def get_baseline_model(args):
vocab = utils.load_vocab(args.vocab_json)
if args.baseline_start_from is not None:
model, kwargs = utils.load_baseline(args.baseline_start_from)
elif args.model_type == 'LSTM':
kwargs = {
'vocab': vocab,
'rnn_wordvec_dim': args.rnn_wordvec_dim,
'rnn_dim': args.rnn_hidden_dim,
'rnn_num_layers': args.rnn_num_layers,
'rnn_dropout': args.rnn_dropout,
'fc_dims': parse_int_list(args.classifier_fc_dims),
'fc_use_batchnorm': args.classifier_batchnorm == 1,
'fc_dropout': args.classifier_dropout,
}
model = LstmModel(**kwargs)
elif args.model_type == 'CNN+LSTM':
kwargs = {
'vocab': vocab,
'rnn_wordvec_dim': args.rnn_wordvec_dim,
'rnn_dim': args.rnn_hidden_dim,
'rnn_num_layers': args.rnn_num_layers,
'rnn_dropout': args.rnn_dropout,
'cnn_feat_dim': parse_int_list(args.feature_dim),
'cnn_num_res_blocks': args.cnn_num_res_blocks,
'cnn_res_block_dim': args.cnn_res_block_dim,
'cnn_proj_dim': args.cnn_proj_dim,
'cnn_pooling': args.cnn_pooling,
'fc_dims': parse_int_list(args.classifier_fc_dims),
'fc_use_batchnorm': args.classifier_batchnorm == 1,
'fc_dropout': args.classifier_dropout,
}
model = CnnLstmModel(**kwargs)
elif args.model_type == 'CNN+LSTM+SA':
kwargs = {
'vocab': vocab,
'rnn_wordvec_dim': args.rnn_wordvec_dim,
'rnn_dim': args.rnn_hidden_dim,
'rnn_num_layers': args.rnn_num_layers,
'rnn_dropout': args.rnn_dropout,
'cnn_feat_dim': parse_int_list(args.feature_dim),
'stacked_attn_dim': args.stacked_attn_dim,
'num_stacked_attn': args.num_stacked_attn,
'fc_dims': parse_int_list(args.classifier_fc_dims),
'fc_use_batchnorm': args.classifier_batchnorm == 1,
'fc_dropout': args.classifier_dropout,
}
model = CnnLstmSaModel(**kwargs)
if model.rnn.token_to_idx != vocab['question_token_to_idx']:
# Make sure new vocab is superset of old
for k, v in model.rnn.token_to_idx.items():
assert k in vocab['question_token_to_idx']
assert vocab['question_token_to_idx'][k] == v
for token, idx in vocab['question_token_to_idx'].items():
model.rnn.token_to_idx[token] = idx
kwargs['vocab'] = vocab
model.rnn.expand_vocab(vocab['question_token_to_idx'])
model.cuda()
model.train()
return model, kwargs
def main(args):
if args.debug_every <= 1:
pdb.set_trace()
model = None
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):
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)
else:
print('Must give either --baseline_model or --program_generator' +
'and --execution_engine')
return
dtype = torch.FloatTensor
if args.use_gpu == 1:
dtype = torch.cuda.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)
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, dtype, loader)
def main(args):
if not args.program_generator:
args.program_generator = args.execution_engine
input_question_h5 = os.path.join(args.data_dir,
'{}_questions.h5'.format(args.part))
input_features_h5 = os.path.join(args.data_dir,
'{}_features.h5'.format(args.part))
model = None
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:
pg, _ = utils.load_program_generator(args.program_generator)
ee, _ = utils.load_execution_engine(args.execution_engine,
verbose=False)
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
dtype = torch.FloatTensor
if args.use_gpu == 1:
dtype = torch.cuda.FloatTensor
if args.question is not None and args.image is not None:
run_single_example(args, model, dtype, args.question)
else:
vocab = load_vocab(args)
loader_kwargs = {
'question_h5': input_question_h5,
'feature_h5': 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, dtype, loader)
dataset = Dataset.create(dtype='agreement', 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))
dataset = torch_util.ShapeWorldDataset(dataset=dataset, # include_model=True
mode=(None if args.sw_mode == 'none' else args.sw_mode), epoch=(args.num_samples is None))
loader = ShapeWorldDataLoader(dataset=dataset, batch_size=args.batch_size) # num_workers=1
model = None
if args.baseline_model is not None:
assert args.model_type in ('LSTM', 'CNN+LSTM', 'CNN+LSTM+SA')
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)
else:
print('Must give either --baseline_model or --program_generator and --execution_engine')
return
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)