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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from mmcv.cnn import build_conv_layer, build_norm_layer | |
| from mmcls.registry import MODELS | |
| from .resnet import Bottleneck as _Bottleneck | |
| from .resnet import ResLayer, ResNet | |
| class Bottleneck(_Bottleneck): | |
| """Bottleneck block for ResNeXt. | |
| Args: | |
| in_channels (int): Input channels of this block. | |
| out_channels (int): Output channels of this block. | |
| groups (int): Groups of conv2. | |
| width_per_group (int): Width per group of conv2. 64x4d indicates | |
| ``groups=64, width_per_group=4`` and 32x8d indicates | |
| ``groups=32, width_per_group=8``. | |
| stride (int): stride of the block. Default: 1 | |
| dilation (int): dilation of convolution. Default: 1 | |
| downsample (nn.Module, optional): downsample operation on identity | |
| branch. Default: None | |
| 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. | |
| conv_cfg (dict, optional): dictionary to construct and config conv | |
| layer. Default: None | |
| norm_cfg (dict): dictionary to construct and config norm layer. | |
| Default: dict(type='BN') | |
| with_cp (bool): Use checkpoint or not. Using checkpoint will save some | |
| memory while slowing down the training speed. | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| base_channels=64, | |
| groups=32, | |
| width_per_group=4, | |
| **kwargs): | |
| super(Bottleneck, self).__init__(in_channels, out_channels, **kwargs) | |
| self.groups = groups | |
| self.width_per_group = width_per_group | |
| # For ResNet bottleneck, middle channels are determined by expansion | |
| # and out_channels, but for ResNeXt bottleneck, it is determined by | |
| # groups and width_per_group and the stage it is located in. | |
| if groups != 1: | |
| assert self.mid_channels % base_channels == 0 | |
| self.mid_channels = ( | |
| groups * width_per_group * self.mid_channels // base_channels) | |
| self.norm1_name, norm1 = build_norm_layer( | |
| self.norm_cfg, self.mid_channels, postfix=1) | |
| self.norm2_name, norm2 = build_norm_layer( | |
| self.norm_cfg, self.mid_channels, postfix=2) | |
| self.norm3_name, norm3 = build_norm_layer( | |
| self.norm_cfg, self.out_channels, postfix=3) | |
| self.conv1 = build_conv_layer( | |
| self.conv_cfg, | |
| self.in_channels, | |
| self.mid_channels, | |
| kernel_size=1, | |
| stride=self.conv1_stride, | |
| bias=False) | |
| self.add_module(self.norm1_name, norm1) | |
| self.conv2 = build_conv_layer( | |
| self.conv_cfg, | |
| self.mid_channels, | |
| self.mid_channels, | |
| kernel_size=3, | |
| stride=self.conv2_stride, | |
| padding=self.dilation, | |
| dilation=self.dilation, | |
| groups=groups, | |
| bias=False) | |
| self.add_module(self.norm2_name, norm2) | |
| self.conv3 = build_conv_layer( | |
| self.conv_cfg, | |
| self.mid_channels, | |
| self.out_channels, | |
| kernel_size=1, | |
| bias=False) | |
| self.add_module(self.norm3_name, norm3) | |
| class ResNeXt(ResNet): | |
| """ResNeXt backbone. | |
| Please refer to the `paper <https://arxiv.org/abs/1611.05431>`__ for | |
| details. | |
| Args: | |
| depth (int): Network depth, from {50, 101, 152}. | |
| groups (int): Groups of conv2 in Bottleneck. Default: 32. | |
| width_per_group (int): Width per group of conv2 in Bottleneck. | |
| Default: 4. | |
| in_channels (int): Number of input image channels. Default: 3. | |
| stem_channels (int): Output channels of the stem layer. Default: 64. | |
| num_stages (int): Stages of the network. Default: 4. | |
| strides (Sequence[int]): Strides of the first block of each stage. | |
| Default: ``(1, 2, 2, 2)``. | |
| dilations (Sequence[int]): Dilation of each stage. | |
| Default: ``(1, 1, 1, 1)``. | |
| out_indices (Sequence[int]): Output from which stages. If only one | |
| stage is specified, a single tensor (feature map) is returned, | |
| otherwise multiple stages are specified, a tuple of tensors will | |
| be returned. Default: ``(3, )``. | |
| 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. | |
| deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv. | |
| Default: False. | |
| avg_down (bool): Use AvgPool instead of stride conv when | |
| downsampling in the bottleneck. Default: False. | |
| frozen_stages (int): Stages to be frozen (stop grad and set eval mode). | |
| -1 means not freezing any parameters. Default: -1. | |
| conv_cfg (dict | None): The config dict for conv layers. Default: None. | |
| norm_cfg (dict): The config dict for norm layers. | |
| 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. | |
| """ | |
| arch_settings = { | |
| 50: (Bottleneck, (3, 4, 6, 3)), | |
| 101: (Bottleneck, (3, 4, 23, 3)), | |
| 152: (Bottleneck, (3, 8, 36, 3)) | |
| } | |
| def __init__(self, depth, groups=32, width_per_group=4, **kwargs): | |
| self.groups = groups | |
| self.width_per_group = width_per_group | |
| super(ResNeXt, self).__init__(depth, **kwargs) | |
| def make_res_layer(self, **kwargs): | |
| return ResLayer( | |
| groups=self.groups, | |
| width_per_group=self.width_per_group, | |
| base_channels=self.base_channels, | |
| **kwargs) | |