(). + + +def assert_shape(tensor, ref_shape): + if tensor.ndim != len(ref_shape): + raise AssertionError( + f"Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}" + ) + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if ref_size is None: + pass + elif isinstance(ref_size, torch.Tensor): + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert( + torch.equal(torch.as_tensor(size), ref_size), + f"Wrong size for dimension {idx}", + ) + elif isinstance(size, torch.Tensor): + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert( + torch.equal(size, torch.as_tensor(ref_size)), + f"Wrong size for dimension {idx}: expected {ref_size}", + ) + elif size != ref_size: + raise AssertionError( + f"Wrong size for dimension {idx}: got {size}, expected {ref_size}" + ) + + +# ---------------------------------------------------------------------------- +# Function decorator that calls torch.autograd.profiler.record_function(). + + +def profiled_function(fn): + def decorator(*args, **kwargs): + with torch.autograd.profiler.record_function(fn.__name__): + return fn(*args, **kwargs) + + decorator.__name__ = fn.__name__ + return decorator + + +# ---------------------------------------------------------------------------- +# Sampler for torch.utils.data.DataLoader that loops over the dataset +# indefinitely, shuffling items as it goes. + + +class InfiniteSampler(torch.utils.data.Sampler): + def __init__( + self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5 + ): + assert len(dataset) > 0 + assert num_replicas > 0 + assert 0 <= rank < num_replicas + assert 0 <= window_size <= 1 + super().__init__(dataset) + self.dataset = dataset + self.rank = rank + self.num_replicas = num_replicas + self.shuffle = shuffle + self.seed = seed + self.window_size = window_size + + def __iter__(self): + order = np.arange(len(self.dataset)) + rnd = None + window = 0 + if self.shuffle: + rnd = np.random.RandomState(self.seed) + rnd.shuffle(order) + window = int(np.rint(order.size * self.window_size)) + + idx = 0 + while True: + i = idx % order.size + if idx % self.num_replicas == self.rank: + yield order[i] + if window >= 2: + j = (i - rnd.randint(window)) % order.size + order[i], order[j] = order[j], order[i] + idx += 1 + + +# ---------------------------------------------------------------------------- +# Utilities for operating with torch.nn.Module parameters and buffers. + + +def params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.parameters()) + list(module.buffers()) + + +def named_params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.named_parameters()) + list(module.named_buffers()) + + +def copy_params_and_buffers(src_module, dst_module, require_all=False): + assert isinstance(src_module, torch.nn.Module) + assert isinstance(dst_module, torch.nn.Module) + src_tensors = { + name: tensor for name, tensor in named_params_and_buffers(src_module) + } + for name, tensor in named_params_and_buffers(dst_module): + assert (name in src_tensors) or (not require_all) + if name in src_tensors: + tensor.copy_(src_tensors[name].detach()).requires_grad_( + tensor.requires_grad + ) + + +# ---------------------------------------------------------------------------- +# Context manager for easily enabling/disabling DistributedDataParallel +# synchronization. + + +@contextlib.contextmanager +def ddp_sync(module, sync): + assert isinstance(module, torch.nn.Module) + if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel): + yield + else: + with module.no_sync(): + yield + + +# ---------------------------------------------------------------------------- +# Check DistributedDataParallel consistency across processes. + + +def check_ddp_consistency(module, ignore_regex=None): + assert isinstance(module, torch.nn.Module) + for name, tensor in named_params_and_buffers(module): + fullname = type(module).__name__ + "." + name + if ignore_regex is not None and re.fullmatch(ignore_regex, fullname): + continue + tensor = tensor.detach() + other = tensor.clone() + torch.distributed.broadcast(tensor=other, src=0) + assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname + + +# ---------------------------------------------------------------------------- +# Print summary table of module hierarchy. + + +def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): + assert isinstance(module, torch.nn.Module) + assert not isinstance(module, torch.jit.ScriptModule) + assert isinstance(inputs, (tuple, list)) + + # Register hooks. + entries = [] + nesting = [0] + + def pre_hook(_mod, _inputs): + nesting[0] += 1 + + def post_hook(mod, _inputs, outputs): + nesting[0] -= 1 + if nesting[0] <= max_nesting: + outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs] + outputs = [t for t in outputs if isinstance(t, torch.Tensor)] + entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) + + hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()] + hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] + + # Run module. + outputs = module(*inputs) + for hook in hooks: + hook.remove() + + # Identify unique outputs, parameters, and buffers. + tensors_seen = set() + for e in entries: + e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen] + e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen] + e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] + tensors_seen |= { + id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs + } + + # Filter out redundant entries. + if skip_redundant: + entries = [ + e + for e in entries + if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs) + ] + + # Construct table. + rows = [ + [type(module).__name__, "Parameters", "Buffers", "Output shape", "Datatype"] + ] + rows += [["---"] * len(rows[0])] + param_total = 0 + buffer_total = 0 + submodule_names = {mod: name for name, mod in module.named_modules()} + for e in entries: + name = "" if e.mod is module else submodule_names[e.mod] + param_size = sum(t.numel() for t in e.unique_params) + buffer_size = sum(t.numel() for t in e.unique_buffers) + output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs] + output_dtypes = [str(t.dtype).split(".")[-1] for t in e.outputs] + rows += [ + [ + name + (":0" if len(e.outputs) >= 2 else ""), + str(param_size) if param_size else "-", + str(buffer_size) if buffer_size else "-", + (output_shapes + ["-"])[0], + (output_dtypes + ["-"])[0], + ] + ] + for idx in range(1, len(e.outputs)): + rows += [ + [name + f":{idx}", "-", "-", output_shapes[idx], output_dtypes[idx]] + ] + param_total += param_size + buffer_total += buffer_size + rows += [["---"] * len(rows[0])] + rows += [["Total", str(param_total), str(buffer_total), "-", "-"]] + + # Print table. + widths = [max(len(cell) for cell in column) for column in zip(*rows)] + print() + for row in rows: + print( + " ".join( + cell + " " * (width - len(cell)) for cell, width in zip(row, widths) + ) + ) + print() + return outputs + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/ops/__init__.py b/stylegan2_ada_pytorch/torch_utils/ops/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..ece0ea08fe2e939cc260a1dafc0ab5b391b773d9 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/stylegan2_ada_pytorch/torch_utils/ops/bias_act.cpp b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.cpp new file mode 100755 index 0000000000000000000000000000000000000000..1802bfaf19a11ed740551387b40f43fdae0738f8 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.cpp @@ -0,0 +1,102 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// All rights reserved. +// +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ + +static bool has_same_layout(torch::Tensor x, torch::Tensor y) +{ + if (x.dim() != y.dim()) + return false; + for (int64_t i = 0; i < x.dim(); i++) + { + if (x.size(i) != y.size(i)) + return false; + if (x.size(i) >= 2 && x.stride(i) != y.stride(i)) + return false; + } + return true; +} + +//------------------------------------------------------------------------ + +static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x"); + TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x"); + TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x"); + TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(b.dim() == 1, "b must have rank 1"); + TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds"); + TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements"); + TORCH_CHECK(grad >= 0, "grad must be non-negative"); + + // Validate layout. + TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense"); + TORCH_CHECK(b.is_contiguous(), "b must be contiguous"); + TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x"); + TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x"); + TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + torch::Tensor y = torch::empty_like(x); + TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x"); + + // Initialize CUDA kernel parameters. + bias_act_kernel_params p; + p.x = x.data_ptr(); + p.b = (b.numel()) ? b.data_ptr() : NULL; + p.xref = (xref.numel()) ? xref.data_ptr() : NULL; + p.yref = (yref.numel()) ? yref.data_ptr() : NULL; + p.dy = (dy.numel()) ? dy.data_ptr() : NULL; + p.y = y.data_ptr(); + p.grad = grad; + p.act = act; + p.alpha = alpha; + p.gain = gain; + p.clamp = clamp; + p.sizeX = (int)x.numel(); + p.sizeB = (int)b.numel(); + p.stepB = (b.numel()) ? (int)x.stride(dim) : 1; + + // Choose CUDA kernel. + void* kernel; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + kernel = choose_bias_act_kernel(p); + }); + TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func"); + + // Launch CUDA kernel. + p.loopX = 4; + int blockSize = 4 * 32; + int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1; + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("bias_act", &bias_act); +} + +//------------------------------------------------------------------------ diff --git a/stylegan2_ada_pytorch/torch_utils/ops/bias_act.cu b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.cu new file mode 100755 index 0000000000000000000000000000000000000000..2313fb7fa8c705c47753ba39d0a7d411c6599a5f --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.cu @@ -0,0 +1,176 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// All rights reserved. +// +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +//------------------------------------------------------------------------ +// CUDA kernel. + +template +__global__ void bias_act_kernel(bias_act_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + int G = p.grad; + scalar_t alpha = (scalar_t)p.alpha; + scalar_t gain = (scalar_t)p.gain; + scalar_t clamp = (scalar_t)p.clamp; + scalar_t one = (scalar_t)1; + scalar_t two = (scalar_t)2; + scalar_t expRange = (scalar_t)80; + scalar_t halfExpRange = (scalar_t)40; + scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946; + scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717; + + // Loop over elements. + int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x; + for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x) + { + // Load. + scalar_t x = (scalar_t)((const T*)p.x)[xi]; + scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0; + scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0; + scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0; + scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one; + scalar_t yy = (gain != 0) ? yref / gain : 0; + scalar_t y = 0; + + // Apply bias. + ((G == 0) ? x : xref) += b; + + // linear + if (A == 1) + { + if (G == 0) y = x; + if (G == 1) y = x; + } + + // relu + if (A == 2) + { + if (G == 0) y = (x > 0) ? x : 0; + if (G == 1) y = (yy > 0) ? x : 0; + } + + // lrelu + if (A == 3) + { + if (G == 0) y = (x > 0) ? x : x * alpha; + if (G == 1) y = (yy > 0) ? x : x * alpha; + } + + // tanh + if (A == 4) + { + if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); } + if (G == 1) y = x * (one - yy * yy); + if (G == 2) y = x * (one - yy * yy) * (-two * yy); + } + + // sigmoid + if (A == 5) + { + if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one); + if (G == 1) y = x * yy * (one - yy); + if (G == 2) y = x * yy * (one - yy) * (one - two * yy); + } + + // elu + if (A == 6) + { + if (G == 0) y = (x >= 0) ? x : exp(x) - one; + if (G == 1) y = (yy >= 0) ? x : x * (yy + one); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one); + } + + // selu + if (A == 7) + { + if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one); + if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha); + } + + // softplus + if (A == 8) + { + if (G == 0) y = (x > expRange) ? x : log(exp(x) + one); + if (G == 1) y = x * (one - exp(-yy)); + if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); } + } + + // swish + if (A == 9) + { + if (G == 0) + y = (x < -expRange) ? 0 : x / (exp(-x) + one); + else + { + scalar_t c = exp(xref); + scalar_t d = c + one; + if (G == 1) + y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d); + else + y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d); + yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain; + } + } + + // Apply gain. + y *= gain * dy; + + // Clamp. + if (clamp >= 0) + { + if (G == 0) + y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp; + else + y = (yref > -clamp & yref < clamp) ? y : 0; + } + + // Store. + ((T*)p.y)[xi] = (T)y; + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p) +{ + if (p.act == 1) return (void*)bias_act_kernel; + if (p.act == 2) return (void*)bias_act_kernel; + if (p.act == 3) return (void*)bias_act_kernel; + if (p.act == 4) return (void*)bias_act_kernel; + if (p.act == 5) return (void*)bias_act_kernel; + if (p.act == 6) return (void*)bias_act_kernel; + if (p.act == 7) return (void*)bias_act_kernel; + if (p.act == 8) return (void*)bias_act_kernel; + if (p.act == 9) return (void*)bias_act_kernel; + return NULL; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/stylegan2_ada_pytorch/torch_utils/ops/bias_act.h b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.h new file mode 100755 index 0000000000000000000000000000000000000000..c39bc72c776e2b910fb8b589773025a5fbbf5dc5 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.h @@ -0,0 +1,41 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// All rights reserved. +// +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct bias_act_kernel_params +{ + const void* x; // [sizeX] + const void* b; // [sizeB] or NULL + const void* xref; // [sizeX] or NULL + const void* yref; // [sizeX] or NULL + const void* dy; // [sizeX] or NULL + void* y; // [sizeX] + + int grad; + int act; + float alpha; + float gain; + float clamp; + + int sizeX; + int sizeB; + int stepB; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/stylegan2_ada_pytorch/torch_utils/ops/bias_act.py b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.py new file mode 100755 index 0000000000000000000000000000000000000000..50b8bd9898a47681907eeed58888b451f795f5aa --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/bias_act.py @@ -0,0 +1,324 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient bias and activation.""" + +import os +import warnings +import numpy as np +import torch +import dnnlib +import traceback + +from .. import custom_ops +from .. import misc + +# ---------------------------------------------------------------------------- + +activation_funcs = { + "linear": dnnlib.EasyDict( + func=lambda x, **_: x, + def_alpha=0, + def_gain=1, + cuda_idx=1, + ref="", + has_2nd_grad=False, + ), + "relu": dnnlib.EasyDict( + func=lambda x, **_: torch.nn.functional.relu(x), + def_alpha=0, + def_gain=np.sqrt(2), + cuda_idx=2, + ref="y", + has_2nd_grad=False, + ), + "lrelu": dnnlib.EasyDict( + func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), + def_alpha=0.2, + def_gain=np.sqrt(2), + cuda_idx=3, + ref="y", + has_2nd_grad=False, + ), + "tanh": dnnlib.EasyDict( + func=lambda x, **_: torch.tanh(x), + def_alpha=0, + def_gain=1, + cuda_idx=4, + ref="y", + has_2nd_grad=True, + ), + "sigmoid": dnnlib.EasyDict( + func=lambda x, **_: torch.sigmoid(x), + def_alpha=0, + def_gain=1, + cuda_idx=5, + ref="y", + has_2nd_grad=True, + ), + "elu": dnnlib.EasyDict( + func=lambda x, **_: torch.nn.functional.elu(x), + def_alpha=0, + def_gain=1, + cuda_idx=6, + ref="y", + has_2nd_grad=True, + ), + "selu": dnnlib.EasyDict( + func=lambda x, **_: torch.nn.functional.selu(x), + def_alpha=0, + def_gain=1, + cuda_idx=7, + ref="y", + has_2nd_grad=True, + ), + "softplus": dnnlib.EasyDict( + func=lambda x, **_: torch.nn.functional.softplus(x), + def_alpha=0, + def_gain=1, + cuda_idx=8, + ref="y", + has_2nd_grad=True, + ), + "swish": dnnlib.EasyDict( + func=lambda x, **_: torch.sigmoid(x) * x, + def_alpha=0, + def_gain=np.sqrt(2), + cuda_idx=9, + ref="x", + has_2nd_grad=True, + ), +} + +# ---------------------------------------------------------------------------- + +_inited = False +_plugin = None +_null_tensor = torch.empty([0]) + + +def _init(): + global _inited, _plugin + if not _inited: + _inited = True + sources = ["bias_act.cpp", "bias_act.cu"] + sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] + try: + _plugin = custom_ops.get_plugin( + "bias_act_plugin", + sources=sources, + extra_cuda_cflags=["--use_fast_math"], + ) + except: + warnings.warn( + "Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n" + + traceback.format_exc() + ) + return _plugin is not None + + +# ---------------------------------------------------------------------------- + + +def bias_act( + x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None, impl="cuda" +): + r"""Fused bias and activation function. + + Adds bias `b` to activation tensor `x`, evaluates activation function `act`, + and scales the result by `gain`. Each of the steps is optional. In most cases, + the fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports first and second order gradients, + but not third order gradients. + + Args: + x: Input activation tensor. Can be of any shape. + b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type + as `x`. The shape must be known, and it must match the dimension of `x` + corresponding to `dim`. + dim: The dimension in `x` corresponding to the elements of `b`. + The value of `dim` is ignored if `b` is not specified. + act: Name of the activation function to evaluate, or `"linear"` to disable. + Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. + See `activation_funcs` for a full list. `None` is not allowed. + alpha: Shape parameter for the activation function, or `None` to use the default. + gain: Scaling factor for the output tensor, or `None` to use default. + See `activation_funcs` for the default scaling of each activation function. + If unsure, consider specifying 1. + clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable + the clamping (default). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the same shape and datatype as `x`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ["ref", "cuda"] + if impl == "cuda" and x.device.type == "cuda" and _init(): + return _bias_act_cuda( + dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp + ).apply(x, b) + return _bias_act_ref( + x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp + ) + + +# ---------------------------------------------------------------------------- + + +@misc.profiled_function +def _bias_act_ref(x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None): + """Slow reference implementation of `bias_act()` using standard TensorFlow ops. + """ + assert isinstance(x, torch.Tensor) + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + # Add bias. + if b is not None: + assert isinstance(b, torch.Tensor) and b.ndim == 1 + assert 0 <= dim < x.ndim + assert b.shape[0] == x.shape[dim] + x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]) + + # Evaluate activation function. + alpha = float(alpha) + x = spec.func(x, alpha=alpha) + + # Scale by gain. + gain = float(gain) + if gain != 1: + x = x * gain + + # Clamp. + if clamp >= 0: + x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type + return x + + +# ---------------------------------------------------------------------------- + +_bias_act_cuda_cache = dict() + + +def _bias_act_cuda(dim=1, act="linear", alpha=None, gain=None, clamp=None): + """Fast CUDA implementation of `bias_act()` using custom ops. + """ + # Parse arguments. + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + # Lookup from cache. + key = (dim, act, alpha, gain, clamp) + if key in _bias_act_cuda_cache: + return _bias_act_cuda_cache[key] + + # Forward op. + class BiasActCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, b): # pylint: disable=arguments-differ + ctx.memory_format = ( + torch.channels_last + if x.ndim > 2 and x.stride()[1] == 1 + else torch.contiguous_format + ) + x = x.contiguous(memory_format=ctx.memory_format) + b = b.contiguous() if b is not None else _null_tensor + y = x + if act != "linear" or gain != 1 or clamp >= 0 or b is not _null_tensor: + y = _plugin.bias_act( + x, + b, + _null_tensor, + _null_tensor, + _null_tensor, + 0, + dim, + spec.cuda_idx, + alpha, + gain, + clamp, + ) + ctx.save_for_backward( + x if "x" in spec.ref or spec.has_2nd_grad else _null_tensor, + b if "x" in spec.ref or spec.has_2nd_grad else _null_tensor, + y if "y" in spec.ref else _null_tensor, + ) + return y + + @staticmethod + def backward(ctx, dy): # pylint: disable=arguments-differ + dy = dy.contiguous(memory_format=ctx.memory_format) + x, b, y = ctx.saved_tensors + dx = None + db = None + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + dx = dy + if act != "linear" or gain != 1 or clamp >= 0: + dx = BiasActCudaGrad.apply(dy, x, b, y) + + if ctx.needs_input_grad[1]: + db = dx.sum([i for i in range(dx.ndim) if i != dim]) + + return dx, db + + # Backward op. + class BiasActCudaGrad(torch.autograd.Function): + @staticmethod + def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ + ctx.memory_format = ( + torch.channels_last + if dy.ndim > 2 and dy.stride()[1] == 1 + else torch.contiguous_format + ) + dx = _plugin.bias_act( + dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp + ) + ctx.save_for_backward(dy if spec.has_2nd_grad else _null_tensor, x, b, y) + return dx + + @staticmethod + def backward(ctx, d_dx): # pylint: disable=arguments-differ + d_dx = d_dx.contiguous(memory_format=ctx.memory_format) + dy, x, b, y = ctx.saved_tensors + d_dy = None + d_x = None + d_b = None + d_y = None + + if ctx.needs_input_grad[0]: + d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) + + if spec.has_2nd_grad and ( + ctx.needs_input_grad[1] or ctx.needs_input_grad[2] + ): + d_x = _plugin.bias_act( + d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp + ) + + if spec.has_2nd_grad and ctx.needs_input_grad[2]: + d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) + + return d_dy, d_x, d_b, d_y + + # Add to cache. + _bias_act_cuda_cache[key] = BiasActCuda + return BiasActCuda + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/ops/conv2d_gradfix.py b/stylegan2_ada_pytorch/torch_utils/ops/conv2d_gradfix.py new file mode 100755 index 0000000000000000000000000000000000000000..26c8454be79b52b1229909a5a743f84073952258 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/conv2d_gradfix.py @@ -0,0 +1,275 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.conv2d` that supports +arbitrarily high order gradients with zero performance penalty.""" + +import warnings +import contextlib +import torch + +# pylint: disable=redefined-builtin +# pylint: disable=arguments-differ +# pylint: disable=protected-access + +# ---------------------------------------------------------------------------- + +enabled = False # Enable the custom op by setting this to true. +weight_gradients_disabled = ( + False +) # Forcefully disable computation of gradients with respect to the weights. + + +@contextlib.contextmanager +def no_weight_gradients(): + global weight_gradients_disabled + old = weight_gradients_disabled + weight_gradients_disabled = True + yield + weight_gradients_disabled = old + + +# ---------------------------------------------------------------------------- + + +def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if _should_use_custom_op(input): + return _conv2d_gradfix( + transpose=False, + weight_shape=weight.shape, + stride=stride, + padding=padding, + output_padding=0, + dilation=dilation, + groups=groups, + ).apply(input, weight, bias) + return