# from the torchvision's implementation of ResNet
class ResNet:
   # ...   self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,                              bias=False)       self.bn1 = norm_layer(self.inplanes)       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, dilate = replace_stride_with_dilation[0])       self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate = replace_stride_with_dilation[1])       self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate = replace_stride_with_dilation[2])       self.avgpool = nn.AdaptiveAvgPool2d((1, 1))       self.fc = nn.Linear(512 * block.expansion, num_classes)
   # ...
   def _forward_impl(self, x):       # See note [TorchScript super()]       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

class FullyConvolutionalResnet18(models.ResNet):   def __init__(self, num_classes=1000, pretrained=False, **kwargs):
       # Start with standard resnet18 defined here       super().__init__(block = models.resnet.BasicBlock, layers = [2, 2, 2, 2], num_classes = num_classes, **kwargs)       if pretrained:           state_dict = load_state_dict_from_url( models.resnet.model_urls["resnet18"], progress=True)           self.load_state_dict(state_dict)
       # Replace AdaptiveAvgPool2d with standard AvgPool2d       self.avgpool = nn.AvgPool2d((7, 7))
       # Convert the original fc layer to a convolutional layer.         self.last_conv = torch.nn.Conv2d( in_channels = self.fc.in_features, out_channels = num_classes, kernel_size = 1)       self.last_conv.weight.data.copy_( self.fc.weight.data.view ( *self.fc.weight.data.shape, 1, 1))       self.last_conv.bias.data.copy_ (self.fc.bias.data)
   # Reimplementing forward pass.   def _forward_impl(self, x):       # Standard forward for resnet18       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)
       # Notice, there is no forward pass       # through the original fully connected layer.       # Instead, we forward pass through the last conv layer       x = self.last_conv(x)       return x

#1. 导入标准库import torchimport torch.nn as nnfrom torchvision import modelsfrom torch.hub import load_state_dict_from_url
from PIL import Imageimport cv2import numpy as npfrom matplotlib import pyplot as plt
#2. 读取ImageNet类ID到名称的映射if __name__ == "__main__":
   # Read ImageNet class id to name mapping   with open('imagenet_classes.txt') as f:       labels = [line.strip() for line in f.readlines()]

# Read imageoriginal_image = cv2.imread('camel.jpg')
# Convert original image to RGB formatimage = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
# Transform input image# 1. Convert to Tensor# 2. Subtract mean# 3. Divide by standard deviation
transform = transforms.Compose([                        transforms.ToTensor(), #Convert image to tensor.            transforms.Normalize(                                  mean=[0.485, 0.456, 0.406],   # Subtract mean            std=[0.229, 0.224, 0.225]     # Divide by standard deviation                        )])
image = transform(image)image = image.unsqueeze(0)

# Load modified resnet18 model with pretrained ImageNet weightsmodel = FullyConvolutionalResnet18(pretrained=True).eval()

with torch.no_grad():   # Perform inference.   # Instead of a 1x1000 vector, we will get a   # 1x1000xnxm output ( i.e. a probabibility map   # of size n x m for each 1000 class,   # where n and m depend on the size of the image.)   preds = model(image)   preds = torch.softmax(preds, dim=1)
   print('Response map shape : ', preds.shape)
   # Find the class with the maximum score in the n x m output map   pred, class_idx = torch.max(preds, dim=1)   print(class_idx)
   row_max, row_idx = torch.max(pred, dim=1)   col_max, col_idx = torch.max(row_max, dim=1)   predicted_class = class_idx[0, row_idx[0, col_idx], col_idx]
   # Print top predicted class   print('Predicted Class : ', labels[predicted_class], predicted_class)

Response map shape :  torch.Size([1, 1000, 3, 8])tensor([[[977, 977, 977, 977, 977, 978, 354, 437],        [978, 977, 980, 977, 858, 970, 354, 461],        [977, 978, 977, 977, 977, 977, 354, 354]]])Predicted Class :  Arabian camel, dromedary, Camelus dromedarius tensor([354])

# Find the n x m score map for the predicted classscore_map = preds[0, predicted_class, :, :].cpu().numpy()score_map = score_map[0]
# Resize score map to the original image sizescore_map = cv2.resize(score_map, (original_image.shape[1], original_image.shape[0]))
# Binarize score map_, score_map_for_contours = cv2.threshold(score_map, 0.25, 1, type=cv2.THRESH_BINARY)score_map_for_contours = score_map_for_contours.astype(np.uint8).copy()
# Find the countour of the binary blobcontours, _ = cv2.findContours(score_map_for_contours, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
# Find bounding box around the object.rect = cv2.boundingRect(contours[0])

# Apply score map as a mask to original imagescore_map = score_map - np.min(score_map[:])score_map = score_map / np.max(score_map[:])
接下来，我们将响应图与原始图像相乘并显示边界框。score_map = cv2.cvtColor(score_map, cv2.COLOR_GRAY2BGR)masked_image = (original_image * score_map).astype(np.uint8)
# Display bounding boxcv2.rectangle(masked_image, rect[:2], (rect[0] + rect[2], rect[1] + rect[3]), (0, 0, 255), 2)
# Display imagescv2.imshow("Original Image", original_image)cv2.imshow("scaled_score_map", score_map)cv2.imshow("activations_and_bbox", masked_image)cv2.waitKey(0)