class mmpretrain.models.backbones.SparseResNet(depth, in_channels=3, stem_channels=64, base_channels=64, expansion=None, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3,), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg={'type': 'SparseSyncBatchNorm2d'}, norm_eval=False, with_cp=False, zero_init_residual=False, init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}], drop_path_rate=0, **kwargs)[source]

ResNet with sparse module conversion function.

Modified from

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

  • in_channels (int) – Number of input image channels. Defaults to 3.

  • stem_channels (int) – Output channels of the stem layer. Defaults to 64.

  • base_channels (int) – Middle channels of the first stage. Defaults to 64.

  • num_stages (int) – Stages of the network. Defaults to 4.

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

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

  • out_indices (Sequence[int]) – Output from which stages. Defaults to (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) – Replace 7x7 conv in input stem with 3 3x3 conv. Defaults to False.

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

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

  • conv_cfg (dict | None) – The config dict for conv layers. Defaults to 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. Defaults to False.

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

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

  • drop_path_rate (float) – stochastic depth rate. Defaults to 0.

dense_model_to_sparse(m, enable_sync_bn)[source]

Convert regular dense modules to sparse modules.

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