pytorch图片分类，使用cutmix进行数据增强，要求绘制gradcam热力图的全部代码

好的，下面是基于PyTorch和CutMix进行数据增强和绘制GradCAM热力图的全部代码： ```python import torch import torch.nn as nn import torch.nn.functional as F from torchvision import models, transforms from PIL import Image import matplotlib.pyplot as plt class CutMixTransform: """ CutMix augmentation Reference: https://arxiv.org/pdf/1905.04899.pdf """ def __init__(self, alpha=1.0): self.alpha = alpha def __call__(self, image, target): r = np.random.rand(1) if r < 0.5: return image, target w, h = image.size cut_rat = np.sqrt(1. - self.alpha) cut_w = np.int(w * cut_rat) cut_h = np.int(h * cut_rat) cx = np.random.randint(w) cy = np.random.randint(h) bbx1 = np.clip(cx - cut_w // 2, 0, w) bby1 = np.clip(cy - cut_h // 2, 0, h) bbx2 = np.clip(cx + cut_w // 2, 0, w) bby2 = np.clip(cy + cut_h // 2, 0, h) image = image.copy() image.paste(image.crop((bbx1, bby1, bbx2, bby2)), (bbx1, bby1, bbx2, bby2)) target_ = target.copy() target = [target, target_] return image, target class Model(nn.Module): """ Pretrained ResNet50 model for image classification """ def __init__(self, num_classes): super().__init__() self.resnet = models.resnet50(pretrained=True) self.resnet.fc = nn.Linear(2048, num_classes) def forward(self, x): x = self.resnet.conv1(x) x = self.resnet.bn1(x) x = self.resnet.relu(x) x = self.resnet.maxpool(x) x = self.resnet.layer1(x) x = self.resnet.layer2(x) x = self.resnet.layer3(x) x = self.resnet.layer4(x) x = self.resnet.avgpool(x) x = torch.flatten(x, 1) x = self.resnet.fc(x) return x def inference(model, image_path): """ Perform inference on single image """ image = Image.open(image_path) transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) input_tensor = transform(image).unsqueeze(0) output_tensor = model(input_tensor) output_probs = F.softmax(output_tensor, dim=1) output_label = torch.argmax(output_probs, dim=1) return input_tensor, output_probs, output_label def gradcam(model, input_tensor, class_idx): """ Calculate GradCAM heatmap """ model.eval() feature_maps, logits = model(input_tensor.cuda()) logits[0, class_idx].backward() gradients = model.resnet.layer4[2].conv3.weight.grad pooled_gradients = torch.mean(gradients, dim=[0, 2, 3]) feature_maps = feature_maps.permute(0, 2, 3, 1) heatmap = torch.zeros_like(feature_maps[:, :, :, 0]) for i in range(pooled_gradients.shape[0]): heatmap += (pooled_gradients[i] * feature_maps[:, :, :, i]) return heatmap # Load model model = Model(num_classes=10) # Load image and perform inference image_path = "cat.jpg" input_tensor, output_probs, output_label = inference(model, image_path) # Choose a random class label to visualize class_idx = torch.randint(0, 10, size=(1,))[0].item() class_name = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"][class_idx] # Perform CutMix augmentation and inference cutmix_transforms = CutMixTransform(alpha=1.0) image_cutmix, target_cutmix = cutmix_transforms(Image.open(image_path).convert("RGB"), class_idx) input_tensor_cutmix, output_probs_cutmix, output_label_cutmix = inference(model, image_cutmix) # Calculate GradCAM heatmap heatmap = gradcam(model, input_tensor_cutmix.cuda(), target_cutmix) # Plot original image and GradCAM heatmap fig, ax = plt.subplots(1, 2, figsize=(15, 5)) ax[0].imshow(Image.open(image_path)) ax[0].set_title(f"True: cat\nPred: {class_name}") ax[1].imshow(heatmap.detach().cpu().numpy(), cmap="jet") ax[1].set_title(f"GradCAM for {class_name}") plt.show() ``` 在这个示例中，我们首先定义了一个`CutMixTransform`类来实现CutMix数据增强，然后定义了一个预训练的ResNet50模型用于图像分类。我们对一张测试图像进行预测，并从预测结果中随机选择一个类别，然后利用`CutMixTransform`对原始图像进行数据增强，并在增强后的图像上进行预测。然后，我们将增强后的图像和原始图像都用于计算GradCAM热力图，并展示出来。