def main(config):
# For fast training.
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.sample_dir):
os.makedirs(config.sample_dir)
if not os.path.exists(config.result_dir):
os.makedirs(config.result_dir)
# Data loader.
celeba_loader = None
rafd_loader = None
if config.dataset in ['CelebA', 'Both']:
celeba_loader = get_loader(config.celeba_image_dir, config.attr_path, config.selected_attrs,
config.celeba_crop_size, config.image_size, config.batch_size,
'CelebA', config.mode, config.num_workers)
if config.dataset in ['RaFD', 'Both']:
rafd_loader = get_loader(config.rafd_image_dir, None, None,
config.rafd_crop_size, config.image_size, config.batch_size,
'RaFD', config.mode, config.num_workers)
# Solver for training and testing StarGAN.
solver = Solver(celeba_loader, rafd_loader, config)
if config.mode == 'train':
if config.dataset in ['CelebA', 'RaFD']:
solver.train()
elif config.dataset in ['Both']:
solver.train_multi()
elif config.mode == 'test':
if config.dataset in ['CelebA', 'RaFD']:
solver.test()
elif config.dataset in ['Both']:
solver.test_multi()
def train(model, elogger, train_set, eval_set):
# record the experiment setting
elogger.log(str(model))
elogger.log(str(args._get_kwargs()))
if torch.cuda.is_available():
model.cuda()
optimizer = optim.Adam(model.parameters(), lr = 1e-3)
for epoch in range(args.epochs):
model.train()
print('Training on epoch {}'.format(epoch))
for input_file in train_set:
print('Train on file {}'.format(input_file))
# data loader, return two dictionaries, attr and traj
data_iter = data_loader.get_loader(input_file, args.batch_size)
running_loss = 0.0
for idx, (attr, traj) in enumerate(data_iter):
# transform the input to pytorch variable
attr, traj = utils.to_var(attr), utils.to_var(traj)
_, loss = model.eval_on_batch(attr, traj, config)
# update the model
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.data.item()
print('\r Progress {:.2f}%, average loss {}'.format((idx + 1) * 100.0 / len(data_iter), running_loss / (idx + 1.0)), end=' ')
print()
elogger.log('Training Epoch {}, File {}, Loss {}'.format(epoch, input_file, running_loss / (idx + 1.0)))
# evaluate the model after each epoch
evaluate(model, elogger, eval_set, save_result = False)
# save the weight file after each epoch
weight_name = '{}_{}'.format(args.log_file, str(datetime.datetime.now()))
elogger.log('Save weight file {}'.format(weight_name))
torch.save(model.state_dict(), './saved_weights/weights')
def main(config):
if config.outf is None:
config.outf = 'samples'
os.system('mkdir {0}'.format(config.outf))
config.manual_seed = random.randint(1, 10000)
print("Random Seed: ", config.manual_seed)
random.seed(config.manual_seed)
torch.manual_seed(config.manual_seed)
if config.cuda:
torch.cuda.manual_seed_all(config.manual_seed)
cudnn.benchmark = True
if torch.cuda.is_available() and not config.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
if not config.training:
config.stage = 2
if config.stage == 1:
config.batch_size = 128
config.image_size = 64
else:
config.batch_size = 40
config.image_size = 256
if config.training:
data_loader = get_loader(_dataset=config.dataset,
dataroot=config.dataroot,
batch_size=config.batch_size,
num_workers=int(config.workers),
image_size=config.image_size)
trainer = Trainer(config, data_loader, None)
trainer.train()
else:
datapath = '%s/test/val_captions.t7' % (config.dataroot)
trainer = Trainer(config, None, datapath)
trainer.sample()
def main():
checkpoint_path = './models/checkpoint_2_7340.pt'
vocab_path = './data/vocab.pkl'
caption_dir = 'data/annotations/'
val_dir = 'data/val2014'
batch_size = 32
num_workers = 2
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
state = torch.load(checkpoint_path)
with open(vocab_path, 'rb') as f:
vocab = pickle.load(f)
encoder = state['encoder'].to(device)
decoder = state['decoder'].to(device)
encoder.eval()
decoder.eval()
transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
val_annotations = os.path.join(
caption_dir, "captions_{}.json".format(os.path.basename(val_dir)))
val_loader = get_loader(val_dir,
val_annotations,
vocab,
transform,
batch_size,
shuffle=True,
num_workers=num_workers)
# ids = val_loader.dataset.ids
# val_loader.dataset.ids = np.random.choice(val_loader.dataset.ids, 2000, replace=False)
print("Scoring model...")
