def serialize_value(value,
value_name,
value_type,
write_file,
id2word=None):
if value_type == 'int':
value = '\n'.join(str(int(x)) for x in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'float':
value = '\n'.join(str(float(x)) for x in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'vector':
value = '\n'.join(','.join(str(x) for x in vector)
for vector in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'text':
assert id2word
value = '\n'.join(' '.join(id2word[word_id]
for word_id in text if word_id)
for text in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'world':
for n in range(len(value)):
image = World.get_image(world=value[n])
image_bytes = BytesIO()
image.save(image_bytes, format='bmp')
write_file('{}-{}.bmp'.format(value_name, n),
image_bytes.getvalue(),
binary=True)
image_bytes.close()
elif value_type == 'model':
value = json.dumps(value)
write_file(value_name + '.json', value)
assert False
elif args.clevr_format:
from shapeworld.world import World
from shapeworld.datasets.clevr_util import parse_program
assert args.type == 'agreement'
worlds = generated['world']
captions = generated['caption']
captions_length = generated['caption_length']
captions_model = generated.get('caption_model')
agreements = generated['agreement']
for n in range(len(worlds)):
index = (shard - 1) * args.instances + n
filename = 'world_{}.png'.format(index)
image_bytes = BytesIO()
World.get_image(world_array=worlds[n]).save(image_bytes,
format='png')
with open(os.path.join(directory, filename),
'wb') as filehandle:
filehandle.write(image_bytes.getvalue())
image_bytes.close()
id2word = dataset.vocabulary(value_type='language')
if 'alternatives' in generated:
captions_iter = zip(captions[n], captions_length[n],
captions_model[n], agreements[n])
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'
def main(args):
if args.debug_every <= 1:
pdb.set_trace()
if args.sw_name is not None or args.sw_config is not None:
assert args.image is None and args.question is None
from shapeworld import Dataset, torch_util
from shapeworld.datasets import clevr_util
class ShapeWorldDataLoader(torch_util.ShapeWorldDataLoader):
def __iter__(self):
for batch in super(ShapeWorldDataLoader, self).__iter__():
if "caption" in batch:
question = batch["caption"].long()
else:
question = batch["question"].long()
if args.sw_features == 1:
image = batch["world_features"]
else:
image = batch["world"]
feats = image
if "agreement" in batch:
answer = batch["agreement"].long()
else:
answer = batch["answer"].long()
if "caption_model" in batch:
assert args.sw_name.startswith(
"clevr") or args.sw_program == 3
program_seq = batch["caption_model"]
# .apply_(callable=(lambda model: clevr_util.parse_program(mode=0, model=model)))
elif "question_model" in batch:
program_seq = batch["question_model"]
elif "caption" in batch:
if args.sw_program == 1:
program_seq = batch["caption_pn"].long()
elif args.sw_program == 2:
program_seq = batch["caption_rpn"].long()
else:
program_seq = [None]
else:
program_seq = [None]
# program_seq = torch.IntTensor([0 for _ in batch['question']])
program_json = dict()
yield question, image, feats, answer, program_seq, program_json
dataset = Dataset.create(
dtype=args.sw_type,
name=args.sw_name,
variant=args.sw_variant,
language=args.sw_language,
config=args.sw_config,
)
print("ShapeWorld dataset: {} (variant: {})".format(
dataset, args.sw_variant))
print("Config: " + str(args.sw_config))
if args.program_generator is not None:
with open(args.program_generator + ".vocab", "r") as filehandle:
vocab = json.load(filehandle)
elif args.execution_engine is not None:
with open(args.execution_engine + ".vocab", "r") as filehandle:
vocab = json.load(filehandle)
elif args.baseline_model is not None:
with open(args.baseline_model + ".vocab", "r") as filehandle:
vocab = json.load(filehandle)
program_token_to_idx = vocab["program_token_to_idx"]
include_model = args.model_type in ("PG", "EE", "PG+EE") and (
args.sw_name.startswith("clevr") or args.sw_program == 3)
if include_model:
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)
if args.sw_name.startswith("clevr"):
preprocessing = dict(question_model=preprocess)
else:
preprocessing = dict(caption_model=preprocess)
elif args.sw_program in (1, 2):
def preprocess(caption_pn):
caption_pn += (caption_pn > 0) * 2
for n, symbol in enumerate(caption_pn):
if symbol == 0:
caption_pn[n] = 2
break
caption_pn = np.concatenate(([1], caption_pn))
return caption_pn
if args.sw_program == 1:
preprocessing = dict(caption_pn=preprocess)
else:
preprocessing = dict(caption_rpn=preprocess)
else:
preprocessing = None
dataset = torch_util.ShapeWorldDataset(
dataset=dataset,
mode=(None if args.sw_mode == "none" else args.sw_mode),
include_model=include_model,
epoch=(args.num_samples is None),
preprocessing=preprocessing,
)
loader = ShapeWorldDataLoader(dataset=dataset,
batch_size=args.batch_size)
model = None
if args.model_type in ("CNN", "LSTM", "CNN+LSTM", "CNN+LSTM+SA"):
assert 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:
pg, _ = utils.load_program_generator(args.program_generator,
args.model_type)
ee, _ = utils.load_execution_engine(args.execution_engine,
verbose=False,
model_type=args.model_type)
