Spaces:
Running
Running
| """Code from : https://debuggercafe.com/implementing-resnet18-in-pytorch-from-scratch/""" | |
| import torch.nn as nn | |
| import torch | |
| from torchvision.ops import RoIPool | |
| from torch import Tensor | |
| from typing import Type | |
| class BasicBlock(nn.Module): | |
| def __init__(self, in_channels: int,out_channels: int,stride: int = 1,expansion: int = 1,downsample: nn.Module = None) -> None: | |
| super(BasicBlock, self).__init__() | |
| # Multiplicative factor for the subsequent conv2d layer's output channels. | |
| # It is 1 for ResNet18 and ResNet34. | |
| self.expansion = expansion | |
| self.downsample = downsample | |
| self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1,bias=False) | |
| self.bn1 = nn.BatchNorm2d(out_channels) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = nn.Conv2d(out_channels, out_channels*self.expansion, kernel_size=3, padding=1,bias=False) | |
| self.bn2 = nn.BatchNorm2d(out_channels*self.expansion) | |
| def forward(self, x: Tensor) -> Tensor: | |
| 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 ResNet18(nn.Module): | |
| def __init__(self, img_channels: int,num_layers: int,block: Type[BasicBlock],num_classes: int = 1000) -> None: | |
| super(ResNet18, self).__init__() | |
| if num_layers == 18: | |
| # The following `layers` list defines the number of `BasicBlock` | |
| # to use to build the network and how many basic blocks to stack | |
| # together. | |
| layers = [2, 2, 2, 2] | |
| self.expansion = 1 | |
| self.in_channels = 64 | |
| # All ResNets (18 to 152) contain a Conv2d => BN => ReLU for the first | |
| # three layers. Here, kernel size is 7. | |
| self.conv1 = nn.Conv2d(in_channels=img_channels,out_channels=self.in_channels,kernel_size=7, stride=2,padding=3,bias=False) | |
| self.bn1 = nn.BatchNorm2d(self.in_channels) | |
| 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) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
| self.fc = nn.Linear(512*self.expansion, num_classes) | |
| def _make_layer(self, block: Type[BasicBlock],out_channels: int,blocks: int,stride: int = 1) -> nn.Sequential: | |
| downsample = None | |
| if stride != 1: | |
| """ | |
| This should pass from `layer2` to `layer4` or | |
| when building ResNets50 and above. Section 3.3 of the paper | |
| Deep Residual Learning for Image Recognition | |
| (https://arxiv.org/pdf/1512.03385v1.pdf). | |
| """ | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.in_channels, out_channels*self.expansion,kernel_size=1,stride=stride,bias=False), | |
| nn.BatchNorm2d(out_channels * self.expansion), | |
| ) | |
| layers = [] | |
| layers.append( | |
| block( | |
| self.in_channels, out_channels, stride, self.expansion, downsample | |
| ) | |
| ) | |
| self.in_channels = out_channels * self.expansion | |
| for i in range(1, blocks): | |
| layers.append(block( | |
| self.in_channels, | |
| out_channels, | |
| expansion=self.expansion | |
| )) | |
| return nn.Sequential(*layers) | |
| def forward(self, x: Tensor) -> Tensor: | |
| 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) | |
| # The spatial dimension of the final layer's feature | |
| # map should be (7, 7) for all ResNets. | |
| #print('Dimensions of the last convolutional feature map: ', x.shape) | |
| x = self.avgpool(x) | |
| x = torch.flatten(x, 1) | |
| #x = self.fc(x) | |
| return x |