import torch.nn as nn import torch.nn.functional as F from torch import set_grad_enabled, flatten, Tensor from .nets_utils import EmbeddingRecorder from torchvision.models import resnet # Acknowledgement to # https://github.com/kuangliu/pytorch-cifar, # https://github.com/BIGBALLON/CIFAR-ZOO, def conv3x3(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(in_planes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, in_planes, planes, stride=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion * planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) out += self.shortcut(x) out = F.relu(out) return out class ResNet_32x32(nn.Module): def __init__(self, block, num_blocks, channel=3, num_classes=10, record_embedding: bool = False, no_grad: bool = False): super().__init__() self.in_planes = 64 self.conv1 = conv3x3(channel, 64) self.bn1 = nn.BatchNorm2d(64) self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) self.linear = nn.Linear(512 * block.expansion, num_classes) self.embedding_recorder = EmbeddingRecorder(record_embedding) self.no_grad = no_grad def get_last_layer(self): return self.linear def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): with set_grad_enabled(not self.no_grad): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.embedding_recorder(out) out = self.linear(out) return out class ResNet_224x224(resnet.ResNet): def __init__(self, block, layers, channel: int, num_classes: int, record_embedding: bool = False, no_grad: bool = False, **kwargs): super().__init__(block, layers, **kwargs) self.embedding_recorder = EmbeddingRecorder(record_embedding) if channel != 3: self.conv1 = nn.Conv2d(channel, 64, kernel_size=7, stride=2, padding=3, bias=False) if num_classes != 1000: self.fc = nn.Linear(self.fc.in_features, num_classes) self.no_grad = no_grad def get_last_layer(self): return self.fc def _forward_impl(self, x: Tensor) -> Tensor: # See note [TorchScript super()] with set_grad_enabled(not self.no_grad): 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 = flatten(x, 1) x = self.embedding_recorder(x) x = self.fc(x) return x def ResNet(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): arch = arch.lower() if pretrained: if arch == "resnet18": net = ResNet_224x224(resnet.BasicBlock, [2, 2, 2, 2], channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet34": net = ResNet_224x224(resnet.BasicBlock, [3, 4, 6, 3], channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet50": net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet101": net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet152": net = ResNet_224x224(resnet.Bottleneck, [3, 8, 36, 3], channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad) else: raise ValueError("Model architecture not found.") from torch.hub import load_state_dict_from_url state_dict = load_state_dict_from_url(resnet.model_urls[arch], progress=True) net.load_state_dict(state_dict) if channel != 3: net.conv1 = nn.Conv2d(channel, 64, kernel_size=7, stride=2, padding=3, bias=False) if num_classes != 1000: net.fc = nn.Linear(net.fc.in_features, num_classes) elif im_size[0] == 224 and im_size[1] == 224: if arch == "resnet18": net = ResNet_224x224(resnet.BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet34": net = ResNet_224x224(resnet.BasicBlock, [3, 4, 6, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet50": net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet101": net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet152": net = ResNet_224x224(resnet.Bottleneck, [3, 8, 36, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) else: raise ValueError("Model architecture not found.") elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( channel == 3 and im_size[0] == 32 and im_size[1] == 32): if arch == "resnet18": net = ResNet_32x32(BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet34": net = ResNet_32x32(BasicBlock, [3, 4, 6, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet50": net = ResNet_32x32(Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet101": net = ResNet_32x32(Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) elif arch == "resnet152": net = ResNet_32x32(Bottleneck, [3, 8, 36, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) else: raise ValueError("Model architecture not found.") else: raise NotImplementedError("Network Architecture for current dataset has not been implemented.") return net def ResNet18(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return ResNet("resnet18", channel, num_classes, im_size, record_embedding, no_grad, pretrained) def ResNet34(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return ResNet("resnet34", channel, num_classes, im_size, record_embedding, no_grad, pretrained) def ResNet50(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return ResNet("resnet50", channel, num_classes, im_size, record_embedding, no_grad, pretrained) def ResNet101(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return ResNet("resnet101", channel, num_classes, im_size, record_embedding, no_grad, pretrained) def ResNet152(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return ResNet("resnet152", channel, num_classes, im_size, record_embedding, no_grad, pretrained)