def main(sents):
parser = utils.make_parser()
args = parser.parse_args()
# build model
# # get data
#data = Dataloader(args.input_json, args.input_ques_h5)
# # make op
op = {
"vocab_sz": 27699, #data.getVocabSize(),
"max_seq_len": 28, #data.getSeqLength(),
"emb_hid_dim": 256, #args.emb_hid_dim,
"emb_dim": 512, #args.emb_dim,
"enc_dim": 512, #args.enc_dim,
"enc_dropout": 0.5, #args.enc_dropout,
"enc_rnn_dim": 512, #args.enc_rnn_dim,
"gen_rnn_dim": 512, #args.gen_rnn_dim,
#"gen_dropout": 0.5,#args.gen_dropout,
#"lr": 0.0008,#args.learning_rate,
#"epochs": 1,#args.n_epoch
}
files = glob("save/*")
files.sort(key=os.path.getmtime)
WEIGHT_PATH = files[-1]
print("### Loading weights from {} ###".format(WEIGHT_PATH))
model = ParaphraseGenerator(op)
model.load_state_dict(torch.load(WEIGHT_PATH))
print("Maximum sequence length = {}".format(28))
with open('data/Vocab_Extra', 'rb') as f:
vocab = pickle.load(f)
sents = inputData(sents, 28, vocab, embed_model)
out, _, _ = model.forward(sents)
def main():
# get arguments ---
print("REMOVED METEOR SCORE FOR NOW")
parser = utils.make_parser()
args = parser.parse_args()
# build model
# # get data
data = Dataloader(args.input_json, args.input_ques_h5)
# # make op
op = {
"vocab_sz": data.getVocabSize(),
"max_seq_len": data.getSeqLength(),
"emb_hid_dim": args.emb_hid_dim,
"emb_dim": args.emb_dim,
"enc_dim": args.enc_dim,
"enc_dropout": args.enc_dropout,
"enc_rnn_dim": args.enc_rnn_dim,
"gen_rnn_dim": args.gen_rnn_dim,
"gen_dropout": args.gen_dropout,
"lr": args.learning_rate,
"epochs": args.n_epoch
}
print(op)
# # instantiate paraphrase generator
pgen = ParaphraseGenerator(op)
pgen.load_state_dict(
torch.load("save/model_epoch_0")) # REMEMBER TO CHANGE
# setup logging
logger = SummaryWriter(os.path.join(LOG_DIR, TIME + args.name))
subprocess.run(['mkdir', os.path.join(GEN_DIR, TIME)],
check=False,
shell=True)
# ready model for training
train_loader = Data.DataLoader(
Data.Subset(data, range(args.train_dataset_len)),
batch_size=args.batch_size,
shuffle=True,
)
test_loader = Data.DataLoader(Data.Subset(
data,
range(args.train_dataset_len,
args.train_dataset_len + args.val_dataset_len)),
batch_size=args.batch_size,
shuffle=True)
pgen_optim = optim.RMSprop(pgen.parameters(), lr=op["lr"])
pgen.train()
# train model
pgen = pgen.to(DEVICE)
cross_entropy_loss = nn.CrossEntropyLoss(ignore_index=data.PAD_token)
for epoch_ in range(op["epochs"]):
epoch = epoch_ + 1 # REMEMBER TO CHANGE
epoch_l1 = 0
epoch_l2 = 0
itr = 0
ph = []
pph = []
gpph = []
pgen.train()
for phrase, phrase_len, para_phrase, para_phrase_len, _ in tqdm(
train_loader, ascii=True, desc="epoch" + str(epoch)):
phrase = phrase.to(DEVICE)
para_phrase = para_phrase.to(DEVICE)
out, enc_out, enc_sim_phrase = pgen(
phrase.t(),
sim_phrase=para_phrase.t(),
train=True,
)
loss_1 = cross_entropy_loss(out.permute(1, 2, 0), para_phrase)
loss_2 = net_utils.JointEmbeddingLoss(enc_out, enc_sim_phrase)
pgen_optim.zero_grad()
(loss_1 + loss_2).backward()
pgen_optim.step()
# accumulate results
epoch_l1 += loss_1.item()
