# 测试部分，每次喂入 batch_size 张图片 with torch.no_grad(): my_resnet.eval() # 测试模式 correct = list(0. for i in range(2)) total = list(0. for i in range(2)) for j, (inputs, label_class) in enumerate(test_loader): inputs = inputs.to(device) # 输入图像 label_class = label_class.to(device) # 类别标签 label_class = label_class.long() outputs_class = my_resnet(inputs) # （输出：类别独热码） loss = loss_func(outputs_class, label_class) valid_loss += loss.item() * inputs.size(0) ret, predictions = torch.max(outputs_class.data, 1) correct_counts = predictions.eq(label_class.data.view_as(predictions)) acc = torch.mean(correct_counts.type(torch.FloatTensor)) valid_class_acc += acc.item() * inputs.size(0) res = predictions == label_class for label_idx in range(len(label_class)): label_single = label_class[label_idx] correct[label_single] += res[label_idx].item() total[label_single] += 1 这部分代码是做什么的 介绍一下 谢谢！！

这部分代码是对训练好的 ResNet 模型进行测试，用于评估模型的性能表现。该代码通过遍历测试集中的数据，将其输入 ResNet 模型，得到模型的输出，即分类独热码。然后计算该输出与实际标签之间的损失，并将该损失值加到 valid_loss 中。接着，根据该输出预测出类别，并计算该预测的准确率。最后，根据预测的结果，统计每个类别的正确数量和总数量，以便计算每个类别的准确率。在这个过程中，模型处于测试模式，不会更新模型的参数。

相关问题

import os import json import torch from PIL import Image from torchvision import transforms from model import resnet34 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") data_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])]) # load image # 指向需要遍历预测的图像文件夹 imgs_root = "../dataset/val" assert os.path.exists(imgs_root), f"file: '{imgs_root}' dose not exist." # 读取指定文件夹下所有jpg图像路径 img_path_list = [os.path.join(imgs_root, i) for i in os.listdir(imgs_root) if i.endswith(".jpg")] # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), f"file: '{json_path}' dose not exist." json_file = open(json_path, "r") class_indict = json.load(json_file) # create model model = resnet34(num_classes=16).to(device) # load model weights weights_path = "./newresNet34.pth" assert os.path.exists(weights_path), f"file: '{weights_path}' dose not exist." model.load_state_dict(torch.load(weights_path, map_location=device)) # prediction model.eval() batch_size = 8 # 每次预测时将多少张图片打包成一个batch with torch.no_grad(): for ids in range(0, len(img_path_list) // batch_size): img_list = [] for img_path in img_path_list[ids * batch_size: (ids + 1) * batch_size]: assert os.path.exists(img_path), f"file: '{img_path}' dose not exist." img = Image.open(img_path) img = data_transform(img) img_list.append(img) # batch img # 将img_list列表中的所有图像打包成一个batch batch_img = torch.stack(img_list, dim=0) # predict class output = model(batch_img.to(device)).cpu() predict = torch.softmax(output, dim=1) probs, classes = torch.max(predict, dim=1) for idx, (pro, cla) in enumerate(zip(probs, classes)): print("image: {} class: {} prob: {:.3}".format(img_path_list[ids * batch_size + idx], class_indict[str(cla.numpy())], pro.numpy())) if __name__ == '__main__': main()

这段代码实现了导入必要的包和模块，包括操作系统、JSON、PyTorch、PIL及其转换模块、还有定义的resnet34模型。在主函数中，首先根据可用GPU情况使用cuda或cpu作为设备，然后定义数据的处理流程，包括缩放、剪裁、转换为Tensor并进行标准化。

为以下每句代码做注释：import torch from model import resnet152 from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt import json device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") data_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])]) img = Image.open("./huanglongbing.JPG") plt.imshow(img) img = data_transform(img) img = torch.unsqueeze(img, dim=0) try: json_file = open('./class_indices.json', 'r') class_indict = json.load(json_file) except Exception as e: print(e) exit(-1) model = resnet152(num_classes=38) model_weight_path = "./resNet152.pth" model.load_state_dict(torch.load(model_weight_path, map_location=device)) model.eval() with torch.no_grad(): output = torch.squeeze(model(img)) predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() print(class_indict[str(predict_cla)], predict[predict_cla].numpy()) plt.show()

# 导入所需的库
import torch
from model import resnet152
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import json

# 判断是否有GPU可用，若有则使用GPU，否则使用CPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 定义数据预处理的步骤，包括图片的resize、中心裁剪、转换为张量、以及标准化
data_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])]
)

# 打开图片并显示
img = Image.open("./huanglongbing.JPG")
plt.imshow(img)

# 对图片进行预处理，并增加一维作为batch_size
img = data_transform(img)
img = torch.unsqueeze(img, dim=0)

# 读取class_indices.json文件，获取类别标签
try:
json_file = open('./class_indices.json', 'r')
class_indict = json.load(json_file)
except Exception as e:
print(e)
exit(-1)

# 加载预训练好的模型，以及其对应的权重文件
model = resnet152(num_classes=38)
model_weight_path = "./resNet152.pth"
model.load_state_dict(torch.load(model_weight_path, map_location=device))
model.eval()

# 在不进行梯度计算的情况下，使用模型进行预测
with torch.no_grad():
output = torch.squeeze(model(img))
predict = torch.softmax(output, dim=0) # 对输出进行softmax处理
predict_cla = torch.argmax(predict).numpy() # 获取预测的类别

# 输出预测结果以及对应的概率，并显示图片
print(class_indict[str(predict_cla)], predict[predict_cla].numpy())
plt.show()