if args.vocab_json is not None:
new_vocab = utils.load_vocab(args.vocab_json)
pg.expand_encoder_vocab(new_vocab["question_token_to_idx"])
model = (pg, ee)
elif args.model_type == "FiLM":
assert args.baseline_model is not None
pg, _ = utils.load_program_generator(args.baseline_model,
args.model_type)
ee, _ = utils.load_execution_engine(args.baseline_model,
verbose=False,
model_type=args.model_type)
if args.vocab_json is not None:
new_vocab = utils.load_vocab(args.vocab_json)
pg.expand_encoder_vocab(new_vocab["question_token_to_idx"])
model = (pg, ee)
else:
print(
"Must give either --baseline_model or --program_generator and --execution_engine"
)
return
if torch.cuda.is_available():
dtype = torch.cuda.FloatTensor
else:
dtype = torch.FloatTensor
if args.question is not None and args.image is not None:
run_single_example(args, model, dtype, args.question)
# Interactive mode
elif (args.image is not None and args.input_question_h5 is None
and args.input_features_h5 is None):
feats_var = extract_image_features(args, dtype)
print(colored("Ask me something!", "cyan"))
while True:
# Get user question
question_raw = input(">>> ")
run_single_example(args, model, dtype, question_raw, feats_var)
elif args.sw_name is not None or args.sw_config is not None:
predictions, visualization = run_batch(args, model, dtype, loader)
if args.sw_pred_dir is not None:
assert args.sw_pred_name is not None
pred_dir = os.path.join(
args.sw_pred_dir,
dataset.dataset.type,
dataset.dataset.name,
dataset.dataset.variant,
)
if not os.path.isdir(pred_dir):
os.makedirs(pred_dir)
id2word = dataset.dataset.vocabulary(value_type="language")
with open(
os.path.join(
pred_dir,
args.sw_pred_name + "-" + args.sw_mode + ".txt"),
"w",
) as filehandle:
filehandle.write("".join(
"{} {} {}\n".format(correct, agreement, " ".join(
id2word[c] for c in caption))
for correct, agreement, caption in zip(
predictions["correct"],
predictions["agreement"],
predictions["caption"],
)))
print("Predictions saved")
if args.sw_vis_dir is not None:
assert args.sw_vis_name is not None
from io import BytesIO
from shapeworld.world import World
vis_dir = os.path.join(
args.sw_vis_dir,
dataset.dataset.type,
dataset.dataset.name,
dataset.dataset.variant,
)
image_dir = os.path.join(vis_dir, args.sw_mode, "images")
if not os.path.isdir(image_dir):
os.makedirs(image_dir)
worlds = np.transpose(visualization["world"], (0, 2, 3, 1))
for n in range(worlds.shape[0]):
image = World.get_image(world_array=worlds[n])
image_bytes = BytesIO()
image.save(image_bytes, format="png")
with open(os.path.join(image_dir, "world-{}.png".format(n)),
"wb") as filehandle:
filehandle.write(image_bytes.getvalue())
image_bytes.close()
with open(
os.path.join(
vis_dir,
args.sw_vis_name + "-" + args.sw_mode + ".html"),
"w",
) as filehandle:
html = dataset.dataset.get_html(
generated=visualization,
image_format="png",
image_dir=(args.sw_mode + "/images/"),
)
filehandle.write(html)
print("Visualization saved")
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.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, dtype, loader)
def serialize_value(value,
value_name,
value_type,
write_file,
concat_worlds=False,
id2word=None):
value_type, alts = alternatives_type(value_type=value_type)
if value_type == 'int':
if alts:
value = '\n'.join(';'.join(str(int(x)) for x in xs)
for xs in value) + '\n'
else:
value = '\n'.join(str(int(x)) for x in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'float':
if alts:
value = '\n'.join(';'.join(str(float(x)) for x in xs)
for xs in value) + '\n'
else:
value = '\n'.join(str(float(x)) for x in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'vector(int)' or value_type == 'vector(float)':
if alts:
value = '\n'.join(';'.join(','.join(str(x) for x in vector)
for vector in vectors)
for vectors in value) + '\n'
else:
value = '\n'.join(','.join(str(x) for x in vector)
for vector in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'text':
assert id2word
if alts:
value = '\n\n'.join('\n'.join(' '.join(id2word[word_id]
for word_id in text
if word_id)
for text in texts)
for texts in value) + '\n\n'
else:
value = '\n'.join(' '.join(id2word[word_id]
for word_id in text if word_id)
for text in value) + '\n'
write_file(value_name + '.txt', value)
elif value_type == 'world':
if concat_worlds:
size = ceil(sqrt(len(value)))
worlds = []
for y in range(ceil(len(value) / size)):
if y < len(value) // size:
worlds.append(
np.concatenate(
[value[y * size + x] for x in range(size)],
axis=1))
else:
worlds.append(
np.concatenate([
value[y * size + x]
for x in range(len(value) % size)
] + [
np.zeros_like(a=value[0])
for _ in range(-len(value) % size)
],
axis=1))
worlds = np.concatenate(worlds, axis=0)
image = World.get_image(world_array=worlds)
image_bytes = BytesIO()
image.save(image_bytes, format='bmp')
write_file(value_name + '.bmp',
image_bytes.getvalue(),
binary=True)
image_bytes.close()
else:
for n in range(len(value)):
image = World.get_image(world_array=value[n])
image_bytes = BytesIO()
image.save(image_bytes, format='bmp')
write_file('{}-{}.bmp'.format(value_name, n),
image_bytes.getvalue(),
binary=True)
image_bytes.close()
elif value_type == 'model':
value = json.dumps(value)
write_file(value_name + '.json', value)