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| import math | |
| from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer | |
| from ..builder import BACKBONES | |
| from ..utils import ResLayer | |
| from .resnet import Bottleneck as _Bottleneck | |
| from .resnet import ResNet | |
| class Bottleneck(_Bottleneck): | |
| """Bottleneck block for ResNeXt. | |
| If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is | |
| "caffe", the stride-two layer is the first 1x1 conv layer. | |
| """ | |
| def __init__(self, | |
| inplanes, | |
| planes, | |
| groups=1, | |
| base_width=4, | |
| base_channels=64, | |
| **kwargs): | |
| super(Bottleneck, self).__init__(inplanes, planes, **kwargs) | |
| if groups == 1: | |
| width = self.planes | |
| else: | |
| width = math.floor(self.planes * | |
| (base_width / base_channels)) * groups | |
| self.norm1_name, norm1 = build_norm_layer( | |
| self.norm_cfg, width, postfix=1) | |
| self.norm2_name, norm2 = build_norm_layer( | |
| self.norm_cfg, width, postfix=2) | |
| self.norm3_name, norm3 = build_norm_layer( | |
| self.norm_cfg, self.planes * self.expansion, postfix=3) | |
| self.conv1 = build_conv_layer( | |
| self.conv_cfg, | |
| self.inplanes, | |
| width, | |
| kernel_size=1, | |
| stride=self.conv1_stride, | |
| bias=False) | |
| self.add_module(self.norm1_name, norm1) | |
| fallback_on_stride = False | |
| self.with_modulated_dcn = False | |
| if self.with_dcn: | |
| fallback_on_stride = self.dcn.pop('fallback_on_stride', False) | |
| if not self.with_dcn or fallback_on_stride: | |
| self.conv2 = build_conv_layer( | |
| self.conv_cfg, | |
| width, | |
| width, | |
| kernel_size=3, | |
| stride=self.conv2_stride, | |
| padding=self.dilation, | |
| dilation=self.dilation, | |
| groups=groups, | |
| bias=False) | |
| else: | |
| assert self.conv_cfg is None, 'conv_cfg must be None for DCN' | |
| self.conv2 = build_conv_layer( | |
| self.dcn, | |
| width, | |
| width, | |
| 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, | |
| width, | |
| self.planes * self.expansion, | |
| kernel_size=1, | |
| bias=False) | |
| self.add_module(self.norm3_name, norm3) | |
| class ResNeXt(ResNet): | |
| """ResNeXt backbone. | |
| Args: | |
| depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. | |
| in_channels (int): Number of input image channels. Normally 3. | |
| num_stages (int): Resnet stages, normally 4. | |
| groups (int): Group of resnext. | |
| base_width (int): Base width of resnext. | |
| strides (Sequence[int]): Strides of the first block of each stage. | |
| 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. | |
| frozen_stages (int): Stages to be frozen (all param fixed). -1 means | |
| not freezing any parameters. | |
| norm_cfg (dict): dictionary to construct and config norm layer. | |
| 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. | |
| with_cp (bool): Use checkpoint or not. Using checkpoint will save some | |
| memory while slowing down the training speed. | |
| zero_init_residual (bool): whether to use zero init for last norm layer | |
| in resblocks to let them behave as identity. | |
| Example: | |
| >>> from annotator.uniformer.mmseg.models import ResNeXt | |
| >>> import torch | |
| >>> self = ResNeXt(depth=50) | |
| >>> 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, 256, 8, 8) | |
| (1, 512, 4, 4) | |
| (1, 1024, 2, 2) | |
| (1, 2048, 1, 1) | |
| """ | |
| arch_settings = { | |
| 50: (Bottleneck, (3, 4, 6, 3)), | |
| 101: (Bottleneck, (3, 4, 23, 3)), | |
| 152: (Bottleneck, (3, 8, 36, 3)) | |
| } | |
| def __init__(self, groups=1, base_width=4, **kwargs): | |
| self.groups = groups | |
| self.base_width = base_width | |
| super(ResNeXt, self).__init__(**kwargs) | |
| def make_res_layer(self, **kwargs): | |
| """Pack all blocks in a stage into a ``ResLayer``""" | |
| return ResLayer( | |
| groups=self.groups, | |
| base_width=self.base_width, | |
| base_channels=self.base_channels, | |
| **kwargs) | |