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train.py
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train.py
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import os
import time
import datetime
import logging
import itertools
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.nn import functional as F
from torchvision.utils import save_image
import numpy as np
from pickle import dump
from discriminator import Discriminator
from generator import Generator
class GAN_CLS(object):
def __init__(self, args, data_loader, SUPERVISED=True):
"""
args : Arguments
data_loader = An instance of class DataLoader for loading our dataset in batches
"""
self.data_loader = data_loader
self.num_epochs = args.num_epochs
self.batch_size = args.batch_size
self.log_step = args.log_step
self.sample_step = args.sample_step
self.log_dir = args.log_dir
self.checkpoint_dir = args.checkpoint_dir
self.sample_dir = args.sample_dir
self.final_model = args.final_model
self.model_save_step = args.model_save_step
#self.dataset = args.dataset
#self.model_name = args.model_name
self.img_size = args.img_size
self.z_dim = args.z_dim
self.text_embed_dim = args.text_embed_dim
self.text_reduced_dim = args.text_reduced_dim
self.learning_rate = args.learning_rate
self.beta1 = args.beta1
self.beta2 = args.beta2
self.l1_coeff = args.l1_coeff
self.resume_epoch = args.resume_epoch
self.resume_idx = args.resume_idx
self.SUPERVISED = SUPERVISED
# Logger setting
log_name = datetime.datetime.now().strftime('%Y-%m-%d')+'.log'
self.logger = logging.getLogger('__name__')
self.logger.setLevel(logging.INFO)
self.formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
self.file_handler = logging.FileHandler(os.path.join(self.log_dir, log_name))
self.file_handler.setFormatter(self.formatter)
self.logger.addHandler(self.file_handler)
self.build_model()
def smooth_label(self, tensor, offset):
return tensor + offset
def dump_imgs(images_Array, name):
with open('{}.pickle'.format(name), 'wb') as file:
dump(images_Array, file)
def build_model(self):
""" A function of defining following instances :
----- Generator
----- Discriminator
----- Optimizer for Generator
----- Optimizer for Discriminator
----- Defining Loss functions
"""
# ---------------------------------------------------------------------#
# 1. Network Initialization #
# ---------------------------------------------------------------------#
self.gen = Generator(batch_size=self.batch_size,
img_size=self.img_size,
z_dim=self.z_dim,
text_embed_dim=self.text_embed_dim,
text_reduced_dim=self.text_reduced_dim)
self.disc = Discriminator(batch_size=self.batch_size,
img_size=self.img_size,
text_embed_dim=self.text_embed_dim,
text_reduced_dim=self.text_reduced_dim)
self.gen_optim = optim.Adam(self.gen.parameters(),
lr=self.learning_rate,
betas=(self.beta1, self.beta2))
self.disc_optim = optim.Adam(self.disc.parameters(),
lr=self.learning_rate,
betas=(self.beta1, self.beta2))
self.cls_gan_optim = optim.Adam(itertools.chain(self.gen.parameters(),
self.disc.parameters()),
lr=self.learning_rate,
betas=(self.beta1, self.beta2))
print ('------------- Generator Model Info ---------------')
self.print_network(self.gen, 'G')
print ('------------------------------------------------')
print ('------------- Discriminator Model Info ---------------')
self.print_network(self.disc, 'D')
print ('------------------------------------------------')
self.criterion = nn.BCELoss().cuda()
# self.CE_loss = nn.CrossEntropyLoss().cuda()
# self.MSE_loss = nn.MSELoss().cuda()
self.gen.train()
self.disc.train()
def print_network(self, model, name):
""" A function for printing total number of model parameters """
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(model)
print(name)
print("Total number of parameters: {}".format(num_params))
def load_checkpoints(self, resume_epoch, idx):
"""Restore the trained generator and discriminator."""
