# Copyright 2020 Erik Härkönen. All rights reserved. # This file is licensed to you 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 REPRESENTATIONS # OF ANY KIND, either express or implied. See the License for the specific language # governing permissions and limitations under the License. import torch import torch.nn as nn import torch.nn.functional as F from collections import OrderedDict from pathlib import Path import requests import pickle import sys import numpy as np # Reimplementation of StyleGAN in PyTorch # Source: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb class MyLinear(nn.Module): """Linear layer with equalized learning rate and custom learning rate multiplier.""" def __init__(self, input_size, output_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True): super().__init__() he_std = gain * input_size**(-0.5) # He init # Equalized learning rate and custom learning rate multiplier. if use_wscale: init_std = 1.0 / lrmul self.w_mul = he_std * lrmul else: init_std = he_std / lrmul self.w_mul = lrmul self.weight = torch.nn.Parameter(torch.randn(output_size, input_size) * init_std) if bias: self.bias = torch.nn.Parameter(torch.zeros(output_size)) self.b_mul = lrmul else: self.bias = None def forward(self, x): bias = self.bias if bias is not None: bias = bias * self.b_mul return F.linear(x, self.weight * self.w_mul, bias) class MyConv2d(nn.Module): """Conv layer with equalized learning rate and custom learning rate multiplier.""" def __init__(self, input_channels, output_channels, kernel_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True, intermediate=None, upscale=False): super().__init__() if upscale: self.upscale = Upscale2d() else: self.upscale = None he_std = gain * (input_channels * kernel_size ** 2) ** (-0.5) # He init self.kernel_size = kernel_size if use_wscale: init_std = 1.0 / lrmul self.w_mul = he_std * lrmul else: init_std = he_std / lrmul self.w_mul = lrmul self.weight = torch.nn.Parameter(torch.randn(output_channels, input_channels, kernel_size, kernel_size) * init_std) if bias: self.bias = torch.nn.Parameter(torch.zeros(output_channels)) self.b_mul = lrmul else: self.bias = None self.intermediate = intermediate def forward(self, x): bias = self.bias if bias is not None: bias = bias * self.b_mul have_convolution = False if self.upscale is not None and min(x.shape[2:]) * 2 >= 128: # this is the fused upscale + conv from StyleGAN, sadly this seems incompatible with the non-fused way # this really needs to be cleaned up and go into the conv... w = self.weight * self.w_mul w = w.permute(1, 0, 2, 3) # probably applying a conv on w would be more efficient. also this quadruples the weight (average)?! w = F.pad(w, (1,1,1,1)) w = w[:, :, 1:, 1:]+ w[:, :, :-1, 1:] + w[:, :, 1:, :-1] + w[:, :, :-1, :-1] x = F.conv_transpose2d(x, w, stride=2, padding=(w.size(-1)-1)//2) have_convolution = True elif self.upscale is not None: x = self.upscale(x) if not have_convolution and self.intermediate is None: return F.conv2d(x, self.weight * self.w_mul, bias, padding=self.kernel_size//2) elif not have_convolution: x = F.conv2d(x, self.weight * self.w_mul, None, padding=self.kernel_size//2) if self.intermediate is not None: x = self.intermediate(x) if bias is not None: x = x + bias.view(1, -1, 1, 1) return x class NoiseLayer(nn.Module): """adds noise. noise is per pixel (constant over channels) with per-channel weight""" def __init__(self, channels): super().__init__() self.weight = nn.Parameter(torch.zeros(channels)) self.noise = None def forward(self, x, noise=None): if noise is None and self.noise is None: noise = torch.randn(x.size(0), 1, x.size(2), x.size(3), device=x.device, dtype=x.dtype) elif noise is None: # here is a little trick: if you get all the noiselayers and set each # modules .noise attribute, you can have pre-defined noise. # Very useful for analysis noise = self.noise x = x + self.weight.view(1, -1, 1, 1) * noise return x class StyleMod(nn.Module): def __init__(self, latent_size, channels, use_wscale): super(StyleMod, self).