-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils4blendtest.py
129 lines (107 loc) · 5.44 KB
/
utils4blendtest.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
import os
import os.path
import torch
import sys
from torchvision.utils import save_image
import numpy as np
import matplotlib.pyplot as plt
class saveData():
def __init__(self, args):
self.args = args
#Generate Savedir folder
self.save_dir = os.path.join(args.saveDir, args.name)
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
#Generate Savedir/model
self.save_dir_model = os.path.join(self.save_dir, 'model')
if not os.path.exists(self.save_dir_model):
os.makedirs(self.save_dir_model)
#Generate Savedir/validation
self.save_dir_validation = os.path.join(self.save_dir, 'validation')
if not os.path.exists(self.save_dir_validation):
os.makedirs(self.save_dir_validation)
#Generate Savedir/checkpoint
self.save_dir_checkpoint = os.path.join(self.save_dir, 'checkpoint')
if not os.path.exists(self.save_dir_checkpoint):
os.makedirs(self.save_dir_checkpoint)
#Generate Savedir/tensorBoard
self.save_dir_tensorBoard = os.path.join(self.save_dir, 'tensorBoard')
if not os.path.exists(self.save_dir_tensorBoard):
os.makedirs(self.save_dir_tensorBoard)
#Generate Savedir/log.txt
if os.path.exists(self.save_dir + '/log.txt'):
self.logFile = open(self.save_dir + '/log.txt', 'a')
else:
self.logFile = open(self.save_dir + '/log.txt', 'w')
def save_log(self, log):
sys.stdout.flush()
self.logFile.write(log + '\n')
self.logFile.flush()
def save_model(self, model, epoch):
torch.save(
model.state_dict(),
self.save_dir_model + '/model_' + str(epoch) + '.pt')
def save_result(self, pred, gt_image, blend_gt, gt_blended_image, blend_input, maksed_input, epoch):
pred = pred.detach().squeeze(1).permute(0,2,1).cpu().numpy()
gt_image = gt_image.detach().squeeze(1).permute(0,2,1).cpu().numpy()
blend_gt = blend_gt.detach().squeeze(1).permute(0,2,1).cpu().numpy()
gt_blended_image = gt_blended_image.detach().squeeze(1).permute(0,2,1).cpu().numpy()
blend_input = blend_input.detach().squeeze(1).permute(0,2,1).cpu().numpy()
maksed_input = maksed_input.detach().squeeze(1).permute(0,2,1).cpu().numpy()
np.save(self.save_dir_validation + '/epoch_' + str(epoch) + "_pred", pred)
np.save(self.save_dir_validation + '/epoch_' + str(epoch) + "_gt_image", gt_image)
np.save(self.save_dir_validation + '/epoch_' + str(epoch) + "_gt_blend", blend_gt)
np.save(self.save_dir_validation + '/epoch_' + str(epoch) + "_gt_blended_image", gt_blended_image)
np.save(self.save_dir_validation + '/epoch_' + str(epoch) + "_blend_input", blend_input)
np.save(self.save_dir_validation + '/epoch_' + str(epoch) + "_Masked_input", maksed_input)
cmap = plt.get_cmap('jet')
for i in range(1):
plt.figure(figsize=(2,4))
plt.matshow(pred[i], cmap=cmap)
plt.clim(-100, 50)
#plt.axis('off')
plt.title("prediction", fontsize=25)
plt.savefig(self.save_dir_validation + '/epoch_'+ str(epoch) +'_prediction_'+str(i)+'.png')
for i in range(1):
plt.figure(figsize=(2,4))
plt.matshow(gt_image[i], cmap=cmap)
plt.clim(-100, 50)
#plt.axis('off')
plt.title("gt_image", fontsize=25)
plt.savefig(self.save_dir_validation + '/epoch_'+ str(epoch) +'_gt_image_'+str(i)+'.png')
for i in range(1):
plt.figure(figsize=(2,4))
plt.matshow(blend_gt[i], cmap=cmap)
#plt.matshow(np.zeros(masked_input[i].shape), cmap=cmap)
plt.clim(-100, 50)
#plt.axis('off')
plt.title("blend_gt", fontsize=25)
plt.savefig(self.save_dir_validation + '/epoch_'+ str(epoch) +'_blend_gt_'+str(i)+'.png')
for i in range(1):
plt.figure(figsize=(2,4))
plt.matshow(gt_blended_image[i], cmap=cmap)
#plt.matshow(np.zeros(masked_input[i].shape), cmap=cmap)
plt.clim(-100, 50)
#plt.axis('off')
plt.title("gt_blended_image", fontsize=25)
plt.savefig(self.save_dir_validation + '/epoch_'+ str(epoch) +'_gt_blended_image_'+str(i)+'.png')
for i in range(1):
plt.figure(figsize=(2,4))
plt.matshow(blend_input[i], cmap=cmap)
#plt.matshow(np.zeros(masked_input[i].shape), cmap=cmap)
plt.clim(-100, 50)
#plt.axis('off')
plt.title("blend_input", fontsize=25)
plt.savefig(self.save_dir_validation + '/epoch_'+ str(epoch) +'_blend_input_'+str(i)+'.png')
for i in range(1):
plt.figure(figsize=(2,4))
plt.matshow(maksed_input[i], cmap=cmap)
#plt.matshow(np.zeros(masked_input[i].shape), cmap=cmap)
plt.clim(-100, 50)
#plt.axis('off')
plt.title("maksed_input", fontsize=25)
plt.savefig(self.save_dir_validation + '/epoch_'+ str(epoch) +'_masked_input'+str(i)+'.png')
plt.close('all')
def save_result_test(self, output, iter, alpha):
output = output.detach().squeeze(1).permute(0,2,1).cpu().numpy()
np.save(self.save_dir_validation + '/epoch_' + str(iter)+"_alpha_"+str(alpha) + "_TestOutput", output)