forked from mrq/DL-Art-School
240 lines
7.9 KiB
Python
240 lines
7.9 KiB
Python
from functools import partial
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from math import log2
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from random import random
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models.archs.stylegan.stylegan2 import attn_and_ff
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from models.steps.losses import ConfigurableLoss
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def leaky_relu(p=0.2):
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return nn.LeakyReLU(p)
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def double_conv(chan_in, chan_out):
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return nn.Sequential(
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nn.Conv2d(chan_in, chan_out, 3, padding=1),
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leaky_relu(),
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nn.Conv2d(chan_out, chan_out, 3, padding=1),
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leaky_relu()
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)
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class Flatten(nn.Module):
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def __init__(self, index):
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super().__init__()
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self.index = index
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def forward(self, x):
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return x.flatten(self.index)
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class DownBlock(nn.Module):
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def __init__(self, input_channels, filters, downsample=True):
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super().__init__()
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self.conv_res = nn.Conv2d(input_channels, filters, 1, stride = (2 if downsample else 1))
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self.net = double_conv(input_channels, filters)
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self.down = nn.Conv2d(filters, filters, 3, padding = 1, stride = 2) if downsample else None
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def forward(self, x):
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res = self.conv_res(x)
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x = self.net(x)
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unet_res = x
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if self.down is not None:
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x = self.down(x)
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x = x + res
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return x, unet_res
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class UpBlock(nn.Module):
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def __init__(self, input_channels, filters):
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super().__init__()
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self.conv_res = nn.ConvTranspose2d(input_channels // 2, filters, 1, stride = 2)
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self.net = double_conv(input_channels, filters)
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self.up = nn.Upsample(scale_factor = 2, mode='bilinear', align_corners=False)
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self.input_channels = input_channels
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self.filters = filters
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def forward(self, x, res):
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*_, h, w = x.shape
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conv_res = self.conv_res(x, output_size = (h * 2, w * 2))
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x = self.up(x)
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x = torch.cat((x, res), dim=1)
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x = self.net(x)
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x = x + conv_res
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return x
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class StyleGan2UnetDiscriminator(nn.Module):
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def __init__(self, image_size, network_capacity = 16, fmap_max = 512, input_filters=3):
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super().__init__()
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num_layers = int(log2(image_size) - 3)
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blocks = []
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filters = [input_filters] + [(network_capacity) * (2 ** i) for i in range(num_layers + 1)]
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set_fmap_max = partial(min, fmap_max)
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filters = list(map(set_fmap_max, filters))
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filters[-1] = filters[-2]
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chan_in_out = list(zip(filters[:-1], filters[1:]))
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chan_in_out = list(map(list, chan_in_out))
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down_blocks = []
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attn_blocks = []
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for ind, (in_chan, out_chan) in enumerate(chan_in_out):
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num_layer = ind + 1
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is_not_last = ind != (len(chan_in_out) - 1)
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block = DownBlock(in_chan, out_chan, downsample = is_not_last)
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down_blocks.append(block)
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attn_fn = attn_and_ff(out_chan)
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attn_blocks.append(attn_fn)
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self.down_blocks = nn.ModuleList(down_blocks)
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self.attn_blocks = nn.ModuleList(attn_blocks)
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last_chan = filters[-1]
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self.to_logit = nn.Sequential(
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leaky_relu(),
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nn.AvgPool2d(image_size // (2 ** num_layers)),
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Flatten(1),
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nn.Linear(last_chan, 1)
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)
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self.conv = double_conv(last_chan, last_chan)
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dec_chan_in_out = chan_in_out[:-1][::-1]
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self.up_blocks = nn.ModuleList(list(map(lambda c: UpBlock(c[1] * 2, c[0]), dec_chan_in_out)))
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self.conv_out = nn.Conv2d(input_filters, 1, 1)
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def forward(self, x):
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b, *_ = x.shape
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residuals = []
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for (down_block, attn_block) in zip(self.down_blocks, self.attn_blocks):
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x, unet_res = down_block(x)
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residuals.append(unet_res)
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if attn_block is not None:
