def main():
# Hyper Parameters
opt = opts.parse_opt()
device_id = opt.gpuid
device_count = len(str(device_id).split(","))
#assert device_count == 1 or device_count == 2
print("use GPU:", device_id, "GPUs_count", device_count, flush=True)
os.environ['CUDA_VISIBLE_DEVICES']=str(device_id)
device_id = 0
torch.cuda.set_device(0)
# Load Vocabulary Wrapper
vocab = deserialize_vocab(os.path.join(opt.vocab_path, '%s_vocab.json' % opt.data_name))
opt.vocab_size = len(vocab)
# Load data loaders
train_loader, val_loader = data.get_loaders(
opt.data_name, vocab, opt.batch_size, opt.workers, opt)
# Construct the model
model = SCAN(opt)
model.cuda()
model = nn.DataParallel(model)
# Loss and Optimizer
criterion = ContrastiveLoss(opt=opt, margin=opt.margin, max_violation=opt.max_violation)
mse_criterion = nn.MSELoss(reduction="batchmean")
optimizer = torch.optim.Adam(model.parameters(), lr=opt.learning_rate)
# optionally resume from a checkpoint
if not os.path.exists(opt.model_name):
os.makedirs(opt.model_name)
start_epoch = 0
best_rsum = 0
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
start_epoch = checkpoint['epoch']
best_rsum = checkpoint['best_rsum']
model.load_state_dict(checkpoint['model'])
print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})"
.format(opt.resume, start_epoch, best_rsum))
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
evalrank(model.module, val_loader, opt)
print(opt, flush=True)
# Train the Model
for epoch in range(start_epoch, opt.num_epochs):
message = "epoch: %d, model name: %s\n" % (epoch, opt.model_name)
log_file = os.path.join(opt.logger_name, "performance.log")
logging_func(log_file, message)
print("model name: ", opt.model_name, flush=True)
adjust_learning_rate(opt, optimizer, epoch)
run_time = 0
for i, (images, captions, lengths, masks, ids, _) in enumerate(train_loader):
start_time = time.time()
model.train()
optimizer.zero_grad()
if device_count != 1:
images = images.repeat(device_count,1,1)
score = model(images, captions, lengths, masks, ids)
loss = criterion(score)
loss.backward()
if opt.grad_clip > 0:
clip_grad_norm_(model.parameters(), opt.grad_clip)
optimizer.step()
run_time += time.time() - start_time
# validate at every val_step
if i % 100 == 0:
log = "epoch: %d; batch: %d/%d; loss: %.4f; time: %.4f" % (epoch,
i, len(train_loader), loss.data.item(), run_time / 100)
print(log, flush=True)
run_time = 0
if (i + 1) % opt.val_step == 0:
evalrank(model.module, val_loader, opt)
print("-------- performance at epoch: %d --------" % (epoch))
# evaluate on validation set
rsum = evalrank(model.module, val_loader, opt)
#rsum = -100
filename = 'model_' + str(epoch) + '.pth.tar'
# remember best R@ sum and save checkpoint
is_best = rsum > best_rsum
best_rsum = max(rsum, best_rsum)
save_checkpoint({
'epoch': epoch + 1,
'model': model.state_dict(),
'best_rsum': best_rsum,
'opt': opt,
}, is_best, filename=filename, prefix=opt.model_name + '/')
def train(self):
model = SCAN(self.params)
model.apply(init_xavier)
model.load_state_dict(torch.load('models/model_weights_5.t7'))
loss_function = MarginLoss(self.params.margin)
if torch.cuda.is_available():
model = model.cuda()
loss_function = loss_function.cuda()
optimizer = torch.optim.Adam(model.parameters(),
lr=self.params.lr,
weight_decay=self.params.wdecay)
try:
prev_best = 0
for epoch in range(self.params.num_epochs):
iters = 1
losses = []
start_time = timer()
num_of_mini_batches = len(
self.data_loader.train_ids) // self.params.batch_size
for (caption, mask, image, neg_cap, neg_mask,
neg_image) in tqdm(self.data_loader.training_data_loader):
# Sample according to hard negative mining
caption, mask, image, neg_cap, neg_mask, neg_image = self.data_loader.hard_negative_mining(
model, caption, mask, image, neg_cap, neg_mask,
neg_image)
model.train()
optimizer.zero_grad()
