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losses.py
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losses.py
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# PyTorch StudioGAN: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
# The MIT License (MIT)
# See license file or visit https://github.com/POSTECH-CVLab/PyTorch-StudioGAN for details
# src/utils/loss.py
from torch.nn import DataParallel
from torch import autograd
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.nn.functional as F
import numpy as np
from utils.style_ops import conv2d_gradfix
import utils.ops as ops
class GatherLayer(torch.autograd.Function):
"""
This file is copied from
https://github.com/open-mmlab/OpenSelfSup/blob/master/openselfsup/models/utils/gather_layer.py
Gather tensors from all process, supporting backward propagation
"""
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
output = [torch.zeros_like(input) for _ in range(dist.get_world_size())]
dist.all_gather(output, input)
return tuple(output)
@staticmethod
def backward(ctx, *grads):
input, = ctx.saved_tensors
grad_out = torch.zeros_like(input)
grad_out[:] = grads[dist.get_rank()]
return grad_out
class CrossEntropyLoss(torch.nn.Module):
def __init__(self):
super(CrossEntropyLoss, self).__init__()
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, cls_output, label, **_):
return self.ce_loss(cls_output, label).mean()
class ConditionalContrastiveLoss(torch.nn.Module):
def __init__(self, num_classes, temperature, master_rank, DDP):
super(ConditionalContrastiveLoss, self).__init__()
self.num_classes = num_classes
self.temperature = temperature
self.master_rank = master_rank
self.DDP = DDP
self.calculate_similarity_matrix = self._calculate_similarity_matrix()
self.cosine_similarity = torch.nn.CosineSimilarity(dim=-1)
def _make_neg_removal_mask(self, labels):
labels = labels.detach().cpu().numpy()
n_samples = labels.shape[0]
mask_multi, target = np.zeros([self.num_classes, n_samples]), 1.0
for c in range(self.num_classes):
c_indices = np.where(labels == c)
mask_multi[c, c_indices] = target
return torch.tensor(mask_multi).type(torch.long).to(self.master_rank)
def _calculate_similarity_matrix(self):
return self._cosine_simililarity_matrix
def _remove_diag(self, M):
h, w = M.shape
assert h == w, "h and w should be same"
mask = np.ones((h, w)) - np.eye(h)
mask = torch.from_numpy(mask)
mask = (mask).type(torch.bool).to(self.master_rank)
return M[mask].view(h, -1)
def _cosine_simililarity_matrix(self, x, y):
v = self.cosine_similarity(x.unsqueeze(1), y.unsqueeze(0))
return v
def forward(self, embed, proxy, label, **_):
if self.DDP:
embed = torch.cat(GatherLayer.apply(embed), dim=0)
proxy = torch.cat(GatherLayer.apply(proxy), dim=0)
label = torch.cat(GatherLayer.apply(label), dim=0)
sim_matrix = self.calculate_similarity_matrix(embed, embed)
sim_matrix = torch.exp(self._remove_diag(sim_matrix) / self.temperature)
neg_removal_mask = self._remove_diag(self._make_neg_removal_mask(label)[label])
sim_pos_only = neg_removal_mask * sim_matrix
emb2proxy = torch.exp(self.cosine_similarity(embed, proxy) / self.temperature)
numerator = emb2proxy + sim_pos_only.sum(dim=1)
denomerator = torch.cat([torch.unsqueeze(emb2proxy, dim=1), sim_matrix], dim=1).sum(dim=1)
return -torch.log(numerator / denomerator).mean()
class Data2DataCrossEntropyLoss(torch.nn.Module):
def __init__(self, num_classes, temperature, m_p, master_rank, DDP):
super(Data2DataCrossEntropyLoss, self).__init__()
self.num_classes = num_classes
self.temperature = temperature
self.m_p = m_p
self.master_rank = master_rank
self.DDP = DDP
self.calculate_similarity_matrix = self._calculate_similarity_matrix()
