def main(args):
# Create model directory
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
if not os.path.exists(args.figure_path):
os.makedirs(args.figure_path)
# Image preprocessing
# For normalization, see https://github.com/pytorch/vision#models
transform = transforms.Compose([
transforms.RandomCrop(args.crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# Load vocabulary wrapper.
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build data loader
data_loader = get_loader(args.image_dir,
args.caption_path,
vocab,
transform,
args.batch_size,
shuffle=True,
num_workers=args.num_workers)
# Build the models (Gen)
generator = Generator(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
# Build the models (Disc)
discriminator = Discriminator(args.embed_size, args.hidden_size,
len(vocab), args.num_layers)
if torch.cuda.is_available():
generator.cuda()
discriminator.cuda()
# Loss and Optimizer (Gen)
mle_criterion = nn.CrossEntropyLoss()
params_gen = list(generator.parameters())
optimizer_gen = torch.optim.Adam(params_gen)
# Loss and Optimizer (Disc)
params_disc = list(discriminator.parameters())
optimizer_disc = torch.optim.Adam(params_disc)
if int(args.pretraining) == 1:
# Pre-training: train generator with MLE and discriminator with 3 losses (real + fake + wrong)
total_steps = len(data_loader)
print(total_steps)
disc_losses = []
gen_losses = []
print('pre-training')
generator.load_state_dict(torch.load(args.pretrained_gen_path))
discriminator.load_state_dict(torch.load(args.pretrained_disc_path))
for epoch in range(
max([
int(args.gen_pretrain_num_epochs),
int(args.disc_pretrain_num_epochs)
])):
if epoch < 5:
continue
# for epoch in range(max([int(args.gen_pretrain_num_epochs), int(args.disc_pretrain_num_epochs)])):
for i, (images, captions, lengths, wrong_captions,
wrong_lengths) in enumerate(data_loader):
images = to_var(images, volatile=True)
captions = to_var(captions)
wrong_captions = to_var(wrong_captions)
targets = pack_padded_sequence(captions,
lengths,
batch_first=True)[0]
if epoch < int(args.gen_pretrain_num_epochs):
generator.zero_grad()
outputs, _ = generator(images, captions, lengths)
loss_gen = mle_criterion(outputs, targets)
# gen_losses.append(loss_gen.cpu().data.numpy()[0])
loss_gen.backward()
optimizer_gen.step()
if epoch < int(args.disc_pretrain_num_epochs):
discriminator.zero_grad()
rewards_real = discriminator(images, captions, lengths)
# rewards_fake = discriminator(images, sampled_captions, sampled_lengths)
rewards_wrong = discriminator(images, wrong_captions,
wrong_lengths)
real_loss = -torch.mean(torch.log(rewards_real))
# fake_loss = -torch.mean(torch.clamp(torch.log(1 - rewards_fake), min=-1000))
wrong_loss = -torch.mean(
torch.clamp(torch.log(1 - rewards_wrong), min=-1000))
loss_disc = real_loss + wrong_loss # + fake_loss, no fake_loss because this is pretraining
# disc_losses.append(loss_disc.cpu().data.numpy()[0])
loss_disc.backward()
optimizer_disc.step()
if (i + 1) % args.log_step == 0:
print(
'Epoch [%d], Step [%d], Disc Loss: %.4f, Gen Loss: %.4f'
% (epoch + 1, i + 1, loss_disc, loss_gen))
if (i + 1) % 500 == 0:
torch.save(
discriminator.state_dict(),
os.path.join(
args.model_path,
'pretrained-discriminator-%d-%d.pkl' %
(int(epoch) + 1, i + 1)))
torch.save(
generator.state_dict(),
os.path.join(
args.model_path, 'pretrained-generator-%d-%d.pkl' %
(int(epoch) + 1, i + 1)))
# Save pretrained models
torch.save(
discriminator.state_dict(),
os.path.join(
args.model_path, 'pretrained-discriminator-%d.pkl' %
int(args.disc_pretrain_num_epochs)))
torch.save(
generator.state_dict(),
os.path.join(
