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L40S
Running
on
L40S
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d(in_planes, | |
| out_planes, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=dilation, | |
| groups=groups, | |
| bias=False, | |
| dilation=dilation) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, | |
| inplanes, | |
| planes, | |
| stride=1, | |
| downsample=None, | |
| groups=1, | |
| base_width=64, | |
| dilation=1, | |
| norm_layer=None, | |
| dcn=None): | |
| super(BasicBlock, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| if groups != 1 or base_width != 64: | |
| raise ValueError( | |
| 'BasicBlock only supports groups=1 and base_width=64') | |
| if dilation > 1: | |
| raise NotImplementedError( | |
| "Dilation > 1 not supported in BasicBlock") | |
| # Both self.conv1 and self.downsample layers downsample the input when stride != 1 | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = norm_layer(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = norm_layer(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| identity = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.downsample is not None: | |
| identity = self.downsample(x) | |
| out += identity | |
| out = self.relu(out) | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, | |
| inplanes, | |
| planes, | |
| stride=1, | |
| downsample=None, | |
| norm_layer=nn.BatchNorm2d, | |
| dcn=None): | |
| super(Bottleneck, self).__init__() | |
| self.dcn = dcn | |
| self.with_dcn = dcn is not None | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = norm_layer(planes, momentum=0.1) | |
| self.conv2 = nn.Conv2d(planes, | |
| planes, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=1, | |
| bias=False) | |
| self.bn2 = norm_layer(planes, momentum=0.1) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = norm_layer(planes * 4, momentum=0.1) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = F.relu(self.bn1(self.conv1(x)), inplace=True) | |
| if not self.with_dcn: | |
| out = F.relu(self.bn2(self.conv2(out)), inplace=True) | |
| elif self.with_modulated_dcn: | |
| offset_mask = self.conv2_offset(out) | |
| offset = offset_mask[:, :18 * self.deformable_groups, :, :] | |
| mask = offset_mask[:, -9 * self.deformable_groups:, :, :] | |
| mask = mask.sigmoid() | |
| out = F.relu(self.bn2(self.conv2(out, offset, mask))) | |
| else: | |
| offset = self.conv2_offset(out) | |
| out = F.relu(self.bn2(self.conv2(out, offset)), inplace=True) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = F.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| """ ResNet """ | |
| def __init__(self, | |
| architecture, | |
| norm_layer=nn.BatchNorm2d, | |
| dcn=None, | |
| stage_with_dcn=(False, False, False, False)): | |
| super(ResNet, self).__init__() | |
| self._norm_layer = norm_layer | |
| assert architecture in [ | |
| "resnet18", "resnet34", "resnet50", "resnet101", 'resnet152' | |
| ] | |
| layers = { | |
| 'resnet18': [2, 2, 2, 2], | |
| 'resnet34': [3, 4, 6, 3], | |
| 'resnet50': [3, 4, 6, 3], | |
| 'resnet101': [3, 4, 23, 3], | |
| 'resnet152': [3, 8, 36, 3], | |
| } | |
| self.inplanes = 64 | |
| if architecture == "resnet18" or architecture == 'resnet34': | |
| self.block = BasicBlock | |
| else: | |
| self.block = Bottleneck | |
| self.layers = layers[architecture] | |
| self.conv1 = nn.Conv2d(3, | |
| 64, | |
| kernel_size=7, | |
| stride=2, | |
| padding=3, | |
| bias=False) | |
| self.bn1 = norm_layer(64, eps=1e-5, momentum=0.1, affine=True) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| stage_dcn = [dcn if with_dcn else None for with_dcn in stage_with_dcn] | |
| self.layer1 = self.make_layer(self.block, | |
| 64, | |
| self.layers[0], | |
| dcn=stage_dcn[0]) | |
| self.layer2 = self.make_layer(self.block, | |
| 128, | |
| self.layers[1], | |
| stride=2, | |
| dcn=stage_dcn[1]) | |
| self.layer3 = self.make_layer(self.block, | |
| 256, | |
| self.layers[2], | |
| stride=2, | |
| dcn=stage_dcn[2]) | |
| self.layer4 = self.make_layer(self.block, | |
| 512, | |
| self.layers[3], | |
| stride=2, | |
| dcn=stage_dcn[3]) | |
| def forward(self, x): | |
| x = self.maxpool(self.relu(self.bn1(self.conv1(x)))) # 64 * h/4 * w/4 | |
| x = self.layer1(x) # 256 * h/4 * w/4 | |
| x = self.layer2(x) # 512 * h/8 * w/8 | |
| x = self.layer3(x) # 1024 * h/16 * w/16 | |
| x = self.layer4(x) # 2048 * h/32 * w/32 | |
| return x | |
| def stages(self): | |
| return [self.layer1, self.layer2, self.layer3, self.layer4] | |
| def make_layer(self, block, planes, blocks, stride=1, dcn=None): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, | |
| planes * block.expansion, | |
| kernel_size=1, | |
| stride=stride, | |
| bias=False), | |
| self._norm_layer(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append( | |
| block(self.inplanes, | |
| planes, | |
| stride, | |
| downsample, | |
| norm_layer=self._norm_layer, | |
| dcn=dcn)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append( | |
| block(self.inplanes, | |
| planes, | |
| norm_layer=self._norm_layer, | |
| dcn=dcn)) | |
| return nn.Sequential(*layers) | |