torch.nn.functional.conv2d( + input=input, + weight=weight, + bias=bias, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + ) + + +def conv_transpose2d( + input, + weight, + bias=None, + stride=1, + padding=0, + output_padding=0, + groups=1, + dilation=1, +): + if _should_use_custom_op(input): + return _conv2d_gradfix( + transpose=True, + weight_shape=weight.shape, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation, + ).apply(input, weight, bias) + return torch.nn.functional.conv_transpose2d( + input=input, + weight=weight, + bias=bias, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation, + ) + + +# ---------------------------------------------------------------------------- + + +def _should_use_custom_op(input): + assert isinstance(input, torch.Tensor) + if (not enabled) or (not torch.backends.cudnn.enabled): + return False + if input.device.type != "cuda": + return False + if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8.", "1.9"]): + return True + warnings.warn( + f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()." + ) + return False + + +def _tuple_of_ints(xs, ndim): + xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim + assert len(xs) == ndim + assert all(isinstance(x, int) for x in xs) + return xs + + +# ---------------------------------------------------------------------------- + +_conv2d_gradfix_cache = dict() + + +def _conv2d_gradfix( + transpose, weight_shape, stride, padding, output_padding, dilation, groups +): + # Parse arguments. + ndim = 2 + weight_shape = tuple(weight_shape) + stride = _tuple_of_ints(stride, ndim) + padding = _tuple_of_ints(padding, ndim) + output_padding = _tuple_of_ints(output_padding, ndim) + dilation = _tuple_of_ints(dilation, ndim) + + # Lookup from cache. + key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) + if key in _conv2d_gradfix_cache: + return _conv2d_gradfix_cache[key] + + # Validate arguments. + assert groups >= 1 + assert len(weight_shape) == ndim + 2 + assert all(stride[i] >= 1 for i in range(ndim)) + assert all(padding[i] >= 0 for i in range(ndim)) + assert all(dilation[i] >= 0 for i in range(ndim)) + if not transpose: + assert all(output_padding[i] == 0 for i in range(ndim)) + else: # transpose + assert all( + 0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim) + ) + + # Helpers. + common_kwargs = dict( + stride=stride, padding=padding, dilation=dilation, groups=groups + ) + + def calc_output_padding(input_shape, output_shape): + if transpose: + return [0, 0] + return [ + input_shape[i + 2] + - (output_shape[i + 2] - 1) * stride[i] + - (1 - 2 * padding[i]) + - dilation[i] * (weight_shape[i + 2] - 1) + for i in range(ndim) + ] + + # Forward & backward. + class Conv2d(torch.autograd.Function): + @staticmethod + def forward(ctx, input, weight, bias): + assert weight.shape == weight_shape + if not transpose: + output = torch.nn.functional.conv2d( + input=input, weight=weight, bias=bias, **common_kwargs + ) + else: # transpose + output = torch.nn.functional.conv_transpose2d( + input=input, + weight=weight, + bias=bias, + output_padding=output_padding, + **common_kwargs, + ) + ctx.save_for_backward(input, weight) + return output + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + grad_input = None + grad_weight = None + grad_bias = None + + if ctx.needs_input_grad[0]: + p = calc_output_padding( + input_shape=input.shape, output_shape=grad_output.shape + ) + grad_input = _conv2d_gradfix( + transpose=(not transpose), + weight_shape=weight_shape, + output_padding=p, + **common_kwargs, + ).apply(grad_output, weight, None) + assert grad_input.shape == input.shape + + if ctx.needs_input_grad[1] and not weight_gradients_disabled: + grad_weight = Conv2dGradWeight.apply(grad_output, input) + assert grad_weight.shape == weight_shape + + if ctx.needs_input_grad[2]: + grad_bias = grad_output.sum([0, 2, 3]) + + return grad_input, grad_weight, grad_bias + + # Gradient with respect to the weights. + class Conv2dGradWeight(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input): + op = torch._C._jit_get_operation( + "aten::cudnn_convolution_backward_weight" + if not transpose + else "aten::cudnn_convolution_transpose_backward_weight" + ) + flags = [ + torch.backends.cudnn.benchmark, + torch.backends.cudnn.deterministic, + torch.backends.cudnn.allow_tf32, + ] + grad_weight = op( + weight_shape, + grad_output, + input, + padding, + stride, + dilation, + groups, + *flags, + ) + assert grad_weight.shape == weight_shape + ctx.save_for_backward(grad_output, input) + return grad_weight + + @staticmethod + def backward(ctx, grad2_grad_weight): + grad_output, input = ctx.saved_tensors + grad2_grad_output = None + grad2_input = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) + assert grad2_grad_output.shape == grad_output.shape + + if ctx.needs_input_grad[1]: + p = calc_output_padding( + input_shape=input.shape, output_shape=grad_output.shape + ) + grad2_input = _conv2d_gradfix( + transpose=(not transpose), + weight_shape=weight_shape, + output_padding=p, + **common_kwargs, + ).apply(grad_output, grad2_grad_weight, None) + assert grad2_input.shape == input.shape + + return grad2_grad_output, grad2_input + + _conv2d_gradfix_cache[key] = Conv2d + return Conv2d + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/ops/conv2d_resample.py b/stylegan2_ada_pytorch/torch_utils/ops/conv2d_resample.py new file mode 100755 index 0000000000000000000000000000000000000000..b32b819a18fd269b8430c4a2cb93845d07fa169e --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/conv2d_resample.py @@ -0,0 +1,219 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""2D convolution with optional up/downsampling.""" + +import torch + +from .. import misc +from . import conv2d_gradfix +from . import upfirdn2d +from .upfirdn2d import _parse_padding +from .upfirdn2d import _get_filter_size + +# ---------------------------------------------------------------------------- + + +def _get_weight_shape(w): + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + shape = [int(sz) for sz in w.shape] + misc.assert_shape(w, shape) + return shape + + +# ---------------------------------------------------------------------------- + + +def _conv2d_wrapper( + x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True +): + """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. + """ + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + + # Flip weight if requested. + if ( + not flip_weight + ): # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). + w = w.flip([2, 3]) + + # Workaround performance pitfall in cuDNN 8.0.5, triggered when using + # 1x1 kernel + memory_format=channels_last + less than 64 channels. + if ( + kw == 1 + and kh == 1 + and stride == 1 + and padding in [0, [0, 0], (0, 0)] + and not transpose + ): + if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64: + if out_channels <= 4 and groups == 1: + in_shape = x.shape + x = w.squeeze(3).squeeze(2) @ x.reshape( + [in_shape[0], in_channels_per_group, -1] + ) + x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) + else: + x = x.to(memory_format=torch.contiguous_format) + w = w.to(memory_format=torch.contiguous_format) + x = conv2d_gradfix.conv2d(x, w, groups=groups) + return x.to(memory_format=torch.channels_last) + + # Otherwise => execute using conv2d_gradfix. + op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d + return op(x, w, stride=stride, padding=padding, groups=groups) + + +# ---------------------------------------------------------------------------- + + +@misc.profiled_function +def conv2d_resample( + x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False +): + r"""2D convolution with optional up/downsampling. + + Padding is performed only once at the beginning, not between the operations. + + Args: + x: Input tensor of shape + `[batch_size, in_channels, in_height, in_width]`. + w: Weight tensor of shape + `[out_channels, in_channels//groups, kernel_height, kernel_width]`. + f: Low-pass filter for up/downsampling. Must be prepared beforehand by + calling upfirdn2d.setup_filter(). None = identity (default). + up: Integer upsampling factor (default: 1). + down: Integer downsampling factor (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + groups: Split input channels into N groups (default: 1). + flip_weight: False = convolution, True = correlation (default: True). + flip_filter: False = convolution, True = correlation (default: False). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + # Validate arguments. + assert isinstance(x, torch.Tensor) and (x.ndim == 4) + assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) + assert f is None or ( + isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32 + ) + assert isinstance(up, int) and (up >= 1) + assert isinstance(down, int) and (down >= 1) + assert isinstance(groups, int) and (groups >= 1) + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + fw, fh = _get_filter_size(f) + px0, px1, py0, py1 = _parse_padding(padding) + + # Adjust padding to account for up/downsampling. + if up > 1: + px0 += (fw + up - 1) // 2 + px1 += (fw - up) // 2 + py0 += (fh + up - 1) // 2 + py1 += (fh - up) // 2 + if down > 1: + px0 += (fw - down + 1) // 2 + px1 += (fw - down) // 2 + py0 += (fh - down + 1) // 2 + py1 += (fh - down) // 2 + + # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. + if kw == 1 and kh == 1 and (down > 1 and up == 1): + x = upfirdn2d.upfirdn2d( + x=x, f=f, down=down, padding=[px0, px1, py0, py1], flip_filter=flip_filter + ) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + return x + + # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. + if kw == 1 and kh == 1 and (up > 1 and down == 1): + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + x = upfirdn2d.upfirdn2d( + x=x, + f=f, + up=up, + padding=[px0, px1, py0, py1], + gain=up ** 2, + flip_filter=flip_filter, + ) + return x + + # Fast path: downsampling only => use strided convolution. + if down > 1 and up == 1: + x = upfirdn2d.upfirdn2d( + x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter + ) + x = _conv2d_wrapper( + x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight + ) + return x + + # Fast path: upsampling with optional downsampling => use transpose strided convolution. + if up > 1: + if groups == 1: + w = w.transpose(0, 1) + else: + w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) + w = w.transpose(1, 2) + w = w.reshape( + groups * in_channels_per_group, out_channels // groups, kh, kw + ) + px0 -= kw - 1 + px1 -= kw - up + py0 -= kh - 1 + py1 -= kh - up + pxt = max(min(-px0, -px1), 0) + pyt = max(min(-py0, -py1), 0) + x = _conv2d_wrapper( + x=x, + w=w, + stride=up, + padding=[pyt, pxt], + groups=groups, + transpose=True, + flip_weight=(not flip_weight), + ) + x = upfirdn2d.upfirdn2d( + x=x, + f=f, + padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt], + gain=up ** 2, + flip_filter=flip_filter, + ) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + + # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. + if up == 1 and down == 1: + if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: + return _conv2d_wrapper( + x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight + ) + + # Fallback: Generic reference implementation. + x = upfirdn2d.upfirdn2d( + x=x, + f=(f if up > 1 else None), + up=up, + padding=[px0, px1, py0, py1], + gain=up ** 2, + flip_filter=flip_filter, + ) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/ops/fma.py b/stylegan2_ada_pytorch/torch_utils/ops/fma.py new file mode 100755 index 0000000000000000000000000000000000000000..f0d2df8fca9e277de7e6378db415a39a714311ff --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/fma.py @@ -0,0 +1,73 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`.""" + +import torch + +# ---------------------------------------------------------------------------- + + +def fma(a, b, c): # => a * b + c + return _FusedMultiplyAdd.apply(a, b, c) + + +# ---------------------------------------------------------------------------- + + +class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c + @staticmethod + def forward(ctx, a, b, c): # pylint: disable=arguments-differ + out = torch.addcmul(c, a, b) + ctx.save_for_backward(a, b) + ctx.c_shape = c.shape + return out + + @staticmethod + def backward(ctx, dout): # pylint: disable=arguments-differ + a, b = ctx.saved_tensors + c_shape = ctx.c_shape + da = None + db = None + dc = None + + if ctx.needs_input_grad[0]: + da = _unbroadcast(dout * b, a.shape) + + if ctx.needs_input_grad[1]: + db = _unbroadcast(dout * a, b.shape) + + if ctx.needs_input_grad[2]: + dc = _unbroadcast(dout, c_shape) + + return da, db, dc + + +# ---------------------------------------------------------------------------- + + +def _unbroadcast(x, shape): + extra_dims = x.ndim - len(shape) + assert extra_dims >= 0 + dim = [ + i + for i in range(x.ndim) + if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1) + ] + if len(dim): + x = x.sum(dim=dim, keepdim=True) + if extra_dims: + x = x.reshape(-1, *x.shape[extra_dims + 1 :]) + assert x.shape == shape + return x + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/ops/grid_sample_gradfix.py b/stylegan2_ada_pytorch/torch_utils/ops/grid_sample_gradfix.py new file mode 100755 index 0000000000000000000000000000000000000000..71209e8d7ddb5d8959ce2dc678c44d5b0d0fd014 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/grid_sample_gradfix.py @@ -0,0 +1,108 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.grid_sample` that +supports arbitrarily high order gradients between the input and output. +Only works on 2D images and assumes +`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`.""" + +import warnings +import torch + +# pylint: disable=redefined-builtin +# pylint: disable=arguments-differ +# pylint: disable=protected-access + +# ---------------------------------------------------------------------------- + +enabled = False # Enable the custom op by setting this to true. + +# ---------------------------------------------------------------------------- + + +def grid_sample(input, grid): + if _should_use_custom_op(): + return _GridSample2dForward.apply(input, grid) + return torch.nn.functional.grid_sample( + input=input, + grid=grid, + mode="bilinear", + padding_mode="zeros", + align_corners=False, + ) + + +# ---------------------------------------------------------------------------- + + +def _should_use_custom_op(): + if not enabled: + return False + if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8.", "1.9"]): + return True + warnings.warn( + f"grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample()." + ) + return False + + +# ---------------------------------------------------------------------------- + + +class _GridSample2dForward(torch.autograd.Function): + @staticmethod + def forward(ctx, input, grid): + assert input.ndim == 4 + assert grid.ndim == 4 + output = torch.nn.functional.grid_sample( + input=input, + grid=grid, + mode="bilinear", + padding_mode="zeros", + align_corners=False, + ) + ctx.save_for_backward(input, grid) + return output + + @staticmethod + def backward(ctx, grad_output): + input, grid = ctx.saved_tensors + grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) + return grad_input, grad_grid + + +# ---------------------------------------------------------------------------- + + +class _GridSample2dBackward(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input, grid): + op = torch._C._jit_get_operation("aten::grid_sampler_2d_backward") + grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) + ctx.save_for_backward(grid) + return grad_input, grad_grid + + @staticmethod + def backward(ctx, grad2_grad_input, grad2_grad_grid): + _ = grad2_grad_grid # unused + grid, = ctx.saved_tensors + grad2_grad_output = None + grad2_input = None + grad2_grid = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) + + assert not ctx.needs_input_grad[2] + return grad2_grad_output, grad2_input, grad2_grid + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.cpp b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.cpp new file mode 100755 index 0000000000000000000000000000000000000000..f18bda3fc2d1c04db1a8e4482c49a87319694303 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.cpp @@ -0,0 +1,106 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// All rights reserved. +// +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ + +static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); + TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); + TORCH_CHECK(x.dim() == 4, "x must be rank 4"); + TORCH_CHECK(f.dim() == 2, "f must be rank 2"); + TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); + TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); + TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; + int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; + TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); + torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); + TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); + + // Initialize CUDA kernel parameters. + upfirdn2d_kernel_params p; + p.x = x.data_ptr(); + p.f = f.data_ptr(); + p.y = y.data_ptr(); + p.up = make_int2(upx, upy); + p.down = make_int2(downx, downy); + p.pad0 = make_int2(padx0, pady0); + p.flip = (flip) ? 1 : 0; + p.gain = gain; + p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); + p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); + p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); + p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); + p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); + p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); + p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; + p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; + + // Choose CUDA kernel. + upfirdn2d_kernel_spec spec; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + spec = choose_upfirdn2d_kernel(p); + }); + + // Set looping options. + p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; + p.loopMinor = spec.loopMinor; + p.loopX = spec.loopX; + p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; + p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; + + // Compute grid size. + dim3 blockSize, gridSize; + if (spec.tileOutW < 0) // large + { + blockSize = dim3(4, 32, 1); + gridSize = dim3( + ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, + (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, + p.launchMajor); + } + else // small + { + blockSize = dim3(256, 1, 1); + gridSize = dim3( + ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, + (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, + p.launchMajor); + } + + // Launch CUDA kernel. + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("upfirdn2d", &upfirdn2d); +} + +//------------------------------------------------------------------------ diff --git a/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.cu b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.cu new file mode 100755 index 0000000000000000000000000000000000000000..ceb2b341af906013e40c7f9a3346cd1aa88efab4 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.cu @@ -0,0 +1,353 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// All rights reserved. +// +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +static __device__ __forceinline__ int floor_div(int a, int b) +{ + int t = 1 - a / b; + return (a + t * b) / b - t; +} + +//------------------------------------------------------------------------ +// Generic CUDA implementation for large filters. + +template static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + + // Calculate thread index. + int minorBase = blockIdx.x * blockDim.x + threadIdx.x; + int outY = minorBase / p.launchMinor; + minorBase -= outY * p.launchMinor; + int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y; + int majorBase = blockIdx.z * p.loopMajor; + if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Setup Y receptive field. + int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y; + int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y); + int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY; + int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y; + if (p.flip) + filterY = p.filterSize.y - 1 - filterY; + + // Loop over major, minor, and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor) + { + int nc = major * p.sizeMinor + minor; + int n = nc / p.inSize.z; + int c = nc - n * p.inSize.z; + for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y) + { + // Setup X receptive field. + int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x; + int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x); + int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX; + int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x; + if (p.flip) + filterX = p.filterSize.x - 1 - filterX; + + // Initialize pointers. + const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y]; + int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x; + int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y; + + // Inner loop. + scalar_t v = 0; + for (int y = 0; y < h; y++) + { + for (int x = 0; x < w; x++) + { + v += (scalar_t)(*xp) * (scalar_t)(*fp); + xp += p.inStride.x; + fp += filterStepX; + } + xp += p.inStride.y - w * p.inStride.x; + fp += filterStepY - w * filterStepX; + } + + // Store result. + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } +} + +//------------------------------------------------------------------------ +// Specialized CUDA implementation for small filters. + +template +static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1; + const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1; + __shared__ volatile scalar_t sf[filterH][filterW]; + __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor]; + + // Calculate tile index. + int minorBase = blockIdx.x; + int tileOutY = minorBase / p.launchMinor; + minorBase -= tileOutY * p.launchMinor; + minorBase *= loopMinor; + tileOutY *= tileOutH; + int tileOutXBase = blockIdx.y * p.loopX * tileOutW; + int majorBase = blockIdx.z * p.loopMajor; + if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Load filter (flipped). + for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x) + { + int fy = tapIdx / filterW; + int fx = tapIdx - fy * filterW; + scalar_t v = 0; + if (fx < p.filterSize.x & fy < p.filterSize.y) + { + int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx; + int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy; + v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y]; + } + sf[fy][fx] = v; + } + + // Loop over major and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + { + int baseNC = major * p.sizeMinor + minorBase; + int n = baseNC / p.inSize.z; + int baseC = baseNC - n * p.inSize.z; + for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW) + { + // Load input pixels. + int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x; + int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y; + int tileInX = floor_div(tileMidX, upx); + int tileInY = floor_div(tileMidY, upy); + __syncthreads(); + for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x) + { + int relC = inIdx; + int relInX = relC / loopMinor; + int relInY = relInX / tileInW; + relC -= relInX * loopMinor; + relInX -= relInY * tileInW; + int c = baseC + relC; + int inX = tileInX + relInX; + int inY = tileInY + relInY; + scalar_t v = 0; + if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z) + v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + sx[relInY][relInX][relC] = v; + } + + // Loop over output pixels. + __syncthreads(); + for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x) + { + int relC = outIdx; + int relOutX = relC / loopMinor; + int relOutY = relOutX / tileOutW; + relC -= relOutX * loopMinor; + relOutX -= relOutY * tileOutW; + int c = baseC + relC; + int outX = tileOutX + relOutX; + int outY = tileOutY + relOutY; + + // Setup receptive field. + int midX = tileMidX + relOutX * downx; + int midY = tileMidY + relOutY * downy; + int inX = floor_div(midX, upx); + int inY = floor_div(midY, upy); + int relInX = inX - tileInX; + int relInY = inY - tileInY; + int filterX = (inX + 1) * upx - midX - 1; // flipped + int filterY = (inY + 1) * upy - midY - 1; // flipped + + // Inner