def compute_qgen_accuracy(sess, dataset, batchifier, evaluator, mode,
tokenizer, save_path, cpu_pool, batch_size,
store_games, dump_suffix):
logger = logging.getLogger()
for m in mode:
if m != "beam_search":
test_iterator = Iterator(dataset,
pool=cpu_pool,
batch_size=batch_size,
batchifier=batchifier,
shuffle=False,
use_padding=True)
test_score = evaluator.process(sess,
test_iterator,
mode=m,
store_games=store_games)
# Retrieve the generated games and dump them as a dataset
if store_games:
generated_dialogues = evaluator.get_storage()
dump_samples_into_dataset(generated_dialogues,
save_path=save_path,
tokenizer=tokenizer,
name=dump_suffix + "." + m)
logger.info("Accuracy ({} - {}): {}".format(
dataset.set, m, test_score))
def test_qgen(sess, testset, tokenizer, qgen, cpu_pool, batch_size, logger):
qgen_sources = qgen.get_sources(sess)
qgen_evaluator = Evaluator(qgen_sources, qgen.scope_name, network=qgen, tokenizer=tokenizer)
qgen_batchifier = QuestionerBatchifier(tokenizer, qgen_sources, status=('success',))
qgen_iterator = Iterator(testset, pool=cpu_pool,
batch_size=batch_size,
batchifier=qgen_batchifier)
[qgen_loss] = qgen_evaluator.process(sess, qgen_iterator, outputs=[qgen.ml_loss])
logger.info("QGen test loss: {}".format(qgen_loss))
def test_guesser(sess, testset, tokenizer, guesser, cpu_pool, batch_size, logger):
guesser_sources = guesser.get_sources(sess)
guesser_evaluator = Evaluator(guesser_sources, guesser.scope_name, network=guesser, tokenizer=tokenizer)
guesser_batchifier = QuestionerBatchifier(tokenizer, guesser_sources, status=('success',))
guesser_iterator = Iterator(testset, pool=cpu_pool,
batch_size=batch_size,
batchifier=guesser_batchifier)
[guesser_loss, guesser_error] = guesser_evaluator.process(sess, guesser_iterator, [guesser.loss, guesser.error])
logger.info("Guesser test loss: {}".format(guesser_loss))
logger.info("Guesser test error: {}".format(guesser_error))
def test_oracle(sess, testset, tokenizer, oracle, cpu_pool, batch_size, logger):
oracle_dataset = OracleDataset(testset)
oracle_sources = oracle.get_sources(sess)
oracle_evaluator = Evaluator(oracle_sources, oracle.scope_name, network=oracle, tokenizer=tokenizer)
oracle_batchifier = OracleBatchifier(tokenizer, oracle_sources, status=('success',))
oracle_iterator = Iterator(oracle_dataset, pool=cpu_pool,
batch_size=batch_size,
batchifier=oracle_batchifier)
[oracle_loss, oracle_error] = oracle_evaluator.process(sess, oracle_iterator, [oracle.loss, oracle.error])
logger.info("Oracle test loss: {}".format(oracle_loss))
logger.info("Oracle test error: {}".format(oracle_error))
# create training tools
evaluator = Evaluator(sources,
network.scope_name,
network=network,
tokenizer=tokenizer)
batchifier = QuestionerBatchifier(tokenizer,
sources,
status=('success', ))
for t in range(start_epoch, no_epoch):
logger.info('Epoch {}..'.format(t + 1))
train_iterator = Iterator(trainset,
batch_size=batch_size,
pool=cpu_pool,
batchifier=batchifier,
shuffle=True)
train_loss, train_accuracy = evaluator.process(sess,
train_iterator,
outputs=outputs +
[optimizer])
valid_iterator = Iterator(validset,
pool=cpu_pool,
batch_size=batch_size * 2,
batchifier=batchifier,
shuffle=False)
valid_loss, valid_accuracy = evaluator.process(sess,
valid_iterator,
outputs=outputs)
def extract_raw(
image_shape,
dataset_cstor,
dataset_args,
batchifier_cstor,
source_name,
out_dir,
set_type,
no_threads,
):
for one_set in set_type:
############################
# LOAD DATASET
############################
print("Load dataset...")
dataset_args["which_set"] = one_set
dataset = dataset_cstor(**dataset_args)
# hack dataset to only keep one game by image
image_id_set = {}
games = []
for game in dataset.games:
if game.image.id not in image_id_set:
games.append(game)
image_id_set[game.image.id] = 1
dataset.games = games
no_images = len(games)
# prepare batch builder
dummy_tokenizer = DummyTokenizer()
batchifier = batchifier_cstor(tokenizer=dummy_tokenizer,
sources=[source_name])
cpu_pool = Pool(no_threads, maxtasksperchild=1000)
iterator = Iterator(dataset,
batch_size=64,
pool=cpu_pool,
batchifier=batchifier)
filepath = os.path.join(out_dir, "{}_features.h5".format(one_set))
with h5py.File(filepath, 'w') as f:
feat_dataset = f.create_dataset('features',
shape=[no_images] + image_shape,
dtype=np.float32)
idx2img = f.create_dataset('idx2img',
shape=[no_images],
dtype=np.int32)
pt_hd5 = 0
for batch in tqdm(iterator):
# Store dataset
batch_size = len(batch["raw"])
feat_dataset[pt_hd5:pt_hd5 + batch_size] = batch[source_name]
# Store idx to image.id
for i, game in enumerate(batch["raw"]):
idx2img[pt_hd5 + i] = game.image.id
# update hd5 pointer
pt_hd5 += batch_size
print("Start dumping file: {}".format(filepath))
print("Finished dumping file: {}".format(filepath))
print("Done!")