self.cosine_similarity = torch.nn.CosineSimilarity(dim=-1)
def _calculate_similarity_matrix(self):
return self._cosine_simililarity_matrix
def _cosine_simililarity_matrix(self, x, y):
v = self.cosine_similarity(x.unsqueeze(1), y.unsqueeze(0))
return v
def make_index_matrix(self, labels):
labels = labels.detach().cpu().numpy()
num_samples = labels.shape[0]
mask_multi, target = np.ones([self.num_classes, num_samples]), 0.0
for c in range(self.num_classes):
c_indices = np.where(labels==c)
mask_multi[c, c_indices] = target
return torch.tensor(mask_multi).type(torch.long).to(self.master_rank)
def remove_diag(self, M):
h, w = M.shape
assert h==w, "h and w should be same"
mask = np.ones((h, w)) - np.eye(h)
mask = torch.from_numpy(mask)
mask = (mask).type(torch.bool).to(self.master_rank)
return M[mask].view(h, -1)
def forward(self, embed, proxy, label, **_):
# If train a GAN throuh DDP, gather all data on the master rank
if self.DDP:
embed = torch.cat(GatherLayer.apply(embed), dim=0)
proxy = torch.cat(GatherLayer.apply(proxy), dim=0)
label = torch.cat(GatherLayer.apply(label), dim=0)
# calculate similarities between sample embeddings
sim_matrix = self.calculate_similarity_matrix(embed, embed) + self.m_p - 1
# remove diagonal terms
sim_matrix = self.remove_diag(sim_matrix/self.temperature)
# for numerical stability
sim_max, _ = torch.max(sim_matrix, dim=1, keepdim=True)
sim_matrix = F.relu(sim_matrix) - sim_max.detach()
# calculate similarities between sample embeddings and the corresponding proxies
smp2proxy = self.cosine_similarity(embed, proxy)
# make false negative removal
removal_fn = self.remove_diag(self.make_index_matrix(label)[label])
# apply the negative removal to the similarity matrix
improved_sim_matrix = removal_fn*torch.exp(sim_matrix)
# compute positive attraction term
pos_attr = F.relu((self.m_p - smp2proxy)/self.temperature)
# compute negative repulsion term
neg_repul = torch.log(torch.exp(-pos_attr) + improved_sim_matrix.sum(dim=1))
# compute data to data cross-entropy criterion
criterion = pos_attr + neg_repul
return criterion.mean()
class PathLengthRegularizer:
def __init__(self, device, pl_decay=0.01, pl_weight=2, pl_no_weight_grad=False):
self.pl_decay = pl_decay
self.pl_weight = pl_weight
self.pl_mean = torch.zeros([], device=device)
self.pl_no_weight_grad = pl_no_weight_grad
def cal_pl_reg(self, fake_images, ws):
#ws refers to weight style
#receives new fake_images of original batch (in original implementation, fakes_images used for calculating g_loss and pl_loss is generated independently)
pl_noise = torch.randn_like(fake_images) / np.sqrt(fake_images.shape[2] * fake_images.shape[3])
with conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad):
pl_grads = torch.autograd.grad(outputs=[(fake_images * pl_noise).sum()], inputs=[ws], create_graph=True, only_inputs=True)[0]
pl_lengths = pl_grads.square().sum(2).mean(1).sqrt()
pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay)
self.pl_mean.copy_(pl_mean.detach())
pl_penalty = (pl_lengths - pl_mean).square()
loss_Gpl = (pl_penalty * self.pl_weight).mean(0)
return loss_Gpl
def enable_allreduce(dict_):
loss = 0
for key, value in dict_.items():
if value is not None and key != "label":
loss += value.mean()*0
return loss
def d_vanilla(d_logit_real, d_logit_fake, DDP):
d_loss = torch.mean(F.softplus(-d_logit_real)) + torch.mean(F.softplus(d_logit_fake))
return d_loss
def g_vanilla(d_logit_fake, DDP):
return torch.mean(F.softplus(-d_logit_fake))
def d_logistic(d_logit_real, d_logit_fake, DDP):
d_loss = F.softplus(-d_logit_real) + F.softplus(d_logit_fake)
return d_loss.mean()
def g_logistic(d_logit_fake, DDP):
