-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
171 lines (147 loc) · 9.42 KB
/
utils.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
import os
import torch
import pickle
import numpy as np
import torchvision
import matplotlib.pyplot as plt
from inspect import signature
from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score
def read_pickle(filename):
infile = open(filename, 'rb')
data = pickle.load(infile)
infile.close()
return data
def dump_pickle(filename, data):
outfile = open(filename, "wb")
pickle.dump(data, filename)
outfile.close()
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
# precision = TP / (TP + FP)
# recall = TP / (TP + FN)
def calculate_precision_recall(prediction, label, mode, batch_idx, epoch):
prediction = prediction.reshape(prediction.shape[0] * prediction.shape[1] * prediction.shape[2] * prediction.shape[3])
label = label.reshape(label.shape[0] * label.shape[1] * label.shape[2] * label.shape[3])
average_precision = average_precision_score(label, prediction)
precision, recall, thresholds = precision_recall_curve(label, prediction)
idx = find_nearest(thresholds, 0.5)
step_kwargs = ({'step': 'post'}if 'step' in signature(plt.fill_between).parameters else {})
plt.step(recall, precision, color='b', alpha=0.2, where='post')
plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(average_precision))
plt.savefig('/home/zzhao/data/uav_regression/regression_process/precision_recall_curve/' + str(mode) + '/precision_recall_curve_epoch_' + str(epoch) + 'batch_' + str (batch_idx) + '.png')
plt.close()
return precision[idx], recall[idx]
# TPR = TP / (TP + FN)
# FPR = FP / (FP + TN)
def draw_roc_curve(prediction, label, mode, epoch, batch_idx):
prediction = prediction.reshape(prediction.shape[0] * prediction.shape[1] * prediction.shape[2] * prediction.shape[3])
label = label.reshape(label.shape[0] * label.shape[1] * label.shape[2] * label.shape[3])
fpr, tpr, thresholds = roc_curve(label, prediction, pos_label=1)
auroc = roc_auc_score(label, prediction)
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % auroc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.savefig('/home/zzhao/data/uav_regression/regression_process/roc_curve/' + str(mode) + '/roc_curve_epoch_' + str(epoch) + '_batch_' + str (batch_idx) + '.png')
plt.close()
return auroc
def visualize_lstm_testing_result(prediction, label, batch_id, epoch):
assert prediction.shape[0] == label.shape[0], "prediction size and label size is not identical"
if not os.path.exists("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch)):
os.mkdir("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch))
if not os.path.exists("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/lstm"):
os.mkdir("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/lstm")
for idx, _ in enumerate(prediction):
prediction_lstm = torch.sum(prediction[idx], dim=0)
label_lstm = torch.sum(label[idx], dim=0)
prediction_lstm = (prediction_lstm - torch.min(prediction_lstm)) / (torch.max(prediction_lstm) - torch.min(prediction_lstm))
# prediction_lstm[prediction_lstm < 0.5] = 0
label_lstm = (label_lstm - torch.min(label_lstm)) / (torch.max(label_lstm) - torch.min(label_lstm))
torchvision.utils.save_image(prediction_lstm, "/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/lstm/" + str(idx + batch_id * 32) + "_prediction.png")
torchvision.utils.save_image(label_lstm, "/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/lstm/" + str(idx + batch_id * 32) + "_label.png")
def visualize_sum_testing_result(path,init, prediction,sub_prediction, label, batch_id, epoch, batch_size):
assert prediction.shape[0] == label.shape[0], "prediction size and label size is not identical"
if not os.path.exists(path):
os.mkdir(path)
if not os.path.exists(path + "/epoch_" + str(epoch)):
os.mkdir(path + "/epoch_" + str(epoch))
if not os.path.exists(path + "/epoch_" + str(epoch) + "/sum"):
os.mkdir(path + "/epoch_" + str(epoch) + "/sum")
# prediction_output_np = prediction.cpu().detach().numpy()
# np.save("/home/zjin04/prediction.npy", prediction_output_np)
# label_output_np = label.cpu().detach().numpy()
# np.save("/home/zjin04/label.npy", label_output_np)
for idx, _ in enumerate(prediction):