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x = attn_block(x)
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x = self.conv(x) + x
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enc_out = self.to_logit(x)
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for (up_block, res) in zip(self.up_blocks, residuals[:-1][::-1]):
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x = up_block(x, res)
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dec_out = self.conv_out(x)
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return dec_out, enc_out
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def warmup(start, end, max_steps, current_step):
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if current_step > max_steps:
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return end
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return (end - start) * (current_step / max_steps) + start
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def mask_src_tgt(source, target, mask):
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return source * mask + (1 - mask) * target
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def cutmix(source, target, coors, alpha = 1.):
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source, target = map(torch.clone, (source, target))
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((y0, y1), (x0, x1)), _ = coors
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source[:, :, y0:y1, x0:x1] = target[:, :, y0:y1, x0:x1]
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return source
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def cutmix_coordinates(height, width, alpha = 1.):
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lam = np.random.beta(alpha, alpha)
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cx = np.random.uniform(0, width)
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cy = np.random.uniform(0, height)
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w = width * np.sqrt(1 - lam)
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h = height * np.sqrt(1 - lam)
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x0 = int(np.round(max(cx - w / 2, 0)))
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x1 = int(np.round(min(cx + w / 2, width)))
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y0 = int(np.round(max(cy - h / 2, 0)))
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y1 = int(np.round(min(cy + h / 2, height)))
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return ((y0, y1), (x0, x1)), lam
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class StyleGan2UnetDivergenceLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.real = opt['real']
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self.fake = opt['fake']
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self.discriminator = opt['discriminator']
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self.for_gen = opt['gen_loss']
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self.gp_frequency = opt['gradient_penalty_frequency']
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self.noise = opt['noise'] if 'noise' in opt.keys() else 0
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self.image_size = opt['image_size']
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def forward(self, net, state):
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real_input = state[self.real]
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fake_input = state[self.fake]
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if self.noise != 0:
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fake_input = fake_input + torch.rand_like(fake_input) * self.noise
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real_input = real_input + torch.rand_like(real_input) * self.noise
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D = self.env['discriminators'][self.discriminator]
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fake_dec, fake_enc = D(fake_input)
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fake_aug_images = D.aug_images
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if self.for_gen:
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return fake_enc.mean() + F.relu(1 + fake_dec).mean()
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else:
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dec_loss_coef = warmup(0, 1., 30000, self.env['step'])
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cutmix_prob = warmup(0, 0.25, 30000, self.env['step'])
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apply_cutmix = random() < cutmix_prob
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real_input.requires_grad_() # <-- Needed to compute gradients on the input.
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real_dec, real_enc = D(real_input)
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real_aug_images = D.aug_images
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enc_divergence = (F.relu(1 + real_enc) + F.relu(1 - fake_enc)).mean()
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dec_divergence = (F.relu(1 + real_dec) + F.relu(1 - fake_dec)).mean()
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divergence_loss = enc_divergence + dec_divergence * dec_loss_coef
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if apply_cutmix:
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mask = cutmix(
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torch.ones_like(real_dec),
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torch.zeros_like(real_dec),
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cutmix_coordinates(self.image_size, self.image_size)
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)
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if random() > 0.5:
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mask = 1 - mask
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cutmix_images = mask_src_tgt(real_aug_images, fake_aug_images, mask)
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cutmix_enc_out, cutmix_dec_out = self.GAN.D(cutmix_images)
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cutmix_enc_divergence = F.relu(1 - cutmix_enc_out).mean()
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cutmix_dec_divergence = F.relu(1 + (mask * 2 - 1) * cutmix_dec_out).mean()
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disc_loss = divergence_loss + cutmix_enc_divergence + cutmix_dec_divergence
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cr_cutmix_dec_out = mask_src_tgt(real_dec, fake_dec, mask)
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cr_loss = F.mse_loss(cutmix_dec_out, cr_cutmix_dec_out) * self.cr_weight
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self.last_cr_loss = cr_loss.clone().detach().item()
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disc_loss = disc_loss + cr_loss * dec_loss_coef
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# Apply gradient penalty. TODO: migrate this elsewhere.
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if self.env['step'] % self.gp_frequency == 0:
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from models.archs.stylegan.stylegan2 import gradient_penalty
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gp = gradient_penalty(real_input, real)
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self.metrics.append(("gradient_penalty", gp.clone().detach()))
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disc_loss = disc_loss + gp
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real_input.requires_grad_(requires_grad=False)
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return disc_loss |