# basically same as g_vanilla.
return F.softplus(-d_logit_fake).mean()
def d_ls(d_logit_real, d_logit_fake, DDP):
d_loss = 0.5 * (d_logit_real - torch.ones_like(d_logit_real))**2 + 0.5 * (d_logit_fake)**2
return d_loss.mean()
def g_ls(d_logit_fake, DDP):
gen_loss = 0.5 * (d_logit_fake - torch.ones_like(d_logit_fake))**2
return gen_loss.mean()
def d_hinge(d_logit_real, d_logit_fake, DDP):
return torch.mean(F.relu(1. - d_logit_real)) + torch.mean(F.relu(1. + d_logit_fake))
def g_hinge(d_logit_fake, DDP):
return -torch.mean(d_logit_fake)
def d_wasserstein(d_logit_real, d_logit_fake, DDP):
return torch.mean(d_logit_fake - d_logit_real)
def g_wasserstein(d_logit_fake, DDP):
return -torch.mean(d_logit_fake)
def crammer_singer_loss(adv_output, label, DDP, **_):
# https://github.com/ilyakava/BigGAN-PyTorch/blob/master/train_fns.py
# crammer singer criterion
num_real_classes = adv_output.shape[1] - 1
mask = torch.ones_like(adv_output).to(adv_output.device)
mask.scatter_(1, label.unsqueeze(-1), 0)
wrongs = torch.masked_select(adv_output, mask.bool()).reshape(adv_output.shape[0], num_real_classes)
max_wrong, _ = wrongs.max(1)
max_wrong = max_wrong.unsqueeze(-1)
target = adv_output.gather(1, label.unsqueeze(-1))
return torch.mean(F.relu(1 + max_wrong - target))
def feature_matching_loss(real_embed, fake_embed):
# https://github.com/ilyakava/BigGAN-PyTorch/blob/master/train_fns.py
# feature matching criterion
fm_loss = torch.mean(torch.abs(torch.mean(fake_embed, 0) - torch.mean(real_embed, 0)))
return fm_loss
def lecam_reg(d_logit_real, d_logit_fake, ema):
reg = torch.mean(F.relu(d_logit_real - ema.D_fake).pow(2)) + \
torch.mean(F.relu(ema.D_real - d_logit_fake).pow(2))
return reg
def cal_deriv(inputs, outputs, device):
grads = autograd.grad(outputs=outputs,
inputs=inputs,
grad_outputs=torch.ones(outputs.size()).to(device),
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
return grads
def latent_optimise(zs, fake_labels, generator, discriminator, batch_size, lo_rate, lo_steps, lo_alpha, lo_beta, eval,
cal_trsp_cost, device):
for step in range(lo_steps - 1):
drop_mask = (torch.FloatTensor(batch_size, 1).uniform_() > 1 - lo_rate).to(device)
zs = autograd.Variable(zs, requires_grad=True)
fake_images = generator(zs, fake_labels, eval=eval)
fake_dict = discriminator(fake_images, fake_labels, eval=eval)
z_grads = cal_deriv(inputs=zs, outputs=fake_dict["adv_output"], device=device)
z_grads_norm = torch.unsqueeze((z_grads.norm(2, dim=1)**2), dim=1)
delta_z = lo_alpha * z_grads / (lo_beta + z_grads_norm)
zs = torch.clamp(zs + drop_mask * delta_z, -1.0, 1.0)
if cal_trsp_cost:
if step == 0:
trsf_cost = (delta_z.norm(2, dim=1)**2).mean()
else:
trsf_cost += (delta_z.norm(2, dim=1)**2).mean()
else:
trsf_cost = None
return zs, trsf_cost
def cal_grad_penalty(real_images, real_labels, fake_images, discriminator, device):
batch_size, c, h, w = real_images.shape
alpha = torch.rand(batch_size, 1)
alpha = alpha.expand(batch_size, real_images.nelement() // batch_size).contiguous().view(batch_size, c, h, w)
alpha = alpha.to(device)
real_images = real_images.to(device)
