def eval(rank, args, shared_model):
print('eval start...')
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
}
# print(eval_loader_kwargs)
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_log_path = os.path.join(args.log_dir,
'eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0
while epoch < int(args.max_epochs):
model.load_state_dict(shared_model.state_dict())
model.eval()
metrics = VqaMetric(info={'split': args.eval_split},
metric_names=[
'loss', 'accuracy', 'mean_rank',
'mean_reciprocal_rank'
],
log_json=args.output_log_path)
if args.input_type == 'ques':
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
print(scores)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = True
#all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
epoch += 1
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.log == True:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def eval(rank, args, shared_model, use_vision, use_language):
gpu_idx = args.gpus.index(args.gpus[rank % len(args.gpus)])
torch.cuda.set_device(gpu_idx)
print("eval gpu:" + str(gpu_idx) + " assigned")
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_log_path = os.path.join(args.log_dir,
'eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0
print(epoch, args.max_epochs) # DEBUG
while epoch < int(args.max_epochs):
print("eval gpu:" + str(gpu_idx) + " running epoch " + str(epoch))
model.load_state_dict(shared_model.state_dict())
model.eval()
metrics = VqaMetric(info={'split': args.eval_split},
metric_names=[
'loss', 'accuracy', 'mean_rank',
'mean_reciprocal_rank'
],
log_json=args.output_log_path)
if args.input_type == 'ques':
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers = batch
# If not using language, replace each question with a start and end token back to back.
if not use_language:
questions = torch.zeros_like(questions)
questions.fill_(
model_kwargs['vocab']['questionTokenToIdx']['<NULL>'])
questions[:, 0] = model_kwargs['vocab'][
'questionTokenToIdx']['<START>']
questions[:, 1] = model_kwargs['vocab'][
'questionTokenToIdx']['<END>']
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
# If not using language, replace each question with a start and end token back to back.
if not use_language:
questions = torch.zeros_like(questions)
questions.fill_(model_kwargs['vocab']
['questionTokenToIdx']['<NULL>'])
questions[:, 0] = model_kwargs['vocab'][
'questionTokenToIdx']['<START>']
questions[:, 1] = model_kwargs['vocab'][
'questionTokenToIdx']['<END>']
# If not using vision, replace all image feature data with zeros.
if not use_vision:
images = torch.zeros_like(images)
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
read_epoch = None
while read_epoch is None or epoch >= read_epoch:
try:
with open(args.identifier + '.shared_epoch.tmp', 'r') as f:
read_epoch = int(f.read().strip())
except (IOError, ValueError):
pass
if read_epoch is None:
# TODO: since merger, this no longer works (hanging); might need to undo changes re: threading that we
# TODO: made or debug them.
print("eval gpu:" + str(gpu_idx) +
" waiting for train thread to finish epoch " +
str(epoch))
time.sleep(
10
) # sleep until the training thread finishes another iteration
epoch = read_epoch
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def train(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
optim = torch.optim.Adam(filter(lambda p: p.requires_grad,
shared_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
metrics = VqaMetric(
info={
'split': 'train',
'thread': rank
},
metric_names=['loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'],
log_json=args.output_log_path)
print('[TRAIN_LOADER] start')
train_loader = EqaDataLoader(**train_loader_kwargs)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch = 0, 0
while epoch < int(args.max_epochs):
# print('epoch no. %d' % epoch)
if args.input_type == 'ques':
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.log == True:
metrics.dump_log()
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = True
#all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cnn.eval()
model.cuda()
idx, questions, answers, images, _, _, _ = batch
print('--- images dim {}'.format(images.size()))
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
print('--- att_probs: {}.'.format(att_probs))
# print('--- answers_var: {}.'.format(answers_var))
# print('--- loss: {}.'.format(loss))
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.item(), accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.log == True:
metrics.dump_log()
if all_envs_loaded == False:
print('[CHECK][Cache:%d][Total:%d]' %
(len(train_loader.dataset.img_data_cache),
len(train_loader.dataset.env_list)))
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
epoch += 1
def train(rank, args, shared_model, use_vision, use_language):
gpu_idx = args.gpus.index(args.gpus[rank % len(args.gpus)])
torch.cuda.set_device(gpu_idx)
print("train gpu:" + str(gpu_idx) + " assigned")
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
optim = torch.optim.Adam(filter(lambda p: p.requires_grad,
shared_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
metrics = VqaMetric(
info={
'split': 'train',
'thread': rank
},
metric_names=['loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'],
log_json=args.output_log_path)
train_loader = EqaDataLoader(**train_loader_kwargs)
if args.input_type == 'ques,image':
train_loader.dataset._load_envs(start_idx=0, in_order=True)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch = 0, 0
while epoch < int(args.max_epochs):
print("train gpu:" + str(gpu_idx) + " running epoch " + str(epoch))
if args.input_type == 'ques':
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers = batch
# If not using language, replace each question with a start and end token back to back.
if not use_language:
questions = torch.zeros_like(questions)
questions.fill_(
model_kwargs['vocab']['questionTokenToIdx']['<NULL>'])
questions[:, 0] = model_kwargs['vocab'][
'questionTokenToIdx']['<START>']
questions[:, 1] = model_kwargs['vocab'][
'questionTokenToIdx']['<END>']
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.to_log == 1:
metrics.dump_log()
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers, images, _, _, _ = batch