def extract_features(
img_input,
ft_output,
network_ckpt,
dataset_cstor,
dataset_args,
batchifier_cstor,
out_dir,
set_type,
batch_size,
no_threads,
gpu_ratio):
# CPU/GPU option
cpu_pool = Pool(no_threads, maxtasksperchild=1000)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_ratio)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, network_ckpt)
for one_set in set_type:
print("Load dataset -> set: {}".format(one_set))
dataset_args["which_set"] = one_set
dataset = dataset_cstor(**dataset_args)
# hack dataset to only keep one game by image
image_id_set = {}
games = []
for game in dataset.games:
if game.image.id not in image_id_set:
games.append(game)
image_id_set[game.image.id] = 1
dataset.games = games
no_images = len(games)
source_name = os.path.basename(img_input.name[:-2])
dummy_tokenizer = DummyTokenizer()
batchifier = batchifier_cstor(tokenizer=dummy_tokenizer, sources=[source_name])
iterator = Iterator(dataset,
batch_size=batch_size,
pool=cpu_pool,
batchifier=batchifier)
############################
# CREATE FEATURES
############################
print("Start computing image features...")
filepath = os.path.join(out_dir, "{}_features.h5".format(one_set))
with h5py.File(filepath, 'w') as f:
ft_shape = [int(dim) for dim in ft_output.get_shape()[1:]]
ft_dataset = f.create_dataset('features', shape=[no_images] + ft_shape, dtype=np.float32)
idx2img = f.create_dataset('idx2img', shape=[no_images], dtype=np.int32)
pt_hd5 = 0
for batch in tqdm(iterator):
feat = sess.run(ft_output, feed_dict={img_input: numpy.array(batch[source_name])})
# Store dataset
batch_size = len(batch["raw"])
ft_dataset[pt_hd5: pt_hd5 + batch_size] = feat
# Store idx to image.id
for i, game in enumerate(batch["raw"]):
idx2img[pt_hd5 + i] = game.image.id
# update hd5 pointer
pt_hd5 += batch_size
print("Start dumping file: {}".format(filepath))
print("Finished dumping file: {}".format(filepath))
print("Done!")
# CPU/GPU option
cpu_pool = Pool(args.no_thread, maxtasksperchild=1000)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_ratio)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, args.ckpt)
features = dict()
for one_set in args.set_type:
print("Load dataset -> set: {}".format(one_set))
dataset = OracleDataset.load(args.data_dir, one_set, image_loader=image_loader, crop_loader=crop_loader)
batchifier = OracleBatchifier(tokenizer=None, sources=[source])
iterator = Iterator(dataset,
batch_size=args.batch_size,
pool=cpu_pool,
batchifier=batchifier)
for batch in tqdm(iterator):
feat = sess.run(end_points[feature_name], feed_dict={images: numpy.array(batch[source])})
for f, game in zip(feat, batch["raw"]):
f = f.squeeze()
if args.mode == "crop":
id = game.object_id
else:
id = game.picture.id
if args.network == "resnet":
np.savez_compressed(os.path.join(out_dir, "{}.npz".format(id)), x="features")
evaluator = Evaluator(loop_sources, qgen_network.scope_name, network=qgen_network, tokenizer=tokenizer)
train_batchifier = LooperBatchifier(tokenizer, loop_sources, train=True)
eval_batchifier = LooperBatchifier(tokenizer, loop_sources, train=False)
# Initialize the looper to eval/train the game-simulation
qgen_network.build_sampling_graph(qgen_config["model"], tokenizer=tokenizer, max_length=loop_config['loop']['max_depth'])
looper_evaluator = BasicLooper(loop_config,
oracle=oracle_network,
guesser=guesser_network,
qgen=qgen_network,
tokenizer=tokenizer)
test_iterator = Iterator(testset, pool=cpu_pool,
batch_size=batch_size,
batchifier=eval_batchifier,
shuffle=False,
use_padding=True)
test_score = looper_evaluator.process(sess, test_iterator, mode="sampling")
logger.info("Test success ratio (Init-Sampling): {}".format(test_score))
logs = []
# Start training
final_val_score = 0.
