train_ssense.py

#!/usr/bin/env python

import argparse
import os
import sys
import datetime
import dateutil
import dateutil.tz

import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.nn as nn
from torch.autograd import Variable
from tqdm import tqdm

import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms

from tensorboard_logger import configure, log_value

from models import Generator, Discriminator, FeatureExtractor
from utils import Visualizer, mkdir_p
from datasets import SSENSE_Dataset

parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='ssense', help='cifar10 | cifar100 | folder | ssense')
parser.add_argument('--split_name', type=str, default='baby')
parser.add_argument('--dataroot', type=str, default='/data/fashion_final', help='path to dataset')
parser.add_argument('--workers', type=int, default=8, help='number of data loading workers')
parser.add_argument('--batchSize', type=int, default=16, help='input batch size')
parser.add_argument('--imageSize', type=int, default=64, help='the low resolution image size')
parser.add_argument('--upSampling', type=int, default=4, help='low to high resolution scaling factor')
parser.add_argument('--nEpochs', type=int, default=100, help='number of epochs to train for')
parser.add_argument('--generatorLR', type=float, default=0.001, help='learning rate for generator')
parser.add_argument('--discriminatorLR', type=float, default=0.0001, help='learning rate for discriminator')
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--nGPU', type=int, default=1, help='number of GPUs to use')
parser.add_argument('--generatorWeights', type=str, default='', help="path to generator weights (to continue training)")
parser.add_argument('--discriminatorWeights', type=str, default='', help="path to discriminator weights (to continue training)")
parser.add_argument('--out', type=str, default='ssense_checkpoints', help='folder to output model checkpoints')
parser.add_argument('--samples_dir', type=str, default='output/training_samples', help='folder to save samples during training')
parser.add_argument('--sample_freq', type=int, default=500, help='sampling freq')

opt = parser.parse_args()
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
opt.samples_dir = os.path.join(opt.samples_dir, timestamp)
mkdir_p(opt.samples_dir)
print(opt)

try:
    os.makedirs(opt.out)
except OSError:
    pass

if torch.cuda.is_available() and not opt.cuda:
    print("WARNING: You have a CUDA device, so you should probably run with --cuda")


transform = transforms.Compose([transforms.ToTensor()])


normalize = transforms.Normalize((0., 0., 0.), (1., 1., 1.))

scale = transforms.Compose([transforms.ToPILImage(),
                            transforms.Scale(opt.imageSize),
                            transforms.ToTensor(),
                            #transforms.Normalize((0, 0., 0.), (1., 1., 1.))
                            ])

if opt.dataset == 'folder':
    # folder dataset
    dataset = datasets.ImageFolder(root=opt.dataroot, transform=transform)
elif opt.dataset == 'cifar10':
    dataset = datasets.CIFAR10(root=opt.dataroot, train=True, download=True, transform=transform)
elif opt.dataset == 'cifar100':
    dataset = datasets.CIFAR100(root=opt.dataroot, train=True, download=True, transform=transform)
elif opt.dataset == 'ssense':
    dataset = SSENSE_Dataset(
        opt.dataroot,
        split_name=opt.split_name,
        orig_imsize=opt.imageSize*opt.upSampling,
        transform=transform)

assert dataset
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
                                         shuffle=True, num_workers=int(opt.workers))

generator = Generator(16, opt.upSampling)
if opt.generatorWeights != '':
    generator.load_state_dict(torch.load(opt.generatorWeights))
print generator

discriminator = Discriminator()
if opt.discriminatorWeights != '':
    discriminator.load_state_dict(torch.load(opt.discriminatorWeights))
print discriminator

# For the content loss
feature_extractor = FeatureExtractor(torchvision.models.vgg19(pretrained=True))
print feature_extractor
content_criterion = nn.MSELoss()
adversarial_criterion = nn.BCELoss()

ones_const = Variable(torch.ones(opt.batchSize, 1))

# if gpu is to be used
if opt.cuda:
    generator.cuda()
    discriminator.cuda()
    feature_extractor.cuda()
    content_criterion.cuda()
    adversarial_criterion.cuda()
    ones_const = ones_const.cuda()

optim_generator = optim.Adam(generator.parameters(), lr=opt.generatorLR)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=opt.discriminatorLR)

configure('logs/' + opt.dataset + '-' + str(opt.batchSize) + '-' + str(opt.generatorLR) + '-' + str(opt.discriminatorLR), flush_secs=5)
visualizer = Visualizer(save_dir=opt.samples_dir, show_step=opt.sample_freq, image_size=opt.imageSize*opt.upSampling)

low_res = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize)

# Pre-train generator using raw MSE loss
print 'Generator pre-training'
for epoch in range(2):
    mean_generator_content_loss = 0.0
    # enumerate(tqdm(dataloader, desc='G pre-training', leave=False), 0)
    for i, data in enumerate(dataloader):
        # Generate data
        high_res_real = data

        # Downsample images to low resolution
        for j in range(opt.batchSize):
            low_res[j] = scale(high_res_real[j])
            # high_res_real[j] = normalize(high_res_real[j])