loop. + if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z) + { + scalar_t v = 0; + #pragma unroll + for (int y = 0; y < filterH / upy; y++) + #pragma unroll + for (int x = 0; x < filterW / upx; x++) + v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx]; + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } + } + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p) +{ + int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y; + + upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large, -1,-1,1, 4}; // contiguous + if (s == 1) spec = {(void*)upfirdn2d_kernel_large, -1,-1,4, 1}; // channels_last + + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + if (s != 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + } + if (s == 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + } + if (s != 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + } + if (s == 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // contiguous + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // channels_last + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // contiguous + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // channels_last + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // contiguous + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // channels_last + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + } + return spec; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.h b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.h new file mode 100755 index 0000000000000000000000000000000000000000..74387cd8d746d43cde2b036627c347baa2059ec8 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.h @@ -0,0 +1,62 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// All rights reserved. +// +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct upfirdn2d_kernel_params +{ + const void* x; + const float* f; + void* y; + + int2 up; + int2 down; + int2 pad0; + int flip; + float gain; + + int4 inSize; // [width, height, channel, batch] + int4 inStride; + int2 filterSize; // [width, height] + int2 filterStride; + int4 outSize; // [width, height, channel, batch] + int4 outStride; + int sizeMinor; + int sizeMajor; + + int loopMinor; + int loopMajor; + int loopX; + int launchMinor; + int launchMajor; +}; + +//------------------------------------------------------------------------ +// CUDA kernel specialization. + +struct upfirdn2d_kernel_spec +{ + void* kernel; + int tileOutW; + int tileOutH; + int loopMinor; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.py b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.py new file mode 100755 index 0000000000000000000000000000000000000000..00c5892800aad064cfed720f9d2bc55ee71596f9 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/ops/upfirdn2d.py @@ -0,0 +1,481 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient resampling of 2D images.""" + +import os +import warnings +import numpy as np +import torch +import traceback + +from .. import custom_ops +from .. import misc +from . import conv2d_gradfix + +# ---------------------------------------------------------------------------- + +_inited = False +_plugin = None + + +def _init(): + global _inited, _plugin + if not _inited: + sources = ["upfirdn2d.cpp", "upfirdn2d.cu"] + sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] + try: + _plugin = custom_ops.get_plugin( + "upfirdn2d_plugin", + sources=sources, + extra_cuda_cflags=["--use_fast_math"], + ) + except: + warnings.warn( + "Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n" + + traceback.format_exc() + ) + return _plugin is not None + + +def _parse_scaling(scaling): + if isinstance(scaling, int): + scaling = [scaling, scaling] + assert isinstance(scaling, (list, tuple)) + assert all(isinstance(x, int) for x in scaling) + sx, sy = scaling + assert sx >= 1 and sy >= 1 + return sx, sy + + +def _parse_padding(padding): + if isinstance(padding, int): + padding = [padding, padding] + assert isinstance(padding, (list, tuple)) + assert all(isinstance(x, int) for x in padding) + if len(padding) == 2: + padx, pady = padding + padding = [padx, padx, pady, pady] + padx0, padx1, pady0, pady1 = padding + return padx0, padx1, pady0, pady1 + + +def _get_filter_size(f): + if f is None: + return 1, 1 + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + fw = f.shape[-1] + fh = f.shape[0] + with misc.suppress_tracer_warnings(): + fw = int(fw) + fh = int(fh) + misc.assert_shape(f, [fh, fw][: f.ndim]) + assert fw >= 1 and fh >= 1 + return fw, fh + + +# ---------------------------------------------------------------------------- + + +def setup_filter( + f, + device=torch.device("cpu"), + normalize=True, + flip_filter=False, + gain=1, + separable=None, +): + r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. + + Args: + f: Torch tensor, numpy array, or python list of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), + `[]` (impulse), or + `None` (identity). + device: Result device (default: cpu). + normalize: Normalize the filter so that it retains the magnitude + for constant input signal (DC)? (default: True). + flip_filter: Flip the filter? (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + separable: Return a separable filter? (default: select automatically). + + Returns: + Float32 tensor of the shape + `[filter_height, filter_width]` (non-separable) or + `[filter_taps]` (separable). + """ + # Validate. + if f is None: + f = 1 + f = torch.as_tensor(f, dtype=torch.float32) + assert f.ndim in [0, 1, 2] + assert f.numel() > 0 + if f.ndim == 0: + f = f[np.newaxis] + + # Separable? + if separable is None: + separable = f.ndim == 1 and f.numel() >= 8 + if f.ndim == 1 and not separable: + f = f.ger(f) + assert f.ndim == (1 if separable else 2) + + # Apply normalize, flip, gain, and device. + if normalize: + f /= f.sum() + if flip_filter: + f = f.flip(list(range(f.ndim))) + f = f * (gain ** (f.ndim / 2)) + f = f.to(device=device) + return f + + +# ---------------------------------------------------------------------------- + + +def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl="cuda"): + r"""Pad, upsample, filter, and downsample a batch of 2D images. + + Performs the following sequence of operations for each channel: + + 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). + + 2. Pad the image with the specified number of zeros on each side (`padding`). + Negative padding corresponds to cropping the image. + + 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it + so that the footprint of all output pixels lies within the input image. + + 4. Downsample the image by keeping every Nth pixel (`down`). + + This sequence of operations bears close resemblance to scipy.signal.upfirdn(). + The fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports gradients of arbitrary order. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ["ref", "cuda"] + if impl == "cuda" and x.device.type == "cuda" and _init(): + return _upfirdn2d_cuda( + up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain + ).apply(x, f) + return _upfirdn2d_ref( + x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain + ) + + +# ---------------------------------------------------------------------------- + + +@misc.profiled_function +def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): + """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops. + """ + # Validate arguments. + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + assert f.dtype == torch.float32 and not f.requires_grad + batch_size, num_channels, in_height, in_width = x.shape + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + # Upsample by inserting zeros. + x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) + x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) + x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) + + # Pad or crop. + x = torch.nn.functional.pad( + x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)] + ) + x = x[ + :, + :, + max(-pady0, 0) : x.shape[2] - max(-pady1, 0), + max(-padx0, 0) : x.shape[3] - max(-padx1, 0), + ] + + # Setup filter. + f = f * (gain ** (f.ndim / 2)) + f = f.to(x.dtype) + if not flip_filter: + f = f.flip(list(range(f.ndim))) + + # Convolve with the filter. + f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) + if f.ndim == 4: + x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) + else: + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) + + # Downsample by throwing away pixels. + x = x[:, :, ::downy, ::downx] + return x + + +# ---------------------------------------------------------------------------- + +_upfirdn2d_cuda_cache = dict() + + +def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): + """Fast CUDA implementation of `upfirdn2d()` using custom ops. + """ + # Parse arguments. + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + # Lookup from cache. + key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + if key in _upfirdn2d_cuda_cache: + return _upfirdn2d_cuda_cache[key] + + # Forward op. + class Upfirdn2dCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, f): # pylint: disable=arguments-differ + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + y = x + if f.ndim == 2: + y = _plugin.upfirdn2d( + y, + f, + upx, + upy, + downx, + downy, + padx0, + padx1, + pady0, + pady1, + flip_filter, + gain, + ) + else: + y = _plugin.upfirdn2d( + y, + f.unsqueeze(0), + upx, + 1, + downx, + 1, + padx0, + padx1, + 0, + 0, + flip_filter, + np.sqrt(gain), + ) + y = _plugin.upfirdn2d( + y, + f.unsqueeze(1), + 1, + upy, + 1, + downy, + 0, + 0, + pady0, + pady1, + flip_filter, + np.sqrt(gain), + ) + ctx.save_for_backward(f) + ctx.x_shape = x.shape + return y + + @staticmethod + def backward(ctx, dy): # pylint: disable=arguments-differ + f, = ctx.saved_tensors + _, _, ih, iw = ctx.x_shape + _, _, oh, ow = dy.shape + fw, fh = _get_filter_size(f) + p = [ + fw - padx0 - 1, + iw * upx - ow * downx + padx0 - upx + 1, + fh - pady0 - 1, + ih * upy - oh * downy + pady0 - upy + 1, + ] + dx = None + df = None + + if ctx.needs_input_grad[0]: + dx = _upfirdn2d_cuda( + up=down, + down=up, + padding=p, + flip_filter=(not flip_filter), + gain=gain, + ).apply(dy, f) + + assert not ctx.needs_input_grad[1] + return dx, df + + # Add to cache. + _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda + return Upfirdn2dCuda + + +# ---------------------------------------------------------------------------- + + +def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl="cuda"): + r"""Filter a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape matches the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [padx0 + fw // 2, padx1 + (fw - 1) // 2, pady0 + fh // 2, pady1 + (fh - 1) // 2] + return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) + + +# ---------------------------------------------------------------------------- + + +def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl="cuda"): + r"""Upsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a multiple of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + upx, upy = _parse_scaling(up) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw + upx - 1) // 2, + padx1 + (fw - upx) // 2, + pady0 + (fh + upy - 1) // 2, + pady1 + (fh - upy) // 2, + ] + return upfirdn2d( + x, + f, + up=up, + padding=p, + flip_filter=flip_filter, + gain=gain * upx * upy, + impl=impl, + ) + + +# ---------------------------------------------------------------------------- + + +def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl="cuda"): + r"""Downsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a fraction of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the input. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw - downx + 1) // 2, + padx1 + (fw - downx) // 2, + pady0 + (fh - downy + 1) // 2, + pady1 + (fh - downy) // 2, + ] + return upfirdn2d( + x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl + ) + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/persistence.py b/stylegan2_ada_pytorch/torch_utils/persistence.py new file mode 100755 index 0000000000000000000000000000000000000000..3b9a8b0d2a74a607a4d4d93620a33d5b45ac1433 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/persistence.py @@ -0,0 +1,284 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for pickling Python code alongside other data. + +The pickled code is automatically imported into a separate Python module +during unpickling. This way, any previously exported pickles will remain +usable even if the original code is no longer available, or if the current +version of the code is not consistent with what was originally pickled.""" + +import sys +import os +import pickle +import io +import inspect +import copy +import uuid +import types + +sys.path.insert(1, os.path.join(sys.path[0], "../stylegan2_ada_pytorch")) +import dnnlib + +# ---------------------------------------------------------------------------- + +_version = 6 # internal version number +_decorators = set() # {decorator_class, ...} +_import_hooks = [] # [hook_function, ...] +_module_to_src_dict = dict() # {module: src, ...} +_src_to_module_dict = dict() # {src: module, ...} + +# ---------------------------------------------------------------------------- + + +def persistent_class(orig_class): + r"""Class decorator that extends a given class to save its source code + when pickled. + + Example: + + from torch_utils import persistence + + @persistence.persistent_class + class MyNetwork(torch.nn.Module): + def __init__(self, num_inputs, num_outputs): + super().__init__() + self.fc = MyLayer(num_inputs, num_outputs) + ... + + @persistence.persistent_class + class MyLayer(torch.nn.Module): + ... + + When pickled, any instance of `MyNetwork` and `MyLayer` will save its + source code alongside other internal state (e.g., parameters, buffers, + and submodules). This way, any previously exported pickle will remain + usable even if the class definitions have been modified or are no + longer available. + + The decorator saves the source code of the entire Python module + containing the decorated class. It does *not* save the source code of + any imported modules. Thus, the imported modules must be available + during unpickling, also including `torch_utils.persistence` itself. + + It is ok to call functions defined in the same module from the + decorated class. However, if the decorated class depends on other + classes defined in the same module, they must be decorated as well. + This is illustrated in the above example in the case of `MyLayer`. + + It is also possible to employ the decorator just-in-time before + calling the constructor. For example: + + cls = MyLayer + if want_to_make_it_persistent: + cls = persistence.persistent_class(cls) + layer = cls(num_inputs, num_outputs) + + As an additional feature, the decorator also keeps track of the + arguments that were used to construct each instance of the decorated + class. The arguments can be queried via `obj.init_args` and + `obj.init_kwargs`, and they are automatically pickled alongside other + object state. A typical use case is to first unpickle a previous + instance of a persistent class, and then upgrade it to use the latest + version of the source code: + + with open('old_pickle.pkl', 'rb') as f: + old_net = pickle.load(f) + new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs) + misc.copy_params_and_buffers(old_net, new_net, require_all=True) + """ + assert isinstance(orig_class, type) + if is_persistent(orig_class): + return orig_class + + assert orig_class.__module__ in sys.modules + orig_module = sys.modules[orig_class.__module__] + orig_module_src = _module_to_src(orig_module) + + class Decorator(orig_class): + _orig_module_src = orig_module_src + _orig_class_name = orig_class.__name__ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._init_args = copy.deepcopy(args) + self._init_kwargs = copy.deepcopy(kwargs) + assert orig_class.__name__ in orig_module.__dict__ + _check_pickleable(self.__reduce__()) + + @property + def init_args(self): + return copy.deepcopy(self._init_args) + + @property + def init_kwargs(self): + return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs)) + + def __reduce__(self): + fields = list(super().__reduce__()) + fields += [None] * max(3 - len(fields), 0) + if fields[0] is not _reconstruct_persistent_obj: + meta = dict( + type="class", + version=_version, + module_src=self._orig_module_src, + class_name=self._orig_class_name, + state=fields[2], + ) + fields[0] = _reconstruct_persistent_obj # reconstruct func + fields[1] = (meta,) # reconstruct args + fields[2] = None # state dict + return tuple(fields) + + Decorator.__name__ = orig_class.__name__ + _decorators.add(Decorator) + return Decorator + + +# ---------------------------------------------------------------------------- + + +def is_persistent(obj): + r"""Test whether the given object or class is persistent, i.e., + whether it will save its source code when pickled. + """ + try: + if obj in _decorators: + return True + except TypeError: + pass + return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck + + +# ---------------------------------------------------------------------------- + + +def import_hook(hook): + r"""Register an import hook that is called whenever a persistent object + is being unpickled. A typical use case is to patch the pickled source + code to avoid errors and inconsistencies when the API of some imported + module has changed. + + The hook should have the following signature: + + hook(meta) -> modified meta + + `meta` is an instance of `dnnlib.EasyDict` with the following fields: + + type: Type of the persistent object, e.g. `'class'`. + version: Internal version number of `torch_utils.persistence`. + module_src Original source code of the Python module. + class_name: Class name in the original Python module. + state: Internal state of the object. + + Example: + + @persistence.import_hook + def wreck_my_network(meta): + if meta.class_name == 'MyNetwork': + print('MyNetwork is being imported. I will wreck it!') + meta.module_src = meta.module_src.replace("True", "False") + return meta + """ + assert callable(hook) + _import_hooks.append(hook) + + +# ---------------------------------------------------------------------------- + + +def _reconstruct_persistent_obj(meta): + r"""Hook that is called internally by the `pickle` module to unpickle + a persistent object. + """ + meta = dnnlib.EasyDict(meta) + meta.state = dnnlib.EasyDict(meta.state) + for hook in _import_hooks: + meta = hook(meta) + assert meta is not None + + assert meta.version == _version + + module = _src_to_module(meta.module_src) + + assert meta.type == "class" + orig_class = module.__dict__[meta.class_name] + decorator_class = persistent_class(orig_class) + obj = decorator_class.__new__(decorator_class) + + setstate = getattr(obj, "__setstate__", None) + if callable(setstate): + setstate(meta.state) # pylint: disable=not-callable + else: + obj.__dict__.update(meta.state) + return obj + + +# ---------------------------------------------------------------------------- + + +def _module_to_src(module): + r"""Query the source code of a given Python module. + """ + src = _module_to_src_dict.get(module, None) + if src is None: + src = inspect.getsource(module) + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + return src + + +def _src_to_module(src): + r"""Get or create a Python module for the given source code. + """ + module = _src_to_module_dict.get(src, None) + if module is None: + module_name = "_imported_module_" + uuid.uuid4().hex + module = types.ModuleType(module_name) + sys.modules[module_name] = module + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + exec(src, module.__dict__) # pylint: disable=exec-used + return module + + +# ---------------------------------------------------------------------------- + + +def _check_pickleable(obj): + r"""Check that the given object is pickleable, raising an exception if + it is not. This function is expected to be considerably more efficient + than actually pickling the object. + """ + + def recurse(obj): + if isinstance(obj, (list, tuple, set)): + return [recurse(x) for x in obj] + if isinstance(obj, dict): + return [[recurse(x), recurse(y)] for x, y in obj.items()] + if isinstance(obj, (str, int, float, bool, bytes, bytearray)): + return None # Python primitive types are pickleable. + if f"{type(obj).__module__}.{type(obj).__name__}" in [ + "numpy.ndarray", + "torch.Tensor", + ]: + return None # NumPy arrays and PyTorch tensors are pickleable. + if is_persistent(obj): + return ( + None + ) # Persistent objects are pickleable, by virtue of the constructor check. + return obj + + with io.BytesIO() as f: + pickle.dump(recurse(obj), f) + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/torch_utils/training_stats.py b/stylegan2_ada_pytorch/torch_utils/training_stats.py new file mode 100755 index 0000000000000000000000000000000000000000..ac150b4757680fbae626d802615c9e2bacd9c558 --- /dev/null +++ b/stylegan2_ada_pytorch/torch_utils/training_stats.py @@ -0,0 +1,290 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for reporting and collecting training statistics across +multiple processes and devices. The interface is designed to minimize +synchronization overhead as well as the amount of boilerplate in user +code.""" + +import re +import numpy as np +import torch +import dnnlib + +from . import misc + +# ---------------------------------------------------------------------------- + +_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] +_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. +_counter_dtype = torch.float64 # Data type to use for the internal counters. +_rank = 0 # Rank of the current process. +_sync_device = ( + None +) # Device to use for multiprocess communication. None = single-process. +_sync_called = False # Has _sync() been called yet? +_counters = ( + dict() +) # Running counters on each device, updated by report(): name => device => torch.Tensor +_cumulative = ( + dict() +) # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor + +# ---------------------------------------------------------------------------- + + +def init_multiprocessing(rank, sync_device): + r"""Initializes `torch_utils.training_stats` for collecting statistics + across multiple processes. + + This function must be called after + `torch.distributed.init_process_group()` and before `Collector.update()`. + The call is not necessary if multi-process collection is not needed. + + Args: + rank: Rank of the current process. + sync_device: PyTorch device to use for inter-process + communication, or None to disable multi-process + collection. Typically `torch.device('cuda', rank)`. + """ + global _rank, _sync_device + assert not _sync_called + _rank = rank + _sync_device = sync_device + + +# ---------------------------------------------------------------------------- + + +@misc.profiled_function +def report(name, value): + r"""Broadcasts the given set of scalars to all interested instances of + `Collector`, across device and process boundaries. + + This function is expected to be extremely cheap and can be safely + called from anywhere in the training loop, loss function, or inside a + `torch.nn.Module`. + + Warning: The current implementation expects the set of unique names to + be consistent across processes. Please make sure that `report()` is + called at least once for each unique name by each process, and in the + same order. If a given process has no scalars to broadcast, it can do + `report(name, [])` (empty list). + + Args: + name: Arbitrary string specifying the name of the statistic. + Averages are accumulated separately for each unique name. + value: Arbitrary set of scalars. Can be a list, tuple, + NumPy array, PyTorch tensor, or Python scalar. + + Returns: + The same `value` that was passed in. + """ + if name not in _counters: + _counters[name] = dict() + + elems = torch.as_tensor(value) + if elems.numel() == 0: + return value + + elems = elems.detach().flatten().to(_reduce_dtype) + moments = torch.stack( + [torch.ones_like(elems).sum(), elems.sum(), elems.square().sum()] + ) + assert moments.ndim == 1 and moments.shape[0] == _num_moments + moments = moments.to(_counter_dtype) + + device = moments.device + if device not in _counters[name]: + _counters[name][device] = torch.zeros_like(moments) + _counters[name][device].add_(moments) + return value + + +# ---------------------------------------------------------------------------- + + +def report0(name, value): + r"""Broadcasts the given set of scalars by the first process (`rank = 0`), + but ignores any scalars provided by the other processes. + See `report()` for further details. + """ + report(name, value if _rank == 0 else []) + return value + + +# ---------------------------------------------------------------------------- + + +class Collector: + r"""Collects the scalars broadcasted by `report()` and `report0()` and + computes their long-term averages (mean and standard deviation) over + user-defined periods of time. + + The averages are first collected into internal counters that are not + directly visible to the user. They are then copied to the user-visible + state as a result of calling `update()` and can then be queried using + `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the + internal counters for the next round, so that the user-visible state + effectively reflects averages collected between the last two calls to + `update()`. + + Args: + regex: Regular expression defining which statistics to + collect. The default is to collect everything. + keep_previous: Whether to retain the previous averages if no + scalars were collected on a given round + (default: True). + """ + + def __init__(self, regex=".