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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.

VGG

class mmcls.models.VGG(depth, num_classes=- 1, num_stages=5, dilations=(1, 1, 1, 1, 1), out_indices=None, frozen_stages=- 1, conv_cfg=None, norm_cfg=None, act_cfg={'type': 'ReLU'}, norm_eval=False, ceil_mode=False, with_last_pool=True, init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1.0, 'layer': ['_BatchNorm']}, {'type': 'Normal', 'std': 0.01, 'layer': ['Linear']}])[source]

VGG backbone.

Parameters
  • depth (int) – Depth of vgg, from {11, 13, 16, 19}.

  • with_norm (bool) – Use BatchNorm or not.

  • num_classes (int) – number of classes for classification.

  • num_stages (int) – VGG stages, normally 5.

  • dilations (Sequence[int]) – Dilation of each stage.

  • out_indices (Sequence[int], optional) – Output from which stages. When it is None, the default behavior depends on whether num_classes is specified. If num_classes <= 0, the default value is (4, ), output the last feature map before classifier. If num_classes > 0, the default value is (5, ), output the classification score. Default: None.

  • frozen_stages (int) – Stages to be frozen (all param fixed). -1 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.

  • ceil_mode (bool) – Whether to use ceil_mode of MaxPool. Default: False.

  • with_last_pool (bool) – Whether to keep the last pooling before classifier. Default: True.

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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