VGG

class mmpretrain.models.backbones.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']}])

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.