def main(args):
# import os
# cwd = os.getcwd()
# print(cwd)
# return
if args.randomize_checkpoint_path == 1:
name, ext = os.path.splitext(args.checkpoint_path)
num = random.randint(1, 1000000)
args.checkpoint_path = '%s_%06d%s' % (name, num, ext)
vocab = utils.load_vocab(args.vocab_json)
if args.use_local_copies == 1:
shutil.copy(args.train_question_h5, '/tmp/train_questions.h5')
shutil.copy(args.train_features_h5, '/tmp/train_features.h5')
shutil.copy(args.val_question_h5, '/tmp/val_questions.h5')
shutil.copy(args.val_features_h5, '/tmp/val_features.h5')
args.train_question_h5 = '/tmp/train_questions.h5'
args.train_features_h5 = '/tmp/train_features.h5'
args.val_question_h5 = '/tmp/val_questions.h5'
args.val_features_h5 = '/tmp/val_features.h5'
question_families = None
if args.family_split_file is not None:
with open(args.family_split_file, 'r') as f:
question_families = json.load(f)
train_loader_kwargs = {
'question_h5': args.train_question_h5,
'feature_h5': args.train_features_h5,
'vocab': vocab,
'batch_size': args.batch_size,
'shuffle': args.shuffle_train_data == 1,
'question_families': question_families,
'max_samples': args.num_train_samples,
'num_workers': args.loader_num_workers,
}
val_loader_kwargs = {
'question_h5': args.val_question_h5,
'feature_h5': args.val_features_h5,
'vocab': vocab,
'batch_size': args.batch_size,
'question_families': question_families,
'max_samples': args.num_val_samples,
'num_workers': args.loader_num_workers,
}
with ClevrDataLoader(**train_loader_kwargs) as train_loader, \
ClevrDataLoader(**val_loader_kwargs) as val_loader:
train_loop(args, train_loader, val_loader)
if args.use_local_copies == 1 and args.cleanup_local_copies == 1:
os.remove('/tmp/train_questions.h5')
os.remove('/tmp/train_features.h5')
os.remove('/tmp/val_questions.h5')
os.remove('/tmp/val_features.h5')
def main(args):
print()
model = None
import pdb
pdb.set_trace()
if args.baseline_model is not None:
print('Loading baseline model from ', args.baseline_model)
model, _ = utils.load_baseline(args.baseline_model)
if args.vocab_json is not None:
new_vocab = utils.load_vocab(args.vocab_json)
model.rnn.expand_vocab(new_vocab['question_token_to_idx'])
elif args.program_generator is not None and args.execution_engine is not None:
print('Loading program generator from ', args.program_generator)
program_generator, _ = utils.load_program_generator(
args.program_generator)
print('Loading execution engine from ', args.execution_engine)
execution_engine, _ = utils.load_execution_engine(
args.execution_engine, verbose=False)
if args.vocab_json is not None:
new_vocab = utils.load_vocab(args.vocab_json)
program_generator.expand_encoder_vocab(
new_vocab['question_token_to_idx'])
model = (program_generator, execution_engine)
else:
print(
'Must give either --baseline_model or --program_generator and --execution_engine'
)
return
if args.question is not None and args.image is not None:
run_single_example(args, model)
else:
vocab = load_vocab(args)
loader_kwargs = {
'question_h5': args.input_question_h5,
'feature_h5': args.input_features_h5,
'vocab': vocab,
'batch_size': args.batch_size,
}
if args.num_samples is not None and args.num_samples > 0:
loader_kwargs['max_samples'] = args.num_samples
if args.family_split_file is not None:
with open(args.family_split_file, 'r') as f:
loader_kwargs['question_families'] = json.load(f)
with ClevrDataLoader(**loader_kwargs) as loader:
run_batch(args, model, loader)
def main(args):
print()
model = init_model(args)
if model is None:
return
if args.question is not None and args.image is not None:
run_single_example(args, model)
else:
vocab = load_vocab(args)
loader_kwargs = {
'question_h5': args.input_question_h5,
'feature_h5': args.input_features_h5,
'vocab': vocab,
'batch_size': args.batch_size,
}
if args.num_samples is not None and args.num_samples > 0:
loader_kwargs['max_samples'] = args.num_samples
if args.family_split_file is not None:
with open(args.family_split_file, 'r') as f:
loader_kwargs['question_families'] = json.load(f)
with ClevrDataLoader(**loader_kwargs) as loader:
run_batch(args, model, loader)