Shortcuts

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.

CSPResNet

class mmcls.models.CSPResNet(depth, in_channels=3, out_indices=(3,), frozen_stages=- 1, deep_stem=False, conv_cfg=None, norm_cfg={'eps': 1e-05, 'type': 'BN'}, act_cfg={'inplace': True, 'type': 'LeakyReLU'}, norm_eval=False, init_cfg={'layer': 'Conv2d', 'type': 'Kaiming'})[source]

CSP-ResNet backbone.

Parameters
  • depth (int) – Depth of CSP-ResNet. 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 CSPResNet
>>> import torch
>>> model = CSPResNet(depth=50, out_indices=(0, 1, 2, 3))
>>> model.eval()
>>> inputs = torch.rand(1, 3, 416, 416)
>>> level_outputs = model(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
...
(1, 128, 104, 104)
(1, 256, 52, 52)
(1, 512, 26, 26)
(1, 1024, 13, 13)
Read the Docs v: latest
Versions
master
latest
1.x
dev-1.x
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.