| # resnext101_32x8d | |
| Implementation of ResNetXt proposed in [\"Aggregated Residual | |
| Transformation for Deep Neural | |
| Networks\"](https://arxiv.org/pdf/1611.05431.pdf) | |
| Create a default model | |
| ``` python | |
| ResNetXt.resnext50_32x4d() | |
| ResNetXt.resnext101_32x8d() | |
| # create a resnetxt18_32x4d | |
| ResNetXt.resnet18(block=ResNetXtBottleNeckBlock, groups=32, base_width=4) | |
| ``` | |
| Examples: | |
| : ``` python | |
| # change activation | |
| ResNetXt.resnext50_32x4d(activation = nn.SELU) | |
| # change number of classes (default is 1000 ) | |
| ResNetXt.resnext50_32x4d(n_classes=100) | |
| # pass a different block | |
| ResNetXt.resnext50_32x4d(block=SENetBasicBlock) | |
| # change the initial convolution | |
| model = ResNetXt.resnext50_32x4d | |
| model.encoder.gate.conv1 = nn.Conv2d(3, 64, kernel_size=3) | |
| # store each feature | |
| x = torch.rand((1, 3, 224, 224)) | |
| model = ResNetXt.resnext50_32x4d() | |
| # first call .features, this will activate the forward hooks and tells the model you'll like to get the features | |
| model.encoder.features | |
| model(torch.randn((1,3,224,224))) | |
| # get the features from the encoder | |
| features = model.encoder.features | |
| print([x.shape for x in features]) | |
| #[torch.Size([1, 64, 112, 112]), torch.Size([1, 64, 56, 56]), torch.Size([1, 128, 28, 28]), torch.Size([1, 256, 14, 14])] | |
| ``` | |