def main():
start = time.time()
parser = args.parse_args()
# run some checks on arguments
check_args(parser)
# format logging
log_name = os.path.join(
parser.run_log,
'{}_run_log_{}.log'.format(parser.experiment,
dt.now().strftime("%Y%m%d_%H%M")))
log.basicConfig(filename=log_name,
format='%(asctime)s | %(name)s -- %(message)s',
level=log.INFO)
os.chmod(log_name, parser.access_mode)
# set device to CPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Starting experiment {} VN -> EN NMT on {}.".format(
parser.experiment, device))
log.info("Starting experiment {} VN -> EN NMT on {}.".format(
parser.experiment, device))
# set seed for replication
random.seed(parser.seed)
np.random.seed(parser.seed)
torch.manual_seed(parser.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(parser.seed)
log.info("For reproducibility, the seed is set to {}.".format(parser.seed))
# set file paths
source_name = parser.source_name
target_name = parser.target_name
# get saved models dir
base_saved_models_dir = parser.save_dir
saved_models_dir = os.path.join(base_saved_models_dir,
source_name + '2' + target_name)
plots_dir = parser.plots_dir
log.info("We will save the models in this directory: {}".format(
saved_models_dir))
log.info("We will save the plots in this directory: {}".format(plots_dir))
# get data dir
main_data_path = parser.data_dir
path_to_train_data = {
'source': main_data_path + 'train.tok.' + source_name,
'target': main_data_path + 'train.tok.' + target_name
}
path_to_dev_data = {
'source': main_data_path + 'dev.tok.' + source_name,
'target': main_data_path + 'dev.tok.' + target_name
}
path_to_test_data = {
'source': main_data_path + 'test.tok.' + source_name,
'target': main_data_path + 'test.tok.' + target_name
}
# Configuration
bs = parser.batch_size
log.info("Batch size = {}.".format(bs))
enc_emb = parser.enc_emb
enc_hidden = parser.enc_hidden
enc_layers = parser.enc_layers
rnn_type = parser.rnn_type
dec_emb = parser.dec_emb
dec_hidden = parser.dec_hidden
dec_layers = parser.dec_layers
learning_rate = parser.learning_rate
num_epochs = parser.epochs
attn_flag = parser.wo_attn
log.info("The attention flag is set to {}.".format(attn_flag))
beam_size = parser.beam_size
log.info("We evaluate using beam size of {}.".format(beam_size))
train, val, test, en_lang, vi_lang = dataset_helper.train_val_load(
"", main_data_path)
# get vocab sizes
log.info('English has vocab size of: {} words.'.format(en_lang.n_words))
log.info('Vietnamese has vocab size of: {} words.'.format(vi_lang.n_words))
# get max sentence length by 95% percentile
MAX_LEN = int(train['en_len'].quantile(0.95))
log.info(
'We will have a max sentence length of {} (95 percentile).'.format(
MAX_LEN))
# set data loaders
bs_dict = {'train': bs, 'validate': 1, 'test': 1}
shuffle_dict = {'train': True, 'validate': False, 'test': False}
train_used = train
val_used = val
collate_fn_dict = {
'train': partial(dataset_helper.vocab_collate_func, MAX_LEN=MAX_LEN),
'validate': dataset_helper.vocab_collate_func_val,
'test': dataset_helper.vocab_collate_func_val
}
transformed_dataset = {
'train': dataset_helper.Vietnamese(train_used),
'validate': dataset_helper.Vietnamese(val_used, val=True),
'test': dataset_helper.Vietnamese(test, val=True)
}
dataloader = {
x: DataLoader(transformed_dataset[x],
batch_size=bs_dict[x],
collate_fn=collate_fn_dict[x],
shuffle=shuffle_dict[x],
num_workers=0)
for x in ['train', 'validate', 'test']
}
# instantiate encoder/decoder
encoder_wo_att = nnet_models.EncoderRNN(input_size=vi_lang.n_words,
embed_dim=enc_emb,
hidden_size=enc_hidden,
n_layers=enc_layers,
rnn_type=rnn_type).to(device)
decoder_wo_att = nnet_models.AttentionDecoderRNN(
output_size=en_lang.n_words,
embed_dim=dec_emb,
hidden_size=dec_hidden,
n_layers=dec_layers,
attention=attn_flag).to(device)
# instantiate optimizer
if parser.optimizer == 'sgd':
encoder_optimizer = optim.SGD(encoder_wo_att.parameters(),
lr=learning_rate,
nesterov=True,
momentum=0.99)
decoder_optimizer = optim.SGD(decoder_wo_att.parameters(),
lr=learning_rate,
nesterov=True,
momentum=0.99)
elif parser.optimizer == 'adam':
# lee kho learning rate
encoder_optimizer = optim.Adam(encoder_wo_att.parameters(), lr=1e-4)
decoder_optimizer = optim.Adam(decoder_wo_att.parameters(), lr=1e-4)
else:
raise ValueError('Invalid optimizer!')
