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| Original file line number | Diff line number | Diff line change |
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@@ -9,3 +9,4 @@ results/* | |
| force_push.sh | ||
| scripts/vis* | ||
| scripts/process_all* | ||
| .idea | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
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| 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) | ||
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| class BasicBlock(nn.Module): | ||
| expansion = 1 | ||
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| 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 | ||
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| def forward(self, x): | ||
| identity = x | ||
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| out = self.conv1(x) | ||
| out = self.bn1(out) | ||
| out = self.relu(out) | ||
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| out = self.conv2(out) | ||
| out = self.bn2(out) | ||
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| if self.downsample is not None: | ||
| identity = self.downsample(x) | ||
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| out += identity | ||
| out = self.relu(out) | ||
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| return out | ||
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| class Bottleneck(nn.Module): | ||
| expansion = 4 | ||
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| 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 | ||
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| 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) | ||
|
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| 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 | ||
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| def forward(self, x): | ||
| residual = x | ||
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| 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) | ||
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| out = self.conv3(out) | ||
| out = self.bn3(out) | ||
|
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| if self.downsample is not None: | ||
| residual = self.downsample(x) | ||
|
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| out += residual | ||
| out = F.relu(out) | ||
|
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| return out | ||
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| class ResNet(nn.Module): | ||
| """ ResNet """ | ||
|
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| 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] | ||
|
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| 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) | ||
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| stage_dcn = [dcn if with_dcn else None for with_dcn in stage_with_dcn] | ||
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| 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]) | ||
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| self.layer4 = self.make_layer( | ||
| self.block, 512, self.layers[3], stride=2, dcn=stage_dcn[3]) | ||
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| 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 | ||
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| def stages(self): | ||
| return [self.layer1, self.layer2, self.layer3, self.layer4] | ||
|
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| 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), | ||
| ) | ||
|
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| 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)) | ||
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| return nn.Sequential(*layers) |
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