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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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