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MobileNetV3

class mmcls.models.MobileNetV3(arch='small', conv_cfg=None, norm_cfg={'eps': 0.001, 'momentum': 0.01, 'type': 'BN'}, out_indices=None, frozen_stages=- 1, norm_eval=False, with_cp=False, init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d'], 'nonlinearity': 'leaky_relu'}, {'type': 'Normal', 'layer': ['Linear'], 'std': 0.01}, {'type': 'Constant', 'layer': ['BatchNorm2d'], 'val': 1}])[source]

MobileNetV3 backbone.

Parameters
  • arch (str) – Architecture of mobilnetv3, from {small, large}. Default: small.

  • 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’).

  • out_indices (None or Sequence[int]) – Output from which stages. Default: None, which means output tensors from final stage.

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

  • 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.

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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