score = bleu_score(val_loader, encoder, decoder, device)
print("BLEU-4 SCORE: {:.4f}".format(score * 100))
def main(config):
# For fast training.
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.sample_dir):
os.makedirs(config.sample_dir)
if not os.path.exists(config.result_dir):
os.makedirs(config.result_dir)
# Data loader.
#celeba_loader = None
#rafd_loader = None
#if config.dataset in ['CelebA', 'Both']:
celeba_loader = get_loader(config.celeba_image_dir, config.attr_path,
config.selected_attrs, config.celeba_crop_size,
config.image_size, config.batch_size, 'CelebA',
config.mode, config.num_workers)
'''
if config.dataset in ['RaFD', 'Both']:
rafd_loader = get_loader(config.rafd_image_dir, None, None,
config.rafd_crop_size, config.image_size, config.batch_size,
'RaFD', config.mode, config.num_workers)
'''
# Solver for training and testing StarGAN.
solver = Solver(
celeba_loader,
config) #solver = Solver(celeba_loader, rafd_loader, config)
if config.mode == 'train':
#if config.dataset in ['CelebA', 'RaFD']:
solver.train()
#elif config.dataset in ['Both']:
# solver.train_multi()
elif config.mode == 'test':
#if config.dataset in ['CelebA', 'RaFD']:
solver.test()
def __init__(self, config):
self.image_size = 299 #Inception net condition
self.lr = 0.0001
self.log_step = 100
self.selected_attrs = config.selected_attrs
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.buildIncNet()
self.save_incDir = config.inc_net_dir
self.pretrained_incNet = config.pretrained_incNet
self.dataset = config.dataset
self.test_dataset = get_loader(config.celeba_image_dir,
config.attr_path,
config.selected_attrs,
image_size=self.image_size,
num_workers=config.num_workers,
dataset=config.dataset,
mode='test')
def train(model):
optimizer = optim.Adam(model.parameters(), lr=1e-3)
data_iter = data_loader.get_loader(batch_size=args.batch_size)
for epoch in range(args.epochs):
model.train()
run_loss = 0.0
for idx, data in enumerate(data_iter):
data = utils.to_var(data)
ret = model.run_on_batch(data, optimizer, epoch)
run_loss += ret['loss'].item()
print '\r Progress epoch {}, {:.2f}%, average loss {}'.format(epoch, (idx + 1) * 100.0 / len(data_iter), run_loss / (idx + 1.0)),
evaluate(model, data_iter)
def main(config):
# For fast training.
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.sample_dir):
os.makedirs(config.sample_dir)
# Data loader.
vcc_loader = get_loader(hparams)
# Solver for training
solver = Solver(vcc_loader, config, hparams)
solver.train()
def main(config):
svhn_loader, mnist_loader, svhn_test_loader, mnist_test_loader = get_loader(config)
solver = Solver(config, svhn_loader, mnist_loader)
cudnn.benchmark = True
# create directories if not exist
if not os.path.exists(config.model_path):
os.makedirs(config.model_path)
if not os.path.exists(config.sample_path):
os.makedirs(config.sample_path)
base = config.log_path
filename = os.path.join(base, str(config.max_items))
if not os.path.isdir(base):
os.mkdir(base)
logging.basicConfig(filename=filename, level=logging.DEBUG)
if config.mode == 'train':
solver.train()
def main():
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_transform = transforms.Compose(
[transforms.Resize(256),
transforms.CenterCrop(224)])
val_loader = get_loader(opts.img_path,
val_transform,
vocab,
opts.data_path,
partition='test',
batch_size=opts.batch_size,
shuffle=False,
num_workers=opts.workers,
pin_memory=True)
print('Validation loader prepared.')
