-
Notifications
You must be signed in to change notification settings - Fork 34
/
testBRDF.py
317 lines (262 loc) · 14.7 KB
/
testBRDF.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
import torch
import numpy as np
from torch.autograd import Variable
import argparse
import random
import os
import models
import utils
import dataLoader
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torchvision.utils as vutils
parser = argparse.ArgumentParser()
# The locationi of testing set
parser.add_argument('--dataRoot', default=None, help='path to real image distorted by water')
parser.add_argument('--experiment', default=None, help='the path to store samples and models')
parser.add_argument('--testRoot', default=None, help='the path to save the testing errors')
# The basic training setting
parser.add_argument('--nepoch0', type=int, default=14, help='the number of epochs for training')
parser.add_argument('--nepoch1', type=int, default=7, help='the number of epochs for training')
parser.add_argument('--batchSize0', type=int, default=16, help='input batch size')
parser.add_argument('--batchSize1', type=int, default=16, help='input batch size')
parser.add_argument('--imHeight0', type=int, default=240, help='the height / width of the input image to network')
parser.add_argument('--imWidth0', type=int, default=320, help='the height / width of the input image to network')
parser.add_argument('--imHeight1', type=int, default=240, help='the height / width of the input image to network')
parser.add_argument('--imWidth1', type=int, default=320, help='the height / width of the input image to network')
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--deviceIds', type=int, nargs='+', default=[0], help='the gpus used for testing network')
# Cascae Level
parser.add_argument('--cascadeLevel', type=int, default=0, help='the casacade level')
opt = parser.parse_args()
print(opt)
opt.gpuId = opt.deviceIds[0]
if opt.cascadeLevel == 0:
opt.nepoch = opt.nepoch0
opt.batchSize = opt.batchSize0
opt.imHeight, opt.imWidth = opt.imHeight0, opt.imWidth0
elif opt.cascadeLevel == 1:
opt.nepoch = opt.nepoch1
opt.batchSize = opt.batchSize1
opt.imHeight, opt.imWidth = opt.imHeight1, opt.imWidth1
if opt.experiment is None:
opt.experiment = 'check_cascade%d_w%d_h%d' % (opt.cascadeLevel,
opt.imWidth, opt.imHeight )
if opt.testRoot is None:
opt.testRoot = opt.experiment.replace('check', 'test')
os.system('mkdir {0}'.format(opt.testRoot) )
os.system('cp *.py %s' % opt.testRoot )
opt.seed = 0
print("Random Seed: ", opt.seed)
random.seed(opt.seed)
torch.manual_seed(opt.seed)
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
# Initial Network
encoder = models.encoder0(cascadeLevel = opt.cascadeLevel)
albedoDecoder = models.decoder0(mode=0 )
normalDecoder = models.decoder0(mode=1 )
roughDecoder = models.decoder0(mode=2 )
depthDecoder = models.decoder0(mode=4 )
####################################################################
#########################################
encoder.load_state_dict(
torch.load('{0}/encoder{1}_{2}.pth'.format(opt.experiment, opt.cascadeLevel, opt.nepoch-1 ) ).state_dict() )
albedoDecoder.load_state_dict(
torch.load('{0}/albedo{1}_{2}.pth'.format(opt.experiment, opt.cascadeLevel, opt.nepoch-1 ) ).state_dict() )
normalDecoder.load_state_dict(
torch.load('{0}/normal{1}_{2}.pth'.format(opt.experiment, opt.cascadeLevel, opt.nepoch-1 ) ).state_dict() )
roughDecoder.load_state_dict(
torch.load('{0}/rough{1}_{2}.pth'.format(opt.experiment, opt.cascadeLevel, opt.nepoch-1 ) ).state_dict() )
depthDecoder.load_state_dict(
torch.load('{0}/depth{1}_{2}.pth'.format(opt.experiment, opt.cascadeLevel, opt.nepoch-1) ).state_dict() )
for param in encoder.parameters():
param.requires_grad = False
for param in albedoDecoder.parameters():
param.requires_grad = False
for param in normalDecoder.parameters():
param.requires_grad = False
for param in roughDecoder.parameters():
param.requires_grad = False
for param in depthDecoder.parameters():
param.requires_grad = False
#########################################
encoder = nn.DataParallel(encoder, device_ids = opt.deviceIds )
albedoDecoder = nn.DataParallel(albedoDecoder, device_ids = opt.deviceIds )
normalDecoder = nn.DataParallel(normalDecoder, device_ids = opt.deviceIds )
roughDecoder = nn.DataParallel(roughDecoder, device_ids = opt.deviceIds )
depthDecoder = nn.DataParallel(depthDecoder, device_ids = opt.deviceIds )
############## ######################
# Send things into GPU
if opt.cuda:
encoder = encoder.cuda(opt.gpuId)
albedoDecoder = albedoDecoder.cuda(opt.gpuId)
normalDecoder = normalDecoder.cuda(opt.gpuId)
roughDecoder = roughDecoder.cuda(opt.gpuId)
depthDecoder = depthDecoder.cuda(opt.gpuId)
####################################
####################################
brdfDataset = dataLoader.BatchLoader( opt.dataRoot, imWidth = opt.imWidth, imHeight = opt.imHeight, rseed = opt.seed,
cascadeLevel = opt.cascadeLevel, phase = 'TEST')
brdfLoader = DataLoader(brdfDataset, batch_size = opt.batchSize, num_workers = 4, shuffle = False)
j = 0
albedoErrsNpList = np.ones( [1, 1], dtype = np.float32 )
normalErrsNpList = np.ones( [1, 1], dtype = np.float32 )
roughErrsNpList= np.ones( [1, 1], dtype = np.float32 )
depthErrