model.py 文件源码

def __init__(self, embedding_tokens):
        super(Net, self).__init__()
        question_features = 1024
        vision_features = config.output_features
        glimpses = 2

        self.text = TextProcessor(
            embedding_tokens=embedding_tokens,
            embedding_features=300,
            lstm_features=question_features,
            drop=0.5,
        )
        self.attention = Attention(
            v_features=vision_features,
            q_features=question_features,
            mid_features=512,
            glimpses=2,
            drop=0.5,
        )
        self.classifier = Classifier(
            in_features=glimpses * vision_features + question_features,
            mid_features=1024,
            out_features=config.max_answers,
            drop=0.5,
        )

        for m in self.modules():
            if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
                init.xavier_uniform(m.weight)
                if m.bias is not None:
                    m.bias.data.zero_()