Note

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.

ConvMixer¶

class mmcls.models.ConvMixer(arch='768/32', in_channels=3, norm_cfg={'type': 'BN'}, act_cfg={'type': 'GELU'}, out_indices=- 1, frozen_stages=0, init_cfg=None)[source]¶

ConvMixer. .

A PyTorch implementation of : Patches Are All You Need?

Modified from the official repo and timm.

Parameters

arch (str | dict) –
The model’s architecture. If string, it should be one of architecture in ConvMixer.arch_settings. And if dict, it should include the following two keys:
- embed_dims (int): The dimensions of patch embedding.
- depth (int): Number of repetitions of ConvMixer Layer.
- patch_size (int): The patch size.
- kernel_size (int): The kernel size of depthwise conv layers.
Defaults to ‘768/32’.
in_channels (int) – Number of input image channels. Defaults to 3.
patch_size (int) – The size of one patch in the patch embed layer. Defaults to 7.
norm_cfg (dict) – The config dict for norm layers. Defaults to dict(type='BN').
act_cfg (dict) – The config dict for activation after each convolution. Defaults to dict(type='GELU').
out_indices (Sequence | int) – Output from which stages. 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