本文整理汇总了Python中models.EncoderRNN方法的典型用法代码示例。如果您正苦于以下问题：Python models.EncoderRNN方法的具体用法？Python models.EncoderRNN怎么用？Python models.EncoderRNN使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类models的用法示例。
在下文中一共展示了models.EncoderRNN方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: import models [as 别名]
# 或者: from models import EncoderRNN [as 别名]
def main(opt):
dataset = VideoDataset(opt, "test")
opt["vocab_size"] = dataset.get_vocab_size()
opt["seq_length"] = dataset.max_len
if opt["model"] == 'S2VTModel':
model = S2VTModel(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
elif opt["model"] == "S2VTAttModel":
encoder = EncoderRNN(opt["dim_vid"], opt["dim_hidden"], bidirectional=opt["bidirectional"],
input_dropout_p=opt["input_dropout_p"], rnn_dropout_p=opt["rnn_dropout_p"])
decoder = DecoderRNN(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
input_dropout_p=opt["input_dropout_p"],
rnn_dropout_p=opt["rnn_dropout_p"], bidirectional=opt["bidirectional"])
model = S2VTAttModel(encoder, decoder).cuda()
#model = nn.DataParallel(model)
# Setup the model
model.load_state_dict(torch.load(opt["saved_model"]))
crit = utils.LanguageModelCriterion()
test(model, crit, dataset, dataset.get_vocab(), opt) 示例2: main
# 需要导入模块: import models [as 别名]
# 或者: from models import EncoderRNN [as 别名]
def main(opt):
dataset = VideoDataset(opt, "test")
opt["vocab_size"] = dataset.get_vocab_size()
opt["seq_length"] = dataset.max_len
if opt['beam_size'] != 1:
assert opt["batch_size"] == 1
if opt["model"] == 'S2VTModel':
model = S2VTModel(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"], opt['dim_vid'],
n_layers=opt['num_layers'],
rnn_cell=opt['rnn_type'],
bidirectional=opt["bidirectional"],
rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
elif opt["model"] == "S2VTAttModel":
encoder = EncoderRNN(opt["dim_vid"], opt["dim_hidden"],
n_layers=opt['num_layers'],
rnn_cell=opt['rnn_type'], bidirectional=opt["bidirectional"],
input_dropout_p=opt["input_dropout_p"], rnn_dropout_p=opt["rnn_dropout_p"])
decoder = DecoderRNN(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
n_layers=opt['num_layers'],
rnn_cell=opt['rnn_type'], input_dropout_p=opt["input_dropout_p"],
rnn_dropout_p=opt["rnn_dropout_p"], bidirectional=opt["bidirectional"])
model = S2VTAttModel(encoder, decoder).cuda()
model = nn.DataParallel(model)
# Setup the model
model.load_state_dict(torch.load(opt["saved_model"]))
crit = utils.LanguageModelCriterion()
test(model, crit, dataset, dataset.get_vocab(), opt) 示例3: main
# 需要导入模块: import models [as 别名]
# 或者: from models import EncoderRNN [as 别名]
def main(opt):
dataset = VideoDataset(opt, 'train')
dataloader = DataLoader(dataset, batch_size=opt["batch_size"], shuffle=True)
opt["vocab_size"] = dataset.get_vocab_size()
if opt["model"] == 'S2VTModel':
model = S2VTModel(
opt["vocab_size"],
opt["max_len"],
opt["dim_hidden"],
opt["dim_word"],
opt['dim_vid'],
rnn_cell=opt['rnn_type'],
n_layers=opt['num_layers'],
bidirectional=opt["bidirectional"],
rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
elif opt["model"] == "S2VTAttModel":
encoder = EncoderRNN(
opt["dim_vid"],
opt["dim_hidden"],
n_layers=opt['num_layers'],
bidirectional=opt["bidirectional"],
input_dropout_p=opt["input_dropout_p"],
rnn_cell=opt['rnn_type'],
rnn_dropout_p=opt["rnn_dropout_p"])
decoder = DecoderRNN(
opt["vocab_size"],
opt["max_len"],
opt["dim_hidden"],
opt["dim_word"],