*", keep_previous=True): + self._regex = re.compile(regex) + self._keep_previous = keep_previous + self._cumulative = dict() + self._moments = dict() + self.update() + self._moments.clear() + + def names(self): + r"""Returns the names of all statistics broadcasted so far that + match the regular expression specified at construction time. + """ + return [name for name in _counters if self._regex.fullmatch(name)] + + def update(self): + r"""Copies current values of the internal counters to the + user-visible state and resets them for the next round. + + If `keep_previous=True` was specified at construction time, the + operation is skipped for statistics that have received no scalars + since the last update, retaining their previous averages. + + This method performs a number of GPU-to-CPU transfers and one + `torch.distributed.all_reduce()`. It is intended to be called + periodically in the main training loop, typically once every + N training steps. + """ + if not self._keep_previous: + self._moments.clear() + for name, cumulative in _sync(self.names()): + if name not in self._cumulative: + self._cumulative[name] = torch.zeros( + [_num_moments], dtype=_counter_dtype + ) + delta = cumulative - self._cumulative[name] + self._cumulative[name].copy_(cumulative) + if float(delta[0]) != 0: + self._moments[name] = delta + + def _get_delta(self, name): + r"""Returns the raw moments that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + assert self._regex.fullmatch(name) + if name not in self._moments: + self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + return self._moments[name] + + def num(self, name): + r"""Returns the number of scalars that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + delta = self._get_delta(name) + return int(delta[0]) + + def mean(self, name): + r"""Returns the mean of the scalars that were accumulated for the + given statistic between the last two calls to `update()`, or NaN if + no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0: + return float("nan") + return float(delta[1] / delta[0]) + + def std(self, name): + r"""Returns the standard deviation of the scalars that were + accumulated for the given statistic between the last two calls to + `update()`, or NaN if no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0 or not np.isfinite(float(delta[1])): + return float("nan") + if int(delta[0]) == 1: + return float(0) + mean = float(delta[1] / delta[0]) + raw_var = float(delta[2] / delta[0]) + return np.sqrt(max(raw_var - np.square(mean), 0)) + + def as_dict(self): + r"""Returns the averages accumulated between the last two calls to + `update()` as an `dnnlib.EasyDict`. The contents are as follows: + + dnnlib.EasyDict( + NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT), + ... + ) + """ + stats = dnnlib.EasyDict() + for name in self.names(): + stats[name] = dnnlib.EasyDict( + num=self.num(name), mean=self.mean(name), std=self.std(name) + ) + return stats + + def __getitem__(self, name): + r"""Convenience getter. + `collector[name]` is a synonym for `collector.mean(name)`. + """ + return self.mean(name) + + +# ---------------------------------------------------------------------------- + + +def _sync(names): + r"""Synchronize the global cumulative counters across devices and + processes. Called internally by `Collector.update()`. + """ + if len(names) == 0: + return [] + global _sync_called + _sync_called = True + + # Collect deltas within current rank. + deltas = [] + device = _sync_device if _sync_device is not None else torch.device("cpu") + for name in names: + delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) + for counter in _counters[name].values(): + delta.add_(counter.to(device)) + counter.copy_(torch.zeros_like(counter)) + deltas.append(delta) + deltas = torch.stack(deltas) + + # Sum deltas across ranks. + if _sync_device is not None: + torch.distributed.all_reduce(deltas) + + # Update cumulative values. + deltas = deltas.cpu() + for idx, name in enumerate(names): + if name not in _cumulative: + _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + _cumulative[name].add_(deltas[idx]) + + # Return name-value pairs. + return [(name, _cumulative[name]) for name in names] + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/train.py b/stylegan2_ada_pytorch/train.py new file mode 100755 index 0000000000000000000000000000000000000000..0106f49b9076e68911c14a4bbaf369964d78ff99 --- /dev/null +++ b/stylegan2_ada_pytorch/train.py @@ -0,0 +1,806 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# All contributions by NVIDIA CORPORATION: +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. + +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Train a GAN using the techniques described in the paper +"Training Generative Adversarial Networks with Limited Data".""" +import sys +import os + +sys.path.insert(1, os.path.join(sys.path[0], "..")) +import click +import re +import json +import tempfile +import torch +import dnnlib + +import numpy as np + +import parser + +from training import training_loop +from metrics import metric_main +from torch_utils import training_stats +from torch_utils import custom_ops + + +# ---------------------------------------------------------------------------- + + +class UserError(Exception): + pass + + +# ---------------------------------------------------------------------------- + + +def setup_training_loop_kwargs( + # General options (not included in desc). + exp_name=None, # Experiment name + slurm=None, # Using SLURM or not + gpus=None, # Number of GPUs: , default = 1 gpu + nodes=None, # Number of nodes: , default = 1 node + snap=None, # Snapshot interval: , default = 50 ticks + metrics=None, # List of metric names: [], ['fid50k_full'] (default), ... + seed=None, # Random seed: , default = 0 + # Dataset. + data=None, # Training dataset (required): + class_cond=None, # Conditioning on a class label + subset=None, # Train with only N images: , default = all + mirror=None, # Augment dataset with x-flips: , default = False + # IC-GAN dataset parameters. + instance_cond=None, # Conditioning on instance features + feature_augmentation=None, # Horizontal flips augmentation to extract instance features + root_feats=None, # Path where to find the hdf5 file with the instance features + root_nns=None, # Path where to find the pre-computed nearest neighbors for each instance + label_dim=None, # Dimensionality of the class embeddings if we use class conditonings . + # Base config. + cfg=None, # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar' + lrate=None, # Override learning rate: + gamma=None, # Override R1 gamma: + kimg=None, # Override training duration: + batch=None, # Override batch size: + num_channel_g=None, # Override width of generator network: + num_channel_d=None, # Override width of discriminator network: + channel_max_g=None, # Override max width of generator network: + channel_max_d=None, # Override max width of discriminator network: + hidden_dim_c=None, # Override embedding dimensionality for class conditioning inside mapping network + hidden_dim_h=None, # Override embedding dimensionality for instance conditioning inside mapping network + es_patience=None, # Early stopping patience in number of seen images: + # Discriminator augmentation. + aug=None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed' + p=None, # Specify p for 'fixed' (required): + target=None, # Override ADA target for 'ada': , default = depends on aug + augpipe=None, # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc' + # Transfer learning. + resume=None, # Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', , + freezed=None, # Freeze-D: , default = 0 discriminator layers + # Performance options (not included in desc). + fp32=None, # Disable mixed-precision training: , default = False + nhwc=None, # Use NHWC memory format with FP16: , default = False + allow_tf32=None, # Allow PyTorch to use TF32 for matmul and convolutions: , default = False + nobench=None, # Disable cuDNN benchmarking: , default = False + workers=None, # Override number of DataLoader workers: , default = 3 + **kwargs, +): + args = dnnlib.EasyDict() + + # ------------------------------------------ + # General options: gpus, snap, metrics, seed + # ------------------------------------------ + + if gpus is None: + gpus = 1 + assert isinstance(gpus, int) + if not (gpus >= 1 and gpus & (gpus - 1) == 0): + raise UserError("--gpus must be a power of two") + args.num_gpus = gpus * nodes + + if snap is None: + snap = 50 + assert isinstance(snap, int) + if snap < 1: + raise UserError("--snap must be at least 1") + args.image_snapshot_ticks = snap + args.network_snapshot_ticks = snap + args.es_patience = es_patience + + if metrics is None: + metrics = ["fid50k_full"] + assert isinstance(metrics, list) + if not all(metric_main.is_valid_metric(metric) for metric in metrics): + raise UserError( + "\n".join( + ["--metrics can only contain the following values:"] + + metric_main.list_valid_metrics() + ) + ) + args.metrics = metrics + + if seed is None: + seed = 0 + assert isinstance(seed, int) + args.random_seed = seed + + # ----------------------------------- + # Dataset: data, cond, subset, mirror + # ----------------------------------- + + assert data is not None + assert isinstance(data, str) + + class_name = "data_utils.datasets_common.ILSVRC_HDF5_feats" + args.class_cond = class_cond + args.instance_cond = instance_cond + + if mirror is None: + mirror = False + assert isinstance(mirror, bool) + + args.training_set_kwargs = dnnlib.EasyDict( + class_name=class_name, + root=data, + max_size=None, + xflip=False, + load_labels=class_cond, + load_features=instance_cond, + root_feats=root_feats, + root_nns=root_nns, + transform=None, + label_dim=label_dim, + feature_dim=2048, + apply_norm=False, + label_onehot=True, + feature_augmentation=feature_augmentation, + ) + args.data_loader_kwargs = dnnlib.EasyDict( + pin_memory=True, num_workers=3, prefetch_factor=2 + ) + try: + training_set = dnnlib.util.construct_class_by_name( + **args.training_set_kwargs + ) # subclass of training.dataset.Dataset + args.training_set_kwargs.resolution = ( + training_set.resolution + ) # be explicit about resolution + args.training_set_kwargs.load_labels = class_cond + args.training_set_kwargs.max_size = len( + training_set + ) # be explicit about dataset size + desc = os.path.splitext(os.path.basename(data))[0] + del training_set # conserve memory + except IOError as err: + raise UserError(f"--data: {err}") + + if mirror: + desc += "-mirror" + args.training_set_kwargs.xflip = True + + # if load_labels: + # if not args.training_set_kwargs.load_labels: + # raise UserError('--cond=True requires labels specified in dataset.json') + # desc += '-cond' + # else: + # args.training_set_kwargs.load_labels = False + # if load_features and not load_labels: + # args.training_set_kwargs.label_dim=2048 + + if subset is not None: + assert isinstance(subset, int) + if not 1 <= subset <= args.training_set_kwargs.max_size: + raise UserError( + f"--subset must be between 1 and {args.training_set_kwargs.max_size}" + ) + desc += f"-subset{subset}" + if subset < args.training_set_kwargs.max_size: + args.training_set_kwargs.max_size = subset + args.training_set_kwargs.random_seed = args.random_seed + + # ------------------------------------ + # Base config: cfg, gamma, kimg, batch + # ------------------------------------ + + if cfg is None: + cfg = "auto" + assert isinstance(cfg, str) + desc += f"-{cfg}" + + cfg_specs = { + "auto": dict( + ref_gpus=-1, + kimg=25000, + mb=-1, + mbstd=-1, + fmaps=-1, + lrate=-1, + gamma=-1, + ema=-1, + ramp=0.05, + map=2, + ), # Populated dynamically based on resolution and GPU count. + "stylegan2": dict( + ref_gpus=8, + kimg=25000, + mb=32, + mbstd=4, + fmaps=1, + lrate=0.002, + gamma=10, + ema=10, + ramp=None, + map=8, + ), # Uses mixed-precision, unlike the original StyleGAN2. + "paper256": dict( + ref_gpus=8, + kimg=25000, + mb=64, + mbstd=8, + fmaps=0.5, + lrate=0.0025, + gamma=1, + ema=20, + ramp=None, + map=8, + ), + "paper512": dict( + ref_gpus=8, + kimg=25000, + mb=64, + mbstd=8, + fmaps=1, + lrate=0.0025, + gamma=0.5, + ema=20, + ramp=None, + map=8, + ), + "paper1024": dict( + ref_gpus=8, + kimg=25000, + mb=32, + mbstd=4, + fmaps=1, + lrate=0.002, + gamma=2, + ema=10, + ramp=None, + map=8, + ), + "cifar": dict( + ref_gpus=2, + kimg=100000, + mb=64, + mbstd=32, + fmaps=1, + lrate=0.0025, + gamma=0.01, + ema=500, + ramp=0.05, + map=2, + ), + } + + assert cfg in cfg_specs + spec = dnnlib.EasyDict(cfg_specs[cfg]) + if cfg == "auto": + desc += f"{gpus:d}" + spec.ref_gpus = args.num_gpus + res = args.training_set_kwargs.resolution + spec.mb = max( + min(args.num_gpus * min(4096 // res, 32), 64), args.num_gpus + ) # keep gpu memory consumption at bay + spec.mbstd = min( + spec.mb // args.num_gpus, 4 + ) # other hyperparams behave more predictably if mbstd group size remains fixed + spec.fmaps = 1 if res >= 512 else 0.5 + spec.lrate = 0.002 if res >= 1024 else 0.0025 + spec.gamma = 0.0002 * (res ** 2) / spec.mb # heuristic formula + spec.ema = spec.mb * 10 / 32 + + args.G_kwargs = dnnlib.EasyDict( + class_name="training.networks.Generator", + z_dim=512, + w_dim=512, + mapping_kwargs=dnnlib.EasyDict(), + synthesis_kwargs=dnnlib.EasyDict(), + ) + args.D_kwargs = dnnlib.EasyDict( + class_name="training.networks.Discriminator", + block_kwargs=dnnlib.EasyDict(), + mapping_kwargs=dnnlib.EasyDict(), + epilogue_kwargs=dnnlib.EasyDict(), + ) + args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int( + spec.fmaps * 32768 + ) + args.G_kwargs.synthesis_kwargs.channel_max = args.D_kwargs.channel_max = 512 + args.G_kwargs.mapping_kwargs.num_layers = spec.map + if hidden_dim_c is not None: + args.G_kwargs.mapping_kwargs.embed_features = hidden_dim_c + args.D_kwargs.mapping_kwargs.embed_features = hidden_dim_c + if hidden_dim_h is not None: + args.G_kwargs.mapping_kwargs.embed_features_feat = hidden_dim_h + args.D_kwargs.mapping_kwargs.embed_features_feat = hidden_dim_h + args.G_kwargs.synthesis_kwargs.num_fp16_res = ( + args.D_kwargs.num_fp16_res + ) = 4 # enable mixed-precision training + args.G_kwargs.synthesis_kwargs.conv_clamp = ( + args.D_kwargs.conv_clamp + ) = 256 # clamp activations to avoid float16 overflow + args.D_kwargs.epilogue_kwargs.mbstd_group_size = spec.mbstd + + args.exp_name = exp_name + if num_channel_d is not None: + args.D_kwargs.channel_base = num_channel_d + if channel_max_d is not None: + args.D_kwargs.channel_max = channel_max_d + if num_channel_g is not None: + args.G_kwargs.synthesis_kwargs.channel_base = num_channel_g + if channel_max_g is not None: + args.G_kwargs.synthesis_kwargs.channel_max = channel_max_g + + if lrate is not None: + spec.lrate = lrate + + args.G_opt_kwargs = dnnlib.EasyDict( + class_name="torch.optim.Adam", lr=spec.lrate, betas=[0, 0.99], eps=1e-8 + ) + args.D_opt_kwargs = dnnlib.EasyDict( + class_name="torch.optim.Adam", lr=spec.lrate, betas=[0, 0.99], eps=1e-8 + ) + args.loss_kwargs = dnnlib.EasyDict( + class_name="training.loss.StyleGAN2Loss", r1_gamma=spec.gamma + ) + + args.total_kimg = spec.kimg + args.batch_size = spec.mb + args.batch_gpu = spec.mb // spec.ref_gpus + args.ema_kimg = spec.ema + args.ema_rampup = spec.ramp + + if cfg == "cifar": + args.loss_kwargs.pl_weight = 0 # disable path length regularization + args.loss_kwargs.style_mixing_prob = 0 # disable style mixing + args.D_kwargs.architecture = "orig" # disable residual skip connections + + if gamma is not None: + assert isinstance(gamma, float) + if not gamma >= 0: + raise UserError("--gamma must be non-negative") + desc += f"-gamma{gamma:g}" + args.loss_kwargs.r1_gamma = gamma + + if kimg is not None: + assert isinstance(kimg, int) + if not kimg >= 1: + raise UserError("--kimg must be at least 1") + desc += f"-kimg{kimg:d}" + args.total_kimg = kimg + + if batch is not None: + assert isinstance(batch, int) + if not (batch >= 1 and batch % args.num_gpus == 0): + raise UserError( + "--batch must be at least 1 and divisible by --gpus and --nodes" + ) + desc += f"-batch{batch}" + args.batch_size = batch + args.batch_gpu = batch // (args.num_gpus) + args.slurm = slurm + + # --------------------------------------------------- + # Discriminator augmentation: aug, p, target, augpipe + # --------------------------------------------------- + + if aug is None: + aug = "ada" + else: + assert isinstance(aug, str) + desc += f"-{aug}" + + if aug == "ada": + args.ada_target = 0.6 + + elif aug == "noaug": + pass + + elif aug == "fixed": + if p is None: + raise UserError(f"--aug={aug} requires specifying --p") + + else: + raise UserError(f"--aug={aug} not supported") + + if p is not None: + assert isinstance(p, float) + if aug != "fixed": + raise UserError("--p can only be specified with --aug=fixed") + if not 0 <= p <= 1: + raise UserError("--p must be between 0 and 1") + desc += f"-p{p:g}" + args.augment_p = p + + if target is not None: + assert isinstance(target, float) + if aug != "ada": + raise UserError("--target can only be specified with --aug=ada") + if not 0 <= target <= 1: + raise UserError("--target must be between 0 and 1") + desc += f"-target{target:g}" + args.ada_target = target + + assert augpipe is None or isinstance(augpipe, str) + if augpipe is None: + augpipe = "bgc" + else: + if aug == "noaug": + raise UserError("--augpipe cannot be specified with --aug=noaug") + desc += f"-{augpipe}" + + augpipe_specs = { + "blit": dict(xflip=1, rotate90=1, xint=1), + "geom": dict(scale=1, rotate=1, aniso=1, xfrac=1), + "color": dict(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1), + "filter": dict(imgfilter=1), + "noise": dict(noise=1), + "cutout": dict(cutout=1), + "bg": dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1), + "bgc": dict( + xflip=1, + rotate90=1, + xint=1, + scale=1, + rotate=1, + aniso=1, + xfrac=1, + brightness=1, + contrast=1, + lumaflip=1, + hue=1, + saturation=1, + ), + "bgcf": dict( + xflip=1, + rotate90=1, + xint=1, + scale=1, + rotate=1, + aniso=1, + xfrac=1, + brightness=1, + contrast=1, + lumaflip=1, + hue=1, + saturation=1, + imgfilter=1, + ), + "bgcfn": dict( + xflip=1, + rotate90=1, + xint=1, + scale=1, + rotate=1, + aniso=1, + xfrac=1, + brightness=1, + contrast=1, + lumaflip=1, + hue=1, + saturation=1, + imgfilter=1, + noise=1, + ), + "bgcfnc": dict( + xflip=1, + rotate90=1, + xint=1, + scale=1, + rotate=1, + aniso=1, + xfrac=1, + brightness=1, + contrast=1, + lumaflip=1, + hue=1, + saturation=1, + imgfilter=1, + noise=1, + cutout=1, + ), + } + + assert augpipe in augpipe_specs + if aug != "noaug": + args.augment_kwargs = dnnlib.EasyDict( + class_name="training.augment.AugmentPipe", **augpipe_specs[augpipe] + ) + + # ---------------------------------- + # Transfer learning: resume, freezed + # ---------------------------------- + + resume_specs = { + "ffhq256": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl", + "ffhq512": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl", + "ffhq1024": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl", + "celebahq256": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl", + "lsundog256": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl", + } + + assert resume is None or isinstance(resume, str) + if resume is None: + resume = "noresume" + elif resume == "noresume": + desc += "-noresume" + elif resume in resume_specs: + desc += f"-resume{resume}" + args.resume_pkl = resume_specs[resume] # predefined url + else: + desc += "-resumecustom" + args.resume_pkl = resume # custom path or url + + if resume != "noresume": + args.ada_kimg = 100 # make ADA react faster at the beginning + args.ema_rampup = None # disable EMA rampup + + if freezed is not None: + assert isinstance(freezed, int) + if not freezed >= 0: + raise UserError("--freezed must be non-negative") + desc += f"-freezed{freezed:d}" + args.D_kwargs.block_kwargs.freeze_layers = freezed + + # ------------------------------------------------- + # Performance options: fp32, nhwc, nobench, workers + # ------------------------------------------------- + + if fp32 is None: + fp32 = False + assert isinstance(fp32, bool) + if fp32: + args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 0 + args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = None + + if nhwc is None: + nhwc = False + assert isinstance(nhwc, bool) + if nhwc: + args.G_kwargs.synthesis_kwargs.fp16_channels_last = ( + args.D_kwargs.block_kwargs.fp16_channels_last + ) = True + + if nobench is None: + nobench = False + assert isinstance(nobench, bool) + if nobench: + args.cudnn_benchmark = False + + if allow_tf32 is None: + allow_tf32 = False + assert isinstance(allow_tf32, bool) + if allow_tf32: + args.allow_tf32 = True + + if workers is not None: + assert isinstance(workers, int) + if not workers >= 1: + raise UserError("--workers must be at least 1") + args.data_loader_kwargs.num_workers = workers + + return desc, args + + +# ---------------------------------------------------------------------------- + + +def subprocess_fn(rank, args, world_size=1, dist_url="", temp_dir="", slurm=False): + dnnlib.util.Logger( + file_name=os.path.join(args.run_dir, "log.txt"), + file_mode="a", + should_flush=True, + ) + + # Init torch.distributed. + if not slurm and args.num_gpus > 1: + init_file = os.path.abspath(os.path.join(temp_dir, ".torch_distributed_init")) + if os.name == "nt": + init_method = "file:///" + init_file.replace("\\", "/") + torch.distributed.init_process_group( + backend="gloo", + init_method=init_method, + rank=rank, + world_size=args.num_gpus, + ) + else: + init_method = f"file://{init_file}" + torch.distributed.init_process_group( + backend="nccl", + init_method=init_method, + rank=rank, + world_size=args.num_gpus, + ) + # Init torch_utils. + sync_device = torch.device("cuda", rank) if args.num_gpus > 1 else None + training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) + local_rank = rank + + elif slurm: + rank = int(os.environ.get("SLURM_PROCID")) + local_rank = int(os.environ.get("SLURM_LOCALID")) + torch.distributed.init_process_group( + backend="nccl", init_method=dist_url, rank=rank, world_size=world_size + ) + else: + rank = local_rank = 0 + + if rank != 0: + custom_ops.verbosity = "none" + + # Execute training loop. + training_loop.training_loop( + rank=rank, local_rank=local_rank, temp_dir=temp_dir, **args + ) + + +# ---------------------------------------------------------------------------- + + +class CommaSeparatedList(click.ParamType): + name = "list" + + def convert(self, value, param, ctx): + _ = param, ctx + if value is None or value.lower() == "none" or value == "": + return [] + return value.split(",") + + +# ---------------------------------------------------------------------------- + + +def main(args, outdir, master_node="", port=40000, dry_run=False, **config_kwargs): + """Train a GAN using the techniques described in the