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