# forward pass.
similarity = model(to_variable(caption), to_variable(mask),
to_variable(image), False)
similarity_neg_1 = model(to_variable(neg_cap),
to_variable(neg_mask),
to_variable(image), False)
similarity_neg_2 = model(to_variable(caption),
to_variable(mask),
to_variable(neg_image), False)
# Compute the loss, gradients, and update the parameters by calling optimizer.step()
loss = loss_function(similarity, similarity_neg_1,
similarity_neg_2)
loss.backward()
losses.append(loss.data.cpu().numpy())
if self.params.clip_value > 0:
torch.nn.utils.clip_grad_norm(model.parameters(),
self.params.clip_value)
optimizer.step()
# sys.stdout.write("[%d/%d] :: Training Loss: %f \r" % (
# iters, num_of_mini_batches, np.asscalar(np.mean(losses))))
# sys.stdout.flush()
iters += 1
if epoch + 1 % self.params.step_size == 0:
optim_state = optimizer.state_dict()
optim_state['param_groups'][0]['lr'] = optim_state[
'param_groups'][0]['lr'] / self.params.gamma
optimizer.load_state_dict(optim_state)
torch.save(
model.state_dict(), self.params.model_dir +
'/model_weights_{}.t7'.format(epoch + 1))
# Calculate r@k after each epoch
if (epoch + 1) % self.params.validate_every == 0:
r_at_1, r_at_5, r_at_10 = self.evaluator.recall(
model, is_test=False)
print(
"Epoch {} : Training Loss: {:.5f}, R@1 : {}, R@5 : {}, R@10 : {}, Time elapsed {:.2f} mins"
.format(epoch + 1, np.asscalar(np.mean(losses)),
r_at_1, r_at_5, r_at_10,
(timer() - start_time) / 60))
if r_at_1 > prev_best:
print("Recall at 1 increased....saving weights !!")
prev_best = r_at_1
torch.save(
model.state_dict(), self.params.model_dir +
'best_model_weights_{}.t7'.format(epoch + 1))
else:
print("Epoch {} : Training Loss: {:.5f}".format(
epoch + 1, np.asscalar(np.mean(losses))))
except KeyboardInterrupt:
print("Interrupted.. saving model !!!")
torch.save(model.state_dict(),
self.params.model_dir + '/model_weights_interrupt.t7')
def main():
# Hyper Parameters
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_path',
default='/data3/zhangyf/cross_modal_retrieval/SCAN/data',
help='path to datasets')
parser.add_argument('--data_name',
default='f30k_precomp',
help='{coco,f30k}_precomp')
parser.add_argument(
'--vocab_path',
default='/data3/zhangyf/cross_modal_retrieval/SCAN/vocab/',
help='Path to saved vocabulary json files.')
parser.add_argument('--margin',
default=0.2,
type=float,
help='Rank loss margin.')
parser.add_argument('--num_epochs',
default=20,
type=int,
help='Number of training epochs.')
parser.add_argument('--batch_size',
default=128,
type=int,
help='Size of a training mini-batch.')
parser.add_argument('--word_dim',
default=300,
type=int,
help='Dimensionality of the word embedding.')
parser.add_argument('--decoder_dim',
default=512,
type=int,
help='Dimensionality of the word embedding.')
parser.add_argument('--embed_size',
default=1024,
type=int,
help='Dimensionality of the joint embedding.')
parser.add_argument('--grad_clip',
default=2.,
type=float,
help='Gradient clipping threshold.')
parser.add_argument('--num_layers',
default=1,
type=int,
help='Number of GRU layers.')
parser.add_argument('--learning_rate',
default=.0002,
type=float,
help='Initial learning rate.')
parser.add_argument('--lr_update',
default=10,
type=int,
help='Number of epochs to update the learning rate.')
parser.add_argument('--workers',
default=4,
type=int,
help='Number of data loader workers.')
parser.add_argument('--log_step',
default=30,
type=int,
help='Number of steps to print and record the log.')
parser.add_argument('--val_step',
default=500,
type=int,
help='Number of steps to run validation.')
parser.add_argument('--logger_name',
default='./runs/runX/log',
help='Path to save Tensorboard log.')
parser.add_argument('--model_name',
default='./runs/runX/checkpoint',
help='Path to save the model.')
parser.add_argument(
'--resume',
default=
'/data3/zhangyf/cross_modal_retrieval/vsepp_next_train_12_31_f30k/run/coco_vse++_ft_128_f30k_next/model_best.pth.tar',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--max_violation',
action='store_true',
help='Use max instead of sum in the rank loss.')
parser.add_argument('--img_dim',
default=2048,
type=int,
help='Dimensionality of the image embedding.')
parser.add_argument('--no_imgnorm',
action='store_true',
help='Do not normalize the image embeddings.')
parser.add_argument('--no_txtnorm',
action='store_true',
help='Do not normalize the text embeddings.')
