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ops.py
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ops.py
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import torch
import torch.nn as nn
import torch.autograd as autograd
import numpy as np
from PIL import Image
def gradient_penalty(x_real, x_fake, D, lambda_=10):
eps = torch.rand(1, 1, 1, 1).to("cuda:0")
x_hat = eps * x_real + (1. - eps) * x_fake
x_hat = autograd.Variable(x_hat, requires_grad=True)
outputs = D(x_hat)
grads = autograd.grad(outputs, x_hat, torch.ones_like(outputs), retain_graph=True, create_graph=True)[0]
penalty = lambda_ * ((torch.norm(grads, p=2, dim=1) - 1) ** 2).mean()
return penalty
def concat_prevG(Gs, fixed_Zs, sigmas, imgs, is_rec=False):
for i, G in enumerate(Gs):
z_h, z_w = fixed_Zs[i].size()[2], fixed_Zs[i].size()[3]
prev_h, prev_w = imgs[i+1].shape[0], imgs[i+1].shape[1]
if i == 0:
if is_rec:
z = fixed_Zs[i]
else:
z = torch.randn(1, 3, z_h, z_w).to("cuda:0")
prev = G(z, torch.zeros_like(z).to("cuda:0"))
upsample = nn.Upsample((prev_h, prev_w))
next_ = upsample(prev)
else:
if is_rec:
z = torch.zeros(1, 3, z_h, z_w).to("cuda:0")#fixed_Zs[i]
else:
z = torch.randn(1, 3, z_h, z_w).to("cuda:0") * sigmas[i]
prev = G(z, next_.detach())
upsample = nn.Upsample((prev_h, prev_w))
next_ = upsample(prev)
return next_
def train_single_scale(Gs, Ds, imgs, sigmas, fixed_Zs):
img = imgs[-1]
img = np.transpose(img, axes=[2, 0, 1])[np.newaxis]
real_img = torch.tensor(img/127.5-1.0, dtype=torch.float32).to("cuda:0")
G = Gs[-1]
D = Ds[-1]
Opt_D = torch.optim.Adam(D.parameters(), lr=5e-4, betas=(0.5, 0.999))
Opt_G = torch.optim.Adam(G.parameters(), lr=5e-4, betas=(0.5, 0.999))
schedulerD = torch.optim.lr_scheduler.MultiStepLR(optimizer=Opt_D, milestones=[1600], gamma=0.1)
schedulerG = torch.optim.lr_scheduler.MultiStepLR(optimizer=Opt_G, milestones=[1600], gamma=0.1)
mse = nn.MSELoss()
if len(Gs) == 1:
for i in range(2000):
sigma = 1.
z = torch.randn(img.shape).to("cuda:0") * sigma
z_rec = torch.randn(img.shape).to("cuda:0") * sigma
for j in range(3):
Opt_D.zero_grad()
fake_img = G(z, torch.zeros_like(z).to("cuda:0"))
fake_logits = D(fake_img.detach())
real_logits = D(real_img)
D_loss = fake_logits.mean() - real_logits.mean() + gradient_penalty(real_img, fake_img, D, lambda_=0.1)
D_loss.backward(retain_graph=True)
Opt_D.step()
for j in range(3):
Opt_G.zero_grad()
fake_img = G(z, torch.zeros_like(z).to("cuda:0"))
fake_logits = D(fake_img)
rec = mse(G(z_rec, torch.zeros_like(z_rec)), real_img)
G_loss = -fake_logits.mean() + rec * 10
G_loss.backward(retain_graph=True)
Opt_G.step()
if i % 100 == 0:
fake_img = fake_img.permute(0, 2, 3, 1).cpu().detach().numpy()[0]
Image.fromarray(np.uint8((fake_img + 1) * 127.5)).save("./results/" + str(fake_img.shape[0]) + "_" + str(i) + ".jpg")
print("Iteration: %d, D_loss: %f, G_loss: %f" % (i, D_loss, G_loss))
schedulerD.step()
schedulerG.step()
else:
for i in range(2000):
init_sigma = 0.1
z_ = torch.randn(1, 3, img.shape[2], img.shape[3]).to("cuda:0")
for j in range(3):
Opt_D.zero_grad()
prev_x = concat_prevG(Gs[:-1], fixed_Zs, sigmas, imgs, is_rec=False)
prev_z = concat_prevG(Gs[:-1], fixed_Zs, sigmas, imgs, is_rec=True)
sigma = torch.sqrt(mse(prev_z, real_img)) * init_sigma
z = sigma * z_
fake_img = G(z, prev_x)
fake_logits = D(fake_img.detach())
real_logits = D(real_img)
D_loss = fake_logits.mean() - real_logits.mean() + gradient_penalty(real_img, fake_img, D, lambda_=0.1)
D_loss.backward(retain_graph=True)
Opt_D.step()
for j in range(3):
Opt_G.zero_grad()
fake_logits = D(fake_img)
rec = mse(G(torch.zeros_like(prev_z).to("cuda:0"), prev_z), real_img)
G_loss = -fake_logits.mean() + rec * 10
G_loss.backward(retain_graph=True)
Opt_G.step()
if i % 100 == 0:
fake_img = fake_img.permute(0, 2, 3, 1).cpu().detach().numpy()[0]
Image.fromarray(np.uint8((fake_img + 1) * 127.5)).save("./results/" + str(fake_img.shape[0]) + "_" + str(i) + ".jpg")
print("Iteration: %d, D_loss: %f, G_loss: %f" % (i, D_loss, G_loss))
schedulerD.step()
schedulerG.step()
sigmas.append(sigma)
fixed_Zs.append(z)
return G, D
pass