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alpha_trainer.py
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import os
import time
import torch
import datetime
import itertools
import torch.nn as nn
from torch.autograd import Variable
from torchvision.utils import save_image
from tensorboardX import SummaryWriter
from alphagan_models import Generator, Discriminator, Encoder, Codecriminator
from utils import *
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def log(x):
return torch.log(x + 1e-10)
class alpha_Trainer(object):
def __init__(self, data_loader, config):
self.data_loader = data_loader
# exact model and loss
self.model = config.model
self.adv_loss = config.adv_loss
# Model hyper-parameters
self.imsize = config.imsize
self.g_num = config.g_num
self.z_dim = config.z_dim
self.g_conv_dim = config.g_conv_dim
self.d_conv_dim = config.d_conv_dim
self.parallel = config.parallel
self.lambda_gp = config.lambda_gp
self.total_step = config.total_step
self.d_iters = config.d_iters
self.batch_size = config.batch_size
self.num_workers = config.num_workers
self.ge_lr = config.ge_lr
self.d_lr = config.d_lr
self.lr_decay = config.lr_decay
self.beta1 = config.beta1
self.beta2 = config.beta2
self.pretrained_model = config.pretrained_model
self.dataset = config.dataset
self.mura_class = config.mura_class
self.mura_type = config.mura_type
self.use_tensorboard = config.use_tensorboard
self.image_path = config.image_path
self.log_path = config.log_path
self.model_save_path = config.model_save_path
self.sample_path = config.sample_path
self.log_step = config.log_step
self.sample_step = config.sample_step
self.model_save_step = config.model_save_step
self.version = config.version
# Path
self.log_path = os.path.join(config.log_path, self.version)
self.sample_path = os.path.join(config.sample_path, self.version)
self.model_save_path = os.path.join(config.model_save_path, self.version)
if self.use_tensorboard:
self.build_tensorboard()
self.build_model()
# Start with trained model
if self.pretrained_model:
self.load_pretrained_model()
def train(self):
'''Training loop'''
# Data iterator
data_iter = iter(self.data_loader)
step_per_epoch = len(self.data_loader)
model_save_step = int(self.model_save_step * step_per_epoch)
# Fixed input for debugging
fixed_img, _ = next(data_iter)
fixed_z = tensor2var(torch.randn(self.batch_size, self.z_dim))
if self.use_tensorboard:
self.writer.add_image('img/fixed_img', denorm(fixed_img.data), 0)
else:
save_image(denorm(fixed_img.data),
os.path.join(self.sample_path, 'fixed_img.png'))
# Start with trained model
if self.pretrained_model:
start = self.pretrained_model + 1
else:
start = 0
self.D.train()
self.E.train()
self.G.train()
self.C.train()
# Start time
start_time = time.time()
for step in range(start, self.total_step):
# Sample from data and prior
try:
real_images, _ = next(data_iter)
except:
data_iter = iter(self.data_loader)
real_images, _ = next(data_iter)
# Create labels for training
real_labels = tensor2var(torch.ones((real_images.size(0))))
fake_labels = tensor2var(torch.zeros((real_images.size(0))))
x_real = tensor2var(real_images)
# Sample from condition
z = tensor2var(torch.randn(real_images.size(0), self.z_dim))
# Encoder
z_mean, z_logvar, _, _ = self.E(x_real)
z_hat = z_mean + z_logvar * torch.randn(z_mean.size()).cuda()
# Decoder (generator)
x_rec, x_rec4, x_rec3 = self.G(z_hat)
x_gen, x_gen4, x_gen3 = self.G(z)
# Discriminator
d_real, d_real4, d_real3 = self.D(x_real)
d_rec, d_rec4, d_rec3 = self.D(x_rec)
d_gen, d_gen4, d_gen3 = self.D(x_gen)
# Codecriminator
c_z_hat = self.C(z_hat)
c_z = self.C(z)
# # ================== Train G and E ================== #
# self.reset_grad()
# l1_loss = 0.01 * self.criterion_l1(x_real, x_rec)
# c_hat_loss = self.criterion_bce(c_z_hat, real_labels)
# # c_loss = self.criterion_bce(z, real_labels)
# # d_real_loss = self.criterion_bce(x_real, fake_labels)
# d_rec_loss = self.criterion_bce(d_rec, real_labels)
# d_gen_loss = self.criterion_bce(d_gen, real_labels)
# ge_loss = l1_loss + c_hat_loss + d_rec_loss + d_gen_loss
# ge_loss.backward(retain_graph=True)
# self.ge_optimizer.step()
# ================== Train E ================== #
self.e_optimizer.zero_grad()
l1_loss = 0.01 * self.l1(x_real, x_rec)
c_hat_loss = self.bce(c_z_hat, real_labels) - self.bce(c_z_hat, fake_labels)
e_loss = l1_loss + c_hat_loss
