| |
| import math |
|
|
| from mmcv.cnn import build_conv_layer, build_norm_layer |
|
|
| from mmdet.registry import MODELS |
| from ..layers import ResLayer |
| from .resnet import Bottleneck as _Bottleneck |
| from .resnet import ResNet |
|
|
|
|
| class Bottleneck(_Bottleneck): |
| expansion = 4 |
|
|
| def __init__(self, |
| inplanes, |
| planes, |
| groups=1, |
| base_width=4, |
| base_channels=64, |
| **kwargs): |
| """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. |
| """ |
| 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) |
|
|
| if self.with_plugins: |
| self._del_block_plugins(self.after_conv1_plugin_names + |
| self.after_conv2_plugin_names + |
| self.after_conv3_plugin_names) |
| self.after_conv1_plugin_names = self.make_block_plugins( |
| width, self.after_conv1_plugins) |
| self.after_conv2_plugin_names = self.make_block_plugins( |
| width, self.after_conv2_plugins) |
| self.after_conv3_plugin_names = self.make_block_plugins( |
| self.planes * self.expansion, self.after_conv3_plugins) |
|
|
| def _del_block_plugins(self, plugin_names): |
| """delete plugins for block if exist. |
| |
| Args: |
| plugin_names (list[str]): List of plugins name to delete. |
| """ |
| assert isinstance(plugin_names, list) |
| for plugin_name in plugin_names: |
| del self._modules[plugin_name] |
|
|
|
|
| @MODELS.register_module() |
| 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. Default: 3. |
| num_stages (int): Resnet stages. Default: 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. |
| """ |
|
|
| 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) |
|
|
