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on
T4
Running
on
T4
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) | |
| class BasicBlock(nn.Module): | |
| def __init__(self, in_chan, out_chan, stride=1): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(in_chan, out_chan, stride) | |
| self.bn1 = nn.BatchNorm2d(out_chan) | |
| self.conv2 = conv3x3(out_chan, out_chan) | |
| self.bn2 = nn.BatchNorm2d(out_chan) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = None | |
| if in_chan != out_chan or stride != 1: | |
| self.downsample = nn.Sequential( | |
| nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(out_chan), | |
| ) | |
| def forward(self, x): | |
| residual = self.conv1(x) | |
| residual = F.relu(self.bn1(residual)) | |
| residual = self.conv2(residual) | |
| residual = self.bn2(residual) | |
| shortcut = x | |
| if self.downsample is not None: | |
| shortcut = self.downsample(x) | |
| out = shortcut + residual | |
| out = self.relu(out) | |
| return out | |
| def create_layer_basic(in_chan, out_chan, bnum, stride=1): | |
| layers = [BasicBlock(in_chan, out_chan, stride=stride)] | |
| for i in range(bnum - 1): | |
| layers.append(BasicBlock(out_chan, out_chan, stride=1)) | |
| return nn.Sequential(*layers) | |
| class ResNet18(nn.Module): | |
| def __init__(self): | |
| super(ResNet18, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) | |
| self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) | |
| self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) | |
| self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = F.relu(self.bn1(x)) | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| feat8 = self.layer2(x) # 1/8 | |
| feat16 = self.layer3(feat8) # 1/16 | |
| feat32 = self.layer4(feat16) # 1/32 | |
| return feat8, feat16, feat32 | |