403 lines
14 KiB
Python
403 lines
14 KiB
Python
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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from models.invertibility.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
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webroot = 'http://dl.yf.io/drn/'
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model_urls = {
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'drn-c-26': webroot + 'drn_c_26-ddedf421.pth',
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'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth',
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'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth',
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'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth',
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'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth',
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'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth',
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'drn-d-105': webroot + 'drn_d_105-12b40979.pth'
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}
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def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=padding, 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,
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dilation=(1, 1), residual=True, BatchNorm=None):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(inplanes, planes, stride,
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padding=dilation[0], dilation=dilation[0])
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self.bn1 = BatchNorm(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes,
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padding=dilation[1], dilation=dilation[1])
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self.bn2 = BatchNorm(planes)
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self.downsample = downsample
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self.stride = stride
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self.residual = residual
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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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if self.downsample is not None:
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residual = self.downsample(x)
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if self.residual:
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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,
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dilation=(1, 1), residual=True, BatchNorm=None):
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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 = BatchNorm(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=dilation[1], bias=False,
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dilation=dilation[1])
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self.bn2 = BatchNorm(planes)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = BatchNorm(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 DRN(nn.Module):
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def __init__(self, block, layers, arch='D',
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channels=(16, 32, 64, 128, 256, 512, 512, 512),
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BatchNorm=None):
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super(DRN, self).__init__()
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self.inplanes = channels[0]
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self.out_dim = channels[-1]
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self.arch = arch
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if arch == 'C':
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self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
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padding=3, bias=False)
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self.bn1 = BatchNorm(channels[0])
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self._make_layer(
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BasicBlock, channels[0], layers[0], stride=1, BatchNorm=BatchNorm)
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self.layer2 = self._make_layer(
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BasicBlock, channels[1], layers[1], stride=2, BatchNorm=BatchNorm)
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elif arch == 'D':
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self.layer0 = nn.Sequential(
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nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3,
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bias=False),
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BatchNorm(channels[0]),
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nn.ReLU(inplace=True)
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)
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self.layer1 = self._make_conv_layers(
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channels[0], layers[0], stride=1, BatchNorm=BatchNorm)
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self.layer2 = self._make_conv_layers(
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channels[1], layers[1], stride=2, BatchNorm=BatchNorm)
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self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2, BatchNorm=BatchNorm)
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self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2, BatchNorm=BatchNorm)
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self.layer5 = self._make_layer(block, channels[4], layers[4],
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dilation=2, new_level=False, BatchNorm=BatchNorm)
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self.layer6 = None if layers[5] == 0 else \
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self._make_layer(block, channels[5], layers[5], dilation=4,
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new_level=False, BatchNorm=BatchNorm)
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if arch == 'C':
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self.layer7 = None if layers[6] == 0 else \
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self._make_layer(BasicBlock, channels[6], layers[6], dilation=2,
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new_level=False, residual=False, BatchNorm=BatchNorm)
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self.layer8 = None if layers[7] == 0 else \
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self._make_layer(BasicBlock, channels[7], layers[7], dilation=1,
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new_level=False, residual=False, BatchNorm=BatchNorm)
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elif arch == 'D':
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self.layer7 = None if layers[6] == 0 else \
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self._make_conv_layers(channels[6], layers[6], dilation=2, BatchNorm=BatchNorm)
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self.layer8 = None if layers[7] == 0 else \
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self._make_conv_layers(channels[7], layers[7], dilation=1, BatchNorm=BatchNorm)
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self._init_weight()
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def _init_weight(self):
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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, SynchronizedBatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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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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new_level=True, residual=True, BatchNorm=None):
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assert dilation == 1 or dilation % 2 == 0
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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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BatchNorm(planes * block.expansion),
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)
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layers = list()
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layers.append(block(
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self.inplanes, planes, stride, downsample,
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dilation=(1, 1) if dilation == 1 else (
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dilation // 2 if new_level else dilation, dilation),
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residual=residual, BatchNorm=BatchNorm))
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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, residual=residual,
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dilation=(dilation, dilation), BatchNorm=BatchNorm))
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return nn.Sequential(*layers)
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def _make_conv_layers(self, channels, convs, stride=1, dilation=1, BatchNorm=None):
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modules = []
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for i in range(convs):
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modules.extend([
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nn.Conv2d(self.inplanes, channels, kernel_size=3,
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stride=stride if i == 0 else 1,
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padding=dilation, bias=False, dilation=dilation),
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BatchNorm(channels),
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nn.ReLU(inplace=True)])
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self.inplanes = channels
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return nn.Sequential(*modules)
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def forward(self, x):
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if self.arch == 'C':
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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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elif self.arch == 'D':
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x = self.layer0(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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low_level_feat = x
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x = self.layer4(x)
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x = self.layer5(x)
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if self.layer6 is not None:
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x = self.layer6(x)
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if self.layer7 is not None:
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x = self.layer7(x)
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if self.layer8 is not None:
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x = self.layer8(x)
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return x, low_level_feat
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class DRN_A(nn.Module):
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def __init__(self, block, layers, BatchNorm=None):
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self.inplanes = 64
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super(DRN_A, self).__init__()
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self.out_dim = 512 * block.expansion
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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 = BatchNorm(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], BatchNorm=BatchNorm)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2, BatchNorm=BatchNorm)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
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dilation=2, BatchNorm=BatchNorm)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
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dilation=4, BatchNorm=BatchNorm)
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self._init_weight()
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def _init_weight(self):
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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, SynchronizedBatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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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, BatchNorm=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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BatchNorm(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, BatchNorm=BatchNorm))
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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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dilation=(dilation, dilation, ), BatchNorm=BatchNorm))
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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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x = self.layer3(x)
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x = self.layer4(x)
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return x
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def drn_a_50(BatchNorm, pretrained=True):
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model = DRN_A(Bottleneck, [3, 4, 6, 3], BatchNorm=BatchNorm)
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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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def drn_c_26(BatchNorm, pretrained=True):
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model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-c-26'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_c_42(BatchNorm, pretrained=True):
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model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-c-42'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_c_58(BatchNorm, pretrained=True):
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model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-c-58'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_d_22(BatchNorm, pretrained=True):
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model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-d-22'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_d_24(BatchNorm, pretrained=True):
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model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-d-24'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_d_38(BatchNorm, pretrained=True):
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model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-d-38'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_d_40(BatchNorm, pretrained=True):
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model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-d-40'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_d_54(BatchNorm, pretrained=True):
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model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-d-54'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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def drn_d_105(BatchNorm, pretrained=True):
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model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', BatchNorm=BatchNorm)
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if pretrained:
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pretrained = model_zoo.load_url(model_urls['drn-d-105'])
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del pretrained['fc.weight']
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del pretrained['fc.bias']
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model.load_state_dict(pretrained)
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return model
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if __name__ == "__main__":
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import torch
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model = drn_a_50(BatchNorm=nn.BatchNorm2d, pretrained=True)
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input = torch.rand(1, 3, 512, 512)
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output, low_level_feat = model(input)
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print(output.size())
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print(low_level_feat.size())
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