531 lines
15 KiB
Python
531 lines
15 KiB
Python
"""
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Code source: https://github.com/pytorch/vision
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"""
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from __future__ import division, absolute_import
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import torch.utils.model_zoo as model_zoo
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from torch import nn
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__all__ = [
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'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
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'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
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]
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model_urls = {
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'resnet18':
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'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34':
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'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50':
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'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101':
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'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152':
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'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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'resnext50_32x4d':
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'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
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'resnext101_32x8d':
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'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
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}
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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(
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in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=dilation,
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groups=groups,
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bias=False,
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dilation=dilation
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)
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(
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in_planes, out_planes, kernel_size=1, stride=stride, bias=False
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)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(
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self,
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inplanes,
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planes,
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stride=1,
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downsample=None,
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groups=1,
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base_width=64,
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dilation=1,
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norm_layer=None
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):
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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(
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'BasicBlock only supports groups=1 and base_width=64'
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)
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if dilation > 1:
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raise NotImplementedError(
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"Dilation > 1 not supported in BasicBlock"
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)
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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__(
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self,
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inplanes,
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planes,
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stride=1,
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downsample=None,
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groups=1,
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base_width=64,
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dilation=1,
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norm_layer=None
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):
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super(Bottleneck, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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width = int(planes * (base_width/64.)) * groups
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# Both self.conv2 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv1x1(inplanes, width)
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self.bn1 = norm_layer(width)
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self.conv2 = conv3x3(width, width, stride, groups, dilation)
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self.bn2 = norm_layer(width)
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self.conv3 = conv1x1(width, planes * self.expansion)
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self.bn3 = norm_layer(planes * self.expansion)
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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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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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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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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 ResNet(nn.Module):
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"""Residual network.
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Reference:
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- He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
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- Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
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Public keys:
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- ``resnet18``: ResNet18.
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- ``resnet34``: ResNet34.
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- ``resnet50``: ResNet50.
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- ``resnet101``: ResNet101.
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- ``resnet152``: ResNet152.
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- ``resnext50_32x4d``: ResNeXt50.
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- ``resnext101_32x8d``: ResNeXt101.
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- ``resnet50_fc512``: ResNet50 + FC.
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"""
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def __init__(
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self,
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num_classes,
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loss,
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block,
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layers,
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zero_init_residual=False,
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groups=1,
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width_per_group=64,
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replace_stride_with_dilation=None,
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norm_layer=None,
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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):
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super(ResNet, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self._norm_layer = norm_layer
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self.loss = loss
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self.feature_dim = 512 * block.expansion
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self.inplanes = 64
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self.dilation = 1
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if replace_stride_with_dilation is None:
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# each element in the tuple indicates if we should replace
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# the 2x2 stride with a dilated convolution instead
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError(
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"replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".