n_layers=opt['num_layers'],
input_dropout_p=opt["input_dropout_p"],
rnn_cell=opt['rnn_type'],
rnn_dropout_p=opt["rnn_dropout_p"],
bidirectional=opt["bidirectional"])
model = S2VTAttModel(encoder, decoder).cuda()
crit = utils.LanguageModelCriterion()
rl_crit = utils.RewardCriterion()
optimizer = optim.Adam(
model.parameters(),
lr=opt["learning_rate"],
weight_decay=opt["weight_decay"])
exp_lr_scheduler = optim.lr_scheduler.StepLR(
optimizer,
step_size=opt["learning_rate_decay_every"],
gamma=opt["learning_rate_decay_rate"])
train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit) 示例4: main
# 需要导入模块: import models [as 别名]
# 或者: from models import EncoderRNN [as 别名]
def main(opt):
dataset = VideoDataset(opt, 'train')
dataloader = DataLoader(dataset, batch_size=opt["batch_size"], shuffle=True)
opt["vocab_size"] = dataset.get_vocab_size()
if opt["model"] == 'S2VTModel':
model = S2VTModel(
opt["vocab_size"],
opt["max_len"],
opt["dim_hidden"],
opt["dim_word"],
opt['dim_vid'],
rnn_cell=opt['rnn_type'],
n_layers=opt['num_layers'],
rnn_dropout_p=opt["rnn_dropout_p"])
elif opt["model"] == "S2VTAttModel":
encoder = EncoderRNN(
opt["dim_vid"],
opt["dim_hidden"],
bidirectional=opt["bidirectional"],
input_dropout_p=opt["input_dropout_p"],
rnn_cell=opt['rnn_type'],
rnn_dropout_p=opt["rnn_dropout_p"])
decoder = DecoderRNN(
opt["vocab_size"],
opt["max_len"],
opt["dim_hidden"],
opt["dim_word"],
input_dropout_p=opt["input_dropout_p"],
rnn_cell=opt['rnn_type'],
rnn_dropout_p=opt["rnn_dropout_p"],
bidirectional=opt["bidirectional"])
model = S2VTAttModel(encoder, decoder)
model = model.cuda()
crit = utils.LanguageModelCriterion()
rl_crit = utils.RewardCriterion()
optimizer = optim.Adam(
model.parameters(),
lr=opt["learning_rate"],
weight_decay=opt["weight_decay"])
exp_lr_scheduler = optim.lr_scheduler.StepLR(
optimizer,
step_size=opt["learning_rate_decay_every"],
gamma=opt["learning_rate_decay_rate"])
train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit) 示例5: main
# 需要导入模块: import models [as 别名]
# 或者: from models import EncoderRNN [as 别名]
def main():
voc = Lang('data/WORDMAP.json')
print("voc.n_words: " + str(voc.n_words))
train_data = SaDataset('train', voc)
val_data = SaDataset('valid', voc)
# Initialize encoder
encoder = EncoderRNN(voc.n_words, hidden_size, encoder_n_layers, dropout)
# Use appropriate device
encoder = encoder.to(device)
# Initialize optimizers
print('Building optimizers ...')
optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
best_acc = 0
epochs_since_improvement = 0
# Epochs
for epoch in range(start_epoch, epochs):
# Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20
if epochs_since_improvement == 20:
break
if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0:
adjust_learning_rate(optimizer, 0.8)
# One epoch's training
train(epoch, train_data, encoder, optimizer)
# One epoch's validation
val_acc, val_loss = valid(val_data, encoder)
print('\n * ACCURACY - {acc:.3f}, LOSS - {loss:.3f}\n'.format(acc=val_acc, loss=val_loss))
# Check if there was an improvement
is_best = val_acc > best_acc
best_acc = max(best_acc, val_acc)
if not is_best:
epochs_since_improvement += 1
print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,))
else:
epochs_since_improvement = 0
# Save checkpoint
save_checkpoint(epoch, encoder, optimizer, val_acc, is_best)
# Reshuffle samples
np.random.shuffle(train_data.samples)
np.random.shuffle(val_data.samples)