sum(param.nelement() for param in nli_model.encoder.parameters()
if param.requires_grad)))
best_nli_dev_acc = 0
best_nli_train_acc = -np.infty
# load trained model from checkpoint
if len(sys.argv) > 1:
checkpoint_dir = sys.argv[1]
print('loading from checkpoint in {}'.format(checkpoint_dir))
model_pipeline_pytorch.load_checkpoint(nli_model,
checkpoint=checkpoint_dir)
nli_state['lr'] = 0.01
print('resetting lr as {}'.format(nli_state['lr']))
nli_model.embed.weight.data = load_embeddings.load_embeddings(
nli_dm.vocab, constants.EMBED_DATA_PATH, nli_args.embedding_size)
squad_model.embed.weight.data = load_embeddings.load_embeddings(
squad_dm.vocab, constants.EMBED_DATA_PATH, nli_args.embedding_size)
nli_criterion = nn.NLLLoss()
squad_criterion = nn.NLLLoss()
for epoch in range(NumEpochs):
nli_dm.shuffle_train_data()
squad_dm.shuffle_train_data()
print('lr {}'.format(nli_state['lr']))
nli_optimizer = optim.SGD(
[param for param in nli_model.parameters() if param.requires_grad],
lr=nli_state['lr'])
squad_optimizer = optim.SGD([
})
squad_state = {k: v for k, v in squad_args.items()}
if __name__ == "__main__":
print(args)
checkpoint = sys.argv[1]
print('found checkpoint dir {}'.format(checkpoint))
dm = wrangle.DataManager(args)
if args.add_squad: # add squad to vocab to match checkpoint
squad_dm = SquadDataManager(squad_args, vocab=dm.vocab)
args.n_embed = dm.vocab.n_words
if args.type == 'siamese':
model = siamese_pytorch.SiameseClassifier(config=args)
model.embed.weight.data = load_embeddings.load_embeddings(
dm.vocab, constants.EMBED_DATA_PATH, args.embedding_size)
elif args.type == 'decomposable':
model = decomposable_pytorch.SNLIClassifier(config=args)
model.encoder.embedding.weight.data = load_embeddings.load_embeddings(
dm.vocab, constants.EMBED_DATA_PATH, args.embedding_size)
else:
model = Seq2SeqPytorch(args=args, vocab=dm.vocab)
model.encoder.embedding.weight.data = load_embeddings.load_embeddings(
dm.vocab, constants.EMBED_DATA_PATH, args.embedding_size)
model_pipeline_pytorch.load_checkpoint(model, checkpoint=checkpoint)
model.eval()
if args.cuda:
model = model.cuda()