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- import torch
- import torch.nn as nn
- import os
-
- __all__ = [
- "ResNet",
- "resnet18",
- "resnet34",
- "resnet50",
- ]
-
-
- 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,
- )
-
-
- def conv1x1(in_planes, out_planes, stride=1):
- """1x1 convolution"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
- 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,
- ):
- 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,
- groups=1,
- base_width=64,
- dilation=1,
- norm_layer=None,
- ):
- super(Bottleneck, self).__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- width = int(planes * (base_width / 64.0)) * groups
- # Both self.conv2 and self.downsample layers downsample the input when stride != 1
- self.conv1 = conv1x1(inplanes, width)
- self.bn1 = norm_layer(width)
- self.conv2 = conv3x3(width, width, stride, groups, dilation)
- self.bn2 = norm_layer(width)
- self.conv3 = conv1x1(width, planes * self.expansion)
- self.bn3 = norm_layer(planes * self.expansion)
- self.relu = nn.ReLU(inplace=True)
- 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)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
-
- return out
-
-
- class ResNet(nn.Module):
- def __init__(
- self,
- block,
- layers,
- num_classes=10,
- zero_init_residual=False,
- groups=1,
- width_per_group=64,
- replace_stride_with_dilation=None,
- norm_layer=None,
- ):
- super(ResNet, self).__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- self._norm_layer = norm_layer
-
- self.inplanes = 64
- self.dilation = 1
- if replace_stride_with_dilation is None:
- # each element in the tuple indicates if we should replace
- # the 2x2 stride with a dilated convolution instead
- replace_stride_with_dilation = [False, False, False]
- if len(replace_stride_with_dilation) != 3:
- raise ValueError(
- "replace_stride_with_dilation should be None "
- "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
- )
- self.groups = groups
- self.base_width = width_per_group
-
- # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1
- self.conv1 = nn.Conv2d(
- 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False
- )
- # END
-
- self.bn1 = norm_layer(self.inplanes)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(
- block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
- )
- self.layer3 = self._make_layer(
- block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
- )
- self.layer4 = self._make_layer(
- block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
- )
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.fc = nn.Linear(512 * block.expansion, num_classes)
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
- elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
-
- # Zero-initialize the last BN in each residual branch,
- # so that the residual branch starts with zeros, and each residual block behaves like an identity.
- # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
- if zero_init_residual:
- for m in self.modules():
- if isinstance(m, Bottleneck):
- nn.init.constant_(m.bn3.weight, 0)
- elif isinstance(m, BasicBlock):
- nn.init.constant_(m.bn2.weight, 0)
-
- def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
- norm_layer = self._norm_layer
- downsample = None
- previous_dilation = self.dilation
- if dilate:
- self.dilation *= stride
- stride = 1
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- conv1x1(self.inplanes, planes * block.expansion, stride),
- norm_layer(planes * block.expansion),
- )
-
- layers = []
- layers.append(
- block(
- self.inplanes,
- planes,
- stride,
- downsample,
- self.groups,
- self.base_width,
- previous_dilation,
- norm_layer,
- )
- )
- self.inplanes = planes * block.expansion
- for _ in range(1, blocks):
- layers.append(
- block(
- self.inplanes,
- planes,
- groups=self.groups,
- base_width=self.base_width,
- dilation=self.dilation,
- norm_layer=norm_layer,
- )
- )
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.avgpool(x)
- x = x.reshape(x.size(0), -1)
- x = self.fc(x)
-
- return x
-
-
- def _resnet(arch, block, layers, pretrained, progress, device, **kwargs):
- model = ResNet(block, layers, **kwargs)
- if pretrained:
- script_dir = os.path.dirname(__file__)
- state_dict = torch.load(
- script_dir + "/state_dicts/" + arch + ".pt", map_location=device
- )
- model.load_state_dict(state_dict)
- return model
-
-
- def resnet18(pretrained=False, progress=True, device="cpu", **kwargs):
- """Constructs a ResNet-18 model.
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet(
- "resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, device, **kwargs
- )
-
-
- def resnet34(pretrained=False, progress=True, device="cpu", **kwargs):
- """Constructs a ResNet-34 model.
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet(
- "resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, device, **kwargs
- )
-
-
- def resnet50(pretrained=False, progress=True, device="cpu", **kwargs):
- """Constructs a ResNet-50 model.
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet(
- "resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, device, **kwargs
- )
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