paper + "Training Generative Adversarial Networks with Limited Data". + + Examples: + + \b + # Train with custom dataset using 1 GPU. + python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1 + + \b + # Train class-conditional CIFAR-10 using 2 GPUs. + python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \\ + --gpus=2 --cfg=cifar --cond=1 + + \b + # Transfer learn MetFaces from FFHQ using 4 GPUs. + python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \\ + --gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10 + + \b + # Reproduce original StyleGAN2 config F. + python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \\ + --gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug + + \b + Base configs (--cfg): + auto Automatically select reasonable defaults based on resolution + and GPU count. Good starting point for new datasets. + stylegan2 Reproduce results for StyleGAN2 config F at 1024x1024. + paper256 Reproduce results for FFHQ and LSUN Cat at 256x256. + paper512 Reproduce results for BreCaHAD and AFHQ at 512x512. + paper1024 Reproduce results for MetFaces at 1024x1024. + cifar Reproduce results for CIFAR-10 at 32x32. + + \b + Transfer learning source networks (--resume): + ffhq256 FFHQ trained at 256x256 resolution. + ffhq512 FFHQ trained at 512x512 resolution. + ffhq1024 FFHQ trained at 1024x1024 resolution. + celebahq256 CelebA-HQ trained at 256x256 resolution. + lsundog256 LSUN Dog trained at 256x256 resolution. + Custom network pickle. + """ + dnnlib.util.Logger(should_flush=True) + + # Setup training options. + config_kwargs = vars(args) + run_desc, args = setup_training_loop_kwargs(**config_kwargs) + args.metrics = ["fid50k_full"] + + if args.exp_name is None: + # Pick output directory. + prev_run_dirs = [] + if os.path.isdir(outdir): + prev_run_dirs = [ + x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x)) + ] + prev_run_ids = [re.match(r"^\d+", x) for x in prev_run_dirs] + prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None] + cur_run_id = max(prev_run_ids, default=-1) + 1 + args.run_dir = os.path.join(outdir, f"{cur_run_id:05d}-{run_desc}") + assert not os.path.exists(args.run_dir) + else: + args.run_dir = os.path.join(outdir, args.exp_name) + + # Print options. + print() + print("Training options:") + # print(json.dumps(args, indent=2)) + print() + print(f"Output directory: {args.run_dir}") + print(f"Training data: {args.training_set_kwargs.root}") + print(f"Training duration: {args.total_kimg} kimg") + print(f"Number of GPUs: {args.num_gpus}") + print(f"Number of images: {args.training_set_kwargs.max_size}") + print(f"Image resolution: {args.training_set_kwargs.resolution}") + print(f"Conditional model: {args.training_set_kwargs.load_labels}") + print(f"Dataset x-flips: {args.training_set_kwargs.xflip}") + print() + + # Dry run? + if dry_run: + print("Dry run; exiting.") + return + + # Create output directory. + print("Creating output directory...") + if not os.path.exists(args.run_dir): + os.makedirs(args.run_dir, exist_ok=True) + with open(os.path.join(args.run_dir, "training_options.json"), "wt") as f: + json.dump(args, f, indent=2) + + ## Multi-gpu or multi-node training ## + if args.slurm: + n_nodes = int(os.environ.get("SLURM_JOB_NUM_NODES")) + n_gpus_per_node = int(os.environ.get("SLURM_TASKS_PER_NODE").split("(")[0]) + world_size = n_gpus_per_node * n_nodes + dist_url = "tcp://" + dist_url += master_node + dist_url += ":" + str(port) + print("Dist url ", dist_url) + temp_dir = "/scratch/slurm_tmpdir/" + str(os.environ.get("SLURM_JOB_ID")) + subprocess_fn( + rank=-1, + args=args, + world_size=world_size, + dist_url=dist_url, + temp_dir=temp_dir, + slurm=args.slurm, + ) + else: + # Launch processes. + print("Launching processes...") + torch.multiprocessing.set_start_method("spawn") + with tempfile.TemporaryDirectory() as temp_dir: + if args.num_gpus == 1: + subprocess_fn(rank=0, args=args, temp_dir=temp_dir) + else: + torch.multiprocessing.spawn( + fn=subprocess_fn, + args=(args, args.num_gpus, "", temp_dir), + nprocs=args.num_gpus, + ) + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + parser_ = parser.get_parser() + args = parser_.parse_args() + main(args) # pylint: disable=no-value-for-parameter + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/training/__init__.py b/stylegan2_ada_pytorch/training/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..e1e1a5ba99e56a56ecaa14f7d4fa41777789c0cf --- /dev/null +++ b/stylegan2_ada_pytorch/training/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/stylegan2_ada_pytorch/training/augment.py b/stylegan2_ada_pytorch/training/augment.py new file mode 100755 index 0000000000000000000000000000000000000000..c11ee1a92027dfec434bd5f2fa52047cfc9a8d41 --- /dev/null +++ b/stylegan2_ada_pytorch/training/augment.py @@ -0,0 +1,829 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import numpy as np +import scipy.signal +import torch +from torch_utils import persistence +from torch_utils import misc +from torch_utils.ops import upfirdn2d +from torch_utils.ops import grid_sample_gradfix +from torch_utils.ops import conv2d_gradfix + +# ---------------------------------------------------------------------------- +# Coefficients of various wavelet decomposition low-pass filters. + +wavelets = { + "haar": [0.7071067811865476, 0.7071067811865476], + "db1": [0.7071067811865476, 0.7071067811865476], + "db2": [ + -0.12940952255092145, + 0.22414386804185735, + 0.836516303737469, + 0.48296291314469025, + ], + "db3": [ + 0.035226291882100656, + -0.08544127388224149, + -0.13501102001039084, + 0.4598775021193313, + 0.8068915093133388, + 0.3326705529509569, + ], + "db4": [ + -0.010597401784997278, + 0.032883011666982945, + 0.030841381835986965, + -0.18703481171888114, + -0.02798376941698385, + 0.6308807679295904, + 0.7148465705525415, + 0.23037781330885523, + ], + "db5": [ + 0.003335725285001549, + -0.012580751999015526, + -0.006241490213011705, + 0.07757149384006515, + -0.03224486958502952, + -0.24229488706619015, + 0.13842814590110342, + 0.7243085284385744, + 0.6038292697974729, + 0.160102397974125, + ], + "db6": [ + -0.00107730108499558, + 0.004777257511010651, + 0.0005538422009938016, + -0.031582039318031156, + 0.02752286553001629, + 0.09750160558707936, + -0.12976686756709563, + -0.22626469396516913, + 0.3152503517092432, + 0.7511339080215775, + 0.4946238903983854, + 0.11154074335008017, + ], + "db7": [ + 0.0003537138000010399, + -0.0018016407039998328, + 0.00042957797300470274, + 0.012550998556013784, + -0.01657454163101562, + -0.03802993693503463, + 0.0806126091510659, + 0.07130921926705004, + -0.22403618499416572, + -0.14390600392910627, + 0.4697822874053586, + 0.7291320908465551, + 0.39653931948230575, + 0.07785205408506236, + ], + "db8": [ + -0.00011747678400228192, + 0.0006754494059985568, + -0.0003917403729959771, + -0.00487035299301066, + 0.008746094047015655, + 0.013981027917015516, + -0.04408825393106472, + -0.01736930100202211, + 0.128747426620186, + 0.00047248457399797254, + -0.2840155429624281, + -0.015829105256023893, + 0.5853546836548691, + 0.6756307362980128, + 0.3128715909144659, + 0.05441584224308161, + ], + "sym2": [ + -0.12940952255092145, + 0.22414386804185735, + 0.836516303737469, + 0.48296291314469025, + ], + "sym3": [ + 0.035226291882100656, + -0.08544127388224149, + -0.13501102001039084, + 0.4598775021193313, + 0.8068915093133388, + 0.3326705529509569, + ], + "sym4": [ + -0.07576571478927333, + -0.02963552764599851, + 0.49761866763201545, + 0.8037387518059161, + 0.29785779560527736, + -0.09921954357684722, + -0.012603967262037833, + 0.0322231006040427, + ], + "sym5": [ + 0.027333068345077982, + 0.029519490925774643, + -0.039134249302383094, + 0.1993975339773936, + 0.7234076904024206, + 0.6339789634582119, + 0.01660210576452232, + -0.17532808990845047, + -0.021101834024758855, + 0.019538882735286728, + ], + "sym6": [ + 0.015404109327027373, + 0.0034907120842174702, + -0.11799011114819057, + -0.048311742585633, + 0.4910559419267466, + 0.787641141030194, + 0.3379294217276218, + -0.07263752278646252, + -0.021060292512300564, + 0.04472490177066578, + 0.0017677118642428036, + -0.007800708325034148, + ], + "sym7": [ + 0.002681814568257878, + -0.0010473848886829163, + -0.01263630340325193, + 0.03051551316596357, + 0.0678926935013727, + -0.049552834937127255, + 0.017441255086855827, + 0.5361019170917628, + 0.767764317003164, + 0.2886296317515146, + -0.14004724044296152, + -0.10780823770381774, + 0.004010244871533663, + 0.010268176708511255, + ], + "sym8": [ + -0.0033824159510061256, + -0.0005421323317911481, + 0.03169508781149298, + 0.007607487324917605, + -0.1432942383508097, + -0.061273359067658524, + 0.4813596512583722, + 0.7771857517005235, + 0.3644418948353314, + -0.05194583810770904, + -0.027219029917056003, + 0.049137179673607506, + 0.003808752013890615, + -0.01495225833704823, + -0.0003029205147213668, + 0.0018899503327594609, + ], +} + +# ---------------------------------------------------------------------------- +# Helpers for constructing transformation matrices. + + +def matrix(*rows, device=None): + assert all(len(row) == len(rows[0]) for row in rows) + elems = [x for row in rows for x in row] + ref = [x for x in elems if isinstance(x, torch.Tensor)] + if len(ref) == 0: + return misc.constant(np.asarray(rows), device=device) + assert device is None or device == ref[0].device + elems = [ + x + if isinstance(x, torch.Tensor) + else misc.constant(x, shape=ref[0].shape, device=ref[0].device) + for x in elems + ] + return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1)) + + +def translate2d(tx, ty, **kwargs): + return matrix([1, 0, tx], [0, 1, ty], [0, 0, 1], **kwargs) + + +def translate3d(tx, ty, tz, **kwargs): + return matrix([1, 0, 0, tx], [0, 1, 0, ty], [0, 0, 1, tz], [0, 0, 0, 1], **kwargs) + + +def scale2d(sx, sy, **kwargs): + return matrix([sx, 0, 0], [0, sy, 0], [0, 0, 1], **kwargs) + + +def scale3d(sx, sy, sz, **kwargs): + return matrix([sx, 0, 0, 0], [0, sy, 0, 0], [0, 0, sz, 0], [0, 0, 0, 1], **kwargs) + + +def rotate2d(theta, **kwargs): + return matrix( + [torch.cos(theta), torch.sin(-theta), 0], + [torch.sin(theta), torch.cos(theta), 0], + [0, 0, 1], + **kwargs + ) + + +def rotate3d(v, theta, **kwargs): + vx = v[..., 0] + vy = v[..., 1] + vz = v[..., 2] + s = torch.sin(theta) + c = torch.cos(theta) + cc = 1 - c + return matrix( + [vx * vx * cc + c, vx * vy * cc - vz * s, vx * vz * cc + vy * s, 0], + [vy * vx * cc + vz * s, vy * vy * cc + c, vy * vz * cc - vx * s, 0], + [vz * vx * cc - vy * s, vz * vy * cc + vx * s, vz * vz * cc + c, 0], + [0, 0, 0, 1], + **kwargs + ) + + +def translate2d_inv(tx, ty, **kwargs): + return translate2d(-tx, -ty, **kwargs) + + +def scale2d_inv(sx, sy, **kwargs): + return scale2d(1 / sx, 1 / sy, **kwargs) + + +def rotate2d_inv(theta, **kwargs): + return rotate2d(-theta, **kwargs) + + +# ---------------------------------------------------------------------------- +# Versatile image augmentation pipeline from the paper +# "Training Generative Adversarial Networks with Limited Data". +# +# All augmentations are disabled by default; individual augmentations can +# be enabled by setting their probability multipliers to 1. + + +@persistence.persistent_class +class AugmentPipe(torch.nn.Module): + def __init__( + self, + xflip=0, + rotate90=0, + xint=0, + xint_max=0.125, + scale=0, + rotate=0, + aniso=0, + xfrac=0, + scale_std=0.2, + rotate_max=1, + aniso_std=0.2, + xfrac_std=0.125, + brightness=0, + contrast=0, + lumaflip=0, + hue=0, + saturation=0, + brightness_std=0.2, + contrast_std=0.5, + hue_max=1, + saturation_std=1, + imgfilter=0, + imgfilter_bands=[1, 1, 1, 1], + imgfilter_std=1, + noise=0, + cutout=0, + noise_std=0.1, + cutout_size=0.5, + ): + super().__init__() + self.register_buffer( + "p", torch.ones([]) + ) # Overall multiplier for augmentation probability. + + # Pixel blitting. + self.xflip = float(xflip) # Probability multiplier for x-flip. + self.rotate90 = float( + rotate90 + ) # Probability multiplier for 90 degree rotations. + self.xint = float(xint) # Probability multiplier for integer translation. + self.xint_max = float( + xint_max + ) # Range of integer translation, relative to image dimensions. + + # General geometric transformations. + self.scale = float(scale) # Probability multiplier for isotropic scaling. + self.rotate = float(rotate) # Probability multiplier for arbitrary rotation. + self.aniso = float(aniso) # Probability multiplier for anisotropic scaling. + self.xfrac = float(xfrac) # Probability multiplier for fractional translation. + self.scale_std = float( + scale_std + ) # Log2 standard deviation of isotropic scaling. + self.rotate_max = float( + rotate_max + ) # Range of arbitrary rotation, 1 = full circle. + self.aniso_std = float( + aniso_std + ) # Log2 standard deviation of anisotropic scaling. + self.xfrac_std = float( + xfrac_std + ) # Standard deviation of frational translation, relative to image dimensions. + + # Color transformations. + self.brightness = float(brightness) # Probability multiplier for brightness. + self.contrast = float(contrast) # Probability multiplier for contrast. + self.lumaflip = float(lumaflip) # Probability multiplier for luma flip. + self.hue = float(hue) # Probability multiplier for hue rotation. + self.saturation = float(saturation) # Probability multiplier for saturation. + self.brightness_std = float(brightness_std) # Standard deviation of brightness. + self.contrast_std = float(contrast_std) # Log2 standard deviation of contrast. + self.hue_max = float(hue_max) # Range of hue rotation, 1 = full circle. + self.saturation_std = float( + saturation_std + ) # Log2 standard deviation of saturation. + + # Image-space filtering. + self.imgfilter = float( + imgfilter + ) # Probability multiplier for image-space filtering. + self.imgfilter_bands = list( + imgfilter_bands + ) # Probability multipliers for individual frequency bands. + self.imgfilter_std = float( + imgfilter_std + ) # Log2 standard deviation of image-space filter amplification. + + # Image-space corruptions. + self.noise = float(noise) # Probability multiplier for additive RGB noise. + self.cutout = float(cutout) # Probability multiplier for cutout. + self.noise_std = float(noise_std) # Standard deviation of additive RGB noise. + self.cutout_size = float( + cutout_size + ) # Size of the cutout rectangle, relative to image dimensions. + + # Setup orthogonal lowpass filter for geometric augmentations. + self.register_buffer("Hz_geom", upfirdn2d.setup_filter(wavelets["sym6"])) + + # Construct filter bank for image-space filtering. + Hz_lo = np.asarray(wavelets["sym2"]) # H(z) + Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z) + Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2 # H(z) * H(z^-1) / 2 + Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2 # H(-z) * H(-z^-1) / 2 + Hz_fbank = np.eye(4, 1) # Bandpass(H(z), b_i) + for i in range(1, Hz_fbank.shape[0]): + Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape( + Hz_fbank.shape[0], -1 + )[:, :-1] + Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2]) + Hz_fbank[ + i, + (Hz_fbank.shape[1] - Hz_hi2.size) + // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) + // 2, + ] += Hz_hi2 + self.register_buffer("Hz_fbank", torch.as_tensor(Hz_fbank, dtype=torch.float32)) + + def forward(self, images, debug_percentile=None): + assert isinstance(images, torch.Tensor) and images.ndim == 4 + batch_size, num_channels, height, width = images.shape + device = images.device + if debug_percentile is not None: + debug_percentile = torch.as_tensor( + debug_percentile, dtype=torch.float32, device=device + ) + + # ------------------------------------- + # Select parameters for pixel blitting. + # ------------------------------------- + + # Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in + I_3 = torch.eye(3, device=device) + G_inv = I_3 + + # Apply x-flip with probability (xflip * strength). + if self.xflip > 0: + i = torch.floor(torch.rand([batch_size], device=device) * 2) + i = torch.where( + torch.rand([batch_size], device=device) < self.xflip * self.p, + i, + torch.zeros_like(i), + ) + if debug_percentile is not None: + i = torch.full_like(i, torch.floor(debug_percentile * 2)) + G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1) + + # Apply 90 degree rotations with probability (rotate90 * strength). + if self.rotate90 > 0: + i = torch.floor(torch.rand([batch_size], device=device) * 4) + i = torch.where( + torch.rand([batch_size], device=device) < self.rotate90 * self.p, + i, + torch.zeros_like(i), + ) + if debug_percentile is not None: + i = torch.full_like(i, torch.floor(debug_percentile * 4)) + G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i) + + # Apply integer translation with probability (xint * strength). + if self.xint > 0: + t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max + t = torch.where( + torch.rand([batch_size, 1], device=device) < self.xint * self.p, + t, + torch.zeros_like(t), + ) + if debug_percentile is not None: + t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max) + G_inv = G_inv @ translate2d_inv( + torch.round(t[:, 0] * width), torch.round(t[:, 1] * height) + ) + + # -------------------------------------------------------- + # Select parameters for general geometric transformations. + # -------------------------------------------------------- + + # Apply isotropic scaling with probability (scale * strength). + if self.scale > 0: + s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std) + s = torch.where( + torch.rand([batch_size], device=device) < self.scale * self.p, + s, + torch.ones_like(s), + ) + if debug_percentile is not None: + s = torch.full_like( + s, + torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std), + ) + G_inv = G_inv @ scale2d_inv(s, s) + + # Apply pre-rotation with probability p_rot. + p_rot = 1 - torch.sqrt( + (1 - self.rotate * self.p).clamp(0, 1) + ) # P(pre OR post) = p + if self.rotate > 0: + theta = ( + (torch.rand([batch_size], device=device) * 2 - 1) + * np.pi + * self.rotate_max + ) + theta = torch.where( + torch.rand([batch_size], device=device) < p_rot, + theta, + torch.zeros_like(theta), + ) + if debug_percentile is not None: + theta = torch.full_like( + theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max + ) + G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling. + + # Apply anisotropic scaling with probability (aniso * strength). + if self.aniso > 0: + s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std) + s = torch.where( + torch.rand([batch_size], device=device) < self.aniso * self.p, + s, + torch.ones_like(s), + ) + if debug_percentile is not None: + s = torch.full_like( + s, + torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std), + ) + G_inv = G_inv @ scale2d_inv(s, 1 / s) + + # Apply post-rotation with probability p_rot. + if self.rotate > 0: + theta = ( + (torch.rand([batch_size], device=device) * 2 - 1) + * np.pi + * self.rotate_max + ) + theta = torch.where( + torch.rand([batch_size], device=device) < p_rot, + theta, + torch.zeros_like(theta), + ) + if debug_percentile is not None: + theta = torch.zeros_like(theta) + G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling. + + # Apply fractional translation with probability (xfrac * strength). + if self.xfrac > 0: + t = torch.randn([batch_size, 2], device=device) * self.xfrac_std + t = torch.where( + torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, + t, + torch.zeros_like(t), + ) + if debug_percentile is not None: + t = torch.full_like( + t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std + ) + G_inv = G_inv @ translate2d_inv(t[:, 0] * width, t[:, 1] * height) + + # ---------------------------------- + # Execute geometric transformations. + # ---------------------------------- + + # Execute if the transform is not identity. + if G_inv is not I_3: + + # Calculate padding. + cx = (width - 1) / 2 + cy = (height - 1) / 2 + cp = matrix( + [-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device + ) # [idx, xyz] + cp = G_inv @ cp.t() # [batch, xyz, idx] + Hz_pad = self.Hz_geom.shape[0] // 4 + margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx] + margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1] + margin = margin + misc.constant( + [Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device + ) + margin = margin.max(misc.constant([0, 0] * 2, device=device)) + margin = margin.min( + misc.constant([width - 1, height - 1] * 2, device=device) + ) + mx0, my0, mx1, my1 = margin.ceil().to(torch.int32) + + # Pad image and adjust origin. + images = torch.nn.functional.pad( + input=images, pad=[mx0, mx1, my0, my1], mode="reflect" + ) + G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv + + # Upsample. + images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2) + G_inv = ( + scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device) + ) + G_inv = ( + translate2d(-0.5, -0.5, device=device) + @ G_inv + @ translate2d_inv(-0.5, -0.5, device=device) + ) + + # Execute transformation. + shape = [ + batch_size, + num_channels, + (height + Hz_pad * 2) * 2, + (width + Hz_pad * 2) * 2, + ] + G_inv = ( + scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) + @ G_inv + @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device) + ) + grid = torch.nn.functional.affine_grid( + theta=G_inv[:, :2, :], size=shape, align_corners=False + ) + images = grid_sample_gradfix.grid_sample(images, grid) + + # Downsample and crop. + images = upfirdn2d.downsample2d( + x=images, f=self.Hz_geom, down=2, padding=-Hz_pad * 2, flip_filter=True + ) + + # -------------------------------------------- + # Select parameters for color transformations. + # -------------------------------------------- + + # Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out + I_4 = torch.eye(4, device=device) + C = I_4 + + # Apply brightness with probability (brightness * strength). + if self.brightness > 0: + b = torch.randn([batch_size], device=device) * self.brightness_std + b = torch.where( + torch.rand([batch_size], device=device) < self.brightness * self.p, + b, + torch.zeros_like(b), + ) + if debug_percentile is not None: + b = torch.full_like( + b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std + ) + C = translate3d(b, b, b) @ C + + # Apply contrast with probability (contrast * strength). + if self.contrast > 0: + c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std) + c = torch.where( + torch.rand([batch_size], device=device) < self.contrast * self.p, + c, + torch.ones_like(c), + ) + if debug_percentile is not None: + c = torch.full_like( + c, + torch.exp2( + torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std + ), + ) + C = scale3d(c, c, c) @ C + + # Apply luma flip with probability (lumaflip * strength). + v = misc.constant( + np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device + ) # Luma axis. + if self.lumaflip > 0: + i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2) + i = torch.where( + torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, + i, + torch.zeros_like(i), + ) + if debug_percentile is not None: + i = torch.full_like(i, torch.floor(debug_percentile * 2)) + C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection. + + # Apply hue rotation with probability (hue * strength). + if self.hue > 0 and num_channels > 1: + theta = ( + (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max + ) + theta = torch.where( + torch.rand([batch_size], device=device) < self.hue * self.p, + theta, + torch.zeros_like(theta), + ) + if debug_percentile is not None: + theta = torch.full_like( + theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max + ) + C = rotate3d(v, theta) @ C # Rotate around v. + + # Apply saturation with probability (saturation * strength). + if self.saturation > 0 and num_channels > 1: + s = torch.exp2( + torch.randn([batch_size, 1, 1], device=device) * self.saturation_std + ) + s = torch.where( + torch.rand([batch_size, 1, 1], device=device) + < self.saturation * self.p, + s, + torch.ones_like(s), + ) + if debug_percentile is not None: + s = torch.full_like( + s, + torch.exp2( + torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std + ), + ) + C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C + + # ------------------------------ + # Execute color transformations. + # ------------------------------ + + # Execute if the transform is not identity. + if C is not I_4: + images = images.reshape([batch_size, num_channels, height * width]) + if num_channels == 3: + images = C[:, :3, :3] @ images + C[:, :3, 3:] + elif num_channels == 1: + C = C[:, :3, :].mean(dim=1, keepdims=True) + images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:] + else: + raise ValueError("Image must be RGB (3 channels) or L (1 channel)") + images = images.reshape([batch_size, num_channels, height, width]) + + # ---------------------- + # Image-space filtering. + # ---------------------- + + if self.imgfilter > 0: + num_bands = self.Hz_fbank.shape[0] + assert len(self.imgfilter_bands) == num_bands + expected_power = misc.constant( + np.array([10, 1, 1, 1]) / 13, device=device + ) # Expected power spectrum (1/f). + + # Apply amplification for each band with probability (imgfilter * strength * band_strength). + g = torch.ones( + [batch_size, num_bands], device=device + ) # Global gain vector (identity). + for i, band_strength in enumerate(self.imgfilter_bands): + t_i = torch.exp2( + torch.randn([batch_size], device=device) * self.imgfilter_std + ) + t_i = torch.where( + torch.rand([batch_size], device=device) + < self.imgfilter * self.p * band_strength, + t_i, + torch.ones_like(t_i), + ) + if debug_percentile is not None: + t_i = ( + torch.full_like( + t_i, + torch.exp2( + torch.erfinv(debug_percentile * 2 - 1) + * self.imgfilter_std + ), + ) + if band_strength > 0 + else torch.ones_like(t_i) + ) + t = torch.ones( + [batch_size, num_bands], device=device + ) # Temporary gain vector. + t[:, i] = t_i # Replace i'th element. + t = ( + t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() + ) # Normalize power. + g = g * t # Accumulate into global gain. + + # Construct combined amplification filter. + Hz_prime = g @ self.Hz_fbank # [batch, tap] + Hz_prime = Hz_prime.unsqueeze(1).repeat( + [1, num_channels, 1] + ) # [batch, channels, tap] + Hz_prime = Hz_prime.reshape( + [batch_size * num_channels, 1, -1] + ) # [batch * channels, 1, tap] + + # Apply filter. + p = self.Hz_fbank.shape[1] // 2 + images = images.reshape([1, batch_size * num_channels, height, width]) + images = torch.nn.functional.pad( + input=images, pad=[p, p, p, p], mode="reflect" + ) + images = conv2d_gradfix.conv2d( + input=images, + weight=Hz_prime.unsqueeze(2), + groups=batch_size * num_channels, + ) + images = conv2d_gradfix.conv2d( + input=images, + weight=Hz_prime.unsqueeze(3), + groups=batch_size * num_channels, + ) + images = images.reshape([batch_size, num_channels, height, width]) + + # ------------------------ + # Image-space corruptions. + # ------------------------ + + # Apply additive RGB noise with probability (noise * strength). + if self.noise > 0: + sigma = ( + torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std + ) + sigma = torch.where( + torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, + sigma, + torch.zeros_like(sigma), + ) + if debug_percentile is not None: + sigma = torch.full_like( + sigma, torch.erfinv(debug_percentile) * self.noise_std + ) + images = ( + images + + torch.randn([batch_size, num_channels, height, width], device=device) + * sigma + ) + + # Apply cutout with probability (cutout * strength). + if self.cutout > 0: + size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device) + size = torch.where( + torch.rand([batch_size, 1, 1, 1, 1], device=device) + < self.cutout * self.p, + size, + torch.zeros_like(size), + ) + center = torch.rand([batch_size, 2, 1, 1, 1], device=device) + if debug_percentile is not None: + size = torch.full_like(size, self.cutout_size) + center = torch.full_like(center, debug_percentile) + coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1]) + coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1]) + mask_x = ((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2 + mask_y = ((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2 + mask = torch.logical_or(mask_x, mask_y).to(torch.float32) + images = images * mask + + return images + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/training/dataset.py b/stylegan2_ada_pytorch/training/dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..292f36496b4205fd997e97db2f1713e3c770b61e --- /dev/null +++ b/stylegan2_ada_pytorch/training/dataset.py @@ -0,0 +1,279 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# All contributions by NVIDIA CORPORATION: +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import numpy as np +import zipfile +import PIL.Image +import json +import torch +import dnnlib +import h5py as h5 + +try: + import pyspng +except ImportError: + pyspng = None + +# ---------------------------------------------------------------------------- + + +class Dataset(torch.utils.data.Dataset): + def __init__( + self, + name, # Name of the dataset. + raw_shape, # Shape of the raw image data (NCHW). + max_size=None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. + load_labels=False, # Enable conditioning labels? False = label dimension is zero. + xflip=False, # Artificially double the size of the dataset via x-flips. Applied after max_size. + random_seed=0, # Random seed to use when applying max_size. + **kwargs, + ): + self._name = name + self._raw_shape = list(raw_shape) + self._load_labels = load_labels + self._raw_labels = None + self._label_shape = None + + # Apply max_size. + self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64) + if (max_size is not None) and (self._raw_idx.size > max_size): + np.random.RandomState(random_seed).shuffle(self._raw_idx) + self._raw_idx = np.sort(self._raw_idx[:max_size]) + + # Apply xflip. + self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) + if xflip: + self._raw_idx = np.tile(self._raw_idx, 2) + self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) + + def _get_raw_labels(self, idx): + if self._raw_labels is None: + self._raw_labels = self._load_raw_labels(idx) if self._load_labels else None + if self._raw_labels is None: + self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) + # assert isinstance(self._raw_labels, np.ndarray) + # assert self._raw_labels.shape[0] == self._raw_shape[0] + assert self._raw_labels.dtype in [np.float32, np.int64] + if self._raw_labels.dtype == np.int64: + # assert self._raw_labels.ndim == 1 + assert np.all(self._raw_labels >= 0) + return self._raw_labels + + def close(self): # to be overridden by subclass + pass + + def _load_raw_image(self, raw_idx): # to be overridden by subclass + raise NotImplementedError + + def _load_raw_labels(self, raw_idx): # to be overridden by subclass + raise NotImplementedError + + def __getstate__(self): + return dict(self.__dict__, _raw_labels=None) + + def __del__(self): + try: + self.close() + except: + pass + + def __len__(self): + return self._raw_idx.size + + def __getitem__(self, idx): + image = self._load_raw_image(self._raw_idx[idx]) + assert isinstance(image, np.ndarray) + assert list(image.shape) == self.image_shape + assert image.dtype == np.uint8 + if self._xflip[idx]: + assert image.ndim == 3 # CHW + image = image[:, :, ::-1] + return image.copy(), self.get_label(idx) + + def get_label(self, idx): + label = self._get_raw_labels(self._raw_idx[idx]) + if label.dtype == np.int64: + onehot = np.zeros(self.label_shape, dtype=np.float32) + onehot[label] = 1 + label = onehot + return label.copy() + + def get_details(self, idx): + d = dnnlib.EasyDict() + d.raw_idx = int(self._raw_idx[idx]) + d.xflip = int(self._xflip[idx]) != 0 + d.raw_label = self._get_raw_labels()[d.raw_idx].copy() + return d + + @property + def name(self): + return self._name + + @property + def image_shape(self): + return list(self._raw_shape[1:]) + + @property + def num_channels(self): + assert len(self.image_shape) == 3 # CHW + return self.image_shape[0] + + @property + def resolution(self): + assert len(self.image_shape) == 3 # CHW + assert self.image_shape[1] == self.image_shape[2] + return self.image_shape[1] + + @property + def label_shape(self): + if self._label_shape is None: + raw_labels = self._get_raw_labels(0) + if raw_labels.dtype == np.int64: + self._label_shape = [int(np.max(raw_labels)) + 1] + else: + self._label_shape = raw_labels.shape[1:] + return list(self._label_shape) + + @property + def label_dim(self): + assert len(self.label_shape) == 1 + return self.label_shape[0] + + @property + def has_labels(self): + return any(x != 0 for x in self.label_shape) + + @property + def has_onehot_labels(self): + return self._get_raw_labels().dtype == np.int64 + + +# ---------------------------------------------------------------------------- + + +class ImageFolderDataset(Dataset): + def __init__( + self, + root, # Path to directory or zip. + resolution=None, # Ensure specific resolution, None = highest available. + **super_kwargs, # Additional arguments for the Dataset base class. + ): + self._path = root + self._zipfile = None + + if os.path.isdir(self._path): + self._type = "dir" + self._all_fnames = { + os.path.relpath(os.path.join(root, fname), start=self._path) + for root, _dirs, files in os.walk(self._path) + for fname in files + } + elif self._file_ext(self._path) == ".zip": + self._type = "zip" + self._all_fnames = set(self._get_zipfile().namelist()) + elif self._file_ext(self._path) == ".hdf5": + self._type = "hdf5" + else: + raise IOError("Path must point to a directory or zip") + + PIL.Image.init() + if self._type in ["dir", "zip"]: + self._image_fnames = sorted( + fname + for fname in self._all_fnames + if self._file_ext(fname) in PIL.Image.EXTENSION + ) + if len(self._image_fnames) == 0: + raise IOError("No image files found in the specified path") + + name = os.path.splitext(os.path.basename(self._path))[0] + if self._type == "hdf5": + with h5.File(self._path, "r") as f: + nb = len(f["imgs"]) + sze = list(f["imgs"][0].shape) + raw_shape = [nb] + sze + else: + raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) + if resolution is not None and ( + raw_shape[2] != resolution or raw_shape[3] != resolution + ): + raise IOError("Image files do not match the specified resolution") + super().__init__(name=name, raw_shape=raw_shape, **super_kwargs) + + @staticmethod + def _file_ext(fname): + return os.path.splitext(fname)[1].lower() + + def _get_zipfile(self): + assert self._type == "zip" + if self._zipfile is None: + self._zipfile = zipfile.ZipFile(self._path) + return self._zipfile + + def _open_file(self, fname): + if self._type == "dir": + return open(os.path.join(self._path, fname), "rb") + if self._type == "zip": + return self._get_zipfile().open(fname, "r") + return None + + def close(self): + try: + if self._zipfile is not None: + self._zipfile.close() + finally: + self._zipfile = None + + def __getstate__(self): + return dict(super().__getstate__(), _zipfile=None) + + def _load_raw_image(self, raw_idx): + if self._type in ["dir", "zip"]: + fname = self._image_fnames[raw_idx] + with self._open_file(fname) as f: + if pyspng is not None and self._file_ext(fname) == ".png": + image = pyspng.load(f.read()) + else: + image = np.array(PIL.Image.open(f)) + if image.ndim == 2: + image = image[:, :, np.newaxis] # HW => HWC + image = image.transpose(2, 0, 1) # HWC => CHW + elif self._type == "hdf5": + with h5.File(self._path, "r") as f: + image = f["imgs"][raw_idx] + return image + + def _load_raw_labels(self, idx): + if self._type in ["dir", "zip"]: + fname = "dataset.json" + if fname not in self._all_fnames: + return None + with self._open_file(fname) as f: + labels = json.load(f)["labels"] + if labels is None: + return None + labels = dict(labels) + labels = [labels[fname.replace("\\", "/")] for fname in self._image_fnames] + labels = np.array(labels) + labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim])[idx] + + elif self._type == "hdf5": + with h5.File(self._path, "r") as f: + labels = f["labels"][idx] + return labels + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/training/loss.py b/stylegan2_ada_pytorch/training/loss.py new file mode 100755 index 0000000000000000000000000000000000000000..ca71307c337d0d7e3987fb1cf5b647812c5dd1ae --- /dev/null +++ b/stylegan2_ada_pytorch/training/loss.py @@ -0,0 +1,197 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import numpy as np +import torch +from torch_utils import training_stats +from torch_utils import misc +from torch_utils.ops import conv2d_gradfix + +# ---------------------------------------------------------------------------- + + +class Loss: + def accumulate_gradients( + self, phase, real_img, real_c, real_h, gen_z, gen_c, gen_h, sync, gain + ): # to be overridden by subclass + raise NotImplementedError() + + +# ---------------------------------------------------------------------------- + + +class StyleGAN2Loss(Loss): + def __init__( + self, + device, + G_mapping, + G_synthesis, + D, + augment_pipe=None, + style_mixing_prob=0.9, + r1_gamma=10, + pl_batch_shrink=2, + pl_decay=0.01, + pl_weight=2, + ): + super().__init__() + self.device = device + self.G_mapping = G_mapping + self.G_synthesis = G_synthesis + self.D = D + self.augment_pipe = augment_pipe + self.style_mixing_prob = style_mixing_prob + self.r1_gamma = r1_gamma + self.pl_batch_shrink = pl_batch_shrink + self.pl_decay = pl_decay + self.pl_weight = pl_weight + self.pl_mean = torch.zeros([], device=device) + + def run_G(self, z, c, h, sync): + with misc.ddp_sync(self.G_mapping, sync): + ws = self.G_mapping(z, c, h) + if self.style_mixing_prob > 0: + with torch.autograd.profiler.record_function("style_mixing"): + cutoff = torch.empty( + [], dtype=torch.int64, device=ws.device + ).random_(1, ws.shape[1]) + cutoff = torch.where( + torch.rand([], device=ws.device) < self.style_mixing_prob, + cutoff, + torch.full_like(cutoff, ws.shape[1]), + ) + ws[:, cutoff:] = self.G_mapping( + torch.randn_like(z), c, h, skip_w_avg_update=True + )[:, cutoff:] + with misc.ddp_sync(self.G_synthesis, sync): + img = self.G_synthesis(ws) + return img, ws + + def run_D(self, img, c, h, sync): + if self.augment_pipe is not None: + img = self.augment_pipe(img) + with misc.ddp_sync(self.D, sync): + logits = self.D(img, c, h) + return logits + + def accumulate_gradients( + self, phase, real_img, real_c, real_h, gen_z, gen_c, gen_h, sync, gain + ): + assert phase in ["Gmain", "Greg", "Gboth", "Dmain", "Dreg", "Dboth"] + do_Gmain = phase in ["Gmain", "Gboth"] + do_Dmain = phase in ["Dmain", "Dboth"] + do_Gpl = (phase in ["Greg", "Gboth"]) and (self.pl_weight != 0) + do_Dr1 = (phase in ["Dreg", "Dboth"]) and (self.r1_gamma != 0) + + # Gmain: Maximize logits for generated images. + if do_Gmain: + with torch.autograd.profiler.record_function("Gmain_forward"): + gen_img, _gen_ws = self.run_G( + gen_z, gen_c, gen_h, sync=(sync and not do_Gpl) + ) # May get synced by Gpl. + gen_logits = self.run_D(gen_img, gen_c, gen_h, sync=False) + training_stats.report("Loss/scores/fake", gen_logits) + training_stats.report("Loss/signs/fake", gen_logits.sign()) + loss_Gmain = torch.nn.functional.softplus( + -gen_logits + ) # -log(sigmoid(gen_logits)) + training_stats.report("Loss/G/loss", loss_Gmain) + with torch.autograd.profiler.record_function("Gmain_backward"): + loss_Gmain.mean().mul(gain).backward() + + # Gpl: Apply path length regularization. + if do_Gpl: + with torch.autograd.profiler.record_function("Gpl_forward"): + batch_size = gen_z.shape[0] // self.pl_batch_shrink + gen_img, gen_ws = self.run_G( + gen_z[:batch_size], + gen_c[:batch_size], + gen_h[:batch_size], + sync=sync, + ) + pl_noise = torch.randn_like(gen_img) / np.sqrt( + gen_img.shape[2] * gen_img.shape[3] + ) + with torch.autograd.profiler.record_function( + "pl_grads" + ), conv2d_gradfix.no_weight_gradients(): + pl_grads = torch.autograd.grad( + outputs=[(gen_img * pl_noise).sum()], + inputs=[gen_ws], + create_graph=True, + only_inputs=True, + )[0] + pl_lengths = pl_grads.square().sum(2).mean(1).sqrt() + pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay) + self.pl_mean.copy_(pl_mean.detach()) + pl_penalty = (pl_lengths - pl_mean).square() + training_stats.report("Loss/pl_penalty", pl_penalty) + loss_Gpl = pl_penalty * self.pl_weight + training_stats.report("Loss/G/reg", loss_Gpl) + with torch.autograd.profiler.record_function("Gpl_backward"): + (gen_img[:, 0, 0, 0] * 0 + loss_Gpl).mean().mul(gain).backward() + + # Dmain: Minimize logits for generated images. + loss_Dgen = 0 + if do_Dmain: + with torch.autograd.profiler.record_function("Dgen_forward"): + gen_img, _gen_ws = self.run_G(gen_z, gen_c, gen_h, sync=False) + gen_logits = self.run_D( + gen_img, gen_c, gen_h, sync=False + ) # Gets synced by loss_Dreal. + training_stats.report("Loss/scores/fake", gen_logits) + training_stats.report("Loss/signs/fake", gen_logits.sign()) + loss_Dgen = torch.nn.functional.softplus( + gen_logits + ) # -log(1 - sigmoid(gen_logits)) + with torch.autograd.profiler.record_function("Dgen_backward"): + loss_Dgen.mean().mul(gain).backward() + + # Dmain: Maximize logits for real images. + # Dr1: Apply R1 regularization. + if do_Dmain or do_Dr1: + name = ( + "Dreal_Dr1" if do_Dmain and do_Dr1 else "Dreal" if do_Dmain else "Dr1" + ) + with torch.autograd.profiler.record_function(name + "_forward"): + real_img_tmp = real_img.detach().requires_grad_(do_Dr1) + real_logits = self.run_D(real_img_tmp, real_c, real_h, sync=sync) + training_stats.report("Loss/scores/real", real_logits) + training_stats.report("Loss/signs/real", real_logits.sign()) + + loss_Dreal = 0 + if do_Dmain: + loss_Dreal = torch.nn.functional.softplus( + -real_logits + ) # -log(sigmoid(real_logits)) + training_stats.report("Loss/D/loss", loss_Dgen + loss_Dreal) + + loss_Dr1 = 0 + if do_Dr1: + with torch.autograd.profiler.record_function( + "r1_grads" + ), conv2d_gradfix.no_weight_gradients(): + r1_grads = torch.autograd.grad( + outputs=[real_logits.sum()], + inputs=[real_img_tmp], + create_graph=True, + only_inputs=True, + )[0] + r1_penalty = r1_grads.square().sum([1, 2, 3]) + loss_Dr1 = r1_penalty * (self.r1_gamma / 2) + training_stats.report("Loss/r1_penalty", r1_penalty) + training_stats.report("Loss/D/reg", loss_Dr1) + + with torch.autograd.profiler.record_function(name + "_backward"): + (real_logits * 0 + loss_Dreal + loss_Dr1).mean().mul(gain).backward() + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/training/networks.py b/stylegan2_ada_pytorch/training/networks.py new file mode 100755 index 0000000000000000000000000000000000000000..6f8a7f92488b3661b293ddf63c866cef088fabe3 --- /dev/null +++ b/stylegan2_ada_pytorch/training/networks.py @@ -0,0 +1,1104 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# All contributions by NVIDIA CORPORATION: +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import numpy as np +import torch +from torch_utils import misc +from torch_utils import persistence +from torch_utils.ops import conv2d_resample +from torch_utils.ops import upfirdn2d +from torch_utils.ops import bias_act +from torch_utils.ops import fma + +# ---------------------------------------------------------------------------- + + +@misc.profiled_function +def normalize_2nd_moment(x, dim=1, eps=1e-8): + return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() + + +# ---------------------------------------------------------------------------- + + +@misc.profiled_function +def modulated_conv2d( + x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. + weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. + styles, # Modulation coefficients of shape [batch_size, in_channels]. + noise=None, # Optional noise tensor to add to the output activations. + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + padding=0, # Padding with respect to the upsampled image. + resample_filter=None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). + demodulate=True, # Apply weight demodulation? + flip_weight=True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). + fused_modconv=True, # Perform modulation, convolution, and demodulation as a single fused operation? +): + batch_size = x.shape[0] + out_channels, in_channels, kh, kw = weight.shape + misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(styles, [batch_size, in_channels]) # [NI] + + # Pre-normalize inputs to avoid FP16 overflow. + if x.dtype == torch.float16 and demodulate: + weight = weight * ( + 1 + / np.sqrt(in_channels * kh * kw) + / weight.norm(float("inf"), dim=[1, 2, 3], keepdim=True) + ) # max_Ikk + styles = styles / styles.norm(float("inf"), dim=1, keepdim=True) # max_I + + # Calculate per-sample weights and demodulation coefficients. + w = None + dcoefs = None + if demodulate or fused_modconv: + w = weight.unsqueeze(0) # [NOIkk] + w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] + if demodulate: + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] + if demodulate and fused_modconv: + w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] + + # Execute by scaling the activations before and after the convolution. + if not fused_modconv: + x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) + x = conv2d_resample.conv2d_resample( + x=x, + w=weight.to(x.dtype), + f=resample_filter, + up=up, + down=down, + padding=padding, + flip_weight=flip_weight, + ) + if demodulate and noise is not None: + x = fma.fma( + x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype) + ) + elif demodulate: + x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) + elif noise is not None: + x = x.add_(noise.to(x.dtype)) + return x + + # Execute as one fused op using grouped convolution. + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + batch_size = int(batch_size) + misc.assert_shape(x, [batch_size, in_channels, None, None]) + x = x.reshape(1, -1, *x.shape[2:]) + w = w.reshape(-1, in_channels, kh, kw) + x = conv2d_resample.conv2d_resample( + x=x, + w=w.to(x.dtype), + f=resample_filter, + up=up, + down=down, + padding=padding, + groups=batch_size, + flip_weight=flip_weight, + ) + x = x.reshape(batch_size, -1, *x.shape[2:]) + if noise is not None: + x = x.add_(noise) + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class FullyConnectedLayer(torch.nn.Module): + def __init__( + self, + in_features, # Number of input features. + out_features, # Number of output features. + bias=True, # Apply additive bias before the activation function? + activation="linear", # Activation function: 'relu', 'lrelu', etc. + lr_multiplier=1, # Learning rate multiplier. + bias_init=0, # Initial value for the additive bias. + ): + super().__init__() + self.activation = activation + self.weight = torch.nn.Parameter( + torch.randn([out_features, in_features]) / lr_multiplier + ) + self.bias = ( + torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) + if bias + else None + ) + self.weight_gain = lr_multiplier / np.sqrt(in_features) + self.bias_gain = lr_multiplier + + def forward(self, x): + w = self.weight.to(x.dtype) * self.weight_gain + b = self.bias + if b is not None: + b = b.to(x.dtype) + if self.bias_gain != 1: + b = b * self.bias_gain + + if self.activation == "linear" and b is not None: + x = torch.addmm(b.unsqueeze(0), x, w.t()) + else: + x = x.matmul(w.t()) + x = bias_act.bias_act(x, b, act=self.activation) + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class Conv2dLayer(torch.nn.Module): + def __init__( + self, + in_channels, # Number of input channels. + out_channels, # Number of output channels. + kernel_size, # Width and height of the convolution kernel. + bias=True, # Apply additive bias before the activation function? + activation="linear", # Activation function: 'relu', 'lrelu', etc. + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + resample_filter=[ + 1, + 3, + 3, + 1, + ], # Low-pass filter to apply when resampling activations. + conv_clamp=None, # Clamp the output to +-X, None = disable clamping. + channels_last=False, # Expect the input to have memory_format=channels_last? + trainable=True, # Update the weights of this layer during training? + ): + super().