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# -----------------------------------------------------
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# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
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# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
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# -----------------------------------------------------
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import torch.nn as nn
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import torch.nn.functional as F
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=dilation, groups=groups, bias=False, dilation=dilation)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
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base_width=64, dilation=1, norm_layer=None):
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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if groups != 1 or base_width != 64:
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raise ValueError('BasicBlock only supports groups=1 and base_width=64')
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if dilation > 1:
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = norm_layer(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = norm_layer(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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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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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1,
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downsample=None, norm_layer=nn.BatchNorm2d, dcn=None):
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super(Bottleneck, self).__init__()
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self.dcn = dcn
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self.with_dcn = dcn is not None
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = norm_layer(planes, momentum=0.1)
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if self.with_dcn:
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fallback_on_stride = dcn.get('FALLBACK_ON_STRIDE', False)
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self.with_modulated_dcn = dcn.get('MODULATED', False)
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if not self.with_dcn or fallback_on_stride:
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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else:
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from .dcn import DeformConv, ModulatedDeformConv
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self.deformable_groups = dcn.get('DEFORM_GROUP', 1)
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if not self.with_modulated_dcn:
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conv_op = DeformConv
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offset_channels = 18
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else:
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conv_op = ModulatedDeformConv
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offset_channels = 27
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self.conv2_offset = nn.Conv2d(
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planes,
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self.deformable_groups * offset_channels,
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kernel_size=3,
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stride=stride,
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padding=1)
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self.conv2 = conv_op(
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planes,
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planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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deformable_groups=self.deformable_groups,
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bias=False)
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self.bn2 = norm_layer(planes, momentum=0.1)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = norm_layer(planes * 4, momentum=0.1)
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self.downsample = downsample
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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 = F.relu(self.bn1(self.conv1(x)), inplace=True)
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if not self.with_dcn:
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out = F.relu(self.bn2(self.conv2(out)), inplace=True)
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elif self.with_modulated_dcn:
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offset_mask = self.conv2_offset(out)
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offset = offset_mask[:, :18 * self.deformable_groups, :, :]
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mask = offset_mask[:, -9 * self.deformable_groups:, :, :]
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mask = mask.sigmoid()
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out = F.relu(self.bn2(self.conv2(out, offset, mask)))
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else:
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offset = self.conv2_offset(out)
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out = F.relu(self.bn2(self.conv2(out, offset)), inplace=True)
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out = self.conv3(out)
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out = self.bn3(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 = F.relu(out)
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return out
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class ResNet(nn.Module):
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""" ResNet """
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def __init__(self, architecture, norm_layer=nn.BatchNorm2d, dcn=None, stage_with_dcn=(False, False, False, False)):
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super(ResNet, self).__init__()
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self._norm_layer = norm_layer
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assert architecture in ["resnet18", "resnet50", "resnet101", 'resnet152']
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layers = {
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'resnet18': [2, 2, 2, 2],
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'resnet34': [3, 4, 6, 3],
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'resnet50': [3, 4, 6, 3],
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'resnet101': [3, 4, 23, 3],
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'resnet152': [3, 8, 36, 3],
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}
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self.inplanes = 64
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if architecture == "resnet18" or architecture == 'resnet34':
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self.block = BasicBlock
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else:
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self.block = Bottleneck
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self.layers = layers[architecture]
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
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stride=2, padding=3, bias=False)
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self.bn1 = norm_layer(64, eps=1e-5, momentum=0.1, affine=True)
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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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stage_dcn = [dcn if with_dcn else None for with_dcn in stage_with_dcn]
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self.layer1 = self.make_layer(
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self.block, 64, self.layers[0], dcn=stage_dcn[0])
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self.layer2 = self.make_layer(
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self.block, 128, self.layers[1], stride=2, dcn=stage_dcn[1])
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self.layer3 = self.make_layer(
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self.block, 256, self.layers[2], stride=2, dcn=stage_dcn[2])
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self.layer4 = self.make_layer(
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self.block, 512, self.layers[3], stride=2, dcn=stage_dcn[3])
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def forward(self, x):
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x = self.maxpool(self.relu(self.bn1(self.conv1(x)))) # 64 * h/4 * w/4
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x = self.layer1(x) # 256 * h/4 * w/4
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x = self.layer2(x) # 512 * h/8 * w/8
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x = self.layer3(x) # 1024 * h/16 * w/16
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x = self.layer4(x) # 2048 * h/32 * w/32
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return x
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def stages(self):
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return [self.layer1, self.layer2, self.layer3, self.layer4]
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def make_layer(self, block, planes, blocks, stride=1, dcn=None):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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self._norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample,
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norm_layer=self._norm_layer, dcn=dcn))
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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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norm_layer=self._norm_layer, dcn=dcn))
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return nn.Sequential(*layers)
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