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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

ResNet_CIFAR

class mmcls.models.ResNet_CIFAR(depth, deep_stem=False, **kwargs)[source]

ResNet backbone for CIFAR.

Compared to standard ResNet, it uses kernel_size=3 and stride=1 in conv1, and does not apply MaxPoolinng after stem. It has been proven to be more efficient than standard ResNet in other public codebase, e.g., https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py.

Parameters
  • depth (int) – Network depth, from {18, 34, 50, 101, 152}.

  • in_channels (int) – Number of input image channels. Default: 3.

  • stem_channels (int) – Output channels of the stem layer. Default: 64.

  • base_channels (int) – Middle channels of the first stage. Default: 64.

  • num_stages (int) – Stages of the network. Default: 4.

  • strides (Sequence[int]) – Strides of the first block of each stage. Default: (1, 2, 2, 2).

  • dilations (Sequence[int]) – Dilation of each stage. Default: (1, 1, 1, 1).

  • out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: (3, ).

  • style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

  • deep_stem (bool) – This network has specific designed stem, thus it is asserted to be False.

  • avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False.

  • frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

  • conv_cfg (dict | None) – The config dict for conv layers. Default: None.

  • norm_cfg (dict) – The config dict for norm layers.

  • norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

  • with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

  • zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

forward(x)[source]

Forward computation.

Parameters

x (tensor | tuple[tensor]) – x could be a Torch.tensor or a tuple of Torch.tensor, containing input data for forward computation.

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