test(val_loader)
def main(config):
# For fast training
cudnn.benchmark = True
# Create directories if not exist
mkdir(config.log_path)
mkdir(config.model_save_path)
mkdir(config.sample_path)
mkdir(config.result_path)
data_loader = get_loader(config.data_path, config.image_size,
config.crop_size, config.batch_size, transform=True, dataset='PascalVOC2012', mode=config.mode)
# Solver
solver = Solver(data_loader, vars(config))
if config.mode == 'train':
solver.train()
elif config.mode == 'test':
solver.test()
def initialize_for_test(params):
data_loader = get_loader(params, mode='test')
encoder_file = os.path.join(params.encoder_save,
'epoch-%d.pkl' % params.num_epochs)
decoder_file = os.path.join(params.decoder_save,
'epoch-%d.pkl' % params.num_epochs)
vocab_size = len(data_loader.dataset.vocab)
# Initialize the encoder and decoder, and set each to inference mode.
encoder = Encoder(params)
decoder = Decoder(params, vocab_size)
encoder.eval()
decoder.eval()
# Load the trained weights.
encoder.load_state_dict(torch.load(encoder_file))
decoder.load_state_dict(torch.load(decoder_file))
encoder.to(params.device)
decoder.to(params.device)
return data_loader, encoder, decoder
def main(config):
cudnn.benchmark = True
data_loader = get_loader(image_path=config.image_path,
image_size=config.image_size,
batch_size=config.batch_size,
num_workers=config.num_workers)
solver = Solver(config, data_loader)
# Create directories if not exist
if not os.path.exists(config.model_path):
os.makedirs(config.model_path)
if not os.path.exists(config.sample_path):
os.makedirs(config.sample_path)
# Train and sample the images
if config.mode == 'train':
solver.train()
elif config.mode == 'sample':
solver.sample()
def main():
transform = transforms.Compose(
[transforms.Resize(30), transforms.ToTensor()])
loader = get_loader("./data/images_background", 1, 10, True, transform)
for i, (images_1, images_2, label) in enumerate(loader):
print("example %d" % i)
draw_image(images_1.numpy())
draw_image(images_2.numpy())
label = label.numpy()
if label == 1:
print("same")
else:
print("different")
images_1 = images_1.to(device)
time.sleep(3)
print("\n\n")
def main(config):
# For fast training.
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.sample_dir):
os.makedirs(config.sample_dir)
# Data loader.
train_loader = get_loader(config.train_data_dir, config.batch_size, 'train', num_workers=config.num_workers)
test_loader = TestDataset(config.test_data_dir, config.wav_dir, src_spk='p262', trg_spk='p272')
# Solver for training and testing StarGAN.
solver = Solver(train_loader, test_loader, config)
if config.mode == 'train':
solver.train()
def main(config):
cudnn.benchmark = True
data_loader = get_loader(image_path=config.image_path,
image_size=config.image_size,
batch_size=config.batch_size,
num_workers=config.num_workers)
solver = Solver(config, data_loader)
# Create directories if not exist
if not os.path.exists(config.model_path):
os.makedirs(config.model_path)
if not os.path.exists(config.sample_path):
os.makedirs(config.sample_path)
# Train and sample the images
if config.mode == 'train':
solver.train()
elif config.mode == 'sample':
solver.sample()
def extract_features(root, files, transform, batch_size, shuffle, num_workers,
model):
dataloader = get_loader(root, files, transform, batch_size, shuffle,
num_workers)
model = model.cuda()
model.eval()
features = []
imnames = []
n_iters = len(dataloader)
for i, (images, names) in enumerate(dataloader):
images = Variable(images).cuda()
feas = model(images).cpu()
features.append(feas.data)
imnames.extend(names)
if (i + 1) % 100 == 0:
print 'iter [%d/%d] finsihed.' % (i, n_iters)
return torch.cat(features, 0), imnames