__init__() + self.activation = activation + self.up = up + self.down = down + self.conv_clamp = conv_clamp + self.register_buffer("resample_filter", upfirdn2d.setup_filter(resample_filter)) + self.padding = kernel_size // 2 + self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) + self.act_gain = bias_act.activation_funcs[activation].def_gain + + memory_format = ( + torch.channels_last if channels_last else torch.contiguous_format + ) + weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( + memory_format=memory_format + ) + bias = torch.zeros([out_channels]) if bias else None + if trainable: + self.weight = torch.nn.Parameter(weight) + self.bias = torch.nn.Parameter(bias) if bias is not None else None + else: + self.register_buffer("weight", weight) + if bias is not None: + self.register_buffer("bias", bias) + else: + self.bias = None + + def forward(self, x, gain=1): + w = self.weight * self.weight_gain + b = self.bias.to(x.dtype) if self.bias is not None else None + flip_weight = self.up == 1 # slightly faster + x = conv2d_resample.conv2d_resample( + x=x, + w=w.to(x.dtype), + f=self.resample_filter, + up=self.up, + down=self.down, + padding=self.padding, + flip_weight=flip_weight, + ) + + act_gain = self.act_gain * gain + act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None + x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class MappingNetwork(torch.nn.Module): + def __init__( + self, + z_dim, # Input latent (Z) dimensionality, 0 = no latent. + c_dim, # Conditioning label (C) dimensionality, 0 = no label. + h_dim, # Conditioning instance (H) dimensionality, 0 = no features. + w_dim, # Intermediate latent (W) dimensionality. + num_ws, # Number of intermediate latents to output, None = do not broadcast. + num_layers=8, # Number of mapping layers. + embed_features=None, # Label embedding dimensionality, None = same as w_dim. + embed_features_feat=None, # Instance embedding dimensionality, None = same as w_dim. + layer_features=None, # Number of intermediate features in the mapping layers, None = same as w_dim. + activation="lrelu", # Activation function: 'relu', 'lrelu', etc. + lr_multiplier=0.01, # Learning rate multiplier for the mapping layers. + w_avg_beta=0.995, # Decay for tracking the moving average of W during training, None = do not track. + ): + super().__init__() + self.z_dim = z_dim + self.c_dim = c_dim + self.h_dim = h_dim + self.w_dim = w_dim + self.num_ws = num_ws + self.num_layers = num_layers + self.w_avg_beta = w_avg_beta + + if embed_features is None: + embed_features = w_dim + if embed_features_feat is None: + embed_features_feat = w_dim + if c_dim == 0: + embed_features = 0 + if h_dim == 0: + embed_features_feat = 0 + if layer_features is None: + layer_features = w_dim + features_list = ( + [z_dim + embed_features + embed_features_feat] + + [layer_features] * (num_layers - 1) + + [w_dim] + ) + + if c_dim > 0: + self.embed = FullyConnectedLayer(c_dim, embed_features) + if h_dim > 0: + self.embed_feats = FullyConnectedLayer(h_dim, embed_features_feat) + for idx in range(num_layers): + in_features = features_list[idx] + out_features = features_list[idx + 1] + layer = FullyConnectedLayer( + in_features, + out_features, + activation=activation, + lr_multiplier=lr_multiplier, + ) + setattr(self, f"fc{idx}", layer) + + if num_ws is not None and w_avg_beta is not None: + self.register_buffer("w_avg", torch.zeros([w_dim])) + + def forward( + self, z, c, h, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False + ): + # Embed, normalize, and concat inputs. + x = None + with torch.autograd.profiler.record_function("input"): + if self.z_dim > 0: + misc.assert_shape(z, [None, self.z_dim]) + x = normalize_2nd_moment(z.to(torch.float32)) + if self.c_dim > 0 and self.h_dim > 0: + misc.assert_shape(c, [None, self.c_dim]) + misc.assert_shape(h, [None, self.h_dim]) + y = torch.cat( + [ + self.embed(c.to(torch.float32)), + self.embed_feats(h.to(torch.float32)), + ], + dim=1, + ) + y = normalize_2nd_moment(y) + x = torch.cat([x, y], dim=1) if x is not None else y + elif self.c_dim > 0: + misc.assert_shape(c, [None, self.c_dim]) + y = normalize_2nd_moment(self.embed(c.to(torch.float32))) + x = torch.cat([x, y], dim=1) if x is not None else y + elif self.h_dim > 0: + misc.assert_shape(h, [None, self.h_dim]) + h = normalize_2nd_moment(self.embed_feats(h.to(torch.float32))) + x = torch.cat([x, h], dim=1) if x is not None else h + + # Main layers. + for idx in range(self.num_layers): + layer = getattr(self, f"fc{idx}") + x = layer(x) + + # Update moving average of W. + if self.w_avg_beta is not None and self.training and not skip_w_avg_update: + with torch.autograd.profiler.record_function("update_w_avg"): + self.w_avg.copy_( + x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta) + ) + + # Broadcast. + if self.num_ws is not None: + with torch.autograd.profiler.record_function("broadcast"): + x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) + + # Apply truncation. + if truncation_psi != 1: + with torch.autograd.profiler.record_function("truncate"): + assert self.w_avg_beta is not None + if self.num_ws is None or truncation_cutoff is None: + x = self.w_avg.lerp(x, truncation_psi) + else: + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi + ) + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class SynthesisLayer(torch.nn.Module): + def __init__( + self, + in_channels, # Number of input channels. + out_channels, # Number of output channels. + w_dim, # Intermediate latent (W) dimensionality. + resolution, # Resolution of this layer. + kernel_size=3, # Convolution kernel size. + up=1, # Integer upsampling factor. + use_noise=True, # Enable noise input? + activation="lrelu", # Activation function: 'relu', 'lrelu', etc. + resample_filter=[ + 1, + 3, + 3, + 1, + ], # Low-pass filter to apply when resampling activations. + conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. + channels_last=False, # Use channels_last format for the weights? + ): + super().__init__() + self.resolution = resolution + self.up = up + self.use_noise = use_noise + self.activation = activation + self.conv_clamp = conv_clamp + self.register_buffer("resample_filter", upfirdn2d.setup_filter(resample_filter)) + self.padding = kernel_size // 2 + self.act_gain = bias_act.activation_funcs[activation].def_gain + + self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) + memory_format = ( + torch.channels_last if channels_last else torch.contiguous_format + ) + self.weight = torch.nn.Parameter( + torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( + memory_format=memory_format + ) + ) + if use_noise: + self.register_buffer("noise_const", torch.randn([resolution, resolution])) + self.noise_strength = torch.nn.Parameter(torch.zeros([])) + self.bias = torch.nn.Parameter(torch.zeros([out_channels])) + + def forward(self, x, w, noise_mode="random", fused_modconv=True, gain=1): + assert noise_mode in ["random", "const", "none"] + in_resolution = self.resolution // self.up + misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution]) + styles = self.affine(w) + + noise = None + if self.use_noise and noise_mode == "random": + noise = ( + torch.randn( + [x.shape[0], 1, self.resolution, self.resolution], device=x.device + ) + * self.noise_strength + ) + if self.use_noise and noise_mode == "const": + noise = self.noise_const * self.noise_strength + + flip_weight = self.up == 1 # slightly faster + x = modulated_conv2d( + x=x, + weight=self.weight, + styles=styles, + noise=noise, + up=self.up, + padding=self.padding, + resample_filter=self.resample_filter, + flip_weight=flip_weight, + fused_modconv=fused_modconv, + ) + + act_gain = self.act_gain * gain + act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None + x = bias_act.bias_act( + x, + self.bias.to(x.dtype), + act=self.activation, + gain=act_gain, + clamp=act_clamp, + ) + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class ToRGBLayer(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + w_dim, + kernel_size=1, + conv_clamp=None, + channels_last=False, + ): + super().__init__() + self.conv_clamp = conv_clamp + self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) + memory_format = ( + torch.channels_last if channels_last else torch.contiguous_format + ) + self.weight = torch.nn.Parameter( + torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( + memory_format=memory_format + ) + ) + self.bias = torch.nn.Parameter(torch.zeros([out_channels])) + self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) + + def forward(self, x, w, fused_modconv=True): + styles = self.affine(w) * self.weight_gain + x = modulated_conv2d( + x=x, + weight=self.weight, + styles=styles, + demodulate=False, + fused_modconv=fused_modconv, + ) + x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class SynthesisBlock(torch.nn.Module): + def __init__( + self, + in_channels, # Number of input channels, 0 = first block. + out_channels, # Number of output channels. + w_dim, # Intermediate latent (W) dimensionality. + resolution, # Resolution of this block. + img_channels, # Number of output color channels. + is_last, # Is this the last block? + architecture="skip", # Architecture: 'orig', 'skip', 'resnet'. + resample_filter=[ + 1, + 3, + 3, + 1, + ], # Low-pass filter to apply when resampling activations. + conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + **layer_kwargs, # Arguments for SynthesisLayer. + ): + assert architecture in ["orig", "skip", "resnet"] + super().__init__() + self.in_channels = in_channels + self.w_dim = w_dim + self.resolution = resolution + self.img_channels = img_channels + self.is_last = is_last + self.architecture = architecture + self.use_fp16 = use_fp16 + self.channels_last = use_fp16 and fp16_channels_last + self.register_buffer("resample_filter", upfirdn2d.setup_filter(resample_filter)) + self.num_conv = 0 + self.num_torgb = 0 + + if in_channels == 0: + self.const = torch.nn.Parameter( + torch.randn([out_channels, resolution, resolution]) + ) + + if in_channels != 0: + self.conv0 = SynthesisLayer( + in_channels, + out_channels, + w_dim=w_dim, + resolution=resolution, + up=2, + resample_filter=resample_filter, + conv_clamp=conv_clamp, + channels_last=self.channels_last, + **layer_kwargs, + ) + self.num_conv += 1 + + self.conv1 = SynthesisLayer( + out_channels, + out_channels, + w_dim=w_dim, + resolution=resolution, + conv_clamp=conv_clamp, + channels_last=self.channels_last, + **layer_kwargs, + ) + self.num_conv += 1 + + if is_last or architecture == "skip": + self.torgb = ToRGBLayer( + out_channels, + img_channels, + w_dim=w_dim, + conv_clamp=conv_clamp, + channels_last=self.channels_last, + ) + self.num_torgb += 1 + + if in_channels != 0 and architecture == "resnet": + self.skip = Conv2dLayer( + in_channels, + out_channels, + kernel_size=1, + bias=False, + up=2, + resample_filter=resample_filter, + channels_last=self.channels_last, + ) + + def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, **layer_kwargs): + misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) + w_iter = iter(ws.unbind(dim=1)) + dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 + memory_format = ( + torch.channels_last + if self.channels_last and not force_fp32 + else torch.contiguous_format + ) + if fused_modconv is None: + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + fused_modconv = (not self.training) and ( + dtype == torch.float32 or int(x.shape[0]) == 1 + ) + + # Input. + if self.in_channels == 0: + x = self.const.to(dtype=dtype, memory_format=memory_format) + x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) + else: + misc.assert_shape( + x, [None, self.in_channels, self.resolution // 2, self.resolution // 2] + ) + x = x.to(dtype=dtype, memory_format=memory_format) + + # Main layers. + if self.in_channels == 0: + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + elif self.architecture == "resnet": + y = self.skip(x, gain=np.sqrt(0.5)) + x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1( + x, + next(w_iter), + fused_modconv=fused_modconv, + gain=np.sqrt(0.5), + **layer_kwargs, + ) + x = y.add_(x) + else: + x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + + # ToRGB. + if img is not None: + misc.assert_shape( + img, + [None, self.img_channels, self.resolution // 2, self.resolution // 2], + ) + img = upfirdn2d.upsample2d(img, self.resample_filter) + if self.is_last or self.architecture == "skip": + y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) + y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) + img = img.add_(y) if img is not None else y + + assert x.dtype == dtype + assert img is None or img.dtype == torch.float32 + return x, img + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class SynthesisNetwork(torch.nn.Module): + def __init__( + self, + w_dim, # Intermediate latent (W) dimensionality. + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + channel_base=32768, # Overall multiplier for the number of channels. + channel_max=512, # Maximum number of channels in any layer. + num_fp16_res=0, # Use FP16 for the N highest resolutions. + **block_kwargs, # Arguments for SynthesisBlock. + ): + assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 + super().__init__() + self.w_dim = w_dim + self.img_resolution = img_resolution + self.img_resolution_log2 = int(np.log2(img_resolution)) + self.img_channels = img_channels + self.block_resolutions = [ + 2 ** i for i in range(2, self.img_resolution_log2 + 1) + ] + channels_dict = { + res: min(channel_base // res, channel_max) for res in self.block_resolutions + } + fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + + self.num_ws = 0 + for res in self.block_resolutions: + in_channels = channels_dict[res // 2] if res > 4 else 0 + out_channels = channels_dict[res] + use_fp16 = res >= fp16_resolution + is_last = res == self.img_resolution + block = SynthesisBlock( + in_channels, + out_channels, + w_dim=w_dim, + resolution=res, + img_channels=img_channels, + is_last=is_last, + use_fp16=use_fp16, + **block_kwargs, + ) + self.num_ws += block.num_conv + if is_last: + self.num_ws += block.num_torgb + setattr(self, f"b{res}", block) + + def forward(self, ws, **block_kwargs): + block_ws = [] + with torch.autograd.profiler.record_function("split_ws"): + misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) + ws = ws.to(torch.float32) + w_idx = 0 + for res in self.block_resolutions: + block = getattr(self, f"b{res}") + block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) + w_idx += block.num_conv + + x = img = None + for res, cur_ws in zip(self.block_resolutions, block_ws): + block = getattr(self, f"b{res}") + x, img = block(x, img, cur_ws, **block_kwargs) + return img + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class Generator(torch.nn.Module): + def __init__( + self, + z_dim, # Input latent (Z) dimensionality. + c_dim, # Conditioning label (C) dimensionality. + h_dim, # Conditioning instance (H) dimensionality. + w_dim, # Intermediate latent (W) dimensionality. + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + synthesis_kwargs={}, # Arguments for SynthesisNetwork. + ): + super().__init__() + self.z_dim = z_dim + self.c_dim = c_dim + self.h_dim = h_dim + self.w_dim = w_dim + self.img_resolution = img_resolution + self.img_channels = img_channels + self.synthesis = SynthesisNetwork( + w_dim=w_dim, + img_resolution=img_resolution, + img_channels=img_channels, + **synthesis_kwargs, + ) + self.num_ws = self.synthesis.num_ws + self.mapping = MappingNetwork( + z_dim=z_dim, + c_dim=c_dim, + h_dim=h_dim, + w_dim=w_dim, + num_ws=self.num_ws, + **mapping_kwargs, + ) + + def forward( + self, z, c, feats, truncation_psi=1, truncation_cutoff=None, **synthesis_kwargs + ): + ws = self.mapping( + z, + c, + feats, + truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff, + ) + img = self.synthesis(ws, **synthesis_kwargs) + return img + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class DiscriminatorBlock(torch.nn.Module): + def __init__( + self, + in_channels, # Number of input channels, 0 = first block. + tmp_channels, # Number of intermediate channels. + out_channels, # Number of output channels. + resolution, # Resolution of this block. + img_channels, # Number of input color channels. + first_layer_idx, # Index of the first layer. + architecture="resnet", # Architecture: 'orig', 'skip', 'resnet'. + activation="lrelu", # Activation function: 'relu', 'lrelu', etc. + resample_filter=[ + 1, + 3, + 3, + 1, + ], # Low-pass filter to apply when resampling activations. + conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + freeze_layers=0, # Freeze-D: Number of layers to freeze. + ): + assert in_channels in [0, tmp_channels] + assert architecture in ["orig", "skip", "resnet"] + super().__init__() + self.in_channels = in_channels + self.resolution = resolution + self.img_channels = img_channels + self.first_layer_idx = first_layer_idx + self.architecture = architecture + self.use_fp16 = use_fp16 + self.channels_last = use_fp16 and fp16_channels_last + self.register_buffer("resample_filter", upfirdn2d.setup_filter(resample_filter)) + + self.num_layers = 0 + + def trainable_gen(): + while True: + layer_idx = self.first_layer_idx + self.num_layers + trainable = layer_idx >= freeze_layers + self.num_layers += 1 + yield trainable + + trainable_iter = trainable_gen() + + if in_channels == 0 or architecture == "skip": + self.fromrgb = Conv2dLayer( + img_channels, + tmp_channels, + kernel_size=1, + activation=activation, + trainable=next(trainable_iter), + conv_clamp=conv_clamp, + channels_last=self.channels_last, + ) + + self.conv0 = Conv2dLayer( + tmp_channels, + tmp_channels, + kernel_size=3, + activation=activation, + trainable=next(trainable_iter), + conv_clamp=conv_clamp, + channels_last=self.channels_last, + ) + + self.conv1 = Conv2dLayer( + tmp_channels, + out_channels, + kernel_size=3, + activation=activation, + down=2, + trainable=next(trainable_iter), + resample_filter=resample_filter, + conv_clamp=conv_clamp, + channels_last=self.channels_last, + ) + + if architecture == "resnet": + self.skip = Conv2dLayer( + tmp_channels, + out_channels, + kernel_size=1, + bias=False, + down=2, + trainable=next(trainable_iter), + resample_filter=resample_filter, + channels_last=self.channels_last, + ) + + def forward(self, x, img, force_fp32=False): + dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 + memory_format = ( + torch.channels_last + if self.channels_last and not force_fp32 + else torch.contiguous_format + ) + + # Input. + if x is not None: + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution] + ) + x = x.to(dtype=dtype, memory_format=memory_format) + + # FromRGB. + if self.in_channels == 0 or self.architecture == "skip": + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution] + ) + img = img.to(dtype=dtype, memory_format=memory_format) + y = self.fromrgb(img) + x = x + y if x is not None else y + img = ( + upfirdn2d.downsample2d(img, self.resample_filter) + if self.architecture == "skip" + else None + ) + + # Main layers. + if self.architecture == "resnet": + y = self.skip(x, gain=np.sqrt(0.5)) + x = self.conv0(x) + x = self.conv1(x, gain=np.sqrt(0.5)) + x = y.add_(x) + else: + x = self.conv0(x) + x = self.conv1(x) + + assert x.dtype == dtype + return x, img + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class MinibatchStdLayer(torch.nn.Module): + def __init__(self, group_size, num_channels=1): + super().__init__() + self.group_size = group_size + self.num_channels = num_channels + + def forward(self, x): + N, C, H, W = x.shape + with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants + G = ( + torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) + if self.group_size is not None + else N + ) + F = self.num_channels + c = C // F + + y = x.reshape( + G, -1, F, c, H, W + ) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. + y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. + y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. + y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. + y = y.mean(dim=[2, 3, 4]) # [nF] Take average over channels and pixels. + y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. + y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. + x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class DiscriminatorEpilogue(torch.nn.Module): + def __init__( + self, + in_channels, # Number of input channels. + cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. + resolution, # Resolution of this block. + img_channels, # Number of input color channels. + architecture="resnet", # Architecture: 'orig', 'skip', 'resnet'. + mbstd_group_size=4, # Group size for the minibatch standard deviation layer, None = entire minibatch. + mbstd_num_channels=1, # Number of features for the minibatch standard deviation layer, 0 = disable. + activation="lrelu", # Activation function: 'relu', 'lrelu', etc. + conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. + ): + assert architecture in ["orig", "skip", "resnet"] + super().__init__() + self.in_channels = in_channels + self.cmap_dim = cmap_dim + self.resolution = resolution + self.img_channels = img_channels + self.architecture = architecture + + if architecture == "skip": + self.fromrgb = Conv2dLayer( + img_channels, in_channels, kernel_size=1, activation=activation + ) + self.mbstd = ( + MinibatchStdLayer( + group_size=mbstd_group_size, num_channels=mbstd_num_channels + ) + if mbstd_num_channels > 0 + else None + ) + self.conv = Conv2dLayer( + in_channels + mbstd_num_channels, + in_channels, + kernel_size=3, + activation=activation, + conv_clamp=conv_clamp, + ) + self.fc = FullyConnectedLayer( + in_channels * (resolution ** 2), in_channels, activation=activation + ) + self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) + + def forward(self, x, img, cmap, force_fp32=False): + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution] + ) # [NCHW] + _ = force_fp32 # unused + dtype = torch.float32 + memory_format = torch.contiguous_format + + # FromRGB. + x = x.to(dtype=dtype, memory_format=memory_format) + if self.architecture == "skip": + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution] + ) + img = img.to(dtype=dtype, memory_format=memory_format) + x = x + self.fromrgb(img) + + # Main layers. + if self.mbstd is not None: + x = self.mbstd(x) + x = self.conv(x) + x = self.fc(x.flatten(1)) + x = self.out(x) + + # Conditioning. + if self.cmap_dim > 0: + misc.assert_shape(cmap, [None, self.cmap_dim]) + x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) + + assert x.dtype == dtype + return x + + +# ---------------------------------------------------------------------------- + + +@persistence.persistent_class +class Discriminator(torch.nn.Module): + def __init__( + self, + c_dim, # Conditioning label (C) dimensionality. + h_dim, # Conditioning instance (H) dimensionality. + img_resolution, # Input resolution. + img_channels, # Number of input color channels. + architecture="resnet", # Architecture: 'orig', 'skip', 'resnet'. + channel_base=32768, # Overall multiplier for the number of channels. + channel_max=512, # Maximum number of channels in any layer. + num_fp16_res=0, # Use FP16 for the N highest resolutions. + conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. + cmap_dim=None, # Dimensionality of mapped conditioning label, None = default. + block_kwargs={}, # Arguments for DiscriminatorBlock. + mapping_kwargs={}, # Arguments for MappingNetwork. + epilogue_kwargs={}, # Arguments for DiscriminatorEpilogue. + ): + super().