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EfficientNet

class mmcls.models.EfficientNet(arch='b0', drop_path_rate=0.0, out_indices=(6,), frozen_stages=0, conv_cfg={'type': 'Conv2dAdaptivePadding'}, norm_cfg={'eps': 0.001, 'type': 'BN'}, act_cfg={'type': 'Swish'}, norm_eval=False, with_cp=False, init_cfg=[{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'layer': ['_BatchNorm', 'GroupNorm'], 'val': 1}])[source]

EfficientNet backbone.

Parameters
  • arch (str) – Architecture of efficientnet. Defaults to b0.

  • out_indices (Sequence[int]) – Output from which stages. Defaults to (6, ).

  • frozen_stages (int) – Stages to be frozen (all param fixed). Defaults to 0, which means not freezing any parameters.

  • conv_cfg (dict) – Config dict for convolution layer. Defaults to None, which means using conv2d.

  • norm_cfg (dict) – Config dict for normalization layer. Defaults to dict(type=’BN’).

  • act_cfg (dict) – Config dict for activation layer. Defaults to dict(type=’Swish’).

  • 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. Defaults to False.

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