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train.py
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train.py
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import argparse
from fastText import FastText
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import torch.optim.lr_scheduler as lr_scheduler
from torch.autograd import Variable
import torchvision.transforms as transforms
from torchvision.utils import save_image
from model import VisualSemanticEmbedding
from model import Generator, Discriminator
from data import ReedICML2016
parser = argparse.ArgumentParser()
parser.add_argument('--img_root', type=str, required=True,
help='root directory that contains images')
parser.add_argument('--caption_root', type=str, required=True,
help='root directory that contains captions')
parser.add_argument('--trainclasses_file', type=str, required=True,
help='text file that contains training classes')
parser.add_argument('--fasttext_model', type=str, required=True,
help='pretrained fastText model (binary file)')
parser.add_argument('--text_embedding_model', type=str, required=True,
help='pretrained text embedding model')
parser.add_argument('--save_filename', type=str, required=True,
help='checkpoint file')
parser.add_argument('--num_threads', type=int, default=4,
help='number of threads for fetching data (default: 4)')
parser.add_argument('--num_epochs', type=int, default=600,
help='number of threads for fetching data (default: 600)')
parser.add_argument('--batch_size', type=int, default=64,
help='batch size (default: 64)')
parser.add_argument('--learning_rate', type=float, default=0.0002,
help='learning rate (dafault: 0.0002)')
parser.add_argument('--lr_decay', type=float, default=0.5,
help='learning rate decay (dafault: 0.5)')
parser.add_argument('--momentum', type=float, default=0.5,
help='beta1 for Adam optimizer (dafault: 0.5)')
parser.add_argument('--embed_ndim', type=int, default=300,
help='dimension of embedded vector (default: 300)')
parser.add_argument('--max_nwords', type=int, default=50,
help='maximum number of words (default: 50)')
parser.add_argument('--use_vgg', action='store_true',
help='use pretrained VGG network for image encoder')
parser.add_argument('--no_cuda', action='store_true',
help='do not use cuda')
parser.add_argument('--fusing_method', type=str, default='',
help='fusing_method')
args = parser.parse_args()
if not args.no_cuda and not torch.cuda.is_available():
print('Warning: cuda is not available on this machine.')
args.no_cuda = True
def preprocess(img, desc, len_desc, txt_encoder):
# img = Variable(img.cuda() if not args.no_cuda else img)
desc = Variable(desc.cuda() if not args.no_cuda else desc)
len_desc = len_desc.numpy()
sorted_indices = np.argsort(len_desc)[::-1]
original_indices = np.argsort(sorted_indices)
packed_desc = nn.utils.rnn.pack_padded_sequence(
desc[torch.LongTensor(sorted_indices.copy()).cuda(), ...].transpose(0, 1),
len_desc[sorted_indices]
)
_, txt_feat = txt_encoder(packed_desc)
txt_feat = txt_feat.squeeze()
txt_feat = txt_feat[original_indices, ...]
txt_feat_np = txt_feat.data.cpu().numpy() if not args.no_cuda else txt_feat.data.numpy()
txt_feat_mismatch = torch.Tensor(np.roll(txt_feat_np, 1, axis=0))
txt_feat_mismatch = Variable(txt_feat_mismatch.cuda() if not args.no_cuda else txt_feat_mismatch)
txt_feat_np_split = np.split(txt_feat_np, [txt_feat_np.shape[0] // 2])
txt_feat_relevant = torch.Tensor(np.concatenate([
np.roll(txt_feat_np_split[0], -1, axis=0),
txt_feat_np_split[1]
]))
txt_feat_relevant = Variable(txt_feat_relevant.cuda() if not args.no_cuda else txt_feat_relevant)
return img, txt_feat, txt_feat_mismatch, txt_feat_relevant
if __name__ == '__main__':
print('Loading a pretrained fastText model...')
word_embedding = FastText.load_model(args.fasttext_model)
print('Loading a dataset...')
train_data = ReedICML2016(args.img_root,
args.caption_root,
args.trainclasses_file,
word_embedding,
args.max_nwords,
transforms.Compose([
transforms.Scale(74),
transforms.RandomCrop(64),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
]))
vgg_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_loader = data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_threads)
word_embedding = None
# pretrained text embedding model
print('Loading a pretrained text embedding model...')