interpolates = alpha * real_images + ((1 - alpha) * fake_images)
interpolates = interpolates.to(device)
interpolates = autograd.Variable(interpolates, requires_grad=True)
fake_dict = discriminator(interpolates, real_labels, eval=False)
grads = cal_deriv(inputs=interpolates, outputs=fake_dict["adv_output"], device=device)
grads = grads.view(grads.size(0), -1)
grad_penalty = ((grads.norm(2, dim=1) - 1)**2).mean() + interpolates[:,0,0,0].mean()*0
return grad_penalty
def cal_dra_penalty(real_images, real_labels, discriminator, device):
batch_size, c, h, w = real_images.shape
alpha = torch.rand(batch_size, 1, 1, 1)
alpha = alpha.to(device)
real_images = real_images.to(device)
differences = 0.5 * real_images.std() * torch.rand(real_images.size()).to(device)
interpolates = real_images + (alpha * differences)
interpolates = interpolates.to(device)
interpolates = autograd.Variable(interpolates, requires_grad=True)
fake_dict = discriminator(interpolates, real_labels, eval=False)
grads = cal_deriv(inputs=interpolates, outputs=fake_dict["adv_output"], device=device)
grads = grads.view(grads.size(0), -1)
grad_penalty = ((grads.norm(2, dim=1) - 1)**2).mean() + interpolates[:,0,0,0].mean()*0
return grad_penalty
def cal_maxgrad_penalty(real_images, real_labels, fake_images, discriminator, device):
batch_size, c, h, w = real_images.shape
alpha = torch.rand(batch_size, 1)
alpha = alpha.expand(batch_size, real_images.nelement() // batch_size).contiguous().view(batch_size, c, h, w)
alpha = alpha.to(device)
real_images = real_images.to(device)
interpolates = alpha * real_images + ((1 - alpha) * fake_images)
interpolates = interpolates.to(device)
interpolates = autograd.Variable(interpolates, requires_grad=True)
fake_dict = discriminator(interpolates, real_labels, eval=False)
grads = cal_deriv(inputs=interpolates, outputs=fake_dict["adv_output"], device=device)
grads = grads.view(grads.size(0), -1)
maxgrad_penalty = torch.max(grads.norm(2, dim=1)**2) + interpolates[:,0,0,0].mean()*0
return maxgrad_penalty
def cal_r1_reg(adv_output, images, device):
batch_size = images.size(0)
grad_dout = cal_deriv(inputs=images, outputs=adv_output.sum(), device=device)
grad_dout2 = grad_dout.pow(2)
assert (grad_dout2.size() == images.size())
r1_reg = 0.5 * grad_dout2.contiguous().view(batch_size, -1).sum(1).mean(0) + images[:,0,0,0].mean()*0
return r1_reg
def adjust_k(current_k, topk_gamma, inf_k):
current_k = max(current_k * topk_gamma, inf_k)
return current_k
def normal_nll_loss(x, mu, var):
# https://github.com/Natsu6767/InfoGAN-PyTorch/blob/master/utils.py
# Calculate the negative log likelihood of normal distribution.
# Needs to be minimized in InfoGAN. (Treats Q(c]x) as a factored Gaussian)
logli = -0.5 * (var.mul(2 * np.pi) + 1e-6).log() - (x - mu).pow(2).div(var.mul(2.0) + 1e-6)
nll = -(logli.sum(1).mean())
return nll
def stylegan_cal_r1_reg(adv_output, images):
with conv2d_gradfix.no_weight_gradients():
r1_grads = torch.autograd.grad(outputs=[adv_output.sum()], inputs=[images], create_graph=True, only_inputs=True)[0]
r1_penalty = r1_grads.square().sum([1,2,3]) / 2
return r1_penalty.mean()