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mmseg实现

mmseg有基于numpy的eval和 基于 tensor的 preval两种模式，好在都是基于

total_area_intersect, total_area_union, total_area_pred_label, total_area_label

这四个值去计算的，在

/mmseg/core/evaluation/metrics.py

里面，同时

freq = np.array(total_area_label / total_area_label.sum())

那在 得到

ret_metrics_class

和

freq

后就简单了

 ret_metrics_class.update({'freq': [np.round(x * 100, 2) for x in freq]})

 fwiou = sum(freq * np.nan_to_num(ret_metrics_class['IoU']))
 fwiou = np.round(fwiou, 2)
 ret_metrics_summary.update({'fwiou': fwiou})

还没在train log里测过，单独测可以的

import mmcv

config_path = 'test.py'

from mmseg.datasets import build_dataloader, build_dataset
cfg = mmcv.Config.fromfile(config_path)
dataset = build_dataset(cfg.data.test)
results = mmcv.load('/home/user/workplace/python/mmsegmentation-0.19.0/results.pkl')
metric = dataset.evaluate(results)

效果就是

per class results:

+-------+-------+-------+-------+
| Class |  IoU  |  Acc  |  freq |
+-------+-------+-------+-------+
|   0   |  nan  |  nan  |  0.0  |
|   1   | 81.94 | 92.16 | 60.02 |
|   2   | 58.06 |  69.5 | 10.26 |
|   3   | 19.32 | 24.99 |  0.58 |
|   4   | 57.82 | 67.98 |  9.09 |
|   5   | 69.95 | 82.31 | 19.66 |
|   6   | 39.31 | 49.66 |  0.39 |
+-------+-------+-------+-------+
Summary:

+-------+------+-------+-------+
|  aAcc | mIoU |  mAcc | fwiou |
+-------+------+-------+-------+
| 85.14 | 54.4 | 64.43 | 74.41 |
+-------+------+-------+-------+

手撸一个fwiou

from tqdm import tqdm
import numpy as np
from PIL import Image
import os
def fast_hist(a, b, n):
    k = (a >= 0) & (a < n)
    return np.bincount(n * a[k].astype(int) + b[k], minlength=n*n).reshape(n, n)

truth_path = '/dev/shm/tmp/gt'
res_path = '/home/user/workplace/python/mmsegmentation-0.19.0/.format_cityscapes'
n_class = 7
hist = np.zeros((n_class, n_class), dtype=np.float)
for name in tqdm(os.listdir(truth_path)):
    truth_img = Image.open(os.path.join(truth_path, name))
    truth_img = np.array(truth_img).flatten()
    res_img = Image.open(os.path.join(res_path, name.replace('LT.tif', 'GF.png')))
    res_img = np.array(res_img).flatten()
    hist += fast_hist(truth_img, res_img, n_class)

iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist) + 1e-8)
freq = hist.sum(1) / hist.sum()
res = (freq[freq > 0] * iu[freq > 0]).sum()
iu[0] = res
output = ' '.join(['{:.3f}'.format(x) for x in iu])
print(output)