dc00752d3ad585cb44eefe7cecf86bfc09890e25,models/densenet.py,DenseNet,__init__,#DenseNet#,84

Before Change

        super().__init__()

        // First convolution
        self.features = nn.Sequential(
            OrderedDict([
                ("conv1",
                 nn.Conv3d(
                     3,
                     num_init_features,
                     kernel_size=(conv1_t_size, 7, 7),
                     stride=(conv1_t_stride, 2, 2),
                     padding=(conv1_t_size // 2, 3, 3),
                     bias=False)),
                ("norm1", nn.BatchNorm3d(num_init_features)),
                ("relu1", nn.ReLU(inplace=True)),
                ("pool1", nn.MaxPool3d(kernel_size=3, stride=2, padding=1)),
            ]))

        // Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):

After Change

            self.features.append(("pool1",
                                  nn.MaxPool3d(
                                      kernel_size=3, stride=2, padding=1)))
        self.features = nn.Sequential(OrderedDict(self.features))

        // Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):

Instances