args.model_path, 'pretrained-generator-%d.pkl' %
int(args.gen_pretrain_num_epochs)))
# Plot pretraining figures
# plt.plot(disc_losses, label='pretraining_disc_loss')
# plt.savefig(args.figure_path + 'pretraining_disc_losses.png')
# plt.clf()
#
# plt.plot(gen_losses, label='pretraining_gen_loss')
# plt.savefig(args.figure_path + 'pretraining_gen_losses.png')
# plt.clf()
else:
generator.load_state_dict(torch.load(args.pretrained_gen_path))
discriminator.load_state_dict(torch.load(args.pretrained_disc_path))
# # Skip the rest for now
# return
# Train the Models
total_step = len(data_loader)
disc_gan_losses = []
gen_gan_losses = []
for epoch in range(args.num_epochs):
for i, (images, captions, lengths, wrong_captions,
wrong_lengths) in enumerate(data_loader):
# Set mini-batch dataset
images = to_var(images, volatile=True)
captions = to_var(captions)
wrong_captions = to_var(wrong_captions)
generator.zero_grad()
outputs, packed_lengths = generator(images, captions, lengths)
outputs = PackedSequence(outputs, packed_lengths)
outputs = pad_packed_sequence(outputs,
batch_first=True) # (b, T, V)
Tmax = outputs[0].size(1)
if torch.cuda.is_available():
rewards = torch.zeros_like(outputs[0]).type(
torch.cuda.FloatTensor)
else:
rewards = torch.zeros_like(outputs[0]).type(torch.FloatTensor)
# getting rewards from disc
# for t in tqdm(range(2, Tmax, 4)):
for t in range(2, Tmax, 2):
# for t in range(2, 4):
if t >= min(
lengths
): # TODO this makes things easier, but could min(lengths) could be too short
break
gen_samples = to_var(torch.zeros(
(captions.size(0), Tmax)).type(torch.FloatTensor),
volatile=True)
# part 1: taken from real caption
gen_samples[:, :t] = captions[:, :t].data
predicted_ids, saved_states = generator.pre_compute(
gen_samples, t)
# for v in range(predicted_ids.size(1)):
v = predicted_ids
# pdb.set_trace()
# part 2: taken from all possible vocabs
# gen_samples[:,t] = predicted_ids[:,v]
gen_samples[:, t] = v
# part 3: taken from rollouts
gen_samples[:, t:] = generator.rollout(gen_samples, t,
saved_states)
sampled_lengths = []
# finding sampled_lengths
for batch in range(int(captions.size(0))):
for b_t in range(Tmax):
if gen_samples[batch,
b_t].cpu().data.numpy() == 2: # <end>
sampled_lengths.append(b_t + 1)
break
elif b_t == Tmax - 1:
sampled_lengths.append(Tmax)
# sort sampled_lengths
sampled_lengths = np.array(sampled_lengths)
sampled_lengths[::-1].sort()
sampled_lengths = sampled_lengths.tolist()
# get rewards from disc
rewards[:, t, v] = discriminator(images, gen_samples.detach(),
sampled_lengths)
# rewards = rewards.detach()
# pdb.set_trace()
rewards_detached = rewards.data
rewards_detached = to_var(rewards_detached)
loss_gen = torch.dot(outputs[0], -rewards_detached)
# gen_gan_losses.append(loss_gen.cpu().data.numpy()[0])
# pdb.set_trace()
loss_gen.backward()
optimizer_gen.step()
# TODO get sampled_captions
sampled_ids = generator.sample(images)
# sampled_captions = torch.zeros_like(sampled_ids).type(torch.LongTensor)
sampled_lengths = []
# finding sampled_lengths
for batch in range(int(captions.size(0))):
for b_t in range(20):
#pdb.set_trace()
#sampled_captions[batch, b_t].data = sampled_ids[batch, b_t].cpu().data.numpy()[0]
if sampled_ids[batch,
b_t].cpu().data.numpy() == 2: # <end>
sampled_lengths.append(b_t + 1)
break
elif b_t == 20 - 1:
sampled_lengths.append(20)
# sort sampled_lengths
sampled_lengths = np.array(sampled_lengths)
sampled_lengths[::-1].sort()
sampled_lengths = sampled_lengths.tolist()
# Train discriminator
discriminator.zero_grad()
images.volatile = False
captions.volatile = False
wrong_captions.volatile = False
