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(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(
index_file=config.index_train,
path_formulas=config.path_formulas_train,
dir_images=config.dir_images_train,
max_iter=config.max_iter,
path_matching=config.path_matching_train,
max_len=config.max_length_formula,
form_prepro=vocab.form_prepro)
val_set = DataGenerator(
index_file=config.index_val,
path_formulas=config.path_formulas_val,
dir_images=config.dir_images_val,
max_iter=config.max_iter,
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)
print len(train_set)
print config.batch_size
print n_batches_epoch
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)
transfer_model = config.transfer_model
# Build model and train
model = Img2SeqModel(config, dir_output, vocab)
model.build_train(config)
if transfer_model and os.path.isdir(transfer_model):
model.restore_session(transfer_model)
model.train(config, train_set, val_set, lr_schedule)
# _attn_cell_config = {
# 'cell_type': 'lstm',
# 'num_units': 12,
# 'dim_e' : 14,
# 'dim_o' : 16,
# 'dim_embeddings': 32,
# }
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,
