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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as cp
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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 ResNetV1d
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class RSoftmax(nn.Module):
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"""Radix Softmax module in ``SplitAttentionConv2d``.
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Args:
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radix (int): Radix of input.
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groups (int): Groups of input.
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"""
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def __init__(self, radix, groups):
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super().__init__()
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self.radix = radix
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self.groups = groups
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def forward(self, x):
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batch = x.size(0)
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if self.radix > 1:
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x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
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x = F.softmax(x, dim=1)
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x = x.reshape(batch, -1)
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else:
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x = torch.sigmoid(x)
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return x
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class SplitAttentionConv2d(nn.Module):
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"""Split-Attention Conv2d in ResNeSt.
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Args:
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in_channels (int): Same as nn.Conv2d.
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out_channels (int): Same as nn.Conv2d.
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kernel_size (int | tuple[int]): Same as nn.Conv2d.
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stride (int | tuple[int]): Same as nn.Conv2d.
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padding (int | tuple[int]): Same as nn.Conv2d.
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dilation (int | tuple[int]): Same as nn.Conv2d.
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groups (int): Same as nn.Conv2d.
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radix (int): Radix of SpltAtConv2d. Default: 2
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reduction_factor (int): Reduction factor of inter_channels. Default: 4.
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conv_cfg (dict): Config dict for convolution layer. Default: None,
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which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer. Default: None.
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dcn (dict): Config dict for DCN. Default: None.
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"""
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def __init__(self,
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in_channels,
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channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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radix=2,
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reduction_factor=4,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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dcn=None):
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super(SplitAttentionConv2d, self).__init__()
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inter_channels = max(in_channels * radix // reduction_factor, 32)
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self.radix = radix
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self.groups = groups
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self.channels = channels
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self.with_dcn = dcn is not None
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self.dcn = dcn
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fallback_on_stride = 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 self.with_dcn and not fallback_on_stride:
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assert conv_cfg is None, 'conv_cfg must be None for DCN'
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conv_cfg = dcn
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self.conv = build_conv_layer(
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conv_cfg,
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in_channels,
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channels * radix,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups * radix,
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bias=False)
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self.norm0_name, norm0 = build_norm_layer(
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norm_cfg, channels * radix, postfix=0)
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self.add_module(self.norm0_name, norm0)
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self.relu = nn.ReLU(inplace=True)
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self.fc1 = build_conv_layer(
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None, channels, inter_channels, 1, groups=self.groups)
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self.norm1_name, norm1 = build_norm_layer(
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norm_cfg, inter_channels, postfix=1)
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self.add_module(self.norm1_name, norm1)
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self.fc2 = build_conv_layer(
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None, inter_channels, channels * radix, 1, groups=self.groups)
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self.rsoftmax = RSoftmax(radix, groups)
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@property
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def norm0(self):
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"""nn.Module: the normalization layer named "norm0" """
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return getattr(self, self.norm0_name)
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@property
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def norm1(self):
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"""nn.Module: the normalization layer named "norm1" """
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return getattr(self, self.norm1_name)
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def forward(self, x):
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x = self.conv(x)
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x = self.norm0(x)
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x = self.relu(x)
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batch, rchannel = x.shape[:2]
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batch = x.size(0)
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if self.radix > 1:
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splits = x.view(batch, self.radix, -1, *x.shape[2:])
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gap = splits.sum(dim=1)
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else:
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gap = x
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gap = F.adaptive_avg_pool2d(gap, 1)
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gap = self.fc1(gap)
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gap = self.norm1(gap)
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gap = self.relu(gap)
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atten = self.fc2(gap)
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atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
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if self.radix > 1:
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attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
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out = torch.sum(attens * splits, dim=1)
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else:
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out = atten * x
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return out.contiguous()
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class Bottleneck(_Bottleneck):
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"""Bottleneck block for ResNeSt.
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Args:
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inplane (int): Input planes of this block.
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planes (int): Middle planes of this block.
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groups (int): Groups of conv2.
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width_per_group (int): Width per group of conv2. 64x4d indicates
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``groups=64, width_per_group=4`` and 32x8d indicates
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``groups=32, width_per_group=8``.
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radix (int): Radix of SpltAtConv2d. Default: 2
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reduction_factor (int): Reduction factor of inter_channels in
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SplitAttentionConv2d. Default: 4.
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avg_down_stride (bool): Whether to use average pool for stride in
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Bottleneck. Default: True.
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kwargs (dict): Key word arguments for base class.
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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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radix=2,
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reduction_factor=4,
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avg_down_stride=True,
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**kwargs):
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"""Bottleneck block for ResNeSt."""
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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.avg_down_stride = avg_down_stride and self.conv2_stride > 1
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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.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.with_modulated_dcn = False
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self.conv2 = SplitAttentionConv2d(
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width,
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width,
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kernel_size=3,
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stride=1 if self.avg_down_stride else 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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radix=radix,
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reduction_factor=reduction_factor,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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dcn=self.dcn)
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delattr(self, self.norm2_name)
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if self.avg_down_stride:
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self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
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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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def forward(self, x):
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def _inner_forward(x):
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identity = x
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.relu(out)
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if self.with_plugins:
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out = self.forward_plugin(out, self.after_conv1_plugin_names)
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out = self.conv2(out)
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if self.avg_down_stride:
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out = self.avd_layer(out)
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if self.with_plugins:
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out = self.forward_plugin(out, self.after_conv2_plugin_names)
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out = self.conv3(out)
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out = self.norm3(out)
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if self.with_plugins:
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out = self.forward_plugin(out, self.after_conv3_plugin_names)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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return out
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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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out = self.relu(out)
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return out
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@BACKBONES.register_module()
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class ResNeSt(ResNetV1d):
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"""ResNeSt backbone.
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Args:
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groups (int): Number of groups of Bottleneck. Default: 1
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base_width (int): Base width of Bottleneck. Default: 4
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radix (int): Radix of SpltAtConv2d. Default: 2
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reduction_factor (int): Reduction factor of inter_channels in
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SplitAttentionConv2d. Default: 4.
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avg_down_stride (bool): Whether to use average pool for stride in
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Bottleneck. Default: True.
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kwargs (dict): Keyword arguments for ResNet.
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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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200: (Bottleneck, (3, 24, 36, 3))
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}
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def __init__(self,
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groups=1,
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base_width=4,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True,
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**kwargs):
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self.groups = groups
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self.base_width = base_width
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self.radix = radix
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self.reduction_factor = reduction_factor
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self.avg_down_stride = avg_down_stride
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super(ResNeSt, 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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radix=self.radix,
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reduction_factor=self.reduction_factor,
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avg_down_stride=self.avg_down_stride,
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**kwargs)
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