print('Loading the trained models from epoch {} and iteration {}...'.format(resume_epoch, idx))
G_path = os.path.join(self.checkpoint_dir, '{}-{}-G.ckpt'.format(resume_epoch, idx))
D_path = os.path.join(self.checkpoint_dir, '{}-{}-D.ckpt'.format(resume_epoch, idx))
self.gen.load_state_dict(torch.load(G_path, map_location=lambda storage, loc: storage))
self.disc.load_state_dict(torch.load(D_path, map_location=lambda storage, loc: storage))
def train_model(self):
data_loader = self.data_loader
start_epoch = 0
if self.resume_epoch >= 0:
start_epoch = self.resume_epoch
self.load_checkpoints(self.resume_epoch, self.resume_idx)
print ('--------------- Model Training Started ---------------')
start_time = time.time()
for epoch in range(start_epoch, self.num_epochs):
print("Epoch: {}".format(epoch+1))
for idx, batch in enumerate(data_loader):
print("Index: {}".format(idx+1), end = "\t")
true_imgs = batch['true_imgs']
true_embed = batch['true_embds']
false_imgs = batch['false_imgs']
real_labels = torch.ones(true_imgs.size(0))
fake_labels = torch.zeros(true_imgs.size(0))
smooth_real_labels = torch.FloatTensor(self.smooth_label(real_labels.numpy(), -0.1))
true_imgs = Variable(true_imgs.float()).cuda()
true_embed = Variable(true_embed.float()).cuda()
false_imgs = Variable(false_imgs.float()).cuda()
real_labels = Variable(real_labels).cuda()
smooth_real_labels = Variable(smooth_real_labels).cuda()
fake_labels = Variable(fake_labels).cuda()
# ---------------------------------------------------------------#
# 2. Training the generator #
# ---------------------------------------------------------------#
self.gen.zero_grad()
z = Variable(torch.randn(true_imgs.size(0), self.z_dim)).cuda()
fake_imgs = self.gen.forward(true_embed, z)
fake_out, fake_logit = self.disc.forward(fake_imgs, true_embed)
fake_out = Variable(fake_out.data, requires_grad=True).cuda()
true_out, true_logit = self.disc.forward(true_imgs, true_embed)
true_out = Variable(true_out.data, requires_grad=True).cuda()
g_sf = self.criterion(fake_out, real_labels)
#g_img = self.l1_coeff * nn.L1Loss()(fake_imgs, true_imgs)
gen_loss = g_sf
gen_loss.backward()
self.gen_optim.step()
# ---------------------------------------------------------------#
# 3. Training the discriminator #
# ---------------------------------------------------------------#
self.disc.zero_grad()
false_out, false_logit = self.disc.forward(false_imgs, true_embed)
false_out = Variable(false_out.data, requires_grad=True)
sr = self.criterion(true_out, smooth_real_labels)
sw = self.criterion(true_out, fake_labels)
sf = self.criterion(false_out, smooth_real_labels)
disc_loss = torch.log(sr) + (torch.log(1-sw) + torch.log(1-sf ))/2
disc_loss.backward()
self.disc_optim.step()
self.cls_gan_optim.step()
# Logging
loss = {}
loss['G_loss'] = gen_loss.item()
loss['D_loss'] = disc_loss.item()
# ---------------------------------------------------------------#
# 4. Logging INFO into log_dir #
# ---------------------------------------------------------------#
log = ""
if (idx + 1) % self.log_step == 0:
end_time = time.time() - start_time
end_time = datetime.timedelta(seconds=end_time)
log = "Elapsed [{}], Epoch [{}/{}], Idx [{}]".format(end_time, epoch + 1, self.num_epochs, idx)
for net, loss_value in loss.items():
log += "{}: {:.4f}".format(net, loss_value)
self.logger.info(log)
print (log)
"""
# ---------------------------------------------------------------#
# 5. Saving generated images #
# ---------------------------------------------------------------#
if (idx + 1) % self.sample_step == 0:
concat_imgs = torch.cat((true_imgs, fake_imgs), 0) # ??????????
concat_imgs = (concat_imgs + 1) / 2
# out.clamp_(0, 1)
save_path = os.path.join(self.sample_dir, '{}-{}-images.jpg'.format(epoch, idx + 1))
# concat_imgs.cpu().detach().numpy()
self.dump_imgs(concat_imgs.cpu().numpy(), save_path)
#save_image(concat_imgs.data.cpu(), self.sample_dir, nrow=1, padding=0)
print ('Saved real and fake images into {}...'.format(self.sample_dir))
"""
# ---------------------------------------------------------------#
# 6. Saving the checkpoints & final model #
# ---------------------------------------------------------------#
if (idx + 1) % self.model_save_step == 0:
G_path = os.path.join(self.checkpoint_dir, '{}-{}-G.ckpt'.format(epoch, idx + 1))
D_path = os.path.join(self.checkpoint_dir, '{}-{}-D.ckpt'.format(epoch, idx + 1))
torch.save(self.gen.state_dict(), G_path)
torch.save(self.disc.state_dict(), D_path)
print('Saved model checkpoints into {}...\n'.format(self.checkpoint_dir))
print ('--------------- Model Training Completed ---------------')
# Saving final model into final_model directory
G_path = os.path.join(self.final_model, '{}-G.pth'.format('final'))
D_path = os.path.join(self.final_model, '{}-D.pth'.format('final'))
torch.save(self.gen.state_dict(), G_path)
torch.save(self.disc.state_dict(), D_path)
print('Saved final model into {}...'.format(self.final_model))