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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)

RegNet backbone.

More details can be found in paper .

参数

• 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.

示例

>>> from mmcls.models import RegNet
>>> import torch
>>> inputs = torch.rand(1, 3, 32, 32)
>>> # use str type 'arch'
>>> # Note that default out_indices is (3,)
>>> regnet_cfg = dict(arch='regnetx_4.0gf')
>>> model = RegNet(**regnet_cfg)
>>> model.eval()
>>> level_outputs = model(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
    (1, 1360, 1, 1)
>>> # use dict type 'arch'
>>> arch_cfg =dict(w0=88, wa=26.31, wm=2.25,
>>>                 group_w=48, depth=25, bot_mul=1.0)
>>> regnet_cfg = dict(arch=arch_cfg, out_indices=(0, 1, 2, 3))
>>> model = RegNet(**regnet_cfg)
>>> model.eval()
>>> level_outputs = model(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)