zy = int(opt.zy)
zx_sample = int(opt.zx_sample)
zy_sample = int(opt.zy_sample)
depth = 5
npx = zx_to_npx(zx, depth)
npy = zx_to_npx(zy, depth)
batch_size = int(opt.batchSize)
print(npx, npy)
if opt.data_iter == 'from_ti':
# texture_dir='D:/gan_for_gradient_based_inv/training/ti/'
texture_dir = 'C:/Users/Fleford/PycharmProjects/gan_for_gradient_based_inv/training/ti/'
data_iter = get_texture2D_iter(texture_dir,
npx=npx,
npy=npy,
mirror=False,
batch_size=batch_size,
n_channel=nc)
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
netG = netG(nc, nz, ngf, gfs, ngpu)
def train(generator, discriminator, init_step, loader, total_iter=600000, max_step=6):
step = init_step # can be 1 = 8, 2 = 16, 3 = 32, 4 = 64, 5 = 128, 6 = 128
# data_loader = sample_data(loader, 4 * 2 ** step)
# dataset = iter(data_loader)
# total_iter = 600000
total_iter_remain = total_iter - (total_iter // max_step) * (step - 1)
pbar = tqdm(range(total_iter_remain))
disc_loss_val = 0
gen_loss_val = 0
grad_loss_val = 0
from datetime import datetime
import os
date_time = datetime.now()
post_fix = '%s_%s_%d_%d.txt' % (trial_name, date_time.date(), date_time.hour, date_time.minute)
log_folder = 'trial_%s_%s_%d_%d' % (trial_name, date_time.date(), date_time.hour, date_time.minute)
os.mkdir(log_folder)
os.mkdir(log_folder + '/checkpoint')
os.mkdir(log_folder + '/sample')
config_file_name = os.path.join(log_folder, 'train_config_' + post_fix)
config_file = open(config_file_name, 'w')
config_file.write(str(args))
config_file.close()
log_file_name = os.path.join(log_folder, 'train_log_' + post_fix)
log_file = open(log_file_name, 'w')
log_file.write('g,d,cntxt_loss,ds_cntxt_loss\n')
log_file.close()
from shutil import copy
copy('train.py', log_folder + '/train_%s.py' % post_fix)
copy('progan_modules.py', log_folder + '/model_%s.py' % post_fix)
copy('utils.py', log_folder + '/utils_%s.py' % post_fix)
alpha = 0
one = torch.FloatTensor([1]).to(device)
# one = torch.tensor(1, dtype=torch.float).to(device)
mone = one * -1
iteration = 0
# Prepare reference batch for display
data_iter_sample = get_texture2D_iter('ti/', batch_size=5 * 10)
real_image_raw_res_sample = torch.Tensor(next(data_iter_sample)).to(device)
cond_array_sample, cond_mask_sample = generate_condition(real_image_raw_res_sample)
# cond_array_sample = torch.zeros(batch_size, 1, 128, 128, device='cuda:0')
# broadcast first cond_array to whole batch
# one_cond_array_sample = torch.zeros_like(cond_array_sample)
for slice in range(len(cond_array_sample) // 2):
cond_array_sample[slice] = cond_array_sample[0]
# cond_array_sample = one_cond_array_sample
data_iter = get_texture2D_iter('ti/', batch_size=batch_size)
cntxt_loss = torch.FloatTensor([69]).to(device)
for i in pbar:
discriminator.zero_grad()
alpha = min(1, (2 / (total_iter // max_step)) * iteration)
if iteration > total_iter // max_step:
alpha = 0
iteration = 0
step += 1
if step > max_step:
alpha = 1
step = max_step
# Scale training image using avg downsampling
real_image_raw_res = torch.Tensor(next(data_iter)).to(device)
kernel_width = 2 ** (6 - step)
avg_downsampler = torch.nn.AvgPool2d((kernel_width, kernel_width), stride=(kernel_width, kernel_width))
cond_downsampler = torch.nn.MaxPool2d((kernel_width, kernel_width), stride=(kernel_width, kernel_width))
real_image = avg_downsampler(real_image_raw_res)
# plt.matshow(real_image[0, 0].cpu().detach().numpy())
# plt.show()
iteration += 1
### 1. train Discriminator
b_size = real_image.size(0)
# label = torch.zeros(b_size).to(device)
real_predict = discriminator(
real_image, step=step, alpha=alpha)
real_predict = real_predict.mean() - 0.001 * (real_predict ** 2).mean()
real_predict.backward(mone)
# sample input data: vector for Generator
gen_z = torch.randn(b_size, input_code_size).to(device)
# generate condition array
cond_array, cond_mask = generate_condition(real_image_raw_res)
# # broadcast first raw image to the whole batch
# # one_real_image_raw_res = torch.zeros_like(real_image_raw_res)
# for slice in range(len(real_image_raw_res)//4):
# real_image_raw_res[slice] = real_image_raw_res[0]
# # real_image_raw_res = one_real_image_raw_res
#
# # broadcast first cond array to the whole batch
# # one_cond_array = torch.zeros_like(cond_array)
# for slice in range(len(cond_array)//4):
# cond_array[slice] = cond_array[0]
# # cond_array = one_cond_array
#
# # broadcast first cond mask to the whole batch
# # one_cond_mask = torch.zeros_like(cond_mask)
# for slice in range(len(cond_mask)//4):
# cond_mask[slice] = cond_mask[0]
# # cond_mask = one_cond_mask
fake_image = generator(gen_z, cond_array, step=step, alpha=alpha)