# If not using language, replace each question with a start and end token back to back.
if not use_language:
questions = torch.zeros_like(questions)
questions.fill_(model_kwargs['vocab']
['questionTokenToIdx']['<NULL>'])
questions[:, 0] = model_kwargs['vocab'][
'questionTokenToIdx']['<START>']
questions[:, 1] = model_kwargs['vocab'][
'questionTokenToIdx']['<END>']
# If not using vision, replace all image feature data with zeros.
if not use_vision:
images = torch.zeros_like(images)
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.to_log == 1:
metrics.dump_log()
if all_envs_loaded == False:
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
# Set shared epoch when it finishes on the training side
with open(args.identifier + '.shared_epoch.tmp', 'w') as f:
f.write(str(epoch))
epoch += 1
def train(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
optim = torch.optim.Adam(filter(lambda p: p.requires_grad,
shared_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
metrics = VqaMetric(
info={
'split': 'train',
'thread': rank
},
metric_names=['loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'],
log_json=args.output_log_path)
eval_loader = EqaDataLoader(**eval_loader_kwargs)
train_loader = EqaDataLoader(**train_loader_kwargs)
if args.input_type == 'ques,image':
train_loader.dataset._load_envs(start_idx=0, in_order=True)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch, best_eval_acc = 0, 0, 0
while epoch < int(args.max_epochs):
if args.input_type == 'ques':
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.to_log == 1:
metrics.dump_log()
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
# accuracy, ranks = metrics.compute_ranks(scores.data.cpu(), answers)
# metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
#if t % args.print_every == 0:
# print(metrics.get_stat_string())
# if args.to_log == 1:
# metrics.dump_log()
if all_envs_loaded == False:
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
env_done = False
env_all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded(
)
while env_done == False:
_loss, _accuracy, _ranks = None, None, None
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
if _loss is None:
_loss = loss.data[0]
_accuracy = accuracy
_ranks = ranks
else:
_loss = torch.cat([_loss, loss.data[0]])
_accuracy = torch.cat([_accuracy, accuracy])
_ranks = torch.cat([_ranks, ranks])
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if env_all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
env_done = True
else:
env_done = True
epoch += 1
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def train(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.forget_rate is None:
forget_rate = args.noise_rate
else:
forget_rate = args.forget_rate
rate_schedule = np.ones(args.max_epochs) * forget_rate
rate_schedule[:args.num_gradual] = np.linspace(0,
forget_rate**args.exponent,
args.num_gradual)
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model1 = VqaLstmCnnAttentionModel(**model_kwargs)
# lossFn = torch.nn.CrossEntropyLoss().cuda()
optim = torch.optim.Adam(filter(lambda p: p.requires_grad,
shared_model.parameters()),
lr=args.learning_rate)
optim1 = torch.optim.Adam(filter(lambda p: p.requires_grad,
model1.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.cache
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
metrics = VqaMetric(
info={
'split': 'train',
'thread': rank
},
metric_names=['loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'],
log_json=args.output_log_path)
train_loader = EqaDataLoader(**train_loader_kwargs)
if args.input_type == 'ques,image':
train_loader.dataset._load_envs(start_idx=0, in_order=True)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch = 0, 0
while epoch < int(args.max_epochs):
if args.input_type == 'ques':
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data.item(), accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.log == True:
metrics.dump_log()
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cnn.eval()
model.cuda()
# model1.load_state_dict(shared_model1.state_dict())
model1.train()
model1.cnn.eval()
model1.cuda()
idx, questions, answers, images, _, _, _ = batch
for i in range(len(answers)):
if random.random() < args.noise_rate:
tempt = random.randint(-10, 10)
answers[i] = answers[i] + tempt
while (answers[i] < 0 or answers[i] > 70):
answers[i] = answers[i] - tempt
tempt = random.randint(-10, 10)
answers[i] = answers[i] + tempt
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
scores1, att_probs1 = model1(images_var, questions_var)
loss, loss1 = loss_coteaching(scores, scores1, answers_var,
rate_schedule[epoch])
# zero grad
optim.zero_grad()
optim1.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data.item(), accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
loss1.backward()
ensure_shared_grads(model.cpu(), shared_model)
# ensure_shared_grads(model1.cpu(), shared_model1)
optim.step()
optim1.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.log == True:
metrics.dump_log()
if all_envs_loaded == False:
print('[CHECK][Cache:%d][Total:%d]' %
(len(train_loader.dataset.img_data_cache),
len(train_loader.dataset.env_list)))
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
epoch += 1
def eval(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank%len(args.gpus)],
'to_cache': args.to_cache
}
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_log_path = os.path.join(args.log_dir,
'eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0
while epoch < int(args.max_epochs):
model.load_state_dict(shared_model.state_dict())
model.eval()
metrics = VqaMetric(
info={'split': args.eval_split},
metric_names=[
'loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'
],
log_json=args.output_log_path)
if args.input_type == 'ques':
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
epoch += 1
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def train(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
optim = torch.optim.Adam(
filter(lambda p: p.requires_grad, shared_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank%len(args.gpus)],
'to_cache': args.to_cache
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
metrics = VqaMetric(
info={'split': 'train',
'thread': rank},
metric_names=['loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'],
log_json=args.output_log_path)
train_loader = EqaDataLoader(**train_loader_kwargs)
if args.input_type == 'ques,image':
train_loader.dataset._load_envs(start_idx=0, in_order=True)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch = 0, 0
while epoch < int(args.max_epochs):
if args.input_type == 'ques':
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.to_log == 1:
metrics.dump_log()
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.to_log == 1:
metrics.dump_log()
if all_envs_loaded == False:
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
epoch += 1