133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
import math
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from mmcv.cnn import build_conv_layer, build_norm_layer
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from ..builder import BACKBONES
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from .resnet import Bottleneck as _Bottleneck
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from .resnet import ResLayer, ResNet
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class Bottleneck(_Bottleneck):
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"""Bottleneck block for ResNeXt.
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Args:
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inplanes (int): inplanes of block.
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planes (int): planes of block.
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groups (int): group of convolution.
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base_width (int): Base width of resnext.
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base_channels (int): Number of base channels of hidden layer.
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stride (int): stride of the block. Default: 1
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dilation (int): dilation of convolution. Default: 1
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downsample (nn.Module): downsample operation on identity branch.
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Default: None
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style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
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layer is the 3x3 conv layer, otherwise the stride-two layer is
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the first 1x1 conv layer.
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conv_cfg (dict): dictionary to construct and config conv layer.
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Default: None
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norm_cfg (dict): dictionary to construct and config norm layer.
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Default: dict(type='BN')
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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"""
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expansion = 4
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def __init__(self,
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inplanes,
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planes,
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groups=1,
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base_width=4,
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base_channels=64,
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**kwargs):
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super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
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if groups == 1:
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width = self.planes
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else:
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width = math.floor(self.planes *
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(base_width / base_channels)) * groups
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self.norm1_name, norm1 = build_norm_layer(
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self.norm_cfg, width, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(
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self.norm_cfg, width, postfix=2)
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self.norm3_name, norm3 = build_norm_layer(
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self.norm_cfg, self.planes * self.expansion, postfix=3)
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self.conv1 = build_conv_layer(
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self.conv_cfg,
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self.inplanes,
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width,
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kernel_size=1,
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stride=self.conv1_stride,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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self.conv2 = build_conv_layer(
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self.conv_cfg,
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width,
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width,
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kernel_size=3,
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stride=self.conv2_stride,
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padding=self.dilation,
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dilation=self.dilation,
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groups=groups,
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bias=False)
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self.add_module(self.norm2_name, norm2)
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self.conv3 = build_conv_layer(
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self.conv_cfg,
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width,
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self.planes * self.expansion,
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kernel_size=1,
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bias=False)
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self.add_module(self.norm3_name, norm3)
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@BACKBONES.register_module()
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class ResNeXt(ResNet):
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"""ResNeXt backbone.
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Args:
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groups (int): Group of resnext.
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base_width (int): Base width of resnext.
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depth (int): Depth of resnext, from {50, 101, 152}.
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in_channels (int): Number of input image channels. Default: 3.
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base_channels (int): Number of base channels of hidden layer.
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num_stages (int): Resnet stages. Default: 4.
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strides (Sequence[int]): Strides of the first block of each stage.
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dilations (Sequence[int]): Dilation of each stage.
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out_indices (Sequence[int]): Output from which stages.
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style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
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layer is the 3x3 conv layer, otherwise the stride-two layer is
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the first 1x1 conv layer.
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frozen_stages (int): Stages to be frozen (all param fixed). -1 means
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not freezing any parameters.
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norm_cfg (dict): dictionary to construct and config norm layer.
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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zero_init_residual (bool): whether to use zero init for last norm layer
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in resblocks to let them behave as identity.
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"""
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arch_settings = {
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50: (Bottleneck, (3, 4, 6, 3)),
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101: (Bottleneck, (3, 4, 23, 3)),
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152: (Bottleneck, (3, 8, 36, 3))
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}
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def __init__(self, groups=1, base_width=4, **kwargs):
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self.groups = groups
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self.base_width = base_width
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super(ResNeXt, self).__init__(**kwargs)
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def make_res_layer(self, **kwargs):
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return ResLayer(
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groups=self.groups,
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base_width=self.base_width,
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base_channels=self.base_channels,
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**kwargs)
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