rewards_real = discriminator(images, captions, lengths)
rewards_fake = discriminator(images, sampled_ids, sampled_lengths)
rewards_wrong = discriminator(images, wrong_captions,
wrong_lengths)
real_loss = -torch.mean(torch.log(rewards_real))
fake_loss = -torch.mean(
torch.clamp(torch.log(1 - rewards_fake), min=-1000))
wrong_loss = -torch.mean(
torch.clamp(torch.log(1 - rewards_wrong), min=-1000))
loss_disc = real_loss + fake_loss + wrong_loss
# disc_gan_losses.append(loss_disc.cpu().data.numpy()[0])
loss_disc.backward()
optimizer_disc.step()
# Print log info
if i % args.log_step == 0:
print(
'Epoch [%d/%d], Step [%d/%d], Disc Loss: %.4f, Gen Loss: %.4f'
% (epoch, args.num_epochs, i, total_step, loss_disc,
loss_gen))
# Save the models
# if (i+1) % args.save_step == 0:
if (
i + 1
) % args.log_step == 0: # jm: saving at the last iteration instead
torch.save(
generator.state_dict(),
os.path.join(
args.model_path,
'generator-gan-%d-%d.pkl' % (epoch + 1, i + 1)))
torch.save(
discriminator.state_dict(),
os.path.join(
args.model_path,
'discriminator-gan-%d-%d.pkl' % (epoch + 1, i + 1)))
def train(args):
writer = SummaryWriter(log_dir=args.tensorboard_path)
create_folder(args.outf)
set_seed(args.manualSeed)
cudnn.benchmark = True
dataset, nc = get_dataset(args)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=args.batchSize,
shuffle=True,
num_workers=int(args.workers))
torch.cuda.set_device(args.local_rank)
device = torch.device(
"cuda",
args.local_rank) #torch.device("cuda:0" if args.cuda else "cpu")
ngpu = 0
nz = int(args.nz)
ngf = int(args.ngf)
ndf = int(args.ndf)
netG = Generator(ngpu, ngf, nc, nz).to(device)
netG.apply(weights_init)
if args.netG != '':
netG.load_state_dict(torch.load(args.netG))
netD = Discriminator(ngpu, ndf, nc).to(device)
netD.apply(weights_init)
if args.netD != '':
netD.load_state_dict(torch.load(args.netD))
criterion = nn.BCELoss()
fixed_noise = torch.randn(args.batchSize, nz, 1, 1, device=device)
real_label = 1
fake_label = 0
# setup optimizer
optimizerD = torch.optim.Adam(netD.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999))
optimizerG = torch.optim.Adam(netG.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999))
model_engineD, optimizerD, _, _ = deepspeed.initialize(
args=args,
model=netD,
model_parameters=netD.parameters(),
optimizer=optimizerD)
model_engineG, optimizerG, _, _ = deepspeed.initialize(
args=args,
model=netG,
model_parameters=netG.parameters(),
optimizer=optimizerG)
torch.cuda.synchronize()
start = time()
for epoch in range(args.epochs):
for i, data in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
# train with real
netD.zero_grad()
real = data[0].to(device)
batch_size = real.size(0)
label = torch.full((batch_size, ),
real_label,
dtype=real.dtype,
device=device)
output = netD(real)
errD_real = criterion(output, label)
model_engineD.backward(errD_real)
D_x = output.mean().item()
# train with fake
noise = torch.randn(batch_size, nz, 1, 1, device=device)
fake = netG(noise)
label.fill_(fake_label)
output = netD(fake.detach())
errD_fake = criterion(output, label)
model_engineD.backward(errD_fake)
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
#optimizerD.step() # alternative (equivalent) step
model_engineD.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
label.fill_(real_label) # fake labels are real for generator cost
output = netD(fake)
errG = criterion(output, label)
model_engineG.backward(errG)
D_G_z2 = output.mean().item()
#optimizerG.step() # alternative (equivalent) step
model_engineG.step()
print(
'[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'
% (epoch, args.epochs, i, len(dataloader), errD.item(),
