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ShuffleNetV1

class mmpretrain.models.backbones.ShuffleNetV1(groups=3, widen_factor=1.0, out_indices=(2,), frozen_stages=-1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU'}, norm_eval=False, with_cp=False, init_cfg=None)[source]

ShuffleNetV1 backbone.

Parameters:

  • groups (int) – The number of groups to be used in grouped 1x1 convolutions in each ShuffleUnit. Default: 3.

  • widen_factor (float) – Width multiplier - adjusts the number of channels in each layer by this amount. Default: 1.0.

  • out_indices (Sequence[int]) – Output from which stages. Default: (2, )

  • 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=’ReLU’).

  • 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.

make_layer(out_channels, num_blocks, first_block=False)[source]

Stack ShuffleUnit blocks to make a layer.

Parameters:

  • out_channels (int) – out_channels of the block.

  • num_blocks (int) – Number of blocks.

  • first_block (bool) – Whether is the first ShuffleUnit of a sequential ShuffleUnits. Default: False, which means using the grouped 1x1 convolution.