Python utils.LanguageModelCriterion方法代码示例

本文整理汇总了Python中misc.utils.LanguageModelCriterion方法的典型用法代码示例。如果您正苦于以下问题：Python utils.LanguageModelCriterion方法的具体用法？Python utils.LanguageModelCriterion怎么用？Python utils.LanguageModelCriterion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misc.utils的用法示例。

在下文中一共展示了utils.LanguageModelCriterion方法的6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: init

# 需要导入模块: from misc import utils [as 别名]
# 或者: from misc.utils import LanguageModelCriterion [as 别名]
def __init__(self, model, opt):
        super(LossWrapper, self).__init__()
        self.opt = opt
        self.model = model
        if opt.label_smoothing > 0:
            self.crit = utils.LabelSmoothing(smoothing=opt.label_smoothing)
        else:
            self.crit = utils.LanguageModelCriterion()
        self.rl_crit = utils.RewardCriterion()
        self.struc_crit = utils.StructureLosses(opt)


        if opt.vse_model != 'None':
            self.vse = VSEFCModel(opt)
            for p in self.vse.parameters():
                p.requires_grad = False
            self.retrieval_reward_weight = opt.retrieval_reward_weight # 

            self.vse.load_state_dict({k[4:]:v for k,v in torch.load(opt.initialize_retrieval).items() if 'vse.' in k})
        self.retrieval_reward_weight = 0

开发者ID:ruotianluo，项目名称:GoogleConceptualCaptioning，代码行数:22，代码来源:loss_wrapper.py

示例2: main

# 需要导入模块: from misc import utils [as 别名]
# 或者: from misc.utils import LanguageModelCriterion [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)

开发者ID:xiadingZ，项目名称:video-caption.pytorch，代码行数:22，代码来源:eval.py

示例3: init

# 需要导入模块: from misc import utils [as 别名]
# 或者: from misc.utils import LanguageModelCriterion [as 别名]
def __init__(self, model, opt):
        super(LossWrapper, self).__init__()
        self.opt = opt
        self.model = model
        if opt.label_smoothing > 0:
            self.crit = utils.LabelSmoothing(smoothing=opt.label_smoothing)
        else:
            self.crit = utils.LanguageModelCriterion()
        self.rl_crit = utils.RewardCriterion()

开发者ID:husthuaan，项目名称:AAT，代码行数:11，代码来源:loss_wrapper.py

示例4: main

# 需要导入模块: from misc import utils [as 别名]
# 或者: from misc.utils import LanguageModelCriterion [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)

开发者ID:Sundrops，项目名称:video-caption.pytorch，代码行数:30，代码来源:eval.py

示例5: main

# 需要导入模块: from misc import utils [as 别名]
# 或者: from misc.utils import LanguageModelCriterion [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)

开发者ID:Sundrops，项目名称:video-caption.pytorch，代码行数:49，代码来源:train.py

示例6: main

# 需要导入模块: from misc import utils [as 别名]
# 或者: from misc.utils import LanguageModelCriterion [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)

开发者ID:xiadingZ，项目名称:video-caption.pytorch，代码行数:47，代码来源:train.py

注：本文中的misc.utils.LanguageModelCriterion方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。