def main(config):
cudnn.benchmark = True
if not os.path.exists(config.save_path):
os.makedirs(config.save_path)
if not os.path.exists(config.infer_path):
os.makedirs(config.infer_path)
num_users, num_items, train_loader, test_loader, infer_loader, num_to_user_id, num_to_item_id \
= get_loader(data_path = config.data_path,
train_negs = config.train_negs,
test_negs = config.test_negs,
batch_size = config.batch_size,
num_workers = config.num_workers)
solver = Solver(config, num_users, num_items)
if config.mode == 'train':
solver.train(train_loader, test_loader)
elif config.mode == 'infer':
solver.infer(infer_loader, num_to_user_id, num_to_item_id)
def main(config):
# For fast training
cudnn.benchmark = True
# Create directories if not exist
mkdir(config.log_path)
mkdir(config.model_save_path)
data_loader = get_loader(config.data_path,
batch_size=config.batch_size,
mode=config.mode)
# Solver
solver = Solver(data_loader, vars(config))
if config.mode == 'train':
solver.train()
elif config.mode == 'test':
solver.test()
return solver
def model_setup(config):
"""
Set up the directories and the data before creating the whole model
:param config: (Hyperparams) Dictionary of the hyperparameters as a class
:return: (nn.Module) The model of the DAGMM
"""
# For fast training
cudnn.benchmark = True # Good if input size doesn't change (bad otherwise)
# Create directories if they don't exist
make_directory(config.model_save_path)
make_directory(config.fig_save_path)
# Create data loader
data_loader = get_loader(config.data_path,
batch_size=config.batch_size,
train_ratio=config.train_ratio)
# Create Model
return Model(data_loader, vars(config))
def train(self, config):
train_dataset = get_loader(config.celeba_image_dir,
config.attr_path,
config.selected_attrs,
image_size=self.image_size,
num_workers=config.num_workers,
dataset=config.dataset)
print('Start Training...')
start_time = time.time()
max_acc, epochs = 0, 50
for p in range(epochs):
for i, data in enumerate(train_dataset):
img, label = data
img = img.to(self.device)
label = label.to(self.device)
batch_pred = self.inc_net(img)
loss = self.classification_loss(batch_pred, label,
config.dataset)
self.opt.zero_grad()
loss.backward()
self.opt.step()
if i % self.log_step == 0:
et = time.time() - start_time
et = str(datetime.timedelta(seconds=et))[:-7]
acc = self.test()
print("Test Accuracy: ", acc)
if acc > max_acc:
path = os.path.join(self.save_incDir,
'{}-{}-incNet.ckpt'.format(p, i))
torch.save(self.inc_net.state_dict(), path)
max_acc = acc
log = "Elapsed [{}], Epoch[{}] - Iteration [{}/{}] , loss [{}], max_acc[{}]".format(
et, p, i + 1, len(train_dataset), loss.item(), max_acc)
print(log)
def evaluate(args, device, model, test_dataset, test_dataset_name):
tqdm.write(f'evaluating for {test_dataset_name}')
tqdm.write('test data size: {}'.format(len(test_dataset)))
# Build data loader
test_loader = get_loader(test_dataset,
args.batch_size,
shuffle=False,
num_workers=args.num_workers,
drop_last=False)
criterion = nn.BCEWithLogitsLoss()
with torch.no_grad():
loss_values = []
all_predictions = []
all_targets = []
for video_ids, frame_ids, images, targets in tqdm(
test_loader, desc=test_dataset_name):
images = images.to(device)
targets = targets.to(device)
outputs = model(images)
loss = criterion(outputs, targets)
loss_values.append(loss.item())
predictions = outputs > 0.0
all_predictions.append(predictions)
all_targets.append(targets)
val_loss = sum(loss_values) / len(loss_values)
all_predictions = torch.cat(all_predictions).int()
all_targets = torch.cat(all_targets).int()
test_accuracy = (all_predictions == all_targets
).sum().float().item() / all_targets.shape[0]