parser.add_argument('--precomp_enc_type',
default="basic",
help='basic|weight_norm')
parser.add_argument('--reset_train',
action='store_true',
help='Ensure the training is always done in '
'train mode (Not recommended).')
parser.add_argument('--finetune',
action='store_true',
help='Fine-tune the image encoder.')
parser.add_argument('--cnn_type',
default='resnet152',
help="""The CNN used for image encoder
(e.g. vgg19, resnet152)""")
parser.add_argument('--crop_size',
default=224,
type=int,
help='Size of an image crop as the CNN input.')
opt = parser.parse_args()
print(opt)
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
tb_logger.configure(opt.logger_name, flush_secs=5)
# Load Vocabulary Wrapper
vocab = pickle.load(
open(os.path.join(opt.vocab_path, '%s_vocab.pkl' % opt.data_name),
'rb'))
opt.vocab_size = len(vocab)
# Load data loaders
train_loader, val_loader = data.get_loaders(opt.data_name, vocab,
opt.batch_size, opt.workers,
opt)
# Construct the model
model = SCAN(opt)
# optionally resume from a checkpoint
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
start_epoch = checkpoint['epoch']
best_rsum = checkpoint['best_rsum']
model.load_state_dict(checkpoint['model'])
# Eiters is used to show logs as the continuation of another
# training
model.Eiters = checkpoint['Eiters']
print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})".format(
opt.resume, start_epoch, best_rsum))
validate(opt, val_loader, model)
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
# Train the Model
best_rsum = 0
for epoch in range(opt.num_epochs):
print(opt.logger_name)
print(opt.model_name)
adjust_learning_rate(opt, model.optimizer, epoch)
# train for one epoch
bset_rsum = train(opt, train_loader, model, epoch, val_loader,
best_rsum)
# evaluate on validation set
rsum = validate(opt, val_loader, model)
# remember best R@ sum and save checkpoint
is_best = rsum > best_rsum
best_rsum = max(rsum, best_rsum)
if not os.path.exists(opt.model_name):
os.mkdir(opt.model_name)
save_checkpoint(
{
'epoch': epoch + 1,
'model': model.state_dict(),
'best_rsum': best_rsum,
'opt': opt,
'Eiters': model.Eiters,
},
is_best,
filename='checkpoint_{}.pth.tar'.format(epoch),
prefix=opt.model_name + '/')
def main():
# Hyper Parameters
parser = argparse.ArgumentParser()
parser.add_argument('--data_path',
default='./data/',
help='path to datasets')
parser.add_argument('--data_name',
default='precomp',
help='{coco,f30k}_precomp')
parser.add_argument('--vocab_path',
default='./vocab/',
help='Path to saved vocabulary json files.')
parser.add_argument('--margin',
default=0.2,
type=float,
help='Rank loss margin.')
parser.add_argument('--num_epochs',
default=30,
type=int,
help='Number of training epochs.')
parser.add_argument('--batch_size',
default=128,
type=int,
help='Size of a training mini-batch.')
parser.add_argument('--word_dim',
default=300,
type=int,
help='Dimensionality of the word embedding.')
parser.add_argument('--embed_size',
default=1024,
type=int,
help='Dimensionality of the joint embedding.')
parser.add_argument('--grad_clip',
default=2.,
type=float,
help='Gradient clipping threshold.')
parser.add_argument('--num_layers',
default=1,
type=int,
help='Number of GRU layers.')
parser.add_argument('--learning_rate',
default=.0002,
type=float,
help='Initial learning rate.')
parser.add_argument('--lr_update',
default=15,
type=int,
help='Number of epochs to update the learning rate.')
parser.add_argument('--workers',
default=10,
type=int,
help='Number of data loader workers.')
parser.add_argument('--log_step',
default=10,
type=int,
help='Number of steps to print and record the log.')
parser.add_argument('--val_step',
default=500,
type=int,
help='Number of steps to run validation.')
parser.add_argument('--logger_name',
default='./runs/runX/log',
help='Path to save Tensorboard log.')
parser.add_argument('--model_name',
default='./runs/runX/checkpoint',
help='Path to save the model.')
parser.add_argument('--resume',
default='',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--max_violation',
action='store_true',
help='Use max instead of sum in the rank loss.')
parser.add_argument('--img_dim',
default=2048,
type=int,
help='Dimensionality of the image embedding.')
parser.add_argument('--no_imgnorm',
action='store_true',
help='Do not normalize the image embeddings.')
parser.add_argument('--no_txtnorm',
action='store_true',
help='Do not normalize the text embeddings.')
