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main_att.py
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main_att.py
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from src_att import utils, encoder, decoder, model, trainer, cnn, embedding
import params
import glob, subprocess
import sys
import argparse
from Evaluation import edit_distance_, bleu_score_
def get_trainer(config):
vocab_config = {
'pathes' : [config['formulas_train_path'], config['formulas_validation_path']],
'unk_token_threshold' : config['unk_token_threshold'],
}
vocab = utils.Vocab(vocab_config)
vocab_size = len(vocab.token2idx.keys())
print(vocab_size)
cnn_model = cnn.CNN(config['cnn_params']).to(config['device'])
encoder_model = encoder.Encoder(config['cnn_params']['conv6_c'], config['encoder_hidden_size'],
config['bidirectional'], config['device']).to(config['device'])
train_loader_config = {
'batch_size' : config['batch_size'],
'images_path' : config['images_train_path'],
'formulas_path' : config['formulas_train_path'],
'sort_by_formulas_len' : False,
'shuffle' : True,
}
train_loader = utils.data_loader(vocab, train_loader_config)
embedding_model = embedding.Embedding(vocab_size, config['embedding_size'], vocab.pad_token).to(config['device'])
decoder_model = decoder.AttnDecoder(config['embedding_size'], config['decoder_hidden_size'],
config['encoder_hidden_size']*(2 if config['bidirectional'] else 1), vocab_size,
config['device']).to(config['device'])
_model = model.Model(cnn_model, encoder_model, embedding_model, decoder_model, config['device'])
trainer_config = {
'device' : config['device'],
'checkpoints_dir' : config['checkpoints_dir'],
'log_dir' : config['log_dir'],
'print_every_batch' : config['print_every_batch'],
'clip': config['clip'],
'learning_rate' : config['learning_rate'],
'learning_rate_decay' : config['learning_rate_decay'],
'learning_rate_decay_step' : config['learning_rate_decay_step'],
'learning_rate_min': config['learning_rate_min'],
'teacher_forcing_ratio' : config['teacher_forcing_ratio'],
'teacher_forcing_ratio_decay' : config['teacher_forcing_ratio_decay'],
'teacher_forcing_ratio_decay_step' : config['teacher_forcing_ratio_decay_step'],
'teacher_forcing_ratio_min': config['teacher_forcing_ratio_min'],
}
_trainer = trainer.Trainer(_model, train_loader, trainer_config)
return _trainer
def train(config):
logger_config = {
'owner' : 'Training.train',
'log_dir' : config['log_dir'],
'timezone' : 'UTC',
}
logger = utils.Logger(logger_config)
_trainer = get_trainer(config)
checkpoints = sorted(glob.glob(_trainer.checkpoints_dir + '/*.pt'))
if len(checkpoints):
_trainer.load(checkpoints[-1])
last_epoch = _trainer.current_epoch
predicted_path = 'tmp/predicted-train.txt'
# if shuffled or sorted by length -> should update target_path accordingly
target_path = config['formulas_train_path']
dot = target_path.rfind('.')
target_path = target_path[:dot] + '_' + target_path[dot:]
if _trainer.train_loader.shuffle or _trainer.train_loader.sort_by_formulas_len:
f = open(target_path, 'w')
for formula in _trainer.train_loader.formulas:
joinedformula = ' '.join(token for token in formula)
f.write(joinedformula + '\n')
f.close()
for epoch in range(last_epoch, config['epochs']):
predictions, epoch_loss, epoch_acc = _trainer.train_one_epoch()
checkpoint = _trainer.save(epoch+1, epoch_loss, epoch_acc)
bleu_message, edit_message = bleu_and_edit_distance(predictions, predicted_path, target_path)
logger(bleu_message)
logger(edit_message)
evaluate('validation', config, checkpoint)
def evaluate(evalset, config, checkpoint=None):
assert(evalset in ['validation', 'test'])
logger_config = {
'owner' : 'Training.evaluate',
'log_dir' : config['log_dir'],
'timezone' : 'UTC',
}
logger = utils.Logger(logger_config)
_trainer = get_trainer(config)
if checkpoint is None:
checkpoints = sorted(glob.glob(_trainer.checkpoints_dir + '/*.pt'))
assert(len(checkpoints))
checkpoint = checkpoints[-1]
_trainer.load(checkpoint)
if evalset == 'validation':
valid_loader_config = {
'batch_size': config['batch_size'],
'images_path': config['images_validation_path'],
'formulas_path': config['formulas_validation_path'],
}
loader = utils.data_loader(_trainer.train_loader.vocab, valid_loader_config)
elif evalset == 'test':
test_loader_config = {
'batch_size': config['batch_size'],
'images_path': config['images_test_path'],
}
loader = utils.data_loader(_trainer.train_loader.vocab, test_loader_config)
if loader.has_label:
predictions, attn_weights, loss, acc = _trainer.evaluate(loader, config['generation_method'])
logger('loss={}, acc={}'.format(loss, acc))
else:
predictions, attn_weights = _trainer.evaluate(loader, config['generation_method'])
target_path = config['formulas_validation_path'] if evalset == 'validation' else config['formulas_test_path']
predicted_path = 'tmp/predicted-{}.txt'.format(evalset)
bleu_message, edit_message = bleu_and_edit_distance(predictions, predicted_path, target_path)
logger(bleu_message)
logger(edit_message)
def bleu_and_edit_distance(predictions, predicted_path, target_path):
# with open(predicted_path, 'w') as f:
# for pred in predictions:
# f.write(pred+'\n')
# output = subprocess.check_output('python src/Evaluation/bleu_score.py --target-formulas {} --predicted-formulas {} --ngram 5'.
# format(target_path, predicted_path), shell=True)
# output = str(output)
# bleu_message = output[output.find('BLEU'):-3]
# output = subprocess.check_output('python src/Evaluation/edit_distance.py --target-formulas {} --predicted-formulas {}'.
# format(target_path, predicted_path), shell=True)
# output = str(output)
# edit_message = output[output.find('Edit'):-3]
# return bleu_message, edit_message
with open(predicted_path, 'w') as f:
for pred in predictions:
f.write(pred+'\n')
bleu_message = bleu_score_.bleu_score(target_path, predicted_path, ngram=5)
edit_message = edit_distance_.edit_distance(target_path, predicted_path)
return bleu_message, edit_message
def process_args():
parser = argparse.ArgumentParser()
parser.add_argument("--train", const=True, nargs='?', help='start training')
parser.add_argument("--evaluate", const=True, nargs='?', help='start evaluation')
parser.add_argument("--evalset", type=str, help='test or validation')
parser.add_argument("--checkpoint", type=str, help='path to checkpoint')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = process_args()
config = params.config
if args.train:
train(config)
elif args.evaluate:
evaluate(args.evalset, params.config, args.checkpoint)