__init__() self.lin = MyLinear(latent_size, channels * 2, gain=1.0, use_wscale=use_wscale) def forward(self, x, latent): style = self.lin(latent) # style => [batch_size, n_channels*2] shape = [-1, 2, x.size(1)] + (x.dim() - 2) * [1] style = style.view(shape) # [batch_size, 2, n_channels, ...] x = x * (style[:, 0] + 1.) + style[:, 1] return x class PixelNormLayer(nn.Module): def __init__(self, epsilon=1e-8): super().__init__() self.epsilon = epsilon def forward(self, x): return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + self.epsilon) class BlurLayer(nn.Module): def __init__(self, kernel=[1, 2, 1], normalize=True, flip=False, stride=1): super(BlurLayer, self).__init__() kernel=[1, 2, 1] kernel = torch.tensor(kernel, dtype=torch.float32) kernel = kernel[:, None] * kernel[None, :] kernel = kernel[None, None] if normalize: kernel = kernel / kernel.sum() if flip: kernel = kernel[:, :, ::-1, ::-1] self.register_buffer('kernel', kernel) self.stride = stride def forward(self, x): # expand kernel channels kernel = self.kernel.expand(x.size(1), -1, -1, -1) x = F.conv2d( x, kernel, stride=self.stride, padding=int((self.kernel.size(2)-1)/2), groups=x.size(1) ) return x def upscale2d(x, factor=2, gain=1): assert x.dim() == 4 if gain != 1: x = x * gain if factor != 1: shape = x.shape x = x.view(shape[0], shape[1], shape[2], 1, shape[3], 1).expand(-1, -1, -1, factor, -1, factor) x = x.contiguous().view(shape[0], shape[1], factor * shape[2], factor * shape[3]) return x class Upscale2d(nn.Module): def __init__(self, factor=2, gain=1): super().__init__() assert isinstance(factor, int) and factor >= 1 self.gain = gain self.factor = factor def forward(self, x): return upscale2d(x, factor=self.factor, gain=self.gain) class G_mapping(nn.Sequential): def __init__(self, nonlinearity='lrelu', use_wscale=True): act, gain = {'relu': (torch.relu, np.sqrt(2)), 'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity] layers = [ ('pixel_norm', PixelNormLayer()), ('dense0', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense0_act', act), ('dense1', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense1_act', act), ('dense2', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense2_act', act), ('dense3', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense3_act', act), ('dense4', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense4_act', act), ('dense5', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense5_act', act), ('dense6', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense6_act', act), ('dense7', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)), ('dense7_act', act) ] super().__init__(OrderedDict(layers)) def forward(self, x): return super().forward(x) class Truncation(nn.Module): def __init__(self, avg_latent, max_layer=8, threshold=0.7): super().__init__() self.max_layer = max_layer self.threshold = threshold self.register_buffer('avg_latent', avg_latent) def forward(self, x): assert x.dim() == 3 interp = torch.lerp(self.avg_latent, x, self.threshold) do_trunc = (torch.arange(x.size(1)) < self.max_layer).view(1, -1, 1) return torch.where(do_trunc, interp, x) class LayerEpilogue(nn.Module): """Things to do at the end of each layer.""" def __init__(self, channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer): super().