-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathunsuper_prob_utils.py
379 lines (297 loc) · 13.9 KB
/
unsuper_prob_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
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
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_ssim
import time
import IPython, cv2
SSIM_WIN = 5
class WrappedModel(nn.Module):
def __init__(self, module):
super(WrappedModel, self).__init__()
self.module = module # that I actually define.
def forward(self, x):
return self.module(x)
def gradient_xy(img):
gx = img[:, :, :, :-1] - img[:, :, :, 1:]
gy = img[:, :, :-1, :] - img[:, :, 1:, :]
return gx, gy
def warp_disp(x, disp, args):
# result + flow(-disp) = x
# warp back to result
N, _, H, W = x.shape
x_ = torch.arange(W).view(1, -1).expand(H, -1)
y_ = torch.arange(H).view(-1, 1).expand(-1, W)
grid = torch.stack([x_, y_], dim=0).float()
if args.cuda:
grid = grid.cuda()
grid = grid.unsqueeze(0).expand(N, -1, -1, -1)
grid[:, 0, :, :] = 2 * grid[:, 0, :, :] / (W - 1) - 1
grid[:, 1, :, :] = 2 * grid[:, 1, :, :] / (H - 1) - 1
# disp = 30*torch.ones(N, H, W).cuda()
grid2 = grid.clone()
grid2[:, 0, :, :] = grid[:, 0, :, :] + 2*disp/W
grid2 = grid2.permute(0, 2, 3, 1)
return F.grid_sample(x, grid2, padding_mode='zeros')
def Loss_prob(y, target, logvar):
y = y*50
target = target*50
thresh = 10
logvar = logvar.clamp(-50, 50)
loss = 2**0.5 * F.smooth_l1_loss(y, target, reduction='none').mean(1) * (torch.exp(-logvar) ) + logvar
loss = loss.clamp(-thresh, thresh).mean()
return loss
# loss1
# appearance loss: the difference between reconstructed image and original image
def criterion1_prob(imgC, imgR, imgL, outputR, outputR_var, outputL, outputL_var, maxdisp, args):
imgR2C = warp_disp(imgR, -outputR, args)
imgL2C = warp_disp(imgL, outputL, args)
imgR2C2 = warp_disp(imgR, -outputL, args)
imgL2C2 = warp_disp(imgL, outputR, args)
alpha = 0.85
crop_edge = 200
if imgC.shape[2] > SSIM_WIN:
ssim_loss = pytorch_ssim.SSIM(window_size = SSIM_WIN)
else:
ssim_loss = pytorch_ssim.SSIM(window_size = imgC.shape[2])
prob_mode = args.prob_mode
if crop_edge == 0:
diff_ssim = (1 - ssim_loss(imgC, imgR2C)) / 2.0 + \
(1 - ssim_loss(imgC, imgL2C)) / 2.0 + \
(1 - ssim_loss(imgC, imgR2C2)) / 2.0 + \
(1 - ssim_loss(imgC, imgL2C2)) / 2.0
if prob_mode == 1:
diff_L1 = Loss_prob(imgR2C, imgC, outputR_var) + \
Loss_prob(imgL2C, imgC, outputL_var) + \
Loss_prob(imgR2C2, imgC, outputL_var) + \
Loss_prob(imgL2C2, imgC, outputR_var)
elif prob_mode == 2:
diff_L1 = (F.smooth_l1_loss(imgC, imgR2C, reduction='mean')) + \
(F.smooth_l1_loss(imgC, imgL2C, reduction='mean')) + \
(F.smooth_l1_loss(imgC, imgR2C2, reduction='mean')) + \
(F.smooth_l1_loss(imgC, imgL2C2, reduction='mean'))
else:
diff_ssim = (1 - ssim_loss(imgC[:,:,:,crop_edge:], imgR2C[:,:,:,crop_edge:])) / 2.0 + \
(1 - ssim_loss(imgC[:,:,:,:-crop_edge], imgL2C[:,:,:,:-crop_edge])) / 2.0 + \
(1 - ssim_loss(imgC[:,:,:,crop_edge:], imgR2C2[:,:,:,crop_edge:])) / 2.0 + \
(1 - ssim_loss(imgC[:,:,:,:-crop_edge], imgL2C2[:,:,:,:-crop_edge])) / 2.0
if prob_mode == 1:
