def __iter__(self):
for batch in super(ShapeWorldDataLoader, self).__iter__():
question = batch['caption'].long()
image = batch['world']
feats = batch['world']
answer = batch['agreement'].long()
if 'caption_model' in batch:
program_seq = batch['caption_model'].apply_(callable=(lambda model: clevr_util.parse_program(mode=0, model=model)))
else:
program_seq = torch.IntTensor([0 for _ in batch['caption']])
program_json = dict()
yield question, image, feats, answer, program_seq, program_json
def preprocess(model):
if args.sw_name.startswith("clevr"):
program_prefix = vr.programs.list_to_prefix(
model["program"])
else:
program_prefix = clevr_util.parse_program(mode=0,
model=model)
program_str = vr.programs.list_to_str(program_prefix)
program_tokens = tokenize(program_str)
program_encoded = encode(program_tokens, program_token_to_idx)
program_encoded += [
program_token_to_idx["<NULL>"]
for _ in range(27 - len(program_encoded))
]
return np.asarray(program_encoded, dtype=np.int64)
else:
captions_iter = zip(
(captions[n], ), (captions_length[n], ),
(captions_model[n], ), (agreements[n], ))
for caption, caption_length, caption_model, agreement in captions_iter:
if agreement == 1.0:
answer = 'true'
elif agreement == 0.0:
answer = 'false'
else:
assert False
answer = 'maybe'
if caption_model is None:
program = None
else:
program = parse_program(model=caption_model)
questions.append(
dict(image_index=index,
program=program,
question_index=0,
image_filename=filename,
question_family_index=0,
split=mode,
answer=answer,
question=' '.join(
id2word[caption[i]]
for i in range(caption_length))))
else:
if args.features:
for value_name, value_type in dataset.values.items():
captions_iter = zip(
(captions[n], ), (captions_length[n], ),
(captions_model[n], ), (agreements[n], ))
for caption, caption_length, caption_model, agreement in captions_iter:
if agreement == 1.0:
answer = 'true'
elif agreement == 0.0:
answer = 'false'
else:
assert False
answer = 'maybe'
for parse_mode in range(2):
if caption_model is None:
program = None
else:
program = clevr_util.parse_program(
mode=parse_mode, model=caption_model)
questions[parse_mode].append(
dict(image_index=index,
program=program,
question_index=0,
image_filename=filename,
question_family_index=0,
split=mode,
answer=answer,
question=' '.join(
id2word[caption[i]]
for i in range(caption_length))))
else:
if args.features:
for value_name, value_type in dataset.values.items():