229 lines
9.2 KiB
Python
229 lines
9.2 KiB
Python
"""ResNeSt-101 Backbone - Standalone implementation for PyTorch 2.x
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Extracted from D-DFFNet/model/ours/backbone/resnet.py, removed 'resnest.torch' dependency.
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"""
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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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from .splat import SplAtConv2d
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__all__ = ['ResNet', 'Bottleneck']
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class Bottleneck(nn.Module):
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"""ResNet Bottleneck with Split-Attention"""
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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radix=1, cardinality=1, bottleneck_width=64,
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avd=False, avd_first=False, dilation=1, is_first=False,
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rectified_conv=False, rectify_avg=False,
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norm_layer=None, dropblock_prob=0.0, last_gamma=False):
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super(Bottleneck, self).__init__()
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group_width = int(planes * (bottleneck_width / 64.)) * cardinality
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self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
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self.bn1 = norm_layer(group_width)
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self.dropblock_prob = dropblock_prob
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self.radix = radix
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self.avd = avd and (stride > 1 or is_first)
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self.avd_first = avd_first
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if self.avd:
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self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
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stride = 1
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if dropblock_prob > 0.0:
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self.dropblock1 = DropBlock2D(dropblock_prob, 3)
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if radix == 1:
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self.dropblock2 = DropBlock2D(dropblock_prob, 3)
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self.dropblock3 = DropBlock2D(dropblock_prob, 3)
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if radix >= 1:
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self.conv2 = SplAtConv2d(
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group_width, group_width, kernel_size=3,
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stride=stride, padding=dilation,
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dilation=dilation, groups=cardinality, bias=False,
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radix=radix, rectify=rectified_conv,
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rectify_avg=rectify_avg,
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norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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elif rectified_conv:
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from rfconv import RFConv2d
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self.conv2 = RFConv2d(
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group_width, group_width, kernel_size=3, stride=stride,
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padding=dilation, dilation=dilation,
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groups=cardinality, bias=False,
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average_mode=rectify_avg)
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self.bn2 = norm_layer(group_width)
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else:
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self.conv2 = nn.Conv2d(
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group_width, group_width, kernel_size=3, stride=stride,
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padding=dilation, dilation=dilation,
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groups=cardinality, bias=False)
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self.bn2 = norm_layer(group_width)
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self.conv3 = nn.Conv2d(
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group_width, planes * 4, kernel_size=1, bias=False)
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self.bn3 = norm_layer(planes * 4)
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if last_gamma:
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from torch.nn.init import zeros_
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zeros_(self.bn3.weight)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.dilation = dilation
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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if self.dropblock_prob > 0.0:
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out = self.dropblock1(out)
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out = self.relu(out)
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if self.avd and self.avd_first:
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out = self.avd_layer(out)
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out = self.conv2(out)
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if self.radix == 0:
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out = self.bn2(out)
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if self.dropblock_prob > 0.0:
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out = self.dropblock2(out)
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out = self.relu(out)
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if self.avd and not self.avd_first:
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out = self.avd_layer(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.dropblock_prob > 0.0:
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out = self.dropblock3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class DropBlock2D(object):
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def __init__(self, *args, **kwargs):
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raise NotImplementedError
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class ResNest101(nn.Module):
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"""ResNeSt-101 backbone.
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NOTE: layer3 and layer4 are split into two branches:
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- layer3_1 / layer4_1: main branch
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- layer3_2 / layer4_2: auxiliary branch
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"""
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def __init__(self, layers, block=Bottleneck, radix=1, groups=1, bottleneck_width=64,
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deep_stem=False, stem_width=64, avg_down=False,
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rectified_conv=False, rectify_avg=False,
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avd=False, avd_first=False, dropblock_prob=0,
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last_gamma=False, norm_layer=nn.BatchNorm2d):
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self.cardinality = groups
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self.bottleneck_width = bottleneck_width
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# ResNet-D params
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self.inplanes = stem_width * 2 if deep_stem else 64
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self.avg_down = avg_down
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self.last_gamma = last_gamma
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# ResNeSt params
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self.radix = radix
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self.avd = avd
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self.avd_first = avd_first
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self.rectified_conv = rectified_conv
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self.rectify_avg = rectify_avg
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super(ResNest101, self).__init__()
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conv_layer = nn.Conv2d
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self.conv1 = nn.Sequential(
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conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False),
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norm_layer(stem_width),
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nn.ReLU(inplace=True),
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conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False),
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norm_layer(stem_width),
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nn.ReLU(inplace=True),
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conv_layer(stem_width, stem_width * 2, kernel_size=3, stride=1, padding=1, bias=False),
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)
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self.bn1 = nn.BatchNorm2d(self.inplanes)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer, is_first=False)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
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self.layer3_1 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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self.layer4_1 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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self.inplanes = 512
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self.layer3_2 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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self.layer4_2 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, norm_layer):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None,
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dropblock_prob=0.0, is_first=True):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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down_layers = []
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down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,
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ceil_mode=True, count_include_pad=False))
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down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=1, bias=False))
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down_layers.append(norm_layer(planes * block.expansion))
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downsample = nn.Sequential(*down_layers)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample=downsample,
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radix=self.radix, cardinality=self.cardinality,
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bottleneck_width=self.bottleneck_width,
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avd=self.avd, avd_first=self.avd_first,
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dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,
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rectify_avg=self.rectify_avg,
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norm_layer=norm_layer, dropblock_prob=dropblock_prob,
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last_gamma=self.last_gamma))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes,
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radix=self.radix, cardinality=self.cardinality,
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bottleneck_width=self.bottleneck_width,
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avd=self.avd, avd_first=self.avd_first,
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dilation=dilation, rectified_conv=self.rectified_conv,
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rectify_avg=self.rectify_avg,
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norm_layer=norm_layer, dropblock_prob=dropblock_prob,
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last_gamma=self.last_gamma))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x1 = self.layer3_1(x)
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x1 = self.layer4_1(x1)
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x2 = self.layer3_2(x)
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x2 = self.layer4_2(x2)
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return x1, x2
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