Method __init__

models/densenet.py:129–135

(self, num_input_features: int, num_output_features: int)

class _Transition(nn.Sequential):
    def __init__(self, num_input_features: int, num_output_features: int) -> None:
        super(_Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                          kernel_size=1, stride=1, bias=False))
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):