__init__() layers = [] if use_noise: layers.append(('noise', NoiseLayer(channels))) layers.append(('activation', activation_layer)) if use_pixel_norm: layers.append(('pixel_norm', PixelNorm())) if use_instance_norm: layers.append(('instance_norm', nn.InstanceNorm2d(channels))) self.top_epi = nn.Sequential(OrderedDict(layers)) if use_styles: self.style_mod = StyleMod(dlatent_size, channels, use_wscale=use_wscale) else: self.style_mod = None def forward(self, x, dlatents_in_slice=None): x = self.top_epi(x) if self.style_mod is not None: x = self.style_mod(x, dlatents_in_slice) else: assert dlatents_in_slice is None return x class InputBlock(nn.Module): def __init__(self, nf, dlatent_size, const_input_layer, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer): super().__init__() self.const_input_layer = const_input_layer self.nf = nf if self.const_input_layer: # called 'const' in tf self.const = nn.Parameter(torch.ones(1, nf, 4, 4)) self.bias = nn.Parameter(torch.ones(nf)) else: self.dense = MyLinear(dlatent_size, nf*16, gain=gain/4, use_wscale=use_wscale) # tweak gain to match the official implementation of Progressing GAN self.epi1 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer) self.conv = MyConv2d(nf, nf, 3, gain=gain, use_wscale=use_wscale) self.epi2 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer) def forward(self, dlatents_in_range): batch_size = dlatents_in_range.size(0) if self.const_input_layer: x = self.const.expand(batch_size, -1, -1, -1) x = x + self.bias.view(1, -1, 1, 1) else: x = self.dense(dlatents_in_range[:, 0]).view(batch_size, self.nf, 4, 4) x = self.epi1(x, dlatents_in_range[:, 0]) x = self.conv(x) x = self.epi2(x, dlatents_in_range[:, 1]) return x class GSynthesisBlock(nn.Module): def __init__(self, in_channels, out_channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer): # 2**res x 2**res # res = 3..resolution_log2 super().__init__() if blur_filter: blur = BlurLayer(blur_filter) else: blur = None self.conv0_up = MyConv2d(in_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale, intermediate=blur, upscale=True) self.epi1 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer) self.conv1 = MyConv2d(out_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale) self.epi2 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer) def forward(self, x, dlatents_in_range): x = self.conv0_up(x) x = self.epi1(x, dlatents_in_range[:, 0]) x = self.conv1(x) x = self.epi2(x, dlatents_in_range[:, 1]) return x class G_synthesis(nn.Module): def __init__(self, dlatent_size = 512, # Disentangled latent (W) dimensionality. num_channels = 3, # Number of output color channels. resolution = 1024, # Output resolution. fmap_base = 8192, # Overall multiplier for the number of feature maps. fmap_decay = 1.0, # log2 feature map reduction when doubling the resolution. fmap_max = 512, # Maximum number of feature maps in any layer. use_styles = True, # Enable style inputs? const_input_layer = True, # First layer is a learned constant? use_noise = True, # Enable noise inputs? randomize_noise = True, # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables. nonlinearity = 'lrelu', # Activation function: 'relu', 'lrelu' use_wscale = True, # Enable equalized learning rate? use_pixel_norm = False, # Enable pixelwise feature vector normalization? use_instance_norm = True, # Enable instance normalization? dtype = torch.float32, # Data type to use for activations and outputs. blur_filter = [1,2,1], # Low-pass filter to apply when resampling activations. None = no filtering. ): super().