        # Generate real and fake inputs
        if opt.cuda:
            high_res_real = Variable(high_res_real.cuda())
            high_res_fake = generator(Variable(low_res).cuda())
        else:
            high_res_real = Variable(high_res_real)
            high_res_fake = generator(Variable(low_res))

        ######### Train generator #########
        generator.zero_grad()

        generator_content_loss = content_criterion(high_res_fake, high_res_real)
        mean_generator_content_loss += generator_content_loss.data[0]

        generator_content_loss.backward()
        optim_generator.step()

        ######### Status and display #########
        # sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' % (epoch, 2, i, len(dataloader), generator_content_loss.data[0]))
        if i % 100 == 0:
            print ('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' % (epoch, 2, i, len(dataloader), generator_content_loss.data[0]))
        # visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
        visualizer.save(low_res, high_res_real.cpu().data, high_res_fake.cpu().data, epoch, i)

    sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f\n' % (epoch, 2, i, len(dataloader), mean_generator_content_loss/len(dataloader)))
    print('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f\n' % (epoch, 2, i, len(dataloader), mean_generator_content_loss/len(dataloader)))
    log_value('generator_mse_loss', mean_generator_content_loss/len(dataloader), epoch)

# Do checkpointing
torch.save(generator.state_dict(), '%s/generator_pretrain.pth' % opt.out)

# SRGAN training
optim_generator = optim.Adam(generator.parameters(), lr=opt.generatorLR*0.1)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=opt.discriminatorLR*0.1)

print 'SRGAN training'
for epoch in range(opt.nEpochs):
    mean_generator_content_loss = 0.0
    mean_generator_adversarial_loss = 0.0
    mean_generator_total_loss = 0.0
    mean_discriminator_loss = 0.0
    #enumerate(tqdm(dataloader, desc='SRGAN training', leave=False), 0)
    for i, data in enumerate(dataloader):
        # Generate data
        high_res_real = data

        # Downsample images to low resolution
        for j in range(opt.batchSize):
            low_res[j] = scale(high_res_real[j])
            # high_res_real[j] = normalize(high_res_real[j])

        # Generate real and fake inputs
        if opt.cuda:
            high_res_real = Variable(high_res_real.cuda())
            high_res_fake = generator(Variable(low_res).cuda())
            target_real = Variable(torch.rand(opt.batchSize,1)*0.5 + 0.7).cuda()
            target_fake = Variable(torch.rand(opt.batchSize,1)*0.3).cuda()
        else:
            high_res_real = Variable(high_res_real)
            high_res_fake = generator(Variable(low_res))
            target_real = Variable(torch.rand(opt.batchSize,1)*0.5 + 0.7)
            target_fake = Variable(torch.rand(opt.batchSize,1)*0.3)

        ######### Train discriminator #########
        discriminator.zero_grad()

        discriminator_loss = adversarial_criterion(discriminator(high_res_real), target_real) + \
                             adversarial_criterion(discriminator(Variable(high_res_fake.data)), target_fake)
        mean_discriminator_loss += discriminator_loss.data[0]

        discriminator_loss.backward()
        optim_discriminator.step()

        ######### Train generator #########
        generator.zero_grad()

        real_features = Variable(feature_extractor(high_res_real).data)
        fake_features = feature_extractor(high_res_fake)

        generator_content_loss = content_criterion(high_res_fake, high_res_real) + 0.006*content_criterion(fake_features, real_features)
        mean_generator_content_loss += generator_content_loss.data[0]
        generator_adversarial_loss = adversarial_criterion(discriminator(high_res_fake), ones_const)
        mean_generator_adversarial_loss += generator_adversarial_loss.data[0]

        generator_total_loss = generator_content_loss + 1e-3*generator_adversarial_loss
        mean_generator_total_loss += generator_total_loss.data[0]

        generator_total_loss.backward()
        optim_generator.step()

        ######### Status and display #########
        # sys.stdout.write
        if i % 100 == 0:
            print ('\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f' % (epoch, opt.nEpochs, i, len(dataloader),
        discriminator_loss.data[0], generator_content_loss.data[0], generator_adversarial_loss.data[0], generator_total_loss.data[0]))
        # visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
        visualizer.save(low_res, high_res_real.cpu().data, high_res_fake.cpu().data, epoch, i)

    print ('\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f\n' % (epoch, opt.nEpochs, i, len(dataloader),
    mean_discriminator_loss/len(dataloader), mean_generator_content_loss/len(dataloader),
    mean_generator_adversarial_loss/len(dataloader), mean_generator_total_loss/len(dataloader)))

    log_value('generator_content_loss', mean_generator_content_loss/len(dataloader), epoch)
    log_value('generator_adversarial_loss', mean_generator_adversarial_loss/len(dataloader), epoch)
    log_value('generator_total_loss', mean_generator_total_loss/len(dataloader), epoch)
    log_value('discriminator_loss', mean_discriminator_loss/len(dataloader), epoch)

    # Do checkpointing
    torch.save(generator.state_dict(), '%s/generator_final.pth' % opt.out)
    torch.save(discriminator.state_dict(), '%s/discriminator_final.pth' % opt.out)

# Avoid closing
while True:
    pass