tqdm.write('Testing results - Loss: {:.3f}, Acc: {:.3f}'.format(
val_loss, test_accuracy))
def main(args):
with open(os.path.join(args.root_dir, 'vocab.pkl'), 'rb') as f:
vocab = pickle.load(f)
data_type = args.data_type
data_loader = get_loader(args.root_dir,
vocab,
args.batch_size,
data_type,
shuffle=True,
num_workers=args.num_workers,
debug=args.debug)
print('Iterating the dataset')
print("Length of data loader: " + str(len(data_loader)))
for i, (features, captions, lengths) in enumerate(data_loader):
print("Index: " + str(i))
#print("Features shape: ")
#print(features.shape)
print("Captions shape: ")
print(captions.shape)
print("Lengths: ")
print(len(lengths))
def eval(nb_test):
train_loader, dataset = get_loader(
"dataset/flickr8k/images/",
annotation_file="dataset/flickr8k/captions.txt",
transform=transform,
num_workers=2)
loop = tqdm(enumerate(train_loader), total=nb_test, leave=False)
embed_size = 256
hidden_size = 256
vocab_size = len(dataset.vocab)
num_layers = 1
model = Img2Text(embed_size, hidden_size, vocab_size,
num_layers).to(device)
checkpoint = torch.load("my_checkpoint.pth.tar")
model.load_state_dict(checkpoint["state_dict"])
fig = plt.figure(figsize=(10, 10))
model.eval()
with torch.no_grad():
for idx, (imgs, captions) in loop:
if idx + 1 == nb_test:
break
ax = fig.add_subplot(2, 2, idx + 1)
predicted_str, predicted_int = model.caption_image(
imgs.to(device), dataset.vocab)
#[dataset.vocab.itos[idx] for idx in result_caption]
captions = [
dataset.vocab.itos[idx]
for idx in captions.squeeze(-1).tolist()
]
score = bleu_score([predicted_str[1:-1]], [captions])
ax.imshow(imgs.squeeze(0).permute(1, 2, 0))
text = f"CORRECT:{captions[1:-1]}\nPREDICTED:{predicted_str[1:-1]}\nBleu score:{score}"
ax.title.set_text(text)
plt.show()
def test_multi(self, sample_dir, result_dir):
"""Translate images using StarGAN trained on multiple datasets."""
# Load the trained generator.
# self.restore_model(self.test_iters)
config = self.config
test_loader = get_loader(config.celeba_image_dir, config.attr_path,
config.selected_attrs,
config.celeba_crop_size, config.image_size,
config.batch_size, 'CelebA', config.mode,
sample_dir, config.num_workers)
with torch.no_grad():
for i, (x_real, c_org) in enumerate(test_loader):
# Prepare input images and target domain labels.
x_real = x_real.to(self.device)
c_rafd_list = self.create_labels(c_org, self.c2_dim, 'RaFD')
zero_celeba = torch.zeros(x_real.size(0), self.c_dim).to(
self.device) # Zero vector for CelebA.
mask_rafd = self.label2onehot(torch.ones(
x_real.size(0)), 2).to(self.device) # Mask vector: [0, 1].
# Translate images.
x_fake_list = [x_real]
for c_rafd in c_rafd_list:
c_trg = torch.cat([zero_celeba, c_rafd, mask_rafd], dim=1)
x_fake_list.append(self.G(x_real, c_trg))
x_concat = torch.cat(x_fake_list, dim=3)
ii = 0
for l in x_fake_list:
result_path = os.path.join(result_dir,
'{}-images.jpg'.format(ii + 1))
save_image(self.denorm(l.data.cpu()),
result_path,
nrow=1,
padding=0)
ii = ii + 1
print('Saved real and fake images into {}...'.format(
result_path))
def main(config):
# For fast training.
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.sample_dir):
os.makedirs(config.sample_dir)
if not os.path.exists(config.speaker_path):
raise Exception(f"speaker list {config.speaker_path} does not exist")
with open(config.speaker_path) as f:
speakers = json.load(f)
print(f"load speakers {speakers}", flush=True)
# Data loader.
train_loader = get_loader(
config.train_data_dir,
config.batch_size,
config.min_length,
'train',
speakers,
num_workers=config.num_workers,
)
test_loader = TestDataset(config.test_data_dir,
config.wav_dir,
speakers,
src_spk=config.test_src_spk,
trg_spk=config.test_trg_spk)
