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import argparse
import os
import numpy as np
import math
import itertools
import sys
import datetime
import time
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from models import *
from datasets import *
import torch.nn as nn
import torch.nn.functional as F
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--dataset_name", type=str, default="edges2shoes", help="name of the dataset")
parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_height", type=int, default=64, help="size of image height")
parser.add_argument("--img_width", type=int, default=64, help="size of image width")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=100, help="interval between saving generator samples")
parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between model checkpoints")
opt = parser.parse_args()
print(opt)
# Create sample and checkpoint directories
os.makedirs("images/%s" % opt.dataset_name, exist_ok=True)
os.makedirs("saved_models/%s" % opt.dataset_name, exist_ok=True)
# Losses
adversarial_loss = torch.nn.MSELoss()
cycle_loss = torch.nn.L1Loss()
pixelwise_loss = torch.nn.L1Loss()
cuda = torch.cuda.is_available()
input_shape = (opt.channels, opt.img_height, opt.img_width)
# Initialize generator and discriminator
G_AB = GeneratorUNet(input_shape)
G_BA = GeneratorUNet(input_shape)
D_A = Discriminator(input_shape)
D_B = Discriminator(input_shape)
if cuda:
G_AB = G_AB.cuda()
G_BA = G_BA.cuda()
D_A = D_A.cuda()
D_B = D_B.cuda()
adversarial_loss.cuda()
cycle_loss.cuda()
pixelwise_loss.cuda()
if opt.epoch != 0:
# Load pretrained models
G_AB.load_state_dict(torch.load("saved_models/%s/G_AB_%d.pth" % (opt.dataset_name, opt.epoch)))
G_BA.load_state_dict(torch.load("saved_models/%s/G_BA_%d.pth" % (opt.dataset_name, opt.epoch)))
D_A.load_state_dict(torch.load("saved_models/%s/D_A_%d.pth" % (opt.dataset_name, opt.epoch)))
D_B.load_state_dict(torch.load("saved_models/%s/D_B_%d.pth" % (opt.dataset_name, opt.epoch)))
else:
# Initialize weights
G_AB.apply(weights_init_normal)
G_BA.apply(weights_init_normal)
D_A.apply(weights_init_normal)
D_B.apply(weights_init_normal)
# Optimizers
optimizer_G = torch.optim.Adam(
itertools.chain(G_AB.parameters(), G_BA.parameters()), lr=opt.lr, betas=(opt.b1, opt.b2)
)
optimizer_D_A = torch.optim.Adam(D_A.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D_B = torch.optim.Adam(D_B.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
# Input tensor type
Tensor = torch.cuda.FloatTensor if cuda else torch.Tensor
# Dataset loader
transforms_ = [
transforms.Resize((opt.img_height, opt.img_width), Image.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_, mode="train"),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_cpu,
)
val_dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_, mode="val"),
batch_size=16,
shuffle=True,
num_workers=opt.n_cpu,
)
def sample_images(batches_done):
"""Saves a generated sample from the validation set"""
imgs = next(iter(val_dataloader))
G_AB.eval()
G_BA.eval()
real_A = Variable(imgs["A"].type(Tensor))
fake_B = G_AB(real_A)
real_B = Variable(imgs["B"].type(Tensor))
fake_A = G_BA(real_B)
img_sample = torch.cat((real_A.data, fake_B.data, real_B.data, fake_A.data), 0)
save_image(img_sample, "images/%s/%s.png" % (opt.dataset_name, batches_done), nrow=8, normalize=True)
# ----------
# Training
# ----------
prev_time = time.time()
for epoch in range(opt.epoch, opt.n_epochs):
for i, batch in enumerate(dataloader):
# Model inputs
real_A = Variable(batch["A"].type(Tensor))
real_B = Variable(batch["B"].type(Tensor))
# Adversarial ground truths
valid = Variable(Tensor(np.ones((real_A.size(0), *D_A.output_shape))), requires_grad=False)
fake = Variable(Tensor(np.zeros((real_A.size(0), *D_A.output_shape))), requires_grad=False)
# ------------------
# Train Generators
# ------------------
G_AB.train()
G_BA.train()
optimizer_G.zero_grad()
# GAN loss
fake_B = G_AB(real_A)
loss_GAN_AB = adversarial_loss(D_B(fake_B), valid)
fake_A = G_BA(real_B)
loss_GAN_BA = adversarial_loss(D_A(fake_A), valid)
loss_GAN = (loss_GAN_AB + loss_GAN_BA) / 2
# Pixelwise translation loss
loss_pixelwise = (pixelwise_loss(fake_A, real_A) + pixelwise_loss(fake_B, real_B)) / 2
# Cycle loss
loss_cycle_A = cycle_loss(G_BA(fake_B), real_A)
loss_cycle_B = cycle_loss(G_AB(fake_A), real_B)
loss_cycle = (loss_cycle_A + loss_cycle_B) / 2
# Total loss
loss_G = loss_GAN + loss_cycle + loss_pixelwise
loss_G.backward()
optimizer_G.step()
# -----------------------
# Train Discriminator A
# -----------------------
optimizer_D_A.zero_grad()
# Real loss
loss_real = adversarial_loss(D_A(real_A), valid)
# Fake loss (on batch of previously generated samples)
loss_fake = adversarial_loss(D_A(fake_A.detach()), fake)
# Total loss
loss_D_A = (loss_real + loss_fake) / 2
loss_D_A.backward()
optimizer_D_A.step()
# -----------------------
# Train Discriminator B
# -----------------------
optimizer_D_B.zero_grad()
# Real loss
loss_real = adversarial_loss(D_B(real_B), valid)
# Fake loss (on batch of previously generated samples)
loss_fake = adversarial_loss(D_B(fake_B.detach()), fake)
# Total loss
loss_D_B = (loss_real + loss_fake) / 2
loss_D_B.backward()
optimizer_D_B.step()
loss_D = 0.5 * (loss_D_A + loss_D_B)
# --------------
# Log Progress
# --------------
# Determine approximate time left
batches_done = epoch * len(dataloader) + i
batches_left = opt.n_epochs * len(dataloader) - batches_done
time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
prev_time = time.time()
# Print log
sys.stdout.write(
"\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, adv: %f, pixel: %f, cycle: %f] ETA: %s"
% (
epoch,
opt.n_epochs,
i,
len(dataloader),
loss_D.item(),
loss_G.item(),
loss_GAN.item(),
loss_pixelwise.item(),
loss_cycle.item(),
time_left,
)
)
# If at sample interval save image
if batches_done % opt.sample_interval == 0:
sample_images(batches_done)
if opt.checkpoint_interval != -1 and epoch % opt.checkpoint_interval == 0:
# Save model checkpoints
torch.save(G_AB.state_dict(), "saved_models/%s/G_AB_%d.pth" % (opt.dataset_name, epoch))
torch.save(G_BA.state_dict(), "saved_models/%s/G_BA_%d.pth" % (opt.dataset_name, epoch))
torch.save(D_A.state_dict(), "saved_models/%s/D_A_%d.pth" % (opt.dataset_name, epoch))
torch.save(D_B.state_dict(), "saved_models/%s/D_B_%d.pth" % (opt.dataset_name, epoch))
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