e7cb8e2da07f62549cc6dbb1241c5dbe6f5622dd,models/AttModel.py,AttModel,__init__,#AttModel#Any#,36

Before Change

        self.fc_embed = nn.Sequential(nn.Linear(self.fc_feat_size, self.rnn_size),
                                    nn.ReLU(),
                                    nn.Dropout(self.drop_prob_lm))
        self.att_embed = nn.Sequential(nn.Linear(self.att_feat_size, self.rnn_size),
                                    nn.ReLU(),
                                    nn.Dropout(self.drop_prob_lm))
        self.logit = nn.Linear(self.rnn_size, self.vocab_size + 1)
        self.ctx2att = nn.Linear(self.rnn_size, self.att_hid_size)

After Change

        self.att_feat_size = opt.att_feat_size
        self.att_hid_size = opt.att_hid_size

        self.use_bn = getattr(opt, "use_bn", 0)

        self.ss_prob = 0.0 // Schedule sampling probability

        self.embed = nn.Sequential(nn.Embedding(self.vocab_size + 1, self.input_encoding_size),
                                nn.ReLU(),
                                nn.Dropout(self.drop_prob_lm))
        self.fc_embed = nn.Sequential(nn.Linear(self.fc_feat_size, self.rnn_size),
                                    nn.ReLU(),
                                    nn.Dropout(self.drop_prob_lm))
        self.att_embed = nn.Sequential(*(
                                    ((nn.BatchNorm1d(self.att_feat_size),) if self.use_bn else ())+
                                    (nn.Linear(self.att_feat_size, self.rnn_size),
                                    nn.ReLU(),
                                    nn.Dropout(self.drop_prob_lm))))
        self.logit = nn.Linear(self.rnn_size, self.vocab_size + 1)
        self.ctx2att = nn.Linear(self.rnn_size, self.att_hid_size)

Instances