txt_encoder = VisualSemanticEmbedding(args.embed_ndim)
txt_encoder.load_state_dict(torch.load(args.text_embedding_model))
txt_encoder = txt_encoder.txt_encoder
for param in txt_encoder.parameters():
param.requires_grad = False
G = Generator(use_vgg=args.use_vgg, fusing=args.fusing_method)
D = Discriminator(fusing_method=args.fusing_method)
if not args.no_cuda:
txt_encoder.cuda()
G.cuda()
D.cuda()
g_optimizer = torch.optim.Adam(filter(lambda x: x.requires_grad, G.parameters()),
lr=args.learning_rate, betas=(args.momentum, 0.999))
d_optimizer = torch.optim.Adam(filter(lambda x: x.requires_grad, D.parameters()),
lr=args.learning_rate, betas=(args.momentum, 0.999))
g_lr_scheduler = lr_scheduler.StepLR(g_optimizer, 100, args.lr_decay)
d_lr_scheduler = lr_scheduler.StepLR(d_optimizer, 100, args.lr_decay)
for epoch in range(args.num_epochs):
d_lr_scheduler.step()
g_lr_scheduler.step()
# training loop
avg_D_real_loss = 0
avg_D_real_m_loss = 0
avg_D_fake_loss = 0
avg_G_fake_loss = 0
avg_kld = 0
for i, (img, desc, len_desc) in enumerate(train_loader):
img, txt_feat, txt_feat_mismatch, txt_feat_relevant = \
preprocess(img, desc, len_desc, txt_encoder)
img_norm = img * 2 - 1
vgg_norm1 = (img[:,0,:,:] - 0.485)/0.229
vgg_norm2 = (img[:, 1, :, :] - 0.456) / 0.224
vgg_norm3 = (img[:, 2, :, :] - 0.406) / 0.225
vgg_norm = torch.cat((vgg_norm1.unsqueeze(1),vgg_norm2.unsqueeze(1),vgg_norm3.unsqueeze(1)), 1)
img_norm = Variable(img_norm.cuda() if not args.no_cuda else img_norm)
img_G = Variable(vgg_norm.cuda() if not args.no_cuda else vgg_norm) if args.use_vgg else img_norm
ONES = Variable(torch.ones(img.size(0)))
ZEROS = Variable(torch.zeros(img.size(0)))
if not args.no_cuda:
ONES, ZEROS = ONES.cuda(), ZEROS.cuda()
if i % 2 == 0:
# UPDATE DISCRIMINATOR
D.zero_grad()
# real image with matching text
real_logit = D(img_norm, txt_feat)
#real_loss = F.binary_cross_entropy_with_logits(real_logit, ONES)
real_loss = F.mse_loss(F.sigmoid(real_logit), ONES)
avg_D_real_loss += real_loss.data[0]
real_loss.backward()
# real image with mismatching text
real_m_logit = D(img_norm, txt_feat_mismatch)
#real_m_loss = 0.5 * F.binary_cross_entropy_with_logits(real_m_logit, ZEROS)
real_m_loss = 0.5 * F.mse_loss(F.sigmoid(real_m_logit), ZEROS)
avg_D_real_m_loss += real_m_loss.data[0]
real_m_loss.backward()
# synthesized image with semantically relevant text
fake, _ = G(img_G, txt_feat_relevant)
fake_logit = D(fake.detach(), txt_feat_relevant)
fake_loss = 0.5 * F.mse_loss(F.sigmoid(fake_logit), ZEROS)
avg_D_fake_loss += fake_loss.data[0]
fake_loss.backward()
d_optimizer.step()
# UPDATE GENERATOR
G.zero_grad()
fake, (z_mean, z_log_stddev) = G(img_G, txt_feat_relevant)
kld = torch.mean(-z_log_stddev + 0.5 * (torch.exp(2 * z_log_stddev) + torch.pow(z_mean, 2) - 1))
avg_kld += kld.data[0]
fake_logit = D(fake, txt_feat_relevant)
fake_loss = F.mse_loss(F.sigmoid(fake_logit), ONES)
avg_G_fake_loss += fake_loss.data[0]
G_loss = fake_loss + kld
G_loss.backward()
g_optimizer.step()
if i % 10 == 0:
print('Epoch [%d/%d], Iter [%d/%d], D_real: %.4f, D_mis: %.4f, D_fake: %.4f, G_fake: %.4f, KLD: %.4f'
% (epoch + 1, args.num_epochs, i + 1, len(train_loader), avg_D_real_loss / (i + 1),
avg_D_real_m_loss / (i + 1), avg_D_fake_loss / (i + 1), avg_G_fake_loss / (i + 1), avg_kld / (i + 1)))
save_image((fake.data + 1) * 0.5, './examples/epoch_%d.png' % (epoch + 1))
if epoch % 10 ==0:
torch.save(G.state_dict(), args.save_filename)