parser.add_argument(
'--raw_feature_norm',
default="clipped_l2norm",
help='clipped_l2norm|l2norm|clipped_l1norm|l1norm|no_norm|softmax')
parser.add_argument('--agg_func',
default="LogSumExp",
help='LogSumExp|Mean|Max|Sum')
parser.add_argument('--cross_attn', default="t2i", help='t2i|i2t')
parser.add_argument('--precomp_enc_type',
default="basic",
help='basic|weight_norm')
parser.add_argument('--bi_gru',
action='store_true',
help='Use bidirectional GRU.')
parser.add_argument('--lambda_lse',
default=6.,
type=float,
help='LogSumExp temp.')
parser.add_argument('--lambda_softmax',
default=9.,
type=float,
help='Attention softmax temperature.')
opt = parser.parse_args()
print(opt)
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
tb_logger.configure(opt.logger_name, flush_secs=5)
# Load Vocabulary Wrapper
vocab = deserialize_vocab(
os.path.join(opt.vocab_path, '%s_vocab.json' % opt.data_name))
opt.vocab_size = len(vocab)
# Load data loaders
train_loader, val_loader = data.get_loaders(opt.data_name, vocab,
opt.batch_size, opt.workers,
opt)
# Construct the model
model = SCAN(opt)
# optionally resume from a checkpoint
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
start_epoch = checkpoint['epoch']
best_rsum = checkpoint['best_rsum']
model.load_state_dict(checkpoint['model'])
# Eiters is used to show logs as the continuation of another
# training
model.Eiters = checkpoint['Eiters']
print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})".format(
opt.resume, start_epoch, best_rsum))
validate(opt, val_loader, model)
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
# Train the Model
best_rsum = 0
for epoch in range(opt.num_epochs):
print(opt.logger_name)
print(opt.model_name)
adjust_learning_rate(opt, model.optimizer, epoch)
# train for one epoch
train(opt, train_loader, model, epoch, val_loader)
# evaluate on validation set
rsum = validate(opt, val_loader, model)
# remember best R@ sum and save checkpoint
is_best = rsum > best_rsum
best_rsum = max(rsum, best_rsum)
if not os.path.exists(opt.model_name):
os.mkdir(opt.model_name)
save_checkpoint(
{
'epoch': epoch + 1,
'model': model.state_dict(),
'best_rsum': best_rsum,
'opt': opt,
'Eiters': model.Eiters,
},
is_best,
filename='checkpoint_{}.pth.tar'.format(epoch),
prefix=opt.model_name + '/')
def start_experiment(opt, seed):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
print("Let's use", torch.cuda.device_count(), "GPUs!")
print("Number threads:", torch.get_num_threads())
# Load Vocabulary Wrapper, create dictionary that can switch between ids and words
vocab = deserialize_vocab("{}/{}/{}_vocab_{}.json".format(
opt.vocab_path, opt.clothing, opt.data_name, opt.version))
opt.vocab_size = len(vocab)
# Load data loaders
train_loader, val_loader = data_ken.get_loaders(opt.data_name, vocab,
opt.batch_size,
opt.workers, opt)
# Construct the model
model = SCAN(opt)
# save hyperparameters in file
save_hyperparameters(opt.logger_name, opt)
best_rsum = 0
start_epoch = 0
# optionally resume from a checkpoint
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
start_epoch = checkpoint['epoch'] + 1
best_rsum = checkpoint['best_rsum']
model.load_state_dict(checkpoint['model'])
# Eiters is used to show logs as the continuation of another
# training
model.Eiters = checkpoint['Eiters']
print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})".format(
opt.resume, start_epoch, best_rsum))
validate(opt, val_loader, model)
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
# Train the Model
for epoch in range(start_epoch, opt.num_epochs):
print(opt.logger_name)
print(opt.model_name)
adjust_learning_rate(opt, model.optimizer, epoch)
# train for one epoch
train(opt, train_loader, model, epoch, val_loader)
# evaluate on validation set
rsum = validate(opt, val_loader, model)
# remember best R@ sum and save checkpoint
is_best = rsum > best_rsum
best_rsum = max(rsum, best_rsum)
if not os.path.exists(opt.model_name):
os.mkdir(opt.model_name)
last_epoch = False
if epoch == (opt.num_epochs - 1):
last_epoch = True
# only save when best epoch, or last epoch for further training
if is_best or last_epoch:
save_checkpoint(
{
'epoch': epoch,
'model': model.state_dict(),
'best_rsum': best_rsum,
'opt': opt,
'Eiters': model.Eiters,
},
is_best,
last_epoch,
filename='checkpoint_{}.pth.tar'.format(epoch),
prefix=opt.model_name + '/')
return best_rsum