errG.item(), D_x, D_G_z1, D_G_z2))
writer.add_scalar("Loss_D", errD.item(),
epoch * len(dataloader) + i)
writer.add_scalar("Loss_G", errG.item(),
epoch * len(dataloader) + i)
if i % 100 == 0:
vutils.save_image(real,
'%s/real_samples.png' % args.outf,
normalize=True)
fake = netG(fixed_noise)
vutils.save_image(fake.detach(),
'%s/fake_samples_epoch_%03d.png' %
(args.outf, epoch),
normalize=True)
# do checkpointing
#torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (args.outf, epoch))
#torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' % (args.outf, epoch))
torch.cuda.synchronize()
stop = time()
print(
f"total wall clock time for {args.epochs} epochs is {stop-start} secs")
optimizerG = optim.Adam(netG.parameters(), lr=0.002, betas=(0.5, 0.999))
optimizerD = optim.Adam(netD.parameters(), lr=0.002, betas=(0.5, 0.999))
fix_noise = torch.randn(batch_size, latent_size, 1, 1).cuda()
lossG = []
lossD = []
Dx = []
DG1 = []
DG2 = []
for epoch in range(epoch_num):
for i, data in enumerate(train_dataloader):
# Update D network
# train with real
if cuda:
data = data.cuda()
netD.zero_grad()
label = torch.full((data.size(0), ), 1).cuda()
output = netD(data)
errD_real = criterion(output, label)
errD_real.backward()
D_x = output.mean().item()
# train with fake
noise = torch.randn(data.size(0), latent_size, 1, 1).cuda()
fake = netG(noise)
label.fill_(0)
output = netD(fake.detach())
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
def train_gan():
batch_size = 64
epochs = 100
disc_update = 1
gen_update = 5
latent_dimension = 100
lambduh = 10
device = torch.device(
'cuda:0') if torch.cuda.is_available() else torch.device('cpu')
# load data
train_loader, valid_loader, test_loader = get_data_loader(
'data', batch_size)
disc_model = Discriminator().to(device)
gen_model = Generator(latent_dimension).to(device)
disc_optim = Adam(disc_model.parameters(), lr=1e-4, betas=(0.5, 0.9))
gen_optim = Adam(gen_model.parameters(), lr=1e-4, betas=(0.5, 0.9))
for e in range(epochs):
disc_loss = 0
gen_loss = 0
for i, (images, _) in enumerate(train_loader):
images = images.to(device)
b_size = images.shape[0]
step = i + 1
if step % disc_update == 0:
disc_model.zero_grad()
# sample noise
noise = torch.randn((b_size, latent_dimension), device=device)
# loss on fake
inputs = gen_model(noise).detach()
f_outputs = disc_model(inputs)
loss = f_outputs.mean()
# loss on real
r_outputs = disc_model(images)
loss -= r_outputs.mean()
# add gradient penalty
loss += lambduh * gradient_penalty(disc_model, images, inputs,
device)
disc_loss += loss
loss.backward()
disc_optim.step()
if step % gen_update == 0:
gen_model.zero_grad()
noise = torch.randn((b_size, latent_dimension)).to(device)
inputs = gen_model(noise)
outputs = disc_model(inputs)
loss = -outputs.mean()
gen_loss += loss
loss.backward()
gen_optim.step()
torch.save(
{
'epoch': e,
'disc_model': disc_model.state_dict(),
'gen_model': gen_model.state_dict(),
'disc_loss': disc_loss,
'gen_loss': gen_loss,
'disc_optim': disc_optim.state_dict(),
'gen_optim': gen_optim.state_dict()
}, "upsample/checkpoint_{}.pth".format(e))
print("Epoch: {} Disc loss: {}".format(
e + 1,
disc_loss.item() / len(train_loader)))
print("Epoch: {} Gen loss: {}".format(
e + 1,
gen_loss.item() / len(train_loader)))
def main(args):
# Create model directory
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
# Image preprocessing
# For normalization, see https://github.com/pytorch/vision#models
transform = transforms.Compose([
transforms.RandomCrop(args.crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# Load vocabulary wrapper.
