Pretrained model output bad segmentation result

[edvardHua]

According to Visualize_video.py, i wrote a infer.py for testing pretrained model located in result folder. But i found output result is terriable.

Below is the complete code of infer.py script.

import cv2
import json
import models
import argparse
import torch
import numpy as np

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-c', '--config', type=str)
    parser.add_argument('-ip', '--img_path', type=str)
    args = parser.parse_args()

    with open(args.config) as fin:
        config = json.load(fin)

    test_config = config['test_config']
    data_config = config['data_config']

    if test_config["loss"] == "Lovasz":
        test_config["num_classes"] = 1
        print("Use Lovasz loss ")
        Lovasz = True
    else:
        print("Use Cross Entropy loss ")
        Lovasz = False

    if test_config['Model'].startswith('Stage2'):
        model = models.__dict__[test_config["Model"]](classes=test_config["num_classes"],
                                                      p=test_config["p"], q=test_config["q"])
    elif test_config["Model"].startswith('Dnc_SINet'):
        model = models.__dict__[test_config["Model"]](
            classes=test_config["num_classes"], p=test_config["p"], q=test_config["q"],
            chnn=test_config["chnn"])

    mean = [107.304565, 115.69884, 132.35703]
    std = [63.97182, 65.1337, 68.29726]
    model.eval()

    image = cv2.imread(args.img_path)
    image = cv2.resize(image, (data_config['w'], data_config['h']))
    img = image.astype(np.float32)

    img = img.astype(np.float32)
    for j in range(3):
        img[:, :, j] -= mean[j]
    for j in range(3):
        img[:, :, j] /= std[j]

    img /= 255.
    img = img.transpose((2, 0, 1))
    img_tensor = torch.from_numpy(img)
    img_tensor = torch.unsqueeze(img_tensor, 0)

    with torch.no_grad():
        img_variable = torch.autograd.Variable(img_tensor)

        if torch.cuda.is_available():
            img_variable = img_variable.cuda()

        out = model(img_variable)

    classMap_numpy = (out[0].data.cpu() > 0).numpy()[0]
    idx_fg = (classMap_numpy == 1)
    seg_img = image * idx_fg[:, :, np.newaxis]
    cv2.imwrite("test.jpg", seg_img.astype(np.uint8))

And run with the following command

python3 infer.py -c setting/Test_ExtremeC3Net.json -ip /Users/edvardzeng/Dataset/PortraitDataset/Nukki/baidu_V1/input/43.png

Output result:

Did i missing something ?

Looking forward to your replay.

Thx.

[darius513]

@edvardHua hello, I use your code for a test, and I get the same bad result as you. Have you solved the problem? waiting for your reply

[YAwei666]

@edvardHua hello, I use your code for a test, and I get the same bad result as you. Have you solved the problem? waiting for your reply
have you solved this problem?

[RORO11]

I also meet this problem.

[zhepherd]

miss load_state_dict? but add load_state_dict is error