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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.

MobileNetV2

class mmcls.models.MobileNetV2(widen_factor=1.0, out_indices=(7,), frozen_stages=- 1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU6'}, norm_eval=False, with_cp=False, init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}])[source]

MobileNetV2 backbone.

Parameters
  • widen_factor (float) – Width multiplier, multiply number of channels in each layer by this amount. Default: 1.0.

  • out_indices (None or Sequence[int]) – Output from which stages. Default: (7, ).

  • frozen_stages (int) – Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters.

  • conv_cfg (dict, optional) – Config dict for convolution layer. Default: None, which means using conv2d.

  • norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).

  • act_cfg (dict) – Config dict for activation layer. Default: dict(type=’ReLU6’).

  • 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.

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.

make_layer(out_channels, num_blocks, stride, expand_ratio)[source]

Stack InvertedResidual blocks to build a layer for MobileNetV2.

Parameters
  • out_channels (int) – out_channels of block.

  • num_blocks (int) – number of blocks.

  • stride (int) – stride of the first block. Default: 1

  • expand_ratio (int) – Expand the number of channels of the hidden layer in InvertedResidual by this ratio. Default: 6.

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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