Spaces:
Build error
Build error
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.utils.model_zoo as model_zoo | |
| # Only keeping ResNet-18 pre-trained model URL | |
| model_urls = { | |
| 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', | |
| } | |
| # Helper function: 3x3 Convolution | |
| 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) | |
| # BasicBlock for ResNet-18 | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, in_planes, planes, stride=1, downsample=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(in_planes, planes, stride) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.downsample = downsample | |
| def forward(self, x): | |
| identity = x | |
| if self.downsample is not None: | |
| identity = self.downsample(x) | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out += identity | |
| out = self.relu(out) | |
| return out | |
| # ResNet Model Definition | |
| class ResNet(nn.Module): | |
| def __init__(self, block, layers, num_classes=14): # Changed num_classes to 14 for Clothing1M | |
| super(ResNet, self).__init__() | |
| self.inplanes = 64 | |
| # Initial Conv Layer | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| # ResNet Layers | |
| 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) | |
| # Adaptive Average Pooling & Fully Connected Layer | |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
| self.fc = nn.Linear(512 * block.expansion, num_classes) # Updated for Clothing1M | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| 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), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for _ in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| 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 = torch.flatten(x, 1) | |
| x = self.fc(x) | |
| return x | |
| # Function to Create ResNet-18 Model | |
| def resnet18(pretrained=False, **kwargs): | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) # ResNet-18 architecture | |
| if pretrained: | |
| model.load_state_dict(torch.load("netBest.pth", map_location=torch.device('cpu'))) # Load our Clothing1M weights | |
| return model | |
| # Updated import statement for modular usage | |
| __all__ = ['resnet18'] | |