diff_L1 = Loss_prob(imgR2C[:,:,:,crop_edge:], imgC[:,:,:,crop_edge:], outputR_var[:,:,crop_edge:]) + \
Loss_prob(imgL2C[:,:,:,:-crop_edge], imgC[:,:,:,:-crop_edge], outputL_var[:,:,:-crop_edge]) + \
Loss_prob(imgR2C2[:,:,:,crop_edge:], imgC[:,:,:,crop_edge:], outputL_var[:,:,crop_edge:]) + \
Loss_prob(imgL2C2[:,:,:,:-crop_edge], imgC[:,:,:,:-crop_edge], outputR_var[:,:,:-crop_edge])
elif prob_mode == 2:
diff_L1 = (F.smooth_l1_loss(imgC[:,:,:,crop_edge:], imgR2C[:,:,:,crop_edge:], reduction='mean')) + \
(F.smooth_l1_loss(imgC[:,:,:,:-crop_edge], imgL2C[:,:,:,:-crop_edge], reduction='mean')) + \
(F.smooth_l1_loss(imgC[:,:,:,crop_edge:], imgR2C2[:,:,:,crop_edge:], reduction='mean')) + \
(F.smooth_l1_loss(imgC[:,:,:,:-crop_edge], imgL2C2[:,:,:,:-crop_edge], reduction='mean'))
if prob_mode == 1:
loss1 = 1.0/4 * diff_ssim
varR_gx, varR_gy = gradient_xy(outputR_var.unsqueeze(1)*50)
# varL_gx, varL_gy = gradient_xy(outputL_var.unsqueeze(1)*50)
intensity_gx, intensity_gy = gradient_xy(imgC)
weights_x = torch.exp(-10 * torch.abs(intensity_gx).mean(1).unsqueeze(1))
weights_y = torch.exp(-10 * torch.abs(intensity_gy).mean(1).unsqueeze(1))
smoothness_x = torch.abs(varR_gx) * weights_x #+ torch.abs(varL_gx) * weights_x
smoothness_y = torch.abs(varR_gy) * weights_y #+ torch.abs(varL_gy) * weights_y
var_smooth = smoothness_x.mean() + smoothness_y.mean()
loss4 = 0.01 * diff_L1 + 0.001 * var_smooth
elif prob_mode == 2:
loss1 = 1.0/4 * (alpha * diff_ssim + (1-alpha) * diff_L1 * 0.2)
loss4 = torch.Tensor([0]).cuda()
return loss1, loss4, imgR2C, imgL2C, imgC, outputR
# loss2
# consistency loss the difference between left output and right output
def criterion2(R, L, R_var=None, L_var=None, args=None):
tau = 10 # truncation for occluded region
# ssim_loss = pytorch_ssim.SSIM(window_size = 11)
# ssimloss = (1 - ssim_loss(R.unsqueeze(1), L.unsqueeze(1))) / 2.0
if (R_var is None):
return F.smooth_l1_loss(R, L, reduction='none').clamp(min=0, max=tau).mean()
prob_mode = args.prob_mode
if prob_mode == 1:
thresh = 1
elif prob_mode == 2:
thresh = 0.2
mask = (R_var < thresh) & (L_var < thresh)
R_mask = (R_var > thresh) & (L_var < thresh/2.0)
L_mask = (L_var > thresh) & (R_var < thresh/2.0)
l1 = F.smooth_l1_loss(R[mask], L[mask], reduction='none').clamp(min=0, max=tau)
l2 = F.smooth_l1_loss(R[R_mask], L.detach()[R_mask], reduction='none').clamp(min=0, max=tau)
l3 = F.smooth_l1_loss(R.detach()[L_mask], L[L_mask], reduction='none').clamp(min=0, max=tau)
# print "%.2f, %.2f, %.2f" % (100.0*len(l1)/R.shape.numel(),
# 100.0*len(l2)/R.shape.numel(),
# 100.0*len(l3)/R.shape.numel())
return torch.cat([l1, l2, l3]).mean()
# loss3
# smooth loss: force grident of intensity to be small
def criterion3(disp, img):
disp = disp.unsqueeze(1)
disp_gx, disp_gy = gradient_xy(disp)
intensity_gx, intensity_gy = gradient_xy(img)
weights_x = torch.exp(-10 * torch.abs(intensity_gx).mean(1).unsqueeze(1))
weights_y = torch.exp(-10 * torch.abs(intensity_gy).mean(1).unsqueeze(1))
disp_gx = torch.abs(disp_gx)
gx = disp_gx.clone()
gx[gx>0.5] = disp_gx[disp_gx>0.5] + 10
