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179 lines
6.4 KiB
179 lines
6.4 KiB
import math
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import torch.nn as nn
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from collections import OrderedDict
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import torch.utils.model_zoo as model_zoo
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from torchvision.models.resnet import model_urls
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from .base import Backbone
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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, dilation=1):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=dilation, bias=False, dilation=dilation)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.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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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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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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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(Backbone):
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""" ResNet network module. Allows extracting specific feature blocks."""
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def __init__(self, block, layers, output_layers, num_classes=1000, inplanes=64, dilation_factor=1, frozen_layers=()):
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self.inplanes = inplanes
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super(ResNet, self).__init__(frozen_layers=frozen_layers)
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self.output_layers = output_layers
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self.conv1 = nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(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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stride = [1 + (dilation_factor < l) for l in (8, 4, 2)]
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self.layer1 = self._make_layer(block, inplanes, layers[0], dilation=max(dilation_factor//8, 1))
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self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=stride[0], dilation=max(dilation_factor//4, 1))
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self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=stride[1], dilation=max(dilation_factor//2, 1))
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self.layer4 = self._make_layer(block, inplanes*8, layers[3], stride=stride[2], dilation=dilation_factor)
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out_feature_strides = {'conv1': 4, 'layer1': 4, 'layer2': 4*stride[0], 'layer3': 4*stride[0]*stride[1],
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'layer4': 4*stride[0]*stride[1]*stride[2]}
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# TODO better way?
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if isinstance(self.layer1[0], Bottleneck):
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base_num_channels = 4 * inplanes
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out_feature_channels = {'conv1': inplanes, 'layer1': base_num_channels, 'layer2': base_num_channels * 2,
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'layer3': base_num_channels * 4, 'layer4': base_num_channels * 8}
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else:
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raise Exception('block not supported')
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self._out_feature_strides = out_feature_strides
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self._out_feature_channels = out_feature_channels
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# self.avgpool = nn.AvgPool2d(7, stride=1)
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self.avgpool = nn.AdaptiveAvgPool2d((1,1))
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self.fc = nn.Linear(inplanes*8 * block.expansion, num_classes)
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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, nn.BatchNorm2d):
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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):
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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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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation))
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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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return nn.Sequential(*layers)
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def _add_output_and_check(self, name, x, outputs, output_layers):
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if name in output_layers:
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outputs[name] = x
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return len(output_layers) == len(outputs)
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def forward(self, x, output_layers=None):
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""" Forward pass with input x. The output_layers specify the feature blocks which must be returned """
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outputs = OrderedDict()
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if output_layers is None:
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output_layers = self.output_layers
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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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if self._add_output_and_check('conv1', x, outputs, output_layers):
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return outputs
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x = self.maxpool(x)
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x = self.layer1(x)
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if self._add_output_and_check('layer1', x, outputs, output_layers):
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return outputs
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x = self.layer2(x)
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if self._add_output_and_check('layer2', x, outputs, output_layers):
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return outputs
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x = self.layer3(x)
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if self._add_output_and_check('layer3', x, outputs, output_layers):
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return outputs
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x = self.layer4(x)
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if self._add_output_and_check('layer4', x, outputs, output_layers):
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return outputs
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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if self._add_output_and_check('fc', x, outputs, output_layers):
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return outputs
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if len(output_layers) == 1 and output_layers[0] == 'default':
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return x
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raise ValueError('output_layer is wrong.')
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def resnet50(output_layers=None, pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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"""
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if output_layers is None:
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output_layers = ['default']
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else:
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for l in output_layers:
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if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']:
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raise ValueError('Unknown layer: {}'.format(l))
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model = ResNet(Bottleneck, [3, 4, 6, 3], output_layers, **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
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return model
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