e_loss.backward(retain_graph=True)
self.e_optimizer.step()
# ================== Train G ================== #
self.g_optimizer.zero_grad()
g_rec_loss = self.bce(d_rec, real_labels) - self.bce(d_rec, fake_labels)
g_gen_loss = self.bce(d_gen, real_labels) - self.bce(d_gen, fake_labels)
g_loss = l1_loss + g_rec_loss + g_gen_loss
g_loss.backward(retain_graph=True)
self.g_optimizer.step()
# ================== Train D ================== #
self.d_optimizer.zero_grad()
d_real_loss = self.bce(d_real, real_labels)
d_rec_loss = self.bce(d_rec, fake_labels)
d_gen_loss = self.bce(d_gen, fake_labels)
d_loss = d_real_loss + d_rec_loss + d_gen_loss
d_loss.backward(retain_graph=True)
self.d_optimizer.step()
# ================== Train C ================== #
self.c_optimizer.zero_grad()
c_hat_loss = self.bce(c_z_hat, fake_labels)
c_z_loss = self.bce(c_z, real_labels)
c_loss = c_hat_loss + c_z_loss
c_loss.backward()
self.c_optimizer.step()
# Print out log info
if (step + 1) % self.log_step == 0:
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))
print(f"Elapsed: [{elapsed}], step: [{step+1}/{self.total_step}], e_loss: {e_loss}, g_loss: {g_loss}, d_loss: {d_loss}, c_loss: {c_loss}")
if self.use_tensorboard:
self.writer.add_scalar('loss/e', e_loss.data, step+1)
self.writer.add_scalar('loss/g', g_loss.data, step+1)
self.writer.add_scalar('loss/d', d_loss.data, step+1)
self.writer.add_scalar('loss/c', c_loss.data, step+1)
# Sample images
if (step + 1) % self.sample_step == 0:
img_from_z, _, _ = self.G(fixed_z)
z_mean, z_log_var, _, _ = self.E(tensor2var(fixed_img))
z_from_img = z_mean + z_log_var * torch.randn(z_mean.size()).cuda()
rec_from_z, _, _ = self.G(z_from_img)
if self.use_tensorboard:
self.writer.add_image('img/rec_from_z', denorm(rec_from_z.data), step + 1)
self.writer.add_image('img/img_from_z', denorm(img_from_z.data), step + 1)
else:
save_image(denorm(img_from_z.data),
os.path.join(self.sample_path, '{}_img_from_z.png'.format(step + 1)))
save_image(denorm(reimg_from_z.data),
os.path.join(self.sample_path, '{}_reimg_from_z.png'.format(step + 1)))
if (step+1) % model_save_step==0:
torch.save(self.G.state_dict(),
os.path.join(self.model_save_path, '{}_G.pth'.format(step + 1)))
torch.save(self.E.state_dict(),
os.path.join(self.model_save_path, '{}_E.pth'.format(step + 1)))
torch.save(self.D.state_dict(),
os.path.join(self.model_save_path, '{}_D.pth'.format(step + 1)))
torch.save(self.C.state_dict(),
os.path.join(self.model_save_path, '{}_C.pth'.format(step + 1)))
def build_model(self):
self.G = Generator(self.batch_size, self.imsize, self.z_dim, self.g_conv_dim).cuda()
self.E = Encoder(self.batch_size, self.imsize, self.z_dim, self.d_conv_dim).cuda()
self.D = Discriminator(self.batch_size,self.imsize, self.d_conv_dim).cuda()
self.C = Codecriminator(self.batch_size, self.z_dim).cuda()
if self.parallel:
self.G = nn.DataParallel(self.G)
self.E = nn.DataParallel(self.E)
self.D = nn.DataParallel(self.D)
self.C = nn.DataParallel(self.C)
# Loss and optimizer
self.g_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.G.parameters()), self.ge_lr, [self.beta1, self.beta2])
self.e_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.E.parameters()), self.ge_lr, [self.beta1, self.beta2])
self.d_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.D.parameters()), self.d_lr, [self.beta1, self.beta2])
self.c_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.C.parameters()), self.d_lr, [self.beta1, self.beta2])
self.l1 = nn.L1Loss()
self.bce = nn.BCELoss()
# print networks
print(self.G)
print(self.E)
print(self.D)
print(self.C)
def build_tensorboard(self):
'''Initialize tensorboard writeri'''
self.writer = SummaryWriter(self.log_path)
def load_pretrained_model(self):
self.G.load_state_dict(torch.load(os.path.join(
self.model_save_path, '{}_G.pth'.format(self.pretrained_model))))
self.E.load_state_dict(torch.load(os.path.join(
self.model_save_path, '{}_E.pth'.format(self.pretrained_model))))
self.D.load_state_dict(torch.load(os.path.join(
self.model_save_path, '{}_D.pth'.format(self.pretrained_model))))
self.C.load_state_dict(torch.load(os.path.join(
self.model_save_path, '{}_C.pth'.format(self.pretrained_model))))
print('loaded trained models (step: {})..!'.format(self.pretrained_model))
def save_sample(self, data_iter):
real_images, _ = next(data_iter)
save_image(denorm(real_images), os.path.join(self.sample_path, 'real.png'))