| 73 | features.append(new_features) |
| 74 | return torch.cat(features, 1) |
| 75 | class _Transition(nn.Sequential): |
| 76 | def __init__(self, input_c, output_c): |
| 77 | super(_Transition, self).__init__() |
| 78 | self.add_module("norm", nn.BatchNorm2d(input_c)) |
| 79 | self.add_module("relu", nn.ReLU(inplace=True)) |
| 80 | self.add_module("conv", nn.Conv2d(input_c, output_c, kernel_size=1, stride=1, bias=False)) |
| 81 | self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2)) |
| 82 | |
| 83 | class DenseNet(nn.Module): |
| 84 | """ |