Python misc.utils.RewardCriterion() Examples
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code examples of misc.utils.RewardCriterion().
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Example #1
Source File: loss_wrapper.py From GoogleConceptualCaptioning with MIT License | 6 votes |
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
Example #2
Source File: loss_wrapper.py From AAT with MIT License | 5 votes |
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()
Example #3
Source File: loss_wrapper.py From AoANet with MIT License | 5 votes |
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()
Example #4
Source File: train.py From video-caption.pytorch with MIT License | 4 votes |
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)
Example #5
Source File: train.py From video-caption.pytorch with MIT License | 4 votes |
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)