for epoch in range(no_epoch):
logger.info("Epoch {}/{}".format(epoch, no_epoch))
train_iterator = Iterator(trainset, batch_size=batch_size,
pool=cpu_pool,
batchifier=train_batchifier,
def extract_features(img_input, ft_output, network_ckpt, dataset_cstor,
dataset_args, batchifier_cstor, out_dir, set_type,
batch_size, no_threads, gpu_ratio):
# CPU/GPU option
cpu_pool = Pool(no_threads, maxtasksperchild=1000)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_ratio)
# gpu_options.allow_growth = True
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
print("+++ 0")
saver.restore(sess, network_ckpt)
print("+++ 1")
# tf.initialize_all_variables().run()
for one_set in set_type:
print("+++ 2")
print("Load dataset -> set: {}".format(one_set))
dataset_args["which_set"] = one_set
dataset = dataset_cstor(**dataset_args)
print("+++ 3")
# hack dataset to only keep one game by image
image_id_set = {}
games = []
print("+++ 4")
nb_total_trouve = 0
nb_nonTrouve = 0
print("+++ 5")
print("[Data Length ] = {}".format(len(dataset.games)))
for game in dataset.games:
if game.image.id not in image_id_set:
games.append(game)
image_id_set[game.image.id] = 1
print("+++ 6")
# img = game.image.url.split("/")[-1]
# img = img + ".jpg"
#
# print("[Img]= {}".format(img))
#
# s = open(os.path.join("data/img/raw/", "42.jpg"))
#
# try:
# print(str(img++".jpg"))
# f = open(os.path.join("data/img/raw/", img))
#
# nb_total_trouve += 1
# except:
# # print("Not Found !!")
# nb_nonTrouve += 1
# print("[Trouve = {} et Non_Trouve = {} ]".format(nb_total_trouve,nb_nonTrouve))
# # print(img ++".jpg")
dataset.games = games
no_images = len(games)
source_name = os.path.basename(img_input.name[:-2])
dummy_tokenizer = DummyTokenizer()
batchifier = batchifier_cstor(tokenizer_question=dummy_tokenizer,
sources=[source_name])
iterator = Iterator(dataset,
batch_size=batch_size,
pool=cpu_pool,
batchifier=batchifier)
############################
# CREATE FEATURES
############################
print("Start computing image features...")
filepath = os.path.join(out_dir, "{}_features.h5".format(one_set))
with h5py.File(filepath, 'w') as f:
print("--- 1")
ft_shape = [int(dim) for dim in ft_output.get_shape()[1:]]
print("--- 2")
ft_dataset = f.create_dataset('features',
shape=[no_images] + ft_shape,
dtype=np.float32)
print("--- 3")
idx2img = f.create_dataset('idx2img',
shape=[no_images],
dtype=np.int32)
print("--- 4")
pt_hd5 = 0
print("--- 5")
for batch in tqdm(iterator):
# print("--- 6")
# print(" ... ",numpy.array(batch[source_name]),numpy.array(batch[source_name]).shape)
feat = sess.run(
ft_output,
feed_dict={img_input: numpy.array(batch[source_name])})
# Store dataset
#print("--- 7")
batch_size = len(batch["raw"])
ft_dataset[pt_hd5:pt_hd5 + batch_size] = feat
#print("--- 8")
# Store idx to image.id
for i, game in enumerate(batch["raw"]):
idx2img[pt_hd5 + i] = game.image.id
#print("--- 9")
# update hd5 pointer
pt_hd5 += batch_size
print("Start dumping file: {}".format(filepath))
print("Finished dumping file: {}".format(filepath))
print("Done!")
from clevr.data_provider.clevr_dataset import CLEVRDataset
from clevr.data_provider.clevr_batchifier import CLEVRBatchifier
if __name__ == "__main__":
feat_dir = "/media/datas2/tmp"
data_dir = "/home/sequel/fstrub/clevr_data"
image_builder = h5FeatureBuilder(img_dir=feat_dir, bufferize=False)
print("Load datasets...")
dataset = CLEVRDataset(folder=data_dir,
which_set="val",
image_builder=image_builder)
cpu_pool = ThreadPool(1)
dummy_tokenizer = DummyTokenizer()
batchifier = CLEVRBatchifier(tokenizer=dummy_tokenizer, sources=["image"])
iterator = Iterator(dataset,
batch_size=64,
pool=cpu_pool,
batchifier=batchifier)
for batch in tqdm(iterator):
pass
print("Done!")
logs = []
# Start training
final_val_score = 0.
for epoch in range(no_epoch):
if args.skip_training:
logger.info("Skip training...")
break
logger.info("Epoch {}/{}".format(epoch, no_epoch))
cpu_pool = create_cpu_pool(args.no_thread, use_process=False)
train_iterator = Iterator(trainset,
batch_size=batch_size,
pool=cpu_pool,
shuffle=True,
batchifier=train_batchifier)
[train_accuracy, _] = game_engine.process(sess, train_iterator,
optimizer=optimizer,
mode="sampling")
valid_iterator = Iterator(validset, pool=cpu_pool,
batch_size=batch_size,
batchifier=eval_batchifier,
shuffle=False)
[val_accuracy, games] = game_engine.process(sess, valid_iterator, mode="sampling")
logger.info("Accuracy (train - sampling) : {}".format(train_accuracy))
logger.info("Accuracy (valid - sampling) : {}".format(val_accuracy))
