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mmcls.models.RegNet

class mmcls.models.RegNet(arch, in_channels=3, stem_channels=32, base_channels=32, strides=(2, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3,), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=True, init_cfg=None)[source]

RegNet backbone.

More details can be found in paper .

Parameters

  • arch (dict) – The parameter of RegNets. - w0 (int): initial width - wa (float): slope of width - wm (float): quantization parameter to quantize the width - depth (int): depth of the backbone - group_w (int): width of group - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck.

  • strides (Sequence[int]) – Strides of the first block of each stage.

  • base_channels (int) – Base channels after stem layer.

  • in_channels (int) – Number of input image channels. Default: 3.

  • dilations (Sequence[int]) – Dilation of each stage.

  • out_indices (Sequence[int]) – Output from which stages.

  • style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: “pytorch”.

  • frozen_stages (int) – Stages to be frozen (all param fixed). -1 means not freezing any parameters. Default: -1.

  • norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’, requires_grad=True).

  • 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. Default: False.

  • with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

  • zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

Example

>>> from mmcls.models import RegNet
>>> import torch
>>> self = RegNet(
        arch=dict(
            w0=88,
            wa=26.31,
            wm=2.25,
            group_w=48,
            depth=25,
            bot_mul=1.0))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 96, 8, 8)
(1, 192, 4, 4)
(1, 432, 2, 2)
(1, 1008, 1, 1)