def main(data, vocab, training, model, output):
# Load configs
dir_output = output
config = Config([data, vocab, training, model])
config.save(dir_output)
vocab = Vocab(config)
# Load datasets
train_set = DataGenerator(path_formulas=config.path_formulas_train,
dir_images=config.dir_images_train, img_prepro=greyscale,
max_iter=config.max_iter, bucket=config.bucket_train,
path_matching=config.path_matching_train,
max_len=config.max_length_formula,
form_prepro=vocab.form_prepro)
val_set = DataGenerator(path_formulas=config.path_formulas_val,
dir_images=config.dir_images_val, img_prepro=greyscale,
max_iter=config.max_iter, bucket=config.bucket_val,
path_matching=config.path_matching_val,
max_len=config.max_length_formula,
form_prepro=vocab.form_prepro)
# Define learning rate schedule
n_batches_epoch = ((len(train_set) + config.batch_size - 1) //
config.batch_size)
lr_schedule = LRSchedule(lr_init=config.lr_init,
start_decay=config.start_decay*n_batches_epoch,
end_decay=config.end_decay*n_batches_epoch,
end_warm=config.end_warm*n_batches_epoch,
lr_warm=config.lr_warm,
lr_min=config.lr_min)
# Build model and train
model = Img2SeqModel(config, dir_output, vocab)
model.build_train(config)
model.train(config, train_set, val_set, lr_schedule)
def main(data, vocab, training, model, output):
# Load configs
dir_output = output
config = Config([data, vocab, training, model])
config.save(dir_output)
vocab = Vocab(config)
# Load datasets
train_set = DataGenerator(path_formulas=config.path_formulas_train,
dir_images=config.dir_images_train, img_prepro=greyscale,
max_iter=config.max_iter, bucket=config.bucket_train,
path_matching=config.path_matching_train,
max_len=config.max_length_formula,
form_prepro=vocab.form_prepro)
all_img = []
all_formula = []
for i, (_img, _formula) in enumerate(minibatches(train_set, batch_size)):
all_img.append(_img)
if _formula is not None:
_formula, _formula_length = pad_batch_formulas(
_formula,
vocab.id_pad,
vocab.id_end
)
all_formula.append(_formula)
np.save('np_formula', np.array(all_formula))
np.save('np_img', np.array(all_img))
print("DONE EXPORTING NUMPY FILES")
return None
val_set = DataGenerator(path_formulas=config.path_formulas_val,
dir_images=config.dir_images_val, img_prepro=greyscale,
max_iter=config.max_iter, bucket=config.bucket_val,
path_matching=config.path_matching_val,
max_len=config.max_length_formula,
form_prepro=vocab.form_prepro)
# Define learning rate schedule
n_batches_epoch = ((len(train_set) + config.batch_size - 1) //
config.batch_size)
lr_schedule = LRSchedule(lr_init=config.lr_init,
start_decay=config.start_decay*n_batches_epoch,
end_decay=config.end_decay*n_batches_epoch,
end_warm=config.end_warm*n_batches_epoch,
lr_warm=config.lr_warm,
lr_min=config.lr_min)
# Build model and train
model = Img2SeqModel(config, dir_output, vocab)
model.build_train(config)
model.train(config, train_set, val_set, lr_schedule)
def main(results):
# restore config and model
dir_output = results
config_data = Config(dir_output + "data.json")
config_vocab = Config(dir_output + "vocab.json")
config_model = Config(dir_output + "model.json")
vocab = Vocab(config_vocab)
model = Img2SeqCtcModel(config_model, dir_output, vocab)
model.build_pred()
model.restore_session(dir_output + "model.weights")
# load dataset
test_set = DataGenerator(path_formulas=config_data.path_formulas_test,
dir_images=config_data.dir_images_test, img_prepro=greyscale,
max_iter=config_data.max_iter, bucket=config_data.bucket_test,
path_matching=config_data.path_matching_test,
max_len=config_data.max_length_formula,
form_prepro=vocab.form_prepro,)
# use model to write predictions in files
config_eval = Config({"dir_answers": dir_output + "formulas_test/",
"batch_size": 20})
files, perplexity = model.write_prediction(config_eval, test_set)
formula_ref, formula_hyp = files[0], files[1]
# score the ref and prediction files
scores = score_files(formula_ref, formula_hyp)
scores["perplexity"] = perplexity
msg = " - ".join(["{} {:04.2f}".format(k, v) for k, v in scores.items()])
model.logger.info("- Test Txt: {}".format(msg))
def main(data, vocab):
data_config = Config(data)
# datasets
train_set = DataGenerator(path_formulas=data_config.path_formulas_train,
dir_images=data_config.dir_images_train,
path_matching=data_config.path_matching_train)
