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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.

Conformer

class mmcls.models.Conformer(arch='tiny', patch_size=16, base_channels=64, mlp_ratio=4.0, qkv_bias=True, with_cls_token=True, drop_path_rate=0.0, norm_eval=True, frozen_stages=0, out_indices=- 1, init_cfg=None)[source]

Conformer backbone.

A PyTorch implementation of : Conformer: Local Features Coupling Global Representations for Visual Recognition

Parameters
  • arch (str | dict) – Conformer architecture. Defaults to ‘tiny’.

  • patch_size (int) – The patch size. Defaults to 16.

  • base_channels (int) – The base number of channels in CNN network. Defaults to 64.

  • mlp_ratio (float) – The expansion ratio of FFN network in transformer block. Defaults to 4.

  • with_cls_token (bool) – Whether use class token or not. Defaults to True.

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

  • out_indices (Sequence | int) – Output from which stages. Defaults to -1, means the last stage.

  • init_cfg (dict, optional) – Initialization config dict. Defaults to None.

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.

init_weights()[source]

Initialize the weights.

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