# instantiate scheduler
enc_scheduler = ReduceLROnPlateau(encoder_optimizer,
min_lr=1e-4,
factor=0.5,
patience=0)
dec_scheduler = ReduceLROnPlateau(decoder_optimizer,
min_lr=1e-4,
factor=0.5,
patience=0)
criterion = nn.NLLLoss(ignore_index=global_variables.PAD_IDX)
log.info(
"Seq2Seq Model with the following parameters: batch_size = {}, learning_rate = {}, rnn_type = {}, enc_emb = {}, enc_hidden = {}, enc_layers = {}, dec_emb = {}, dec_hidden = {}, dec_layers = {}, num_epochs = {}, source_name = {}, target_name = {}"
.format(bs, learning_rate, rnn_type, enc_emb, enc_hidden, enc_layers,
dec_emb, dec_hidden, dec_layers, num_epochs, source_name,
target_name))
# do we want to train again?
train_again = False
encoder_save = '{}_wo_att_{}bs_{}hs_{}_{}beam_enc_{}_layer'.format(
rnn_type, bs, enc_hidden, parser.optimizer, beam_size, enc_layers)
decoder_save = '{}_wo_att_{}bs_{}hs_{}_{}beam_dec_{}_layer'.format(
rnn_type, bs, enc_hidden, parser.optimizer, beam_size, dec_layers)
if os.path.exists(utils.get_full_filepath(
saved_models_dir, encoder_save)) and os.path.exists(
utils.get_full_filepath(saved_models_dir,
decoder_save)) and (not train_again):
log.info("Retrieving saved encoder from {}".format(
utils.get_full_filepath(saved_models_dir, encoder_save)))
log.info("Retrieving saved decoder from {}".format(
utils.get_full_filepath(saved_models_dir, decoder_save)))
encoder_wo_att.load_state_dict(
torch.load(utils.get_full_filepath(saved_models_dir,
encoder_save)))
decoder_wo_att.load_state_dict(
torch.load(utils.get_full_filepath(saved_models_dir,
decoder_save)))
else:
log.info("Check if encoder path exists: {}".format(
utils.get_full_filepath(saved_models_dir, encoder_save)))
log.info("Check if decoder path exists: {}".format(
utils.get_full_filepath(saved_models_dir, decoder_save)))
log.info("Encoder and Decoder do not exist! Starting to train...")
encoder_wo_att, decoder_wo_att, loss_hist, acc_hist = train_utilities.train_model(
encoder_optimizer,
decoder_optimizer,
encoder_wo_att,
decoder_wo_att,
criterion,
"no_attention",
dataloader,
en_lang,
vi_lang,
saved_models_dir,
encoder_save,
decoder_save,
num_epochs=num_epochs,
rm=0.95,
enc_scheduler=enc_scheduler,
dec_scheduler=dec_scheduler)
log.info("Total time is: {} min : {} s".format(
(time.time() - start) // 60, (time.time() - start) % 60))
log.info(
"We will save the encoder/decoder in this directory: {}".format(
saved_models_dir))
# BLEU with beam size
bleu_no_unk, att_score_wo, pred_wo, src_wo = train_utilities.validation_beam_search(
encoder_wo_att,
decoder_wo_att,
dataloader['validate'],
en_lang,
vi_lang,
'no_attention',
beam_size,
verbose=False)
log.info("Bleu-{} Score (No UNK): {}".format(beam_size, bleu_no_unk))
print("Bleu-{} Score (No UNK): {}".format(beam_size, bleu_no_unk))
bleu_unk, att_score_wo, pred_wo, src_wo = train_utilities.validation_beam_search(
encoder_wo_att,
decoder_wo_att,
dataloader['validate'],
en_lang,
vi_lang,
'no_attention',
beam_size,
verbose=False,
replace_unk=True)
log.info("Bleu-{} Score (UNK): {}".format(beam_size, bleu_unk))
print("Bleu-{} Score (UNK): {}".format(beam_size, bleu_unk))
# generate 5 random predictions
indexes = range(len(pred_wo))
for i in np.random.choice(indexes, 5):
print('Source: {} \nPrediction: {}\n---'.format(src_wo[i], pred_wo[i]))
log.info('Source: {} \nPrediction: {}\n---'.format(
src_wo[i], pred_wo[i]))
log.info("Exported Binned Bleu Score Plot to {}!".format(plots_dir))
_, _, fig = utils.get_binned_bl_score(
encoder=encoder_wo_att,
decoder=decoder_wo_att,
val_dataset=transformed_dataset['validate'],
attn_flag=attn_flag,
beam_size=beam_size,
location=plots_dir,
collate=collate_fn_dict['validate'],
lang_en=en_lang,
lang_vi=vi_lang)
DataLoader(dataset_dict['val'],
batch_size=1,
collate_fn=dataset_helper.vocab_collate_func_val,
shuffle=True,
num_workers=0),
'test':
DataLoader(dataset_dict['test'],
batch_size=1,
collate_fn=dataset_helper.vocab_collate_func_val,
shuffle=True,
num_workers=0)
}
encoder = nnet_models.EncoderRNN(
dataset_dict['train'].source_lang_obj.n_words,
embed_dim=source_embed_dim,
hidden_size=source_hidden_size,
rnn_layers=source_rnn_layers,
rnn_type=source_rnn_type).to(device)
decoder = nnet_models.DecoderRNN(
dataset_dict['train'].target_lang_obj.n_words,
embed_dim=target_embed_dim,
hidden_size=target_hidden_size,
n_layers=target_rnn_layers,
attention=attention).to(device)
encoder_optimizer = optim.SGD(encoder.parameters(),
lr=0.25,
nesterov=True,
momentum=0.99)
enc_scheduler = ReduceLROnPlateau(encoder_optimizer,