disp_gy = torch.abs(disp_gy)
gy = disp_gy.clone()
gy[gy>0.5] = disp_gy[disp_gy>0.5] + 10
smoothness_x = gx * weights_x
smoothness_y = gy * weights_y
return smoothness_x.mean() + smoothness_y.mean()
# loss4
# regularization term:
def criterion4(disp, maxdisp):
# r1 = disp.mean()
# r = torch.exp(-1 / 5.0 * disp) + torch.exp(1 / 5.0 * (disp - 90))
# r = torch.exp(-1 / 5.0 * disp)
r = (disp*2/maxdisp - 1).pow(2)
return r.mean()
def evaluate(model, imgL, imgC, imgR, gt, args, maxd):
use_cuda = args.cuda
# use_cuda = False
height = imgL.shape[1]
width = imgL.shape[2]
maxdisp = maxd
pad_h = (height // 32 + 1) * 32
pad_w = (width // 32 + 1) * 32
imgL = np.reshape(imgL, [1, imgL.shape[0], imgL.shape[1], imgL.shape[2]])
imgR = np.reshape(imgR, [1, imgR.shape[0], imgR.shape[1], imgR.shape[2]])
if imgC is not None:
imgC = np.reshape(imgC, [1, imgC.shape[0], imgC.shape[1], imgC.shape[2]])
# pad to (M x 32, N x 32)
top_pad = pad_h - imgL.shape[2]
left_pad = pad_w - imgL.shape[3]
imgL = np.lib.pad(imgL, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgR = np.lib.pad(imgR, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
if imgC is not None:
imgC = np.lib.pad(imgC, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgL = torch.from_numpy(imgL)
imgR = torch.from_numpy(imgR)
if imgC is not None:
imgC = torch.from_numpy(imgC)
model.eval()
if imgC is not None:
# multiscopic mode
imgC_rot = imgC.flip(2).flip(3)
imgL_rot = imgL.flip(2).flip(3)
if use_cuda:
imgL, imgR, imgC, imgC_rot, imgL_rot = \
imgL.cuda(), imgR.cuda(), imgC.cuda(), imgC_rot.cuda(), imgL_rot.cuda()
if args.model == 'stackhourglass':
outputR, outputR_var, _, _ = model(imgC, imgR, maxdisp)
if args.cuda and (not use_cuda):
outputR = outputR.cpu()
outputR_var = outputR_var.cpu()
outputL_rot, outputL_var_rot, _, _ = model(imgC_rot, imgL_rot, maxdisp)
outputL = outputL_rot.flip(1).flip(2)
outputL_var = outputL_var_rot.flip(2).flip(3)
if args.cuda and (not use_cuda):
outputL = outputL.cpu()
outputL_var = outputL_var.cpu()
elif args.model == 'basic':
outputR = model(imgC, imgR, maxdisp)
outputL_rot = model(imgC_rot, imgL_rot)
outputL = outputL_rot.flip(1).flip(2)
# mindisp = torch.min(torch.cat([outputR, outputL]), 0)[0]
# diff = (outputR - outputL).squeeze()
# outputR = outputR.squeeze()
# outputL = outputL.squeeze()
# outputR[diff>3] = mindisp[diff>3]
disp = outputR.squeeze()[top_pad:, :-left_pad]
var = outputR_var.squeeze()[top_pad:, :-left_pad]
else:
# stereo mode
if use_cuda:
imgL, imgR = imgL.cuda(), imgR.cuda()
if args.model == 'stackhourglass':
output, var, _, _ = model(imgL, imgR, maxdisp)
elif args.model == 'basic':
output = model(imgL, imgR, maxdisp)
if args.cuda and (not use_cuda):
output = output.cpu()
disp = output.squeeze()[top_pad:, :-left_pad]
var = var.squeeze()[top_pad:, :-left_pad]
gt = torch.from_numpy(gt).float()
if(use_cuda): gt = gt.cuda()
mask = (gt != 0)
diff = torch.abs(disp[mask] - gt[mask])
avgerr = torch.mean(diff)
rms = torch.sqrt( (diff**2).mean() )
bad05 = len(diff[diff>0.5])/float(len(diff))
bad1 = len(diff[diff>1])/float(len(diff))
