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import torch |
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import torch.nn as nn |
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import os |
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__all__ = [ |
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"ResNet", |
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"resnet18_with_dropout", |
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"resnet18", |
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"dropout_resnet18" |
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] |
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): |
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"""3x3 convolution with padding""" |
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return nn.Conv2d( |
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in_planes, |
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out_planes, |
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kernel_size=3, |
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stride=stride, |
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padding=dilation, |
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groups=groups, |
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bias=False, |
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dilation=dilation, |
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) |
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def conv1x1(in_planes, out_planes, stride=1): |
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"""1x1 convolution""" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__( |
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self, |
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inplanes, |
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planes, |
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stride=1, |
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downsample=None, |
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groups=1, |
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base_width=64, |
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dilation=1, |
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norm_layer=None, |
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): |
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super(BasicBlock, self).__init__() |
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if norm_layer is None: |
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norm_layer = nn.BatchNorm2d |
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if groups != 1 or base_width != 64: |
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raise ValueError("BasicBlock only supports groups=1 and base_width=64") |
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if dilation > 1: |
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock") |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = norm_layer(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(planes, planes) |
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self.bn2 = norm_layer(planes) |
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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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identity = 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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identity = self.downsample(x) |
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out += identity |
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out = self.relu(out) |
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return out |
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class BasicBlock_withDropout(nn.Module): |
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expansion = 1 |
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def __init__( |
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self, |
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inplanes, |
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planes, |
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stride=1, |
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downsample=None, |
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groups=1, |
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base_width=64, |
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dilation=1, |
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norm_layer=None, |
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): |
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super(BasicBlock_withDropout, self).__init__() |
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if norm_layer is None: |
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norm_layer = nn.BatchNorm2d |
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if groups != 1 or base_width != 64: |
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raise ValueError("BasicBlock only supports groups=1 and base_width=64") |
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if dilation > 1: |
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock") |
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self.dropout = nn.Dropout(p=0.5) |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = norm_layer(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(planes, planes) |
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self.bn2 = norm_layer(planes) |
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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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identity = 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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identity = self.downsample(x) |
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out += identity |
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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__( |
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self, |
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inplanes, |
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planes, |
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stride=1, |
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downsample=None, |
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groups=1, |
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base_width=64, |
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dilation=1, |
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norm_layer=None, |
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): |
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super(Bottleneck, self).__init__() |
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if norm_layer is None: |
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norm_layer = nn.BatchNorm2d |
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width = int(planes * (base_width / 64.0)) * groups |
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self.conv1 = conv1x1(inplanes, width) |
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self.bn1 = norm_layer(width) |
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self.conv2 = conv3x3(width, width, stride, groups, dilation) |
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self.bn2 = norm_layer(width) |
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self.conv3 = conv1x1(width, planes * self.expansion) |
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self.bn3 = norm_layer(planes * self.expansion) |
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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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identity = 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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identity = self.downsample(x) |
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out += identity |
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out = self.relu(out) |
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return out |
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class ResNet(nn.Module): |
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def __init__( |
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self, |
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block, |
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layers, |
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with_dropout, |
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num_classes=10, |
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zero_init_residual=False, |
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groups=1, |
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width_per_group=64, |
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replace_stride_with_dilation=None, |
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norm_layer=None, |
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): |
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super(ResNet, self).__init__() |
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if norm_layer is None: |
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norm_layer = nn.BatchNorm2d |
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self._norm_layer = norm_layer |
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self.inplanes = 64 |
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self.dilation = 1 |
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if replace_stride_with_dilation is None: |
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replace_stride_with_dilation = [False, False, False] |
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if len(replace_stride_with_dilation) != 3: |
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raise ValueError( |
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"replace_stride_with_dilation should be None " |
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation) |
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) |
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self.with_dropout = with_dropout |
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self.groups = groups |
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self.base_width = width_per_group |
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self.conv1 = nn.Conv2d( |
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3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False |
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) |
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self.bn1 = norm_layer(self.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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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer( |
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block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0] |
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) |
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self.layer3 = self._make_layer( |
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block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1] |
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) |
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self.layer4 = self._make_layer( |
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block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2] |
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) |
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
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self.fc = nn.Linear(512 * block.expansion, num_classes) |
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if self.with_dropout: |
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self.fc = nn.Sequential(nn.Flatten(),nn.Dropout(0.5),nn.Linear(512 * 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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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") |
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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if zero_init_residual: |
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for m in self.modules(): |
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if isinstance(m, Bottleneck): |
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nn.init.constant_(m.bn3.weight, 0) |
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elif isinstance(m, BasicBlock): |
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nn.init.constant_(m.bn2.weight, 0) |
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def _make_layer(self, block, planes, blocks, stride=1, dilate=False): |
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norm_layer = self._norm_layer |
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downsample = None |
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previous_dilation = self.dilation |
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if dilate: |
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self.dilation *= stride |
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stride = 1 |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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conv1x1(self.inplanes, planes * block.expansion, stride), |
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norm_layer(planes * block.expansion), |
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) |
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layers = [] |
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layers.append( |
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block( |
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self.inplanes, |
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planes, |
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stride, |
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downsample, |
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self.groups, |
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self.base_width, |
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previous_dilation, |
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norm_layer, |
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) |
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) |
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self.inplanes = planes * block.expansion |
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for _ in range(1, blocks): |
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layers.append( |
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block( |
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self.inplanes, |
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planes, |
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groups=self.groups, |
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base_width=self.base_width, |
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dilation=self.dilation, |
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norm_layer=norm_layer, |
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) |
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) |
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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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x = self.avgpool(x) |
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x = x.reshape(x.size(0), -1) |
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x = self.fc(x) |
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return x |
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def feature(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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x = self.avgpool(x) |
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x = x.reshape(x.size(0), -1) |
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return x |
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def prediction(self,x): |
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x = self.fc(x) |
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return x |
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def _resnet(arch, block, layers, pretrained, progress, device, with_dropout, **kwargs): |
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model = ResNet(block, layers, with_dropout, **kwargs) |
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if pretrained: |
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script_dir = os.path.dirname(__file__) |
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state_dict = torch.load( |
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script_dir + "/state_dicts/" + arch + ".pt", map_location=device |
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) |
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model.load_state_dict(state_dict) |
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return model |
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def resnet18_with_dropout(pretrained=False, progress=True, device="cpu", **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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progress (bool): If True, displays a progress bar of the download to stderr |
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""" |
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return _resnet( |
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"resnet18", BasicBlock_withDropout, [2, 2, 2, 2], pretrained, progress, device, with_dropout = True, **kwargs |
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) |
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def resnet18(pretrained=False, progress=True, device="cpu", **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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progress (bool): If True, displays a progress bar of the download to stderr |
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""" |
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return _resnet( |
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"resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, device, with_dropout = False, **kwargs |
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) |
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def resnet34(pretrained=False, progress=True, device="cpu", **kwargs): |
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"""Constructs a ResNet-34 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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progress (bool): If True, displays a progress bar of the download to stderr |
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""" |
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return _resnet( |
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"resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, device, **kwargs |
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) |
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def resnet50(pretrained=False, progress=True, device="cpu", **kwargs): |
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"""Constructs a ResNet-50 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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progress (bool): If True, displays a progress bar of the download to stderr |
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""" |
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return _resnet( |
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"resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, device, **kwargs |
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) |
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