init_output = init[idx].cpu().detach()
init_output = torch.squeeze(init_output)
prediction_output = prediction[idx].cpu().detach()
prediction_output = torch.squeeze(prediction_output)
#print("sub_prediction.shape ", sub_prediction.shape)
#4, 1, 60, 100, 100
sub_prediction_output = sub_prediction[idx][0][40].cpu().detach()
sub_prediction_output = torch.squeeze(sub_prediction_output)
#print("ub_prediction_output.shape ", sub_prediction_output.shape)
label_output = label[idx].cpu().detach()
label_output = torch.squeeze(label_output)
# print(label_output.shape)
# prediction_output[prediction_output < 0.30] = 0
# output[output >= 0.50] = 1
# plt.imshow(prediction_output)
# plt.savefig("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * 32) + "_prediction.png")
# plt.imshow(label_output)
# plt.savefig("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * 32) + "_label.png")
# plt.close()
torchvision.utils.save_image(init_output, path + "/epoch_" + str(\
epoch) + "/sum" + "/" + str(idx + batch_id * batch_size) + "_init.png")
torchvision.utils.save_image(sub_prediction_output, path + "/epoch_" + str(
epoch) + "/sum" + "/" + str(idx + batch_id * batch_size) + "_sub_prediction.png")
torchvision.utils.save_image(prediction_output, path + "/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * batch_size) + "_prediction.png")
torchvision.utils.save_image(label_output, path + "/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * batch_size ) + "_label.png")
def visualize_sum_training_result(init, prediction,sub_prediction, label, batch_id, epoch, batch_size):
assert prediction.shape[0] == label.shape[0], "prediction size and label size is not identical"
if not os.path.exists("/home/zjin04/data/uav_regression/training_result/epoch_" + str(epoch)):
os.mkdir("/home/zjin04/data/uav_regression/training_result/epoch_" + str(epoch))
if not os.path.exists("/home/zjin04/data/uav_regression/training_result/epoch_" + str(epoch) + "/sum"):
os.mkdir("/home/zjin04/data/uav_regression/training_result/epoch_" + str(epoch) + "/sum")
# prediction_output_np = prediction.cpu().detach().numpy()
# np.save("/home/zjin04/prediction.npy", prediction_output_np)
# label_output_np = label.cpu().detach().numpy()
# np.save("/home/zjin04/label.npy", label_output_np)
for idx, _ in enumerate(prediction):
init_output = init[idx].cpu().detach()
init_output = torch.squeeze(init_output)
prediction_output = prediction[idx].cpu().detach()
prediction_output = torch.squeeze(prediction_output)
#print("sub_prediction.shape ", sub_prediction.shape)
sub_prediction_output = sub_prediction[idx][0][1].cpu().detach()
sub_prediction_output = torch.squeeze(sub_prediction_output)
#print("ub_prediction_output.shape ", sub_prediction_output.shape)
label_output = label[idx].cpu().detach()
label_output = torch.squeeze(label_output)
# print(label_output.shape)
# prediction_output[prediction_output < 0.30] = 0
# output[output >= 0.50] = 1
# plt.imshow(prediction_output)
# plt.savefig("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * 32) + "_prediction.png")
# plt.imshow(label_output)
# plt.savefig("/home/zjin04/data/uav_regression/testing_result/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * 32) + "_label.png")
# plt.close()
torchvision.utils.save_image(init_output, "/home/zjin04/data/uav_regression/training_result/epoch_" + str(\
epoch) + "/sum" + "/" + str(idx + batch_id * batch_size) + "_init.png")
torchvision.utils.save_image(sub_prediction_output, "/home/zjin04/data/uav_regression/training_result/epoch_" + str(
epoch) + "/sum" + "/" + str(idx + batch_id * batch_size) + "_sub_prediction.png")
torchvision.utils.save_image(prediction_output, "/home/zjin04/data/uav_regression/training_result/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * batch_size) + "_prediction.png")
torchvision.utils.save_image(label_output, "/home/zjin04/data/uav_regression/training_result/epoch_" + str(epoch) + "/sum" + "/" + str(idx + batch_id * batch_size ) + "_label.png")