bad2 = len(diff[diff>2])/float(len(diff))
bad3 = len(diff[diff>3])/float(len(diff))
return [avgerr.data.item(), rms.data.item(), bad05, bad1, bad2, bad3], disp.cpu().numpy(), var.cpu().numpy()
def evaluate_kitti(model, imgL, imgR, gt_occ, gt_noc, args, maxd=160):
height = imgL.shape[1]
width = imgL.shape[2]
maxdisp = maxd
pad_h = (height / 32 + 1) * 32
pad_w = (width / 32 + 1) * 32
imgL = np.reshape(imgL, [1, imgL.shape[0], imgL.shape[1], imgL.shape[2]])
imgR = np.reshape(imgR, [1, imgR.shape[0], imgR.shape[1], imgR.shape[2]])
# pad to (M x 32, N x 32)
top_pad = pad_h - imgL.shape[2]
left_pad = pad_w - imgL.shape[3]
imgL = np.lib.pad(imgL, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgR = np.lib.pad(imgR, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgL = torch.from_numpy(imgL)
imgR = torch.from_numpy(imgR)
model.eval()
if args.cuda:
imgL, imgR = imgL.cuda(), imgR.cuda()
if args.model == 'stackhourglass':
output, var, _, _ = model(imgL, imgR, maxdisp)
elif args.model == 'basic':
output = model(imgL, imgR, maxdisp)
disp = output.squeeze()[top_pad:, :-left_pad]
var = var.squeeze()[top_pad:, :-left_pad]
if gt_noc is None:
return disp.cpu().numpy(), var.cpu().numpy()
gt_occ = torch.from_numpy(gt_occ).float()
gt_noc = torch.from_numpy(gt_noc).float()
if args.cuda:
gt_noc = gt_noc.cuda()
gt_occ = gt_occ.cuda()
mask_occ = (gt_occ != 0)
mask_noc = (gt_noc != 0)
mask_o = mask_occ.clone()
mask_o[gt_noc != 0] = 0
diff_occ = torch.abs(disp[mask_occ] - gt_occ[mask_occ])
diff_noc = torch.abs(disp[mask_noc] - gt_noc[mask_noc])
# bad3_occ = len(diff_occ[diff_occ>3])/float(len(diff_occ))
# bad3_noc = len(diff_noc[diff_noc>3])/float(len(diff_noc))
bad3_occ = torch.sum((diff_occ>3) & (diff_occ/gt_occ[mask_occ]>0.05)).float() / float(len(diff_occ))
bad3_noc = torch.sum((diff_noc>3) & (diff_noc/gt_noc[mask_noc]>0.05)).float() / float(len(diff_noc))
if(mask_o.sum()>0):
diff_o = torch.abs(disp[mask_o] - gt_occ[mask_o])
bad3_o = torch.sum((diff_o>3) & (diff_o/gt_occ[mask_o]>0.05)).float() / float(len(diff_o))
else:
diff_o = torch.Tensor([0]).cuda()
bad3_o = torch.Tensor([0]).cuda().sum()
return [bad3_occ, bad3_noc, bad3_o, diff_o.mean()], disp.cpu().numpy(), var.cpu().numpy()
def predict(model, imgL, imgR, args, maxd):
height = imgL.shape[1]
width = imgL.shape[2]
pad_h = (height / 32 + 1) * 32
pad_w = (width / 32 + 1) * 32
imgL = np.reshape(imgL, [1, imgL.shape[0], imgL.shape[1], imgL.shape[2]])
imgR = np.reshape(imgR, [1, imgR.shape[0], imgR.shape[1], imgR.shape[2]])
# pad to (M x 32, N x 32)
top_pad = pad_h - imgL.shape[2]
left_pad = pad_w - imgL.shape[3]
imgL = np.lib.pad(imgL, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgR = np.lib.pad(imgR, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgL = torch.from_numpy(imgL)
imgR = torch.from_numpy(imgR)
model.eval()
if args.cuda:
imgL, imgR = imgL.cuda(), imgR.cuda()
if args.model == 'stackhourglass':
output, _, _, _ = model(imgL, imgR, maxd)
elif args.model == 'basic':
output = model(imgL, imgR, maxd)
disp = output.squeeze()[top_pad:, :-left_pad]
return disp.cpu().numpy()