修改predict实现acc计算

最新推荐文章于 2024-11-24 18:00:00 发布

原创最新推荐文章于 2024-11-24 18:00:00 发布 · 3.4k 阅读

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该博客介绍了一个使用EfficientNet_B0模型进行图像分类的Python代码实现。它加载预训练权重，对指定目录下的图像进行预测，并计算No-Anomaly类别的准确率。代码包括数据预处理、模型创建、权重加载和预测过程。

import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

from model import efficientnet_b0 as create_model


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    img_size = {"B0": 224,
                "B1": 240,
                "B2": 260,
                "B3": 300,
                "B4": 380,
                "B5": 456,
                "B6": 528,
                "B7": 600}
    num_model = "B0"

    data_transform = transforms.Compose(
        [transforms.Resize(img_size[num_model]),
         transforms.CenterCrop(img_size[num_model]),
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    # 建立循环，将目标目录下的图片路径取出
    # 需要改动的有两个部分，输入文件夹的路径、预测结果与文件夹对应结果的对比
    path = r'C:\Users\sun\Desktop\b_classification\classification-pytorch-main\color_datasets\train\No-Anomaly'
    # 获取该路径下所有图片
    filelist = os.listdir(path)
    a = 1
    for files in filelist:
    # load image
        img_path = os.path.join(path, files)
        assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
        img = Image.open(img_path)
        plt.imshow(img)
        # [N, C, H, W]
        img = data_transform(img)
        # expand batch dimension
        img = torch.unsqueeze(img, dim=0)

        # read class_indict
        json_path = './class_indices.json'
        assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)

        json_file = open(json_path, "r")
        class_indict = json.load(json_file)

        # create model
        model = create_model(num_classes=5).to(device)
        # load model weights
        model_weight_path = "./weights/model-29.pth"
        model.load_state_dict(torch.load(model_weight_path, map_location=device))
        model.eval()
        with torch.no_grad():
            # predict class
            output = torch.squeeze(model(img.to(device))).cpu()
            predict = torch.softmax(output, dim=0)
            predict_cla = torch.argmax(predict).numpy()
            if predict_cla == "No-Anomaly":
                a = a + 1
            else:
                a = a
    # 这里的result就是该路径下的图片得到的准确率
    result = a / 2000
    print(result)

        # print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
        #                                              predict[predict_cla].numpy())

        # 不需要展示运行结果所以该部分注释掉
        # plt.title(print_res)
        # for i in range(len(predict)):
        #     print("class: {:10}   prob: {:.3}".format(class_indict[str(i)],
        #                                               predict[i].numpy()))
        # plt.show()


if __name__ == '__main__':
    main()