epoch_l2 += loss_2.item()
ph += net_utils.decode_sequence(data.ix_to_word, phrase)
pph += net_utils.decode_sequence(data.ix_to_word, para_phrase)
gpph += net_utils.decode_sequence(data.ix_to_word,
torch.argmax(out, dim=-1).t())
itr += 1
#torch.cuda.empty_cache()
# log results
logger.add_scalar("l2_train", epoch_l2 / itr, epoch)
logger.add_scalar("l1_train", epoch_l1 / itr, epoch)
scores = evaluate_scores(gpph, pph)
for key in scores:
logger.add_scalar(key + "_train", scores[key], epoch)
#dump_samples(ph, pph, gpph,
# os.path.join(GEN_DIR, TIME,
# str(epoch) + "_train.txt"))
# start validation
epoch_l1 = 0
epoch_l2 = 0
itr = 0
ph = []
pph = []
gpph = []
pgen.eval()
with torch.no_grad():
for phrase, phrase_len, para_phrase, para_phrase_len, _ in tqdm(
test_loader, ascii=True, desc="val" + str(epoch)):
phrase = phrase.to(DEVICE)
para_phrase = para_phrase.to(DEVICE)
out, enc_out, enc_sim_phrase = pgen(phrase.t(),
sim_phrase=para_phrase.t())
loss_1 = cross_entropy_loss(out.permute(1, 2, 0), para_phrase)
loss_2 = net_utils.JointEmbeddingLoss(enc_out, enc_sim_phrase)
epoch_l1 += loss_1.item()
epoch_l2 += loss_2.item()
ph += net_utils.decode_sequence(data.ix_to_word, phrase)
pph += net_utils.decode_sequence(data.ix_to_word, para_phrase)
gpph += net_utils.decode_sequence(
data.ix_to_word,
torch.argmax(out, dim=-1).t())
itr += 1
torch.cuda.empty_cache()
logger.add_scalar("l2_val", epoch_l2 / itr, epoch)
logger.add_scalar("l1_val", epoch_l1 / itr, epoch)
scores = evaluate_scores(gpph, pph)
for key in scores:
logger.add_scalar(key + "_val", scores[key], epoch)
#dump_samples(ph, pph, gpph,
# os.path.join(GEN_DIR, TIME,
# str(epoch) + "_val.txt"))
#save_model(pgen, pgen_optim, epoch, os.path.join(SAVE_DIR, TIME, str(epoch)))
save_model(pgen, pgen_optim, epoch,
"save/model_epoch_{}".format(epoch))
# wrap ups
logger.close()
print("Done !!")
def main():
# get arguments ---
parser = utils.make_parser()
args = parser.parse_args()
# build model
# # get data
if args.dataset_name == 'cub':
# length 21 since 75% of captions have length <= 19
dataset, train_loader = get_cub_200_2011_paraphrase_combined_vocab(
split='train_val',
no_start_end=args.no_start_end,
should_pad=True,
pad_to_length=21,
d_batch=args.batch_size)
_, test_loader = get_cub_200_2011_paraphrase_combined_vocab(
split='test',
no_start_end=args.no_start_end,
should_pad=True,
pad_to_length=21,
d_batch=args.batch_size)
elif args.dataset_name == 'quora':
# length 26 since that was used by the paper authors
dataset, train_loader = get_quora_paraphrase_dataset_combined_vocab(
split='train',
no_start_end=args.no_start_end,
should_pad=True,
pad_to_length=26,
d_batch=args.batch_size)
_, test_loader = get_quora_paraphrase_dataset_combined_vocab(
split='test',
no_start_end=args.no_start_end,
should_pad=True,
pad_to_length=26,
d_batch=args.batch_size)
else:
raise NotImplementedError
# # make op
op = {
"vocab_sz": dataset.d_vocab,
"max_seq_len": dataset.pad_to_length,
"emb_hid_dim": args.emb_hid_dim,
"emb_dim": args.emb_dim,