# test_set = DataGenerator(
# path_formulas=data_config.path_formulas_test,
# dir_images=data_config.dir_images_test,
# path_matching=data_config.path_matching_test)
# val_set = DataGenerator(
# path_formulas=data_config.path_formulas_val,
# dir_images=data_config.dir_images_val,
# path_matching=data_config.path_matching_val)
#
# # produce images and matching files
# train_set.build(buckets=data_config.buckets)
# test_set.build(buckets=data_config.buckets)
# val_set.build(buckets=data_config.buckets)
# vocab
vocab_config = Config(vocab)
vocab = build_vocab([train_set], min_count=vocab_config.min_count_tok)
write_vocab(vocab, vocab_config.path_vocab)
def main(results):
# restore config and model
dir_output = results
config_data = Config(dir_output + "data.json")
config_vocab = Config(dir_output + "vocab.json")
config_model = Config(dir_output + "model.json")
vocab = Vocab(config_vocab)
model = Img2SeqModel(config_model, dir_output, vocab)
model.build_pred()
model.restore_session(dir_output + "model.weights/")
# load dataset
test_set = DataGenerator(path_formulas=config_data.path_formulas_test,
dir_images=config_data.dir_images_test, img_prepro=greyscale,
max_iter=config_data.max_iter, bucket=config_data.bucket_test,
path_matching=config_data.path_matching_test,
max_len=config_data.max_length_formula,
form_prepro=vocab.form_prepro,)
# build images from formulas
formula_ref = path.join(dir_output, "formulas_test/ref.txt")
formula_hyp = path.join(dir_output, "formulas_test/hyp_0.txt")
images_ref = path.join(dir_output, "images_test/ref/")
images_test = path.join(dir_output, "images_test/hyp_0/")
build_images(load_formulas(formula_ref), images_ref)
build_images(load_formulas(formula_hyp), images_test)
# score the repositories
scores = score_dirs(images_ref, images_test, greyscale)
msg = " - ".join(["{} {:04.2f}".format(k, v) for k, v in scores.items()])
model.logger.info("- Eval Img: {}".format(msg))
def main(data, vocab):
data_config = Config(data)
# datasets
train_set = DataGenerator(path_formulas=data_config.path_formulas_train,
dir_images=data_config.dir_images_train,
path_matching=data_config.path_matching_train)
test_set = DataGenerator(path_formulas=data_config.path_formulas_test,
dir_images=data_config.dir_images_test,
path_matching=data_config.path_matching_test)
val_set = DataGenerator(path_formulas=data_config.path_formulas_val,
dir_images=data_config.dir_images_val,
path_matching=data_config.path_matching_val)
# produce images and matching files
train_set.build(buckets=data_config.buckets, fontIndex=0, fontLength=7)
test_set.build(buckets=data_config.buckets, fontIndex=0, fontLength=7)
val_set.build(buckets=data_config.buckets, fontIndex=0, fontLength=7)
# vocab
vocab_config = Config(vocab)
vocab = build_vocab([train_set], min_count=vocab_config.min_count_tok)
write_vocab(vocab, vocab_config.path_vocab)
def main(data, vocab):
data_config = Config(data)
# datasets
train_set = DataGenerator(path_formulas=data_config.path_formulas_train,
dir_images=data_config.dir_images_train,
path_matching=data_config.path_matching_train)
test_set = DataGenerator(path_formulas=data_config.path_formulas_test,
dir_images=data_config.dir_images_test,
path_matching=data_config.path_matching_test)
val_set = DataGenerator(path_formulas=data_config.path_formulas_val,
dir_images=data_config.dir_images_val,
path_matching=data_config.path_matching_val)
# produce images and matching files
# train_set.build(buckets=None, n_threads=1)
train_set.build(buckets=None)
test_set.build(buckets=None)
val_set.build(buckets=None)
# train_set.build(buckets=data_config.buckets)
# test_set.build(buckets=data_config.buckets)
# val_set.build(buckets=data_config.buckets)
# p = Augmentor.Pipeline(data_config.dir_images_train)
# p.zoom(probability=1, min_factor=1.1, max_factor=1.5)
# # p.process()
# augmented_images, labels = p.sample(3)
# print labels
# vocab
vocab_config = Config(vocab)
vocab = build_vocab([train_set], min_count=vocab_config.min_count_tok)
write_vocab(vocab, vocab_config.path_vocab)
dir_output = "results/small/"
config = Config([
"configs/data_small.json",
"configs/vocab_small.json",
"configs/training_small.json",
"configs/model.json",
])
config.save(dir_output)
vocab = Vocab(config)
train_set = DataGenerator(
path_formulas=config.path_formulas_train,
dir_images=config.dir_images_train,
img_prepro=greyscale,
max_iter=config.max_iter,
bucket=config.bucket_train,
path_matching=config.path_matching_train,
max_len=config.max_length_formula,
form_prepro=vocab.form_prepro
)
val_set = DataGenerator(
path_formulas=config.path_formulas_val,
dir_images=config.dir_images_val,
img_prepro=greyscale,
max_iter=config.max_iter,
bucket=config.bucket_val,
path_matching=config.path_matching_val,
max_len=config.max_length_formula,
form_prepro=vocab.form_prepro
)
