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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.

PoolFormer

class mmcls.models.PoolFormer(arch='s12', pool_size=3, norm_cfg={'num_groups': 1, 'type': 'GN'}, act_cfg={'type': 'GELU'}, in_patch_size=7, in_stride=4, in_pad=2, down_patch_size=3, down_stride=2, down_pad=1, drop_rate=0.0, drop_path_rate=0.0, out_indices=- 1, frozen_stages=0, init_cfg=None)[source]

PoolFormer.

A PyTorch implementation of PoolFormer introduced by: MetaFormer is Actually What You Need for Vision

Modified from the official repo <https://github.com/sail-sg/poolformer/blob/main/models/poolformer.py>.

Parameters
  • arch (str | dict) –

    The model’s architecture. If string, it should be one of architecture in PoolFormer.arch_settings. And if dict, it should include the following two keys:

    • layers (list[int]): Number of blocks at each stage.

    • embed_dims (list[int]): The number of channels at each stage.

    • mlp_ratios (list[int]): Expansion ratio of MLPs.

    • layer_scale_init_value (float): Init value for Layer Scale.

    Defaults to ‘S12’.

  • norm_cfg (dict) – The config dict for norm layers. Defaults to dict(type='LN2d', eps=1e-6).

  • act_cfg (dict) – The config dict for activation between pointwise convolution. Defaults to dict(type='GELU').

  • in_patch_size (int) – The patch size of input image patch embedding. Defaults to 7.

  • in_stride (int) – The stride of input image patch embedding. Defaults to 4.

  • in_pad (int) – The padding of input image patch embedding. Defaults to 2.

  • down_patch_size (int) – The patch size of downsampling patch embedding. Defaults to 3.

  • down_stride (int) – The stride of downsampling patch embedding. Defaults to 2.

  • down_pad (int) – The padding of downsampling patch embedding. Defaults to 1.

  • drop_rate (float) – Dropout rate. Defaults to 0.

  • drop_path_rate (float) – Stochastic depth rate. Defaults to 0.

  • out_indices (Sequence | int) – Output from which network position. Index 0-6 respectively corresponds to [stage1, downsampling, stage2, downsampling, stage3, downsampling, stage4] Defaults to -1, means the last stage.

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

  • init_cfg (dict, optional) – Initialization config dict

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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