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format(replace_stride_with_dilation)
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)
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv2d(
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3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
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)
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self.bn1 = norm_layer(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])
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self.layer2 = self._make_layer(
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block,
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128,
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layers[1],
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stride=2,
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dilate=replace_stride_with_dilation[0]
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)
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self.layer3 = self._make_layer(
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block,
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256,
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layers[2],
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stride=2,
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dilate=replace_stride_with_dilation[1]
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)
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self.layer4 = self._make_layer(
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block,
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512,
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layers[3],
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stride=last_stride,
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dilate=replace_stride_with_dilation[2]
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)
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self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = self._construct_fc_layer(
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fc_dims, 512 * block.expansion, dropout_p
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)
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self.classifier = nn.Linear(self.feature_dim, num_classes)
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self._init_params()
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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0)
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elif isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0)
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def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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conv1x1(self.inplanes, planes * block.expansion, stride),
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norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(
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block(
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self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer
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)
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)
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(
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block(
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self.inplanes,
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planes,
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groups=self.groups,
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base_width=self.base_width,
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dilation=self.dilation,
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norm_layer=norm_layer
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)
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)
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return nn.Sequential(*layers)
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def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
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"""Constructs fully connected layer
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Args:
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fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
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input_dim (int): input dimension
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dropout_p (float): dropout probability, if None, dropout is unused
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"""
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if fc_dims is None:
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self.feature_dim = input_dim
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return None
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assert isinstance(
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fc_dims, (list, tuple)
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), 'fc_dims must be either list or tuple, but got {}'.format(
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type(fc_dims)
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)
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layers = []
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for dim in fc_dims:
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layers.append(nn.Linear(input_dim, dim))
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layers.append(nn.BatchNorm1d(dim))
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layers.append(nn.ReLU(inplace=True))
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if dropout_p is not None:
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layers.append(nn.Dropout(p=dropout_p))
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input_dim = dim
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self.feature_dim = fc_dims[-1]
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return nn.Sequential(*layers)
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def _init_params(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(
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m.weight, mode='fan_out', nonlinearity='relu'
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)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm1d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def featuremaps(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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x = self.layer3(x)
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x = self.layer4(x)
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return x
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def forward(self, x):
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f = self.featuremaps(x)
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v = self.global_avgpool(f)
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v = v.view(v.size(0), -1)
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if self.fc is not None:
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v = self.fc(v)
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if not self.training:
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return v
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y = self.classifier(v)
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if self.loss == 'softmax':
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return y
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elif self.loss == 'triplet':
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return y, v
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else:
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raise KeyError("Unsupported loss: {}".format(self.loss))
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def init_pretrained_weights(model, model_url):
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"""Initializes model with pretrained weights.
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Layers that don't match with pretrained layers in name or size are kept unchanged.
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"""
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pretrain_dict = model_zoo.load_url(model_url)
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model_dict = model.state_dict()
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pretrain_dict = {
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k: v
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for k, v in pretrain_dict.items()
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if k in model_dict and model_dict[k].size() == v.size()
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}
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model_dict.update(pretrain_dict)
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model.load_state_dict(model_dict)
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"""ResNet"""
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def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = ResNet(
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num_classes=num_classes,
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loss=loss,
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block=BasicBlock,
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layers=[2, 2, 2, 2],
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnet18'])
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return model
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def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = ResNet(
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num_classes=num_classes,
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loss=loss,
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block=BasicBlock,
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layers=[3, 4, 6, 3],
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnet34'])
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return model
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def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = ResNet(
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num_classes=num_classes,
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loss=loss,
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnet50'])
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return model
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|
|
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def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = ResNet(
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num_classes=num_classes,
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loss=loss,
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block=Bottleneck,
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layers=[3, 4, 23, 3],
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnet101'])
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return model
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|
|
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def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = ResNet(
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num_classes=num_classes,
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loss=loss,
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block=Bottleneck,
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layers=[3, 8, 36, 3],
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnet152'])
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return model
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|
|
|
|
"""ResNeXt"""
|
|
|
|
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|
def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
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|
model = ResNet(
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|
num_classes=num_classes,
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loss=loss,
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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groups=32,
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width_per_group=4,
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**kwargs
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)
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if pretrained:
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|
init_pretrained_weights(model, model_urls['resnext50_32x4d'])
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return model
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|
|
|
|
|
def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
|
|
model = ResNet(
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|
num_classes=num_classes,
|
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loss=loss,
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block=Bottleneck,
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layers=[3, 4, 23, 3],
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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groups=32,
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width_per_group=8,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnext101_32x8d'])
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return model
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|
|
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|
"""
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|
ResNet + FC
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|
"""
|
|
|
|
|
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def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
|
|
model = ResNet(
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|
num_classes=num_classes,
|
|
loss=loss,
|
|
block=Bottleneck,
|
|
layers=[3, 4, 6, 3],
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last_stride=1,
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fc_dims=[512],
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dropout_p=None,
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**kwargs
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)
|
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if pretrained:
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|
init_pretrained_weights(model, model_urls['resnet50'])
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return model
|