# Solver for training and testing StarGAN.
solver = Solver(train_loader, test_loader, config)
if config.mode == 'train':
solver.train()
def main(config):
prepare_dirs_and_logger(config)
torch.manual_seed(config.random_seed)
if config.num_gpu > 0:
torch.cuda.manual_seed(config.random_seed)
if config.is_train:
data_path = config.data_path
batch_size = config.batch_size
else:
if config.test_data_path is None:
data_path = config.data_path
else:
data_path = config.test_data_path
batch_size = config.sample_per_image
if config.dataset == 'celebA':
a_data_loader, b_data_loader = get_celebA_loader(
data_path, batch_size, config.input_scale_size, config.style_A,
config.style_B, config.constraint, config.constraint_type,
config.num_worker, config.skip_pix2pix_processing)
else:
a_data_loader, b_data_loader = get_loader(
data_path, batch_size, config.input_scale_size, config.num_worker,
config.skip_pix2pix_processing)
trainer = Trainer(config, a_data_loader, b_data_loader)
if config.is_train:
save_config(config)
trainer.train()
else:
if not config.load_path:
raise Exception(
"[!] You should specify `load_path` to load a pretrained model"
)
trainer.test()
def main():
args = configs()
if args.training_instance:
args.load_path = os.path.join(args.load_path, args.training_instance)
args.summary_path = os.path.join(args.summary_path,
args.training_instance)
else:
args.load_path = os.path.join(
args.load_path,
"evflownet_{}".format(datetime.now().strftime("%m%d_%H%M%S")))
args.summary_path = os.path.join(
args.summary_path,
"evflownet_{}".format(datetime.now().strftime("%m%d_%H%M%S")))
if not os.path.exists(args.load_path):
os.makedirs(args.load_path)
if not os.path.exists(args.summary_path):
os.makedirs(args.summary_path)
# Fix the random seed for reproducibility.
# Remove this if you are using this code for something else!
tf.set_random_seed(12345)
event_img_loader, prev_img_loader, next_img_loader, _, n_ima = get_loader(
args.data_path,
args.batch_size,
args.image_width,
args.image_height,
split='train',
shuffle=True)
print("Number of images: {}".format(n_ima))
trainer = EVFlowNet(args,
event_img_loader,
prev_img_loader,
next_img_loader,
n_ima,
is_training=True)
trainer.train()
def evaluate(model, elogger, files, save_result = False):
model.eval()
if save_result:
fs = open('%s' % args.result_file, 'w')
for input_file in files:
running_loss = 0.0
data_iter = data_loader.get_loader(input_file, args.batch_size)
for idx, (attr, traj) in enumerate(data_iter):
attr, traj = utils.to_var(attr), utils.to_var(traj)
pred_dict, loss = model.eval_on_batch(attr, traj, config)
if save_result: write_result(fs, pred_dict, attr)
running_loss += loss.data.item()
print('Evaluate on file {}, loss {}'.format(input_file, running_loss / (idx + 1.0)))
elogger.log('Evaluate File {}, Loss {}'.format(input_file, running_loss / (idx + 1.0)))
if save_result:
fs.close()
def main(config):
# For fast training.
cudnn.benchmark = True
# Data loader.
train_loader_casia = get_loader(config.train_data_dir_casia,
config.target_speaker,
config.source_emotion,
config.target_emotion,
config.batch_size,
'train',
num_workers=config.num_workers)
test_loader = TestDataset(config.test_data_dir,
config.src_wav_dir,
config.target_speaker,
config.source_emotion,
config.target_emotion)
# Solver for training and testing StarGAN.
solver = Solver(train_loader_casia, test_loader, config, log)
if config.mode == 'train':
solver.train()
def main(config): if config.gpu > -1: os.environ["CUDA_VISIBLE_DEVICES"] = str(config.gpu) generator_one = GeneratorCNN_Pose_UAEAfterResidual_256(21, config.z_num, config.repeat_num, config.hidden_num) generator_two = UAE_noFC_AfterNoise(6, config.repeat_num - 2, config.hidden_num) discriminator = DCGANDiscriminator_256(use_gpu=config.use_gpu) if config.use_gpu: generator_one.cuda() generator_two.cuda() discriminator.cuda() L1_criterion = nn.L1Loss() BCE_criterion = nn.BCELoss() gen_train_op1 = optim.Adam(generator_one.parameters(), lr=config.g_lr, betas=(config.beta1, config.beta2)) gen_train_op2 = optim.Adam(generator_two.parameters(), lr=config.g_lr, betas=(config.beta1, config.beta2)) dis_train_op1 = optim.Adam(discriminator.parameters(), lr=config.d_lr, betas=(config.beta1, config.beta2)) pose_loader = data_loader.get_loader(os.path.join(config.data_dir, 'DF_img_pose'), config.batch_size) train(generator_one, generator_two, discriminator, L1_criterion, BCE_criterion, gen_train_op1, gen_train_op2, dis_train_op1, pose_loader, config)
def main(config):
