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import math
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from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer
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from ..builder import BACKBONES
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from ..utils import ResLayer
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from .resnet import Bottleneck as _Bottleneck
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from .resnet import ResNet
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class Bottleneck(_Bottleneck):
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"""Bottleneck block for ResNeXt.
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If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is
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"caffe", the stride-two layer is the first 1x1 conv layer.
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"""
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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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fallback_on_stride = False
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self.with_modulated_dcn = False
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if self.with_dcn:
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fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
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if not self.with_dcn or fallback_on_stride:
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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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else:
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assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
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self.conv2 = build_conv_layer(
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self.dcn,
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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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depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
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in_channels (int): Number of input image channels. Normally 3.
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num_stages (int): Resnet stages, normally 4.
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groups (int): Group of resnext.
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base_width (int): Base width of resnext.
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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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Example:
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>>> from annotator.uniformer.mmseg.models import ResNeXt
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>>> import torch
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>>> self = ResNeXt(depth=50)
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>>> self.eval()
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>>> inputs = torch.rand(1, 3, 32, 32)
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>>> level_outputs = self.forward(inputs)
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>>> for level_out in level_outputs:
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... print(tuple(level_out.shape))
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(1, 256, 8, 8)
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(1, 512, 4, 4)
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(1, 1024, 2, 2)
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(1, 2048, 1, 1)
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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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"""Pack all blocks in a stage into a ``ResLayer``"""
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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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