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build data loader
data_loader = get_loader(args.image_dir,
args.caption_path,
vocab,
transform,
args.batch_size,
shuffle=True,
num_workers=args.num_workers)
# Build the models (Gen)
# TODO: put these in generator
encoder = EncoderCNN(args.embed_size)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
# Build the models (Disc)
discriminator = Discriminator(args.embed_size, args.hidden_size,
len(vocab), args.num_layers)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
discriminator.cuda()
# Loss and Optimizer (Gen)
criterion = nn.CrossEntropyLoss()
params = list(decoder.parameters()) + list(
encoder.linear.parameters()) + list(encoder.bn.parameters())
optimizer = torch.optim.Adam(params, lr=args.learning_rate)
# Loss and Optimizer (Disc)
params_disc = list(discriminator.parameters())
optimizer_disc = torch.optim.Adam(params_disc)
# Train the Models
total_step = len(data_loader)
disc_losses = []
for epoch in range(args.num_epochs):
for i, (images, captions, lengths, wrong_captions,
wrong_lengths) in enumerate(data_loader):
# pdb.set_trace()
# TODO: train disc before gen
# Set mini-batch dataset
images = to_var(images, volatile=True)
captions = to_var(captions)
wrong_captions = to_var(wrong_captions)
targets = pack_padded_sequence(captions, lengths,
batch_first=True)[0]
# Forward, Backward and Optimize
decoder.zero_grad()
encoder.zero_grad()
features = encoder(images)
outputs = decoder(features, captions, lengths)
sampled_captions = decoder.sample(features)
# sampled_captions = torch.zeros_like(sampled_ids)
sampled_lengths = []
for row in range(sampled_captions.size(0)):
for index, word_id in enumerate(sampled_captions[row, :]):
# pdb.set_trace()
word = vocab.idx2word[word_id.cpu().data.numpy()[0]]
# sampled_captions[row, index].data = word
if word == '<end>':
sampled_lengths.append(index + 1)
break
elif index == sampled_captions.size(1) - 1:
sampled_lengths.append(sampled_captions.size(1))
break
sampled_lengths = np.array(sampled_lengths)
sampled_lengths[::-1].sort()
sampled_lengths = sampled_lengths.tolist()
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
# Train discriminator
discriminator.zero_grad()
rewards_real = discriminator(images, captions, lengths)
rewards_fake = discriminator(images, sampled_captions,
sampled_lengths)
rewards_wrong = discriminator(images, wrong_captions,
wrong_lengths)
real_loss = -torch.mean(torch.log(rewards_real))
fake_loss = -torch.mean(
torch.clamp(torch.log(1 - rewards_fake), min=-1000))
wrong_loss = -torch.mean(
torch.clamp(torch.log(1 - rewards_wrong), min=-1000))
loss_disc = real_loss + fake_loss + wrong_loss
disc_losses.append(loss_disc.cpu().data.numpy()[0])
loss_disc.backward()
optimizer_disc.step()
# print('iteration %i' % i)
# Print log info
if i % args.log_step == 0:
print(
'Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f'
% (epoch, args.num_epochs, i, total_step, loss.data[0],
np.exp(loss.data[0])))
# Save the models
# if (i+1) % args.save_step == 0:
if (
i + 1
) % total_step == 0: # jm: saving at the last iteration instead
torch.save(
decoder.state_dict(),
os.path.join(args.model_path,
'decoder-%d-%d.pkl' % (epoch + 1, i + 1)))
torch.save(
encoder.state_dict(),
os.path.join(args.model_path,
'encoder-%d-%d.pkl' % (epoch + 1, i + 1)))
torch.save(
discriminator.state_dict(),
os.path.join(
args.model_path,
'discriminator-%d-%d.pkl' % (epoch + 1, i + 1)))
# plot at the end of every epoch
plt.plot(disc_losses, label='disc loss')
plt.savefig('disc_losses.png')
plt.clf()