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 import src.models.big.layers as layers from src.models.parameter import labels_dim, parameter from src.models.neuralnetwork import NeuralNetwork # 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(NeuralNetwork): def __init__(self, G_ch=64, dim_z=128, bottom_width=4, resolution=64, labels_dim=labels_dim, G_kernel_size=3, G_attn='64', n_classes=1, 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 # 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] # 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) 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.z_chunk_size if self.G_shared else self.n_classes), norm_style=self.norm_style, eps=self.BN_eps) # Prepare model # prepare label input self.transform_label_layer = torch.nn.Linear(labels_dim, 128) # 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.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: 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 self.set_optimizer(parameter.optimizer, lr=parameter.learning_rate, betas=parameter.betas) # 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) def transform_labels(self, labels): """ prepore labels for input to generator """ return self.transform_label_layer(labels) # 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, y): # If hierarchical, concatenate zs and ys y = self.transform_labels(y) 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.sigmoid(self.output_layer(h)) # 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(NeuralNetwork): def __init__(self, D_ch=64, D_wide=True, resolution=64, labels_dim=labels_dim, D_kernel_size=3, D_attn='64', n_classes=1, 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 # 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 # prepare label input self.transform_label_layer = torch.nn.Linear(labels_dim, 1024) # 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 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 self.set_optimizer(parameter.optimizer, lr=parameter.learning_rate*3, betas=parameter.betas) # 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 transform_labels(self, labels): """ prepore labels for input to discriminator """ return self.transform_label_layer(labels) def forward(self, x, y=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) # Get projection of final featureset onto class vectors and add to evidence y = self.transform_labels(y) out = out + torch.sum(y * h, 1, keepdim=True) # out = out + torch.sum(self.embed(y) * h, 1, keepdim=True) ## use y = torch.tensor(0) 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