"enc_dim": args.enc_dim,
"enc_dropout": args.enc_dropout,
"enc_rnn_dim": args.enc_rnn_dim,
"gen_rnn_dim": args.gen_rnn_dim,
"gen_dropout": args.gen_dropout,
"lr": args.learning_rate,
"epochs": args.n_epoch
}
# # instantiate paraphrase generator
pgen = ParaphraseGenerator(op)
# setup logging
logger = SummaryWriter(os.path.join(LOG_DIR, TIME + args.name))
subprocess.run(
['mkdir',
os.path.join(GEN_DIR, TIME),
os.path.join(SAVE_DIR, TIME)],
check=False)
# ready model for training
pgen_optim = optim.RMSprop(pgen.parameters(), lr=op["lr"])
pgen.train()
# train model
pgen = pgen.to(DEVICE)
cross_entropy_loss = nn.CrossEntropyLoss(ignore_index=dataset.pad_token)
for epoch in range(op["epochs"]):
epoch_l1 = 0
epoch_l2 = 0
itr = 0
ph = []
pph = []
gpph = []
pgen.train()
for phrase, phrase_len, para_phrase, para_phrase_len in tqdm(
train_loader, ascii=True, desc="epoch" + str(epoch)):
phrase = phrase.to(DEVICE)
para_phrase = para_phrase.to(DEVICE)
out, enc_out, enc_sim_phrase = pgen(phrase.t(),
sim_phrase=para_phrase.t(),
train=True)
loss_1 = cross_entropy_loss(out.permute(1, 2, 0), para_phrase)
loss_2 = net_utils.JointEmbeddingLoss(enc_out, enc_sim_phrase)
pgen_optim.zero_grad()
(loss_1 + loss_2).backward()
pgen_optim.step()
# accumulate results
epoch_l1 += loss_1.item()
epoch_l2 += loss_2.item()
ph += [dataset.decode_caption(p) for p in phrase]
pph += [dataset.decode_caption(p) for p in para_phrase]
gpph += [
dataset.decode_caption(p)
for p in torch.argmax(out, dim=-1).t()
]
itr += 1
torch.cuda.empty_cache()
# log results
logger.add_scalar("l2_train", epoch_l2 / itr, epoch)
logger.add_scalar("l1_train", epoch_l1 / itr, epoch)
scores = evaluate_scores(gpph, pph)
for key in scores:
logger.add_scalar(key + "_train", scores[key], epoch)
dump_samples(ph, pph, gpph,
os.path.join(GEN_DIR, TIME,
str(epoch) + "_train.txt"))
# start validation
epoch_l1 = 0
epoch_l2 = 0
itr = 0
ph = []
pph = []
gpph = []
pgen.eval()
with torch.no_grad():
for phrase, phrase_len, para_phrase, para_phrase_len in tqdm(
test_loader, ascii=True, desc="val" + str(epoch)):
phrase = phrase.to(DEVICE)
para_phrase = para_phrase.to(DEVICE)
out, enc_out, enc_sim_phrase = pgen(phrase.t(),
sim_phrase=para_phrase.t())
loss_1 = cross_entropy_loss(out.permute(1, 2, 0), para_phrase)
loss_2 = net_utils.JointEmbeddingLoss(enc_out, enc_sim_phrase)
epoch_l1 += loss_1.item()
epoch_l2 += loss_2.item()
ph += [dataset.decode_caption(p) for p in phrase]
pph += [dataset.decode_caption(p) for p in para_phrase]
gpph += [
dataset.decode_caption(p)
for p in torch.argmax(out, dim=-1).t()
]
itr += 1
torch.cuda.empty_cache()
logger.add_scalar("l2_val", epoch_l2 / itr, epoch)
logger.add_scalar("l1_val", epoch_l1 / itr, epoch)
scores = evaluate_scores(gpph, pph)
for key in scores:
logger.add_scalar(key + "_val", scores[key], epoch)
dump_samples(ph, pph, gpph,
os.path.join(GEN_DIR, TIME,
str(epoch) + "_val.txt"))
save_model(pgen, pgen_optim, epoch,
os.path.join(SAVE_DIR, TIME, str(epoch)))
# wrap ups
logger.close()
print("Done !!")