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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.

RepVGG

class mmcls.models.RepVGG(arch, in_channels=3, base_channels=64, out_indices=(3,), strides=(2, 2, 2, 2), dilations=(1, 1, 1, 1), frozen_stages=- 1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU'}, with_cp=False, deploy=False, norm_eval=False, add_ppf=False, init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}])[source]

RepVGG backbone.

A PyTorch impl of : RepVGG: Making VGG-style ConvNets Great Again

Parameters
  • arch (str | dict) –

    RepVGG architecture. If use string, choose from ‘A0’, ‘A1`’, ‘A2’, ‘B0’, ‘B1’, ‘B1g2’, ‘B1g4’, ‘B2’ , ‘B2g2’, ‘B2g4’, ‘B3’, ‘B3g2’, ‘B3g4’ or ‘D2se’. If use dict,

    it should have below keys:

    • num_blocks (Sequence[int]): Number of blocks in each stage.

    • width_factor (Sequence[float]): Width deflator in each stage.

    • group_layer_map (dict | None): RepVGG Block that declares the need to apply group convolution.

    • se_cfg (dict | None): Se Layer config.

    • stem_channels (int, optional): The stem channels, the final

      stem channels will be min(stem_channels, base_channels*width_factor[0]).

      If not set here, 64 is used by default in the code.

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

  • base_channels (int) – Base channels of RepVGG backbone, work with width_factor together. Defaults to 64.

  • out_indices (Sequence[int]) – Output from which stages. Default: (3, ).

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

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

  • frozen_stages (int) – Stages to be frozen (all param fixed). -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. Default: dict(type=’BN’).

  • act_cfg (dict) – Config dict for activation layer. Default: dict(type=’ReLU’).

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

  • deploy (bool) – Whether to switch the model structure to deployment mode. Default: False.

  • 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.

  • add_ppf (bool) – Whether to use the MTSPPF block. Default: False.

  • init_cfg (dict or list[dict], optional) – Initialization config dict.

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.

train(mode=True)[source]

Set module status before forward computation.

Parameters

mode (bool) – Whether it is train_mode or test_mode

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