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.

CSPResNeXt¶

class mmcls.models.CSPResNeXt(*args, **kwargs)[source]¶

CSP-ResNeXt backbone.

Parameters

depth (int) – Depth of CSP-ResNeXt. Default: 50.
out_indices (Sequence[int]) – Output from which stages. Default: (4, ).
frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.
conv_cfg (dict) – Config dict for convolution layer. Default: None.
norm_cfg (dict) – Dictionary to construct and config norm layer. Default: dict(type=’BN’, requires_grad=True).
act_cfg (dict) – Config dict for activation layer. Default: dict(type=’LeakyReLU’, negative_slope=0.1).
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.
init_cfg (dict or list[dict], optional) – Initialization config dict. Default: None.

Example

>>> from mmcls.models import CSPResNeXt
>>> import torch
>>> model = CSPResNeXt(depth=50, out_indices=(0, 1, 2, 3))
>>> model.eval()
>>> inputs = torch.rand(1, 3, 224, 224)
>>> level_outputs = model(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
...
(1, 256, 56, 56)
(1, 512, 28, 28)
(1, 1024, 14, 14)
(1, 2048, 7, 7)