-
Notifications
You must be signed in to change notification settings - Fork 6
/
eval_cam_with_crf.py
165 lines (139 loc) · 5.64 KB
/
eval_cam_with_crf.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
import torch
import os
import numpy as np
import torch.nn.functional as F
import joblib
import multiprocessing
import pydensecrf.densecrf as dcrf
import pydensecrf.utils as utils
import cv2
from PIL import Image
import argparse
class DenseCRF(object):
def __init__(self, iter_max, pos_w, pos_xy_std, bi_w, bi_xy_std, bi_rgb_std):
self.iter_max = iter_max
self.pos_w = pos_w
self.pos_xy_std = pos_xy_std
self.bi_w = bi_w
self.bi_xy_std = bi_xy_std
self.bi_rgb_std = bi_rgb_std
def __call__(self, image, probmap):
C, H, W = probmap.shape
U = utils.unary_from_softmax(probmap)
U = np.ascontiguousarray(U)
image = np.ascontiguousarray(image)
d = dcrf.DenseCRF2D(W, H, C)
d.setUnaryEnergy(U)
d.addPairwiseGaussian(sxy=self.pos_xy_std, compat=self.pos_w)
d.addPairwiseBilateral(
sxy=self.bi_xy_std, srgb=self.bi_rgb_std, rgbim=image, compat=self.bi_w
)
Q = d.inference(self.iter_max)
Q = np.array(Q).reshape((C, H, W))
return Q
def makedirs(dirs):
if not os.path.exists(dirs):
os.makedirs(dirs)
def _fast_hist(label_true, label_pred, n_class):
mask = (label_true >= 0) & (label_true < n_class)
hist = np.bincount(
n_class * label_true[mask].astype(int) + label_pred[mask],
minlength=n_class ** 2,
).reshape(n_class, n_class)
return hist
def scores(label_trues, label_preds, n_class):
hist = np.zeros((n_class, n_class))
for lt, lp in zip(label_trues, label_preds):
hist += _fast_hist(lt.flatten(), lp.flatten(), n_class)
acc = np.diag(hist).sum() / hist.sum()
acc_cls = np.diag(hist) / hist.sum(axis=1)
acc_cls = np.nanmean(acc_cls)
iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))
valid = hist.sum(axis=1) > 0 # added
mean_iu = np.nanmean(iu[valid])
freq = hist.sum(axis=1) / hist.sum()
fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
cls_iu = dict(zip(range(n_class), iu))
return {
"Pixel Accuracy": acc,
"Mean Accuracy": acc_cls,
"Frequency Weighted IoU": fwavacc,
"Mean IoU": mean_iu,
"Class IoU": cls_iu,
}
def crf(n_jobs, is_coco=False):
"""
CRF post-processing on pre-computed logits
"""
# Configuration
torch.set_grad_enabled(False)
print("# jobs:", n_jobs)
# CRF post-processor
postprocessor = DenseCRF(
iter_max=10,
pos_xy_std=1,
pos_w=3,
bi_xy_std=67,
bi_rgb_std=3,
bi_w=4,
)
# Process per sample
def process(i):
image_id = eval_list[i]
image_path = os.path.join(args.image_root, image_id + '.jpg')
image = cv2.imread(image_path, cv2.IMREAD_COLOR).astype(np.float32)
label_path = os.path.join(args.gt_root, image_id + '.png')
gt_label = np.asarray(Image.open(label_path), dtype=np.int32)
# Mean subtraction
image -= mean_bgr
# HWC -> CHW
image = image.transpose(2, 0, 1)
filename = os.path.join(args.cam_out_dir, image_id + ".npy")
cam_dict = np.load(filename, allow_pickle=True).item()
cams = cam_dict['attn_highres']
bg_score = np.power(1 - np.max(cams, axis=0, keepdims=True), 1)
cams = np.concatenate((bg_score, cams), axis=0)
prob = cams
image = image.astype(np.uint8).transpose(1, 2, 0)
prob = postprocessor(image, prob)
label = np.argmax(prob, axis=0)
keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant')
label = keys[label]
if not args.eval_only:
confidence = np.max(prob, axis=0)
label[confidence < 0.95] = 255
cv2.imwrite(os.path.join(args.pseudo_mask_save_path, image_id + '.png'), label.astype(np.uint8))
return label.astype(np.uint8), gt_label.astype(np.uint8)
# CRF in multi-process
results = joblib.Parallel(n_jobs=n_jobs, verbose=10, pre_dispatch="all")(
[joblib.delayed(process)(i) for i in range(len(eval_list))]
)
if args.eval_only:
preds, gts = zip(*results)
# Pixel Accuracy, Mean Accuracy, Class IoU, Mean IoU, Freq Weighted IoU
score = scores(gts, preds, n_class=21 if not is_coco else 81)
print(score)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--cam_out_dir", default="./cam_out", type=str)
parser.add_argument("--pseudo_mask_save_path", default="/home/xxx/code/code48/ablation/usss/voc/val_attn07_crf", type=str)
parser.add_argument("--split_file", default="/home/xxx/datasets/VOC2012/ImageSets/Segmentation/train.txt",
type=str)
parser.add_argument("--cam_eval_thres", default=2, type=float)
parser.add_argument("--gt_root", default="/home/xxx/datasets/VOC2012/SegmentationClassAug", type=str)
parser.add_argument("--image_root", default="/home/xxx/datasets/VOC2012/JPEGImages", type=str)
parser.add_argument("--eval_only", action="store_true")
args = parser.parse_args()
is_coco = 'coco' in args.cam_out_dir
if 'voc' in args.cam_out_dir:
eval_list = list(np.loadtxt(args.split_file, dtype=str))
elif 'coco' in args.cam_out_dir:
file_list = tuple(open(args.split_file, "r"))
file_list = [id_.rstrip().split(" ") for id_ in file_list]
eval_list = [x[0] for x in file_list]#[:2000]
print('{} images to eval'.format(len(eval_list)))
if not args.eval_only and not os.path.exists(args.pseudo_mask_save_path):
os.makedirs(args.pseudo_mask_save_path)
mean_bgr = (104.008, 116.669, 122.675)
n_jobs =multiprocessing.cpu_count()
crf(n_jobs, is_coco)