Rework stylegan2 divergence losses
Notably: include unet loss
This commit is contained in:
James Betker
parent
ea94b93a37
commit
99f0cfaab5
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@ -0,0 +1,14 @@
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from models.archs.stylegan.stylegan2 import StyleGan2DivergenceLoss, StyleGan2PathLengthLoss
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from models.archs.stylegan.stylegan2_unet_disc import StyleGan2UnetDivergenceLoss
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def create_stylegan2_loss(opt_loss, env):
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type = opt_loss['type']
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if type == 'stylegan2_divergence':
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return StyleGan2DivergenceLoss(opt_loss, env)
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elif type == 'stylegan2_pathlen':
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return StyleGan2PathLengthLoss(opt_loss, env)
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elif type == 'stylegan2_unet_divergence':
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return StyleGan2UnetDivergenceLoss(opt_loss, env)
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else:
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raise NotImplementedError
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@ -13,6 +13,7 @@ from torch import nn
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from torch.autograd import grad as torch_grad
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from vector_quantize_pytorch import VectorQuantize
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from models.steps.losses import ConfigurableLoss
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from utils.util import checkpoint
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try:
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@ -304,6 +305,9 @@ class StyleGan2Augmentor(nn.Module):
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if detach:
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images = images.detach()
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# Save away for use elsewhere (e.g. unet loss)
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self.aug_images = images
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return self.D(images)
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@ -693,4 +697,69 @@ class StyleGan2Discriminator(nn.Module):
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def _init_weights(self):
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for m in self.modules():
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if type(m) in {nn.Conv2d, nn.Linear}:
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nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
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nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
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class StyleGan2DivergenceLoss(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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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 = D(fake_input)
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if self.for_gen:
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return fake.mean()
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else:
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real_input.requires_grad_() # <-- Needed to compute gradients on the input.
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real = D(real_input)
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divergence_loss = (F.relu(1 + real) + F.relu(1 - fake)).mean()
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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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divergence_loss = divergence_loss + gp
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real_input.requires_grad_(requires_grad=False)
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return divergence_loss
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class StyleGan2PathLengthLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.w_styles = opt['w_styles']
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self.gen = opt['gen']
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self.pl_mean = None
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from models.archs.stylegan.stylegan2 import EMA
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self.pl_length_ma = EMA(.99)
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def forward(self, net, state):
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w_styles = state[self.w_styles]
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gen = state[self.gen]
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from models.archs.stylegan.stylegan2 import calc_pl_lengths
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pl_lengths = calc_pl_lengths(w_styles, gen)
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avg_pl_length = np.mean(pl_lengths.detach().cpu().numpy())
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from models.archs.stylegan.stylegan2 import is_empty
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if not is_empty(self.pl_mean):
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pl_loss = ((pl_lengths - self.pl_mean) ** 2).mean()
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if not torch.isnan(pl_loss):
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return pl_loss
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else:
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print("Path length loss returned NaN!")
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self.pl_mean = self.pl_length_ma.update_average(self.pl_mean, avg_pl_length)
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return 0
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@ -1,10 +1,14 @@
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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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@ -132,4 +136,105 @@ class StyleGan2UnetDiscriminator(nn.Module):
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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
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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
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@ -15,6 +15,9 @@ def create_loss(opt_loss, env):
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if 'teco_' in type:
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from models.steps.tecogan_losses import create_teco_loss
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return create_teco_loss(opt_loss, env)
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elif 'stylegan2_' in type:
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from models.archs.stylegan import create_stylegan2_loss
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return create_stylegan2_loss(opt_loss, env)
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elif type == 'pix':
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return PixLoss(opt_loss, env)
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elif type == 'direct':
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@ -37,10 +40,6 @@ def create_loss(opt_loss, env):
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return RecurrentLoss(opt_loss, env)
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elif type == 'for_element':
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return ForElementLoss(opt_loss, env)
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elif type == 'stylegan2_divergence':
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return StyleGan2DivergenceLoss(opt_loss, env)
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elif type == 'stylegan2_pathlen':
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return StyleGan2PathLengthLoss(opt_loss, env)
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else:
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raise NotImplementedError
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@ -487,68 +486,3 @@ class ForElementLoss(ConfigurableLoss):
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def clear_metrics(self):
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self.loss.clear_metrics()
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class StyleGan2DivergenceLoss(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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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 = D(fake_input)
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if self.for_gen:
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return fake.mean()
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else:
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real_input.requires_grad_() # <-- Needed to compute gradients on the input.
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real = D(real_input)
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divergence_loss = (F.relu(1 + real) + F.relu(1 - fake)).mean()
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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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divergence_loss = divergence_loss + gp
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real_input.requires_grad_(requires_grad=False)
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return divergence_loss
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class StyleGan2PathLengthLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.w_styles = opt['w_styles']
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self.gen = opt['gen']
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self.pl_mean = None
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from models.archs.stylegan.stylegan2 import EMA
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self.pl_length_ma = EMA(.99)
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def forward(self, net, state):
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w_styles = state[self.w_styles]
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gen = state[self.gen]
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from models.archs.stylegan.stylegan2 import calc_pl_lengths
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pl_lengths = calc_pl_lengths(w_styles, gen)
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avg_pl_length = np.mean(pl_lengths.detach().cpu().numpy())
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from models.archs.stylegan.stylegan2 import is_empty
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if not is_empty(self.pl_mean):
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pl_loss = ((pl_lengths - self.pl_mean) ** 2).mean()
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if not torch.isnan(pl_loss):
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return pl_loss
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else:
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print("Path length loss returned NaN!")
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self.pl_mean = self.pl_length_ma.update_average(self.pl_mean, avg_pl_length)
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return 0
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