-
Notifications
You must be signed in to change notification settings - Fork 26
/
metrics.py
195 lines (154 loc) · 7.13 KB
/
metrics.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
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import math
import numpy as np
import torch
def log10(x):
"""Convert a new tensor with the base-10 logarithm of the elements of x. """
return torch.log(x) / math.log(10)
class Result(object):
def __init__(self,threshold=0):
self.irmse, self.imae = 0, 0
self.mse, self.rmse, self.mae = 0, 0, 0
self.absrel, self.lg10 = 0, 0
self.delta1, self.delta2, self.delta3 = 0, 0, 0
self.data_time, self.gpu_time = 0, 0
self.loss0, self.loss1,self.loss2 = 0, 0,0
self.threshold = threshold # 0.18291457286432158
def set_to_worst(self):
self.irmse, self.imae = np.inf, np.inf
self.mse, self.rmse, self.mae = np.inf, np.inf, np.inf
self.absrel, self.lg10 = np.inf, np.inf
self.delta1, self.delta2, self.delta3 = 0, 0, 0
self.data_time, self.gpu_time = 0, 0
self.loss0, self.loss1, self.loss2 = np.inf, np.inf, np.inf
def update(self, irmse, imae, mse, rmse, mae, absrel, lg10, delta1, delta2, delta3, gpu_time, data_time,loss0,loss1,loss2):
self.irmse, self.imae = irmse, imae
self.mse, self.rmse, self.mae = mse, rmse, mae
self.absrel, self.lg10 = absrel, lg10
self.delta1, self.delta2, self.delta3 = delta1, delta2, delta3
self.data_time, self.gpu_time = data_time, gpu_time
self.loss0, self.loss1, self.loss2 = loss0,loss1,loss2
def evaluate(self, output, target,confidence = None):
if confidence is None:
valid_mask = target>0
else:
valid_mask = (target>0) & (confidence > self.threshold)
output = output[valid_mask]
target = target[valid_mask]
abs_diff = (output - target).abs()
self.mse = float((torch.pow(abs_diff, 2)).mean())
self.rmse = math.sqrt(self.mse)
self.mae = float(abs_diff.mean())
self.lg10 = float((log10(output) - log10(target)).abs().mean())
self.absrel = float((abs_diff / target).mean())
maxRatio = torch.max(output / target, target / output)
self.delta1 = float((maxRatio < 1.25).float().mean())
self.delta2 = float((maxRatio < 1.25 ** 2).float().mean())
self.delta3 = float((maxRatio < 1.25 ** 3).float().mean())
self.data_time = 0
self.gpu_time = 0
inv_output = 1 / output
inv_target = 1 / target
abs_inv_diff = (inv_output - inv_target).abs()
self.irmse = math.sqrt((torch.pow(abs_inv_diff, 2)).mean())
self.imae = float(abs_inv_diff.mean())
class ConfidencePixelwiseAverageMeter(object):
def __init__(self,num_bins=1000):
self.num_bins = num_bins
self.reset()
def reset(self):
self.count = np.zeros([self.num_bins], np.uint64)
self.absrel = np.zeros([self.num_bins], np.uint64)
def hash_index(self,confidence): #confidence is a matrix between 0 and 1
indexs = np.floor((confidence - 10e-15) * self.num_bins)
return indexs.astype(int)
def evaluate(self, depth, confidence, target):
valid_mask = target > 0
depth = depth[valid_mask]
target = target[valid_mask]
confidence = confidence[valid_mask]
indexes = self.hash_index(confidence.cpu().numpy())
abs_diff = (depth - target).abs()
absrel = ((abs_diff / target)*1000).cpu().numpy()
for img_index, conf_index in np.ndenumerate(indexes):
self.count[conf_index] += 1
self.absrel[conf_index] += absrel[img_index]
def result(self):
res = [None] * self.num_bins
for pos, conf_index in np.ndenumerate(self.count):
if self.count[pos] > 0:
res[pos[0]] = self.absrel[pos]/ self.count[pos]
return res
class ConfidencePixelwiseThrAverageMeter(object):
def __init__(self,num_bins=500,top= 1.0):# 200, top 0.7
self.num_bins = num_bins
self.thresholds = np.linspace(0, top, num_bins, endpoint=True)
self.reset()
def reset(self):
self.count = np.zeros([self.num_bins], np.uint64)
self.absrel = np.zeros([self.num_bins], np.uint64)
self.recall = np.zeros([self.num_bins], np.uint64)
def evaluate(self, depth, confidence, target):
valid_mask = target > 0
depth = depth[valid_mask]
target = target[valid_mask]
confidence = confidence[valid_mask]
abs_diff = (depth - target).abs()
absrel = ((abs_diff / target) * 1000.0) #.cpu().numpy()
num_valids = valid_mask.sum()/1000.0
for img_index, curr_conf in np.ndenumerate(self.thresholds):
mask = confidence > curr_conf
absrel_medio = absrel[mask].float().mean()
valids = mask.sum() / num_valids
if(math.isfinite(absrel_medio) and math.isfinite(valids)):
self.count[img_index] += 1
self.absrel[img_index] += int(absrel_medio)
self.recall[img_index] += int(valids)
def result(self):
res = [(None,None,None)] * self.num_bins
for pos, conf_index in np.ndenumerate(self.count):
if self.count[pos] > 0:
res[pos[0]] = (self.absrel[pos]/ self.count[pos],self.recall[pos]/ self.count[pos],self.thresholds[pos])
return res
def print(self,filename):
lines = self.result()
with open(filename, 'w') as csvfile:
for absrel, recall,thr in lines:
if recall is not None:
csvfile.write('{},{},{}\n'.format(thr,recall/1000.0,absrel/1000.0))
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.count = 0.0
self.sum_irmse, self.sum_imae = 0, 0
self.sum_mse, self.sum_rmse, self.sum_mae = 0, 0, 0
self.sum_absrel, self.sum_lg10 = 0, 0
self.sum_delta1, self.sum_delta2, self.sum_delta3 = 0, 0, 0
self.sum_data_time, self.sum_gpu_time = 0, 0
self.sum_loss0, self.sum_loss1, self.sum_loss2 = 0,0,0
def update(self, result, gpu_time, data_time,loss, n=1):
self.count += n
self.sum_irmse += n*result.irmse
self.sum_imae += n*result.imae
self.sum_mse += n*result.mse
self.sum_rmse += n*result.rmse
self.sum_mae += n*result.mae
self.sum_absrel += n*result.absrel
self.sum_lg10 += n*result.lg10
self.sum_delta1 += n*result.delta1
self.sum_delta2 += n*result.delta2
self.sum_delta3 += n*result.delta3
self.sum_data_time += n*data_time
self.sum_gpu_time += n*gpu_time
self.sum_loss0 += n * loss[0]
self.sum_loss1 += n * loss[1]
self.sum_loss2 += n * loss[2]
def average(self):
avg = Result()
avg.update(
self.sum_irmse / self.count, self.sum_imae / self.count,
self.sum_mse / self.count, self.sum_rmse / self.count, self.sum_mae / self.count,
self.sum_absrel / self.count, self.sum_lg10 / self.count,
self.sum_delta1 / self.count, self.sum_delta2 / self.count, self.sum_delta3 / self.count,
self.sum_gpu_time / self.count, self.sum_data_time / self.count, self.sum_loss0 / self.count, self.sum_loss1 / self.count, self.sum_loss2 / self.count)
return avg