__init__() + self.c_dim = c_dim + self.h_dim = h_dim + self.img_resolution = img_resolution + self.img_resolution_log2 = int(np.log2(img_resolution)) + self.img_channels = img_channels + self.block_resolutions = [ + 2 ** i for i in range(self.img_resolution_log2, 2, -1) + ] + channels_dict = { + res: min(channel_base // res, channel_max) + for res in self.block_resolutions + [4] + } + fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + + if cmap_dim is None: + cmap_dim = channels_dict[4] + if c_dim == 0 and h_dim == 0: + cmap_dim = 0 + + common_kwargs = dict( + img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp + ) + cur_layer_idx = 0 + for res in self.block_resolutions: + in_channels = channels_dict[res] if res < img_resolution else 0 + tmp_channels = channels_dict[res] + out_channels = channels_dict[res // 2] + use_fp16 = res >= fp16_resolution + block = DiscriminatorBlock( + in_channels, + tmp_channels, + out_channels, + resolution=res, + first_layer_idx=cur_layer_idx, + use_fp16=use_fp16, + **block_kwargs, + **common_kwargs, + ) + setattr(self, f"b{res}", block) + cur_layer_idx += block.num_layers + if c_dim > 0 or h_dim > 0: + self.mapping = MappingNetwork( + z_dim=0, + c_dim=c_dim, + h_dim=h_dim, + w_dim=cmap_dim, + num_ws=None, + w_avg_beta=None, + **mapping_kwargs, + ) + self.b4 = DiscriminatorEpilogue( + channels_dict[4], + cmap_dim=cmap_dim, + resolution=4, + **epilogue_kwargs, + **common_kwargs, + ) + + def forward(self, img, c, h, **block_kwargs): + x = None + for res in self.block_resolutions: + block = getattr(self, f"b{res}") + x, img = block(x, img, **block_kwargs) + + cmap = None + if self.c_dim > 0 or self.h_dim > 0: + cmap = self.mapping(None, c, h) + x = self.b4(x, img, cmap) + return x + + +# ---------------------------------------------------------------------------- diff --git a/stylegan2_ada_pytorch/training/training_loop.py b/stylegan2_ada_pytorch/training/training_loop.py new file mode 100755 index 0000000000000000000000000000000000000000..357131522b6571a8e88507aea9bc2dce4c2ea8c4 --- /dev/null +++ b/stylegan2_ada_pytorch/training/training_loop.py @@ -0,0 +1,733 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +# All contributions by NVIDIA CORPORATION: +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import time +import copy +import json +import pickle +import psutil +import PIL.Image +import numpy as np +import shutil + +import torch +import dnnlib +from torch_utils import misc +from torch_utils import training_stats +from torch_utils.ops import conv2d_gradfix +from torch_utils.ops import grid_sample_gradfix + +from torchvision import transforms + +import legacy +from metrics import metric_main +import torch.distributed as dist + +# ---------------------------------------------------------------------------- + + +def setup_snapshot_image_grid(training_set, random_seed=0): + rnd = np.random.RandomState(random_seed) + gw = np.clip(7680 // training_set.image_shape[2], 7, 32) + gh = np.clip(4320 // training_set.image_shape[1], 4, 32) + + # No labels => show random subset of training samples. + if not training_set.has_labels: + all_indices = list(range(len(training_set))) + rnd.shuffle(all_indices) + grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)] + + else: + # Group training samples by label. + label_groups = dict() # label => [idx, ...] + for idx in range(len(training_set)): + label = tuple(training_set.get_details(idx).raw_label.flat[::-1]) + if label not in label_groups: + label_groups[label] = [] + label_groups[label].append(idx) + + # Reorder. + label_order = sorted(label_groups.keys()) + for label in label_order: + rnd.shuffle(label_groups[label]) + + # Organize into grid. + grid_indices = [] + for y in range(gh): + label = label_order[y % len(label_order)] + indices = label_groups[label] + grid_indices += [indices[x % len(indices)] for x in range(gw)] + label_groups[label] = [ + indices[(i + gw) % len(indices)] for i in range(len(indices)) + ] + + # Load data. + images, labels = zip(*[training_set[i] for i in grid_indices]) + return (gw, gh), np.stack(images), np.stack(labels) + + +# ---------------------------------------------------------------------------- + + +def save_image_grid(img, fname, drange, grid_size): + lo, hi = drange + img = np.asarray(img, dtype=np.float32) + img = (img - lo) * (255 / (hi - lo)) + img = np.rint(img).clip(0, 255).astype(np.uint8) + + gw, gh = grid_size + _N, C, H, W = img.shape + img = img.reshape(gh, gw, C, H, W) + img = img.transpose(0, 3, 1, 4, 2) + img = img.reshape(gh * H, gw * W, C) + + assert C in [1, 3] + if C == 1: + PIL.Image.fromarray(img[:, :, 0], "L").save(fname) + if C == 3: + PIL.Image.fromarray(img, "RGB").save(fname) + + +# ---------------------------------------------------------------------------- + + +def training_loop( + exp_name="default_name", + run_dir=".", # Output directory. + temp_dir=".", # Temporary directory. + training_set_kwargs={}, # Options for training set. + data_loader_kwargs={}, # Options for torch.utils.data.DataLoader. + G_kwargs={}, # Options for generator network. + D_kwargs={}, # Options for discriminator network. + G_opt_kwargs={}, # Options for generator optimizer. + D_opt_kwargs={}, # Options for discriminator optimizer. + augment_kwargs=None, # Options for augmentation pipeline. None = disable. + loss_kwargs={}, # Options for loss function. + class_cond=False, # Condition on class labels. + instance_cond=False, # Condition on instance features. + metrics=[], # Metrics to evaluate during training. + random_seed=0, # Global random seed. + num_gpus=1, # Number of GPUs participating in the training. + slurm=False, # Launching the experiment in SLURM. + rank=0, # Rank of the current process in [0, num_gpus[. + local_rank=0, # Local rank of the current process inside each node [0, num_gpus_per_node] + batch_size=4, # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. + batch_gpu=4, # Number of samples processed at a time by one GPU. + ema_kimg=10, # Half-life of the exponential moving average (EMA) of generator weights. + ema_rampup=None, # EMA ramp-up coefficient. + G_reg_interval=4, # How often to perform regularization for G? None = disable lazy regularization. + D_reg_interval=16, # How often to perform regularization for D? None = disable lazy regularization. + augment_p=0, # Initial value of augmentation probability. + ada_target=None, # ADA target value. None = fixed p. + ada_interval=4, # How often to perform ADA adjustment? + ada_kimg=500, # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. + total_kimg=25000, # Total length of the training, measured in thousands of real images. + kimg_per_tick=4, # Progress snapshot interval. + image_snapshot_ticks=50, # How often to save image snapshots? None = disable. + network_snapshot_ticks=50, # How often to save network snapshots? None = disable. + es_patience=100000000, # Early stopping patience expressed in number of images seen. + resume_pkl=None, # Network pickle to resume training from. + cudnn_benchmark=True, # Enable torch.backends.cudnn.benchmark? + allow_tf32=False, # Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32? + abort_fn=None, # Callback function for determining whether to abort training. Must return consistent results across ranks. + progress_fn=None, # Callback function for updating training progress. Called for all ranks. +): + # Initialize. + start_time = time.time() + + device = "cuda:{}".format(local_rank) + torch.cuda.set_device(device) + # device = torch.device('cuda', rank) + np.random.seed(random_seed * num_gpus + rank) + torch.manual_seed(random_seed * num_gpus + rank) + torch.backends.cudnn.benchmark = cudnn_benchmark # Improves training speed. + torch.backends.cuda.matmul.allow_tf32 = ( + allow_tf32 + ) # Allow PyTorch to internally use tf32 for matmul + torch.backends.cudnn.allow_tf32 = ( + allow_tf32 + ) # Allow PyTorch to internally use tf32 for convolutions + conv2d_gradfix.enabled = True # Improves training speed. + grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. + + if slurm: + img_filename = os.path.basename(training_set_kwargs.root) + tmp_file_img = os.path.join(temp_dir, img_filename) + if instance_cond: + feats_filename = os.path.basename(training_set_kwargs.root_feats) + tmp_file_feats = os.path.join(temp_dir, feats_filename) + if local_rank == 0: + print("start copying data locally") + if not os.path.exists(tmp_file_img): + shutil.copy2(training_set_kwargs.root, tmp_file_img) + if instance_cond and not os.path.exists(tmp_file_feats): + shutil.copy2(training_set_kwargs.root_feats, tmp_file_feats) + print("finished copying data locally") + dist.barrier() + training_set_kwargs.root = tmp_file_img + if instance_cond: + training_set_kwargs.root_feats = tmp_file_feats + print("Final path dataset ", training_set_kwargs.root) + if instance_cond: + print("Final path dataset (feats)", training_set_kwargs.root_feats) + + # Load training set. + if rank == 0: + print("Loading training set...") + if training_set_kwargs.xflip: + transform = transforms.RandomHorizontalFlip() + else: + transform = None + training_set = dnnlib.util.construct_class_by_name( + **{**training_set_kwargs, "transform": transform} + ) # subclass of training.dataset.Dataset + training_set_sampler = misc.InfiniteSampler( + dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed + ) + training_set_iterator = iter( + torch.utils.data.DataLoader( + dataset=training_set, + sampler=training_set_sampler, + batch_size=batch_size // num_gpus, + **data_loader_kwargs, + ) + ) + if rank == 0: + print() + print("Num images: ", len(training_set)) + print("Image shape:", training_set.resolution) + print("Label shape:", training_set.label_dim) + print("Features shape:", training_set.feature_dim) + print() + + # Construct networks. + if rank == 0: + print("Constructing networks...") + common_kwargs = dict( + c_dim=training_set.label_dim if class_cond else 0, + h_dim=training_set.feature_dim if instance_cond else 0, + img_resolution=training_set.resolution, + img_channels=3, + ) + G = ( + dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs) + .train() + .requires_grad_(False) + .to(device) + ) # subclass of torch.nn.Module + D = ( + dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs) + .train() + .requires_grad_(False) + .to(device) + ) # subclass of torch.nn.Module + G_ema = copy.deepcopy(G).eval() + + snapshot_pkl_last = os.path.join(run_dir, "last_net") + # Resume from existing pickle. + if num_gpus > 1: + dist.barrier() + if (resume_pkl is not None) and (rank == 0): + print(f'Resuming from "{resume_pkl}"') + with dnnlib.util.open_url(resume_pkl) as f: + resume_data = legacy.load_network_pkl(f) + for name, module in [("G", G), ("D", D), ("G_ema", G_ema)]: + misc.copy_params_and_buffers(resume_data[name], module, require_all=False) + print("Successfully loaded G,D,G_ema from specific pkl checkpoint") + else: + try: + print(f'Resuming from "{snapshot_pkl_last}".pkl') + with dnnlib.util.open_url(snapshot_pkl_last + ".pkl") as f: + resume_data = legacy.load_network_pkl(f) + for name, module in [("G", G), ("D", D), ("G_ema", G_ema)]: + misc.copy_params_and_buffers( + resume_data[name], module, require_all=False + ) + print("Successfully loaded G,D,G_ema from last checkpoint") + except: + print("Starting training from scratch") + + # Print network summary tables. + if rank == 0: + z = torch.empty([batch_gpu, G.z_dim], device=device) + c = torch.empty([batch_gpu, G.c_dim], device=device) + h = torch.empty([batch_gpu, G.h_dim], device=device) + img = misc.print_module_summary(G, [z, c, h]) + misc.print_module_summary(D, [img, c, h]) + + # Setup augmentation. + if rank == 0: + print("Setting up augmentation...") + augment_pipe = None + ada_stats = None + if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None): + augment_pipe = ( + dnnlib.util.construct_class_by_name(**augment_kwargs) + .train() + .requires_grad_(False) + .to(device) + ) # subclass of torch.nn.Module + augment_pipe.p.copy_(torch.as_tensor(augment_p)) + if ada_target is not None: + ada_stats = training_stats.Collector(regex="Loss/signs/real") + + # Distribute across GPUs. + if rank == 0: + print(f"Distributing across {num_gpus} GPUs...") + ddp_modules = dict() + for name, module in [ + ("G_mapping", G.mapping), + ("G_synthesis", G.synthesis), + ("D", D), + (None, G_ema), + ("augment_pipe", augment_pipe), + ]: + if ( + (num_gpus > 1) + and (module is not None) + and len(list(module.parameters())) != 0 + ): + module.requires_grad_(True) + module = torch.nn.parallel.DistributedDataParallel( + module, device_ids=[device], broadcast_buffers=False + ) + module.requires_grad_(False) + if name is not None: + ddp_modules[name] = module + + # Setup training phases. + if rank == 0: + print("Setting up training phases...") + loss = dnnlib.util.construct_class_by_name( + device=device, **ddp_modules, **loss_kwargs + ) # subclass of training.loss.Loss + phases = [] + for name, module, opt_kwargs, reg_interval in [ + ("G", G, G_opt_kwargs, G_reg_interval), + ("D", D, D_opt_kwargs, D_reg_interval), + ]: + if reg_interval is None: + opt = dnnlib.util.construct_class_by_name( + params=module.parameters(), **opt_kwargs + ) # subclass of torch.optim.Optimizer + phases += [ + dnnlib.EasyDict(name=name + "both", module=module, opt=opt, interval=1) + ] + else: # Lazy regularization. + mb_ratio = reg_interval / (reg_interval + 1) + opt_kwargs = dnnlib.EasyDict(opt_kwargs) + opt_kwargs.lr = opt_kwargs.lr * mb_ratio + opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] + opt = dnnlib.util.construct_class_by_name( + module.parameters(), **opt_kwargs + ) # subclass of torch.optim.Optimizer + phases += [ + dnnlib.EasyDict(name=name + "main", module=module, opt=opt, interval=1) + ] + phases += [ + dnnlib.EasyDict( + name=name + "reg", module=module, opt=opt, interval=reg_interval + ) + ] + for phase in phases: + phase.start_event = None + phase.end_event = None + if rank == 0: + phase.start_event = torch.cuda.Event(enable_timing=True) + phase.end_event = torch.cuda.Event(enable_timing=True) + + # Resume from existing checkpoint. + if num_gpus > 1: + dist.barrier() + print("Resuming optimizers ") + try: + for phase in phases: + phase["opt"].load_state_dict( + torch.load( + snapshot_pkl_last + phase["name"] + "_opt.pth", map_location=device + ) + ) + print("All optimizers loaded from checkpoint! ") + except: + print("Could not load checkpoint! ", snapshot_pkl_last) + print("Starting training from scratch") + + # Export sample images. + grid_size = None + grid_z = None + grid_c = None + # if rank == 0 and False: + # print('Exporting sample images...') + # grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set) + # save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size) + # grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu) + # grid_c = torch.from_numpy(labels).to(device).split(batch_gpu) + # images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() + # save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size) + + # Initialize logs. + if rank == 0: + print("Initializing logs...") + stats_collector = training_stats.Collector(regex=".*") + stats_metrics = dict() + stats_jsonl = None + stats_tfevents = None + if rank == 0: + stats_jsonl = open(os.path.join(run_dir, "stats.jsonl"), "wt") + try: + import torch.utils.tensorboard as tensorboard + + stats_tfevents = tensorboard.SummaryWriter(run_dir) + except ImportError as err: + print("Skipping tfevents export:", err) + + # Train. + if rank == 0: + print(f"Training for {total_kimg} kimg...") + print() + cur_nimg = 0 + cur_tick = 0 + try: + (cur_tick, cur_nimg) = np.load( + snapshot_pkl_last + "last_itr.npy", allow_pickle=True + ) + print("Loading last tick and nimg ", cur_tick, cur_nimg) + except: + print("No last iter to load, starting from scratch") + + best_fid = 1000000 + best_fid_nimg = 0 + try: + (_, best_fid_nimg, best_fid) = np.load( + os.path.join(run_dir, "best_fid_itr.npy"), allow_pickle=True + ) + print(f"Loading best fid itr {best_fid_nimg}, value of fid is {best_fid}") + except: + print("No last iter to load for best fid, starting from scratch") + + tick_start_nimg = cur_nimg + tick_start_time = time.time() + maintenance_time = tick_start_time - start_time + batch_idx = 0 + if progress_fn is not None: + progress_fn(cur_nimg, total_kimg) + while True: + # Fetch training data. + with torch.autograd.profiler.record_function("data_fetch"): + batch = next(training_set_iterator) + if instance_cond and class_cond: + phase_real_img, phase_real_c, phase_real_h, _ = batch + elif instance_cond: + phase_real_img, phase_real_h, _ = batch + phase_real_c = torch.empty([batch_gpu, G.c_dim], device=device) + elif class_cond: + phase_real_img, phase_real_c = batch + phase_real_h = torch.empty([batch_gpu, G.h_dim], device=device) + else: + phase_real_img = batch + phase_real_c = torch.empty([batch_gpu, G.c_dim], device=device) + phase_real_h = torch.empty([batch_gpu, G.h_dim], device=device) + + phase_real_img = ( + phase_real_img.to(device).to(torch.float32) / 127.5 - 1 + ).split(batch_gpu) + all_gen_c = [ + training_set.get_label(np.random.randint(len(training_set))) + for _ in range(len(phases) * batch_size) + ] # take random labels + all_gen_h = [ + training_set.get_instance_features(np.random.randint(len(training_set))) + for _ in range(len(phases) * batch_size) + ] # take random instance features + + if class_cond: + phase_real_c = phase_real_c.to(device).split(batch_gpu) + if instance_cond: + phase_real_h = phase_real_h.to(device).split(batch_gpu) + all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device) + all_gen_z = [ + phase_gen_z.split(batch_gpu) + for phase_gen_z in all_gen_z.split(batch_size) + ] + all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device) + all_gen_c = [ + phase_gen_c.split(batch_gpu) + for phase_gen_c in all_gen_c.split(batch_size) + ] + all_gen_h = torch.from_numpy(np.stack(all_gen_h)).pin_memory().to(device) + all_gen_h = [ + phase_gen_h.split(batch_gpu) + for phase_gen_h in all_gen_h.split(batch_size) + ] + # Execute training phases. + for phase, phase_gen_z, phase_gen_c, phase_gen_h in zip( + phases, all_gen_z, all_gen_c, all_gen_h + ): + if batch_idx % phase.interval != 0: + continue + + # Initialize gradient accumulation. + if phase.start_event is not None: + phase.start_event.record(torch.cuda.current_stream(device)) + phase.opt.zero_grad(set_to_none=True) + phase.module.requires_grad_(True) + + # Accumulate gradients over multiple rounds. + for round_idx, (real_img, real_c, real_h, gen_z, gen_c, gen_h) in enumerate( + zip( + phase_real_img, + phase_real_c, + phase_real_h, + phase_gen_z, + phase_gen_c, + phase_gen_h, + ) + ): + sync = round_idx == batch_size // (batch_gpu * num_gpus) - 1 + gain = phase.interval + loss.accumulate_gradients( + phase=phase.name, + real_img=real_img, + real_c=real_c, + real_h=real_h, + gen_z=gen_z, + gen_c=gen_c, + gen_h=gen_h, + sync=sync, + gain=gain, + ) + + # Update weights. + phase.module.requires_grad_(False) + with torch.autograd.profiler.record_function(phase.name + "_opt"): + for param in phase.module.parameters(): + if param.grad is not None: + misc.nan_to_num( + param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad + ) + phase.opt.step() + if phase.end_event is not None: + phase.end_event.record(torch.cuda.current_stream(device)) + + # Update G_ema. + with torch.autograd.profiler.record_function("Gema"): + ema_nimg = ema_kimg * 1000 + if ema_rampup is not None: + ema_nimg = min(ema_nimg, cur_nimg * ema_rampup) + ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8)) + for p_ema, p in zip(G_ema.parameters(), G.parameters()): + p_ema.copy_(p.lerp(p_ema, ema_beta)) + for b_ema, b in zip(G_ema.buffers(), G.buffers()): + b_ema.copy_(b) + + # Update state. + cur_nimg += batch_size + batch_idx += 1 + + # Execute ADA heuristic. + if (ada_stats is not None) and (batch_idx % ada_interval == 0): + ada_stats.update() + adjust = ( + np.sign(ada_stats["Loss/signs/real"] - ada_target) + * (batch_size * ada_interval) + / (ada_kimg * 1000) + ) + augment_pipe.p.copy_( + (augment_pipe.p + adjust).max(misc.constant(0, device=device)) + ) + + # Perform maintenance tasks once per tick. + done = cur_nimg >= total_kimg * 1000 + if ( + (not done) + and (cur_tick != 0) + and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000) + ): + continue + + # Print status line, accumulating the same information in stats_collector. + tick_end_time = time.time() + fields = [] + fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] + fields += [ + f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}" + ] + fields += [ + f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}" + ] + fields += [ + f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}" + ] + fields += [ + f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}" + ] + fields += [ + f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}" + ] + fields += [ + f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}" + ] + fields += [ + f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}" + ] + torch.cuda.reset_peak_memory_stats() + fields += [ + f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}" + ] + training_stats.report0( + "Timing/total_hours", (tick_end_time - start_time) / (60 * 60) + ) + training_stats.report0( + "Timing/total_days", (tick_end_time - start_time) / (24 * 60 * 60) + ) + if rank == 0: + print(" ".join(fields)) + + # Check for abort. + if (not done) and (abort_fn is not None) and abort_fn(): + done = True + if rank == 0: + print() + print("Aborting...") + + # # Save image snapshot. + # if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0): + # images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() + # save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size) + + # Save network snapshot. + snapshot_pkl = None + snapshot_data = None + if (network_snapshot_ticks is not None) and ( + done or cur_tick % network_snapshot_ticks == 0 + ): + snapshot_data = dict(training_set_kwargs=dict(training_set_kwargs)) + snapshot_pkl = os.path.join( + run_dir, f"network-snapshot-{cur_nimg//1000:06d}.pkl" + ) + for name, module in [ + ("G", G), + ("D", D), + ("G_ema", G_ema), + ("augment_pipe", augment_pipe), + ]: + if module is not None: + # if not slurm and num_gpus > 1: + # misc.check_ddp_consistency(module, ignore_regex=r'.*\.w_avg') + module = copy.deepcopy(module).eval().requires_grad_(False).cpu() + snapshot_data[name] = module + del module # conserve memory + # if rank == 0: + # with open(snapshot_pkl, 'wb') as f: + # pickle.dump(snapshot_data, f) + + # Save last checkpoint as well + with open(snapshot_pkl_last + ".pkl", "wb") as f: + pickle.dump(snapshot_data, f) + for phase in phases: + torch.save( + phase["opt"].state_dict(), + snapshot_pkl_last + phase["name"] + "_opt.pth", + ) + np.save(snapshot_pkl_last + "last_itr", (cur_tick, cur_nimg)) + + # Evaluate metrics. + if (snapshot_data is not None) and (len(metrics) > 0): + if rank == 0: + print("Evaluating metrics...") + for metric in metrics: + result_dict = metric_main.calc_metric( + metric=metric, + G=snapshot_data["G_ema"], + dataset_kwargs=training_set_kwargs, + num_gpus=num_gpus, + rank=rank, + device=device, + ) + if rank == 0: + metric_main.report_metric( + result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl + ) + stats_metrics.update(result_dict.results) + + if metric == "fid50k_full" and rank == 0: + cur_fid = result_dict["results"]["fid50k_full"] + if cur_fid < best_fid: + print("Saving network snapshot with best FID") + best_fid = cur_fid + best_fid_nimg = cur_nimg + snapshot_best_pkl = os.path.join( + run_dir, f"best-network-snapshot.pkl" + ) + with open(snapshot_best_pkl, "wb") as f: + pickle.dump(snapshot_data, f) + np.save( + os.path.join(run_dir, "best_fid_itr"), + (cur_tick, cur_nimg, best_fid), + ) + else: # stopping criterion : if fid stops decreasing during es_patience nimg: stop training + if (cur_nimg - best_fid_nimg) > es_patience: + done = True + print("Stopping training due to early stopping.") + del snapshot_data # conserve memory + + # Collect statistics. + for phase in phases: + value = [] + if (phase.start_event is not None) and (phase.end_event is not None): + phase.end_event.synchronize() + value = phase.start_event.elapsed_time(phase.end_event) + training_stats.report0("Timing/" + phase.name, value) + stats_collector.update() + stats_dict = stats_collector.as_dict() + + # Update logs. + timestamp = time.time() + if stats_jsonl is not None: + fields = dict(stats_dict, timestamp=timestamp) + stats_jsonl.write(json.dumps(fields) + "\n") + stats_jsonl.flush() + if stats_tfevents is not None: + global_step = int(cur_nimg / 1e3) + walltime = timestamp - start_time + for name, value in stats_dict.items(): + stats_tfevents.add_scalar( + name, value.mean, global_step=global_step, walltime=walltime + ) + for name, value in stats_metrics.items(): + stats_tfevents.add_scalar( + f"Metrics/{name}", value, global_step=global_step, walltime=walltime + ) + stats_tfevents.flush() + if progress_fn is not None: + progress_fn(cur_nimg // 1000, total_kimg) + + # Update state. + cur_tick += 1 + tick_start_nimg = cur_nimg + tick_start_time = time.time() + maintenance_time = tick_start_time - tick_end_time + if done: + break + + # Done. + if rank == 0: + print() + print("Exiting...") + + +# ----------------------------------------------------------------------------