__init__() def nf(stage): return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max) self.dlatent_size = dlatent_size resolution_log2 = int(np.log2(resolution)) assert resolution == 2**resolution_log2 and resolution >= 4 act, gain = {'relu': (torch.relu, np.sqrt(2)), 'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity] num_layers = resolution_log2 * 2 - 2 num_styles = num_layers if use_styles else 1 torgbs = [] blocks = [] for res in range(2, resolution_log2 + 1): channels = nf(res-1) name = '{s}x{s}'.format(s=2**res) if res == 2: blocks.append((name, InputBlock(channels, dlatent_size, const_input_layer, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, act))) else: blocks.append((name, GSynthesisBlock(last_channels, channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, act))) last_channels = channels self.torgb = MyConv2d(channels, num_channels, 1, gain=1, use_wscale=use_wscale) self.blocks = nn.ModuleDict(OrderedDict(blocks)) def forward(self, dlatents_in): # Input: Disentangled latents (W) [minibatch, num_layers, dlatent_size]. # lod_in = tf.cast(tf.get_variable('lod', initializer=np.float32(0), trainable=False), dtype) batch_size = dlatents_in.size(0) for i, m in enumerate(self.blocks.values()): if i == 0: x = m(dlatents_in[:, 2*i:2*i+2]) else: x = m(x, dlatents_in[:, 2*i:2*i+2]) rgb = self.torgb(x) return rgb class StyleGAN_G(nn.Sequential): def __init__(self, resolution, truncation=1.0): self.resolution = resolution self.layers = OrderedDict([ ('g_mapping', G_mapping()), #('truncation', Truncation(avg_latent)), ('g_synthesis', G_synthesis(resolution=resolution)), ]) super().__init__(self.layers) def forward(self, x, latent_is_w=False): if isinstance(x, list): assert len(x) == 18, 'Must provide 1 or 18 latents' if not latent_is_w: x = [self.layers['g_mapping'].forward(l) for l in x] x = torch.stack(x, dim=1) else: if not latent_is_w: x = self.layers['g_mapping'].forward(x) x = x.unsqueeze(1).expand(-1, 18, -1) x = self.layers['g_synthesis'].forward(x) return x # From: https://github.com/lernapparat/lernapparat/releases/download/v2019-02-01/ def load_weights(self, checkpoint): self.load_state_dict(torch.load(checkpoint)) def export_from_tf(self, pickle_path): module_path = Path(__file__).parent / 'stylegan_tf' sys.path.append(str(module_path.resolve())) import dnnlib, dnnlib.tflib, pickle, torch, collections dnnlib.tflib.init_tf() weights = pickle.load(open(pickle_path,'rb')) weights_pt = [collections.OrderedDict([(k, torch.from_numpy(v.value().eval())) for k,v in w.trainables.items()]) for w in weights] #torch.save(weights_pt, pytorch_name) # then on the PyTorch side run state_G, state_D, state_Gs = weights_pt #torch.load('./karras2019stylegan-ffhq-1024x1024.pt') def key_translate(k): k = k.lower().split('/') if k[0] == 'g_synthesis': if not k[1].startswith('torgb'): k.insert(1, 'blocks') k = '.'.join(k) k = (k.replace('const.const','const').replace('const.bias','bias').replace('const.stylemod','epi1.style_mod.lin') .replace('const.noise.weight','epi1.top_epi.noise.weight') .replace('conv.noise.weight','epi2.top_epi.noise.weight') .replace('conv.stylemod','epi2.style_mod.lin') .replace('conv0_up.noise.weight', 'epi1.top_epi.noise.weight') .replace('conv0_up.stylemod','epi1.style_mod.lin') .replace('conv1.noise.weight', 'epi2.top_epi.noise.weight') .replace('conv1.stylemod','epi2.style_mod.lin') .replace('torgb_lod0','torgb')) else: k = '.'.join(k) return k def weight_translate(k, w): k = key_translate(k) if k.endswith('.weight'): if w.dim() == 2: w = w.t() elif w.dim() == 1: pass else: assert w.dim() == 4 w = w.permute(3, 2, 0, 1) return w # we delete the useless torgb filters param_dict = {key_translate(k) : weight_translate(k, v) for k,v in state_Gs.items() if 'torgb_lod' not in key_translate(k)} if 1: sd_shapes = {k : v.shape for k,v in self.state_dict().items()} param_shapes = {k : v.shape for k,v in param_dict.items() } for k in list(sd_shapes)+list(param_shapes): pds = param_shapes.get(k) sds = sd_shapes.get(k) if pds is None: print ("sd only", k, sds) elif sds is None: print ("pd only", k, pds) elif sds != pds: print ("mismatch!", k, pds, sds) self.load_state_dict(param_dict, strict=False) # needed for the blur kernels torch.save(self.state_dict(), Path(pickle_path).with_suffix('.pt'))