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import torch.nn as nn |
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import math |
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import torch.utils.model_zoo as model_zoo |
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BatchNorm2d = nn.BatchNorm2d |
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', |
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'resnet152'] |
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model_urls = { |
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', |
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', |
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', |
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} |
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def constant_init(module, constant, bias=0): |
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nn.init.constant_(module.weight, constant) |
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if hasattr(module, 'bias'): |
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nn.init.constant_(module.bias, bias) |
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def conv3x3(in_planes, out_planes, stride=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=1, bias=False) |
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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, dcn=None): |
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super(BasicBlock, self).__init__() |
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self.with_dcn = dcn is not None |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.with_modulated_dcn = False |
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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, |
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padding=1, bias=False) |
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else: |
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deformable_groups = dcn.get('deformable_groups', 1) |
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if not self.with_modulated_dcn: |
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from assets.ops.dcn import DeformConv |
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conv_op = DeformConv |
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offset_channels = 18 |
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else: |
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from assets.ops.dcn import ModulatedDeformConv |
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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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deformable_groups * offset_channels, |
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kernel_size=3, |
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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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padding=1, |
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deformable_groups=deformable_groups, |
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bias=False) |
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self.bn2 = BatchNorm2d(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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residual = 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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if not self.with_dcn: |
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out = self.conv2(out) |
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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, :, :] |
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mask = offset_mask[:, -9:, :, :].sigmoid() |
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out = self.conv2(out, offset, mask) |
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else: |
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offset = self.conv2_offset(out) |
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out = self.conv2(out, offset) |
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out = self.bn2(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 Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None, dcn=None): |
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super(Bottleneck, self).__init__() |
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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 = BatchNorm2d(planes) |
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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 = 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, |
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stride=stride, padding=1, bias=False) |
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else: |
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deformable_groups = dcn.get('deformable_groups', 1) |
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if not self.with_modulated_dcn: |
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from assets.ops.dcn import DeformConv |
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conv_op = DeformConv |
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offset_channels = 18 |
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else: |
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from assets.ops.dcn import ModulatedDeformConv |
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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, deformable_groups * offset_channels, |
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kernel_size=3, |
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padding=1) |
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self.conv2 = conv_op( |
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planes, planes, kernel_size=3, padding=1, stride=stride, |
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deformable_groups=deformable_groups, bias=False) |
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self.bn2 = BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
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self.bn3 = BatchNorm2d(planes * 4) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = downsample |
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self.stride = stride |
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self.dcn = dcn |
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self.with_dcn = dcn is not None |
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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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out = self.relu(out) |
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if not self.with_dcn: |
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out = self.conv2(out) |
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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, :, :] |
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mask = offset_mask[:, -9:, :, :].sigmoid() |
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out = self.conv2(out, offset, mask) |
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else: |
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offset = self.conv2_offset(out) |
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out = self.conv2(out, offset) |
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out = self.bn2(out) |
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out = self.relu(out) |
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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 = self.relu(out) |
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return out |
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class ResNet(nn.Module): |
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def __init__(self, block, layers, num_classes=1000, |
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dcn=None, stage_with_dcn=(False, False, False, False)): |
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self.dcn = dcn |
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self.stage_with_dcn = stage_with_dcn |
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self.inplanes = 64 |
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super(ResNet, self).__init__() |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
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bias=False) |
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self.bn1 = BatchNorm2d(64) |
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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]) |
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self.layer2 = self._make_layer( |
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block, 128, layers[1], stride=2, dcn=dcn) |
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self.layer3 = self._make_layer( |
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block, 256, layers[2], stride=2, dcn=dcn) |
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self.layer4 = self._make_layer( |
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block, 512, layers[3], stride=2, dcn=dcn) |
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self.avgpool = nn.AvgPool2d(7, stride=1) |
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self.fc = nn.Linear(512 * block.expansion, num_classes) |
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self.smooth = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=1) |
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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, BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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if self.dcn is not None: |
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for m in self.modules(): |
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if isinstance(m, Bottleneck) or isinstance(m, BasicBlock): |
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if hasattr(m, 'conv2_offset'): |
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constant_init(m.conv2_offset, 0) |
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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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BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, |
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stride, downsample, 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, dcn=dcn)) |
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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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x2 = self.layer1(x) |
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x3 = self.layer2(x2) |
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x4 = self.layer3(x3) |
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x5 = self.layer4(x4) |
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return x2, x3, x4, x5 |
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def resnet18(pretrained=True, **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url( |
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model_urls['resnet18']), strict=False) |
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return model |
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def deformable_resnet18(pretrained=True, **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [2, 2, 2, 2], |
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dcn=dict(modulated=True, |
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deformable_groups=1, |
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fallback_on_stride=False), |
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stage_with_dcn=[False, True, True, True], **kwargs) |
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return model |
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def resnet34(pretrained=True, **kwargs): |
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"""Constructs a ResNet-34 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url( |
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model_urls['resnet34']), strict=False) |
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return model |
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def resnet50(pretrained=True, **kwargs): |
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"""Constructs a ResNet-50 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url( |
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model_urls['resnet50']), strict=False) |
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return model |
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def deformable_resnet50(pretrained=True, **kwargs): |
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"""Constructs a ResNet-50 model with deformable conv. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 6, 3], |
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dcn=dict(modulated=True, |
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deformable_groups=1, |
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fallback_on_stride=False), |
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stage_with_dcn=[False, True, True, True], |
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**kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url( |
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model_urls['resnet50']), strict=False) |
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return model |
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def resnet101(pretrained=True, **kwargs): |
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"""Constructs a ResNet-101 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url( |
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model_urls['resnet101']), strict=False) |
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return model |
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def resnet152(pretrained=True, **kwargs): |
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"""Constructs a ResNet-152 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url( |
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model_urls['resnet152']), strict=False) |
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return model |
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