# For fast training.
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.sample_dir):
os.makedirs(config.sample_dir)
if not os.path.exists(config.result_dir):
os.makedirs(config.result_dir)
# Data loader.
data_loader = get_loader(config.image_dir, config.crop_size,
config.image_size, config.batch_size, 'test',
config.num_workers)
# Solver for training and testing StarGAN.
solver = Solver(data_loader, config)
solver.test()
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
encoder = EncoderCNN(args.embed_size)
decoder = DecoderRNN(args.embed_size, args.hidden_size,
len(vocab), args.num_layers)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
params = list(decoder.parameters()) + list(encoder.linear.parameters()) + list(encoder.bn.parameters())
optimizer = torch.optim.Adam(params, lr=args.learning_rate)
# Train the Models
total_step = len(data_loader)
for epoch in range(args.num_epochs):
for i, (images, captions, lengths) in enumerate(data_loader):
# Set mini-batch dataset
images = to_var(images, volatile=True)
captions = to_var(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)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
# 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:
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)))
def main(args):
# Create model directory
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
# Image preprocessing, normalization for the pretrained resnet
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
encoder = EncoderCNN(args.embed_size).to(device)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab), args.num_layers).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
params = list(decoder.parameters()) + list(encoder.linear.parameters()) + list(encoder.bn.parameters())
optimizer = torch.optim.Adam(params, lr=args.learning_rate)
# Train the models
total_step = len(data_loader)
for epoch in range(args.num_epochs):
for i, (images, captions, lengths) in enumerate(data_loader):
# Set mini-batch dataset
images = images.to(device)
captions = captions.to(device)
targets = pack_padded_sequence(captions, lengths, batch_first=True)[0]
# Forward, backward and optimize
features = encoder(images)
outputs = decoder(features, captions, lengths)
loss = criterion(outputs, targets)
decoder.zero_grad()
encoder.zero_grad()
loss.backward()
optimizer.step()
# Print log info
if i % args.log_step == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Perplexity: {:5.4f}'
.format(epoch, args.num_epochs, i, total_step, loss.item(), np.exp(loss.item())))
# Save the model checkpoints
if (i+1) % args.save_step == 0:
torch.save(decoder.state_dict(), os.path.join(
args.model_path, 'decoder-{}-{}.ckpt'.format(epoch+1, i+1)))
torch.save(encoder.state_dict(), os.path.join(
args.model_path, 'encoder-{}-{}.ckpt'.format(epoch+1, i+1)))
# (Optional) TODO #2: Amend the image transform below.
transform_train = transforms.Compose([
transforms.Resize(256), # smaller edge of image resized to 256
transforms.RandomRotation(5.0), # Rotate the image randomly
transforms.RandomCrop(224), # get 224x224 crop from random location
transforms.RandomHorizontalFlip(), # horizontally flip image with probability=0.5
transforms.ColorJitter(0.05, 0.05, 0.05), # Jitter the color a little
transforms.ToTensor(), # convert the PIL Image to a tensor
transforms.Normalize((0.485, 0.456, 0.406), # normalize image for pre-trained model
(0.229, 0.224, 0.225))])
# Build data loader.
data_loader = get_loader(transform=transform_train,
mode='train',
batch_size=batch_size,
vocab_threshold=vocab_threshold,
vocab_from_file=vocab_from_file,
cocoapi_loc=COCOPATH)
# The size of the vocabulary.
vocab_size = len(data_loader.dataset.vocab)
# Initialize the encoder and decoder.
encoder = EncoderCNN(embed_size)
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
# Move models to GPU if CUDA is available.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder.to(device)
decoder.to(device)
