dataloader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
collate_fn=dataset.collate_fn)
# iterations per epoch
setattr(args, 'iter_per_epoch',
math.ceil(dataset.num_data_points[args.split] / args.batch_size))
print("{} iter per epoch.".format(args.iter_per_epoch))
# ----------------------------------------------------------------------------
# setup the model
# ----------------------------------------------------------------------------
encoder = Encoder(model_args)
encoder.load_state_dict(components['encoder'])
decoder = Decoder(model_args, encoder)
decoder.load_state_dict(components['decoder'])
print("Loaded model from {}".format(args.load_path))
if args.gpuid >= 0:
encoder = encoder.cuda()
decoder = decoder.cuda()
# ----------------------------------------------------------------------------
# evaluation
# ----------------------------------------------------------------------------
print("Evaluation start time: {}".format(
datetime.datetime.strftime(datetime.datetime.utcnow(),
# ----------------------------------------------------------------------------
# setup the model
# ----------------------------------------------------------------------------
encoder = Encoder(model_args)
decoder = Decoder(model_args, encoder)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(list(encoder.parameters()) + list(decoder.parameters()),
lr=args.lr,
weight_decay=args.weight_decay)
encoder.word_embed.init_embedding('data/glove/glove6b_init_300d_1.0.npy')
start_epoch = 0
if args.load_path != '':
components = torch.load(args.load_path)
encoder.load_state_dict(components.get('encoder', components))
decoder.load_state_dict(components.get('decoder', components))
optimizer.load_state_dict(components.get('optimizer', components))
start_epoch = components['epoch']
print("Loaded model from {}".format(args.load_path))
print("Decoder: {}".format(args.decoder))
args_for_save = encoder.args
encoder = nn.DataParallel(encoder).cuda()
decoder = nn.DataParallel(decoder).cuda()
criterion = criterion.cuda()
# ----------------------------------------------------------------------------
# training
# ----------------------------------------------------------------------------
encoder.train()