fake_predict = discriminator(
fake_image.detach(), step=step, alpha=alpha)
fake_predict = fake_predict.mean()
fake_predict.backward(one)
### gradient penalty for D
eps = torch.rand(b_size, 1, 1, 1).to(device)
x_hat = eps * real_image.data + (1 - eps) * fake_image.detach().data
x_hat.requires_grad = True
hat_predict = discriminator(x_hat, step=step, alpha=alpha)
grad_x_hat = grad(outputs=hat_predict.sum(), inputs=x_hat, create_graph=True)[0]
grad_penalty = ((grad_x_hat.view(grad_x_hat.size(0), -1).norm(2, dim=1) - 1) ** 2).mean()
grad_penalty = 10 * grad_penalty
grad_penalty.backward(one)
grad_loss_val += grad_penalty.item()
disc_loss_val += (real_predict - fake_predict).item()
d_optimizer.step()
### 2. train Generator
if (i + 1) % n_critic == 0:
generator.zero_grad()
discriminator.zero_grad()
predict = discriminator(fake_image, step=step, alpha=alpha)
# Calculate context loss (conditioning hard data)
fake_image_upsampled = F.interpolate(fake_image, size=(128, 128), mode="nearest")
# real_image_upsampled = F.interpolate(real_image, size=(128, 128), mode="nearest")
# context_loss_array = ((fake_image_upsampled - real_image_upsampled) ** 2) * cond_mask
context_loss_array = ((fake_image_upsampled - real_image_raw_res) ** 2) * cond_mask
# ds_cond_mask = cond_downsampler(cond_mask)
ds_context_loss_array = ((fake_image_upsampled - real_image_raw_res) ** 2) * cond_mask
ds_context_loss_value = torch.sum(ds_context_loss_array)
ds_cntxt_loss = ds_context_loss_value.item()
context_loss_value = torch.sum(context_loss_array).log()
loss = -predict.mean() + 1.0 * context_loss_value
gen_loss_val += loss.item()
cntxt_loss = context_loss_value.item()
loss.backward()
g_optimizer.step()
accumulate(g_running, generator)
if (i + 1) % 1000 == 0 or i == 0:
with torch.no_grad():
images = g_running(torch.randn(5 * 10, input_code_size).to(device),
cond_array_sample, step=step, alpha=alpha).data.cpu()
images = F.interpolate(images, size=(128, 128), mode="nearest")
utils.save_image(
images,
f'{log_folder}/sample/{str(i + 1).zfill(6)}.png',
nrow=10,
normalize=True,
range=(-1, 1))
if (i + 1) % 10000 == 0 or i == 0:
try:
torch.save(g_running.state_dict(), f'{log_folder}/checkpoint/{str(i + 1).zfill(6)}_g.model')
torch.save(discriminator.state_dict(), f'{log_folder}/checkpoint/{str(i + 1).zfill(6)}_d.model')
except:
pass
if (i + 1) % 500 == 0:
state_msg = (f'{i + 1}; G: {gen_loss_val / (500 // n_critic):.3f}; D: {disc_loss_val / 500:.3f};'
f' Grad: {grad_loss_val / 500:.3f}; Alpha: {alpha:.3f}; Step: {step:.3f}; Iteration: {iteration:.3f};'
f' Context Loss: {cntxt_loss:.3f};' f' DS Context Loss: {ds_cntxt_loss:.3f};')
print(real_image.shape)
log_file = open(log_file_name, 'a+')
new_line = "%.5f,%.5f,%.5f,%.5f\n" % (
gen_loss_val / (500 // n_critic), disc_loss_val / 500, cntxt_loss, ds_cntxt_loss)
log_file.write(new_line)
log_file.close()
disc_loss_val = 0
gen_loss_val = 0
grad_loss_val = 0
print(state_msg)
input_code_size = 128
generator = Generator(in_channel=64,
input_code_dim=128,
pixel_norm=False,
tanh=False).to(device)
# generator.load_state_dict(torch.load('trial_test18_2020-10-12_22_29/checkpoint/160000_g.model'))
generator.load_state_dict(
torch.load('trial_test18_2020-10-18_17_37/checkpoint/140000_g.model'))
# sample input data: vector for Generator
gen_z = torch.randn(b_size, input_code_size).to(device)
# generate condition array
data_iter = get_texture2D_iter('ti/', batch_size=b_size)
real_image_raw_res = torch.Tensor(next(data_iter)).to(device)
cond_array, cond_mask = generate_condition(real_image_raw_res)
cond_downsampler = torch.nn.MaxPool2d((8, 8), stride=(8, 8))
# broadcast first cond_array to whole batch
one_cond_array = torch.zeros_like(cond_array)
for slice in range(len(cond_array)):
one_cond_array[slice] = cond_array[0]
cond_array = one_cond_array
# broadcast first cond array to the whole batch (cond_mask)
one_cond_mask = torch.zeros_like(cond_mask)
for slice in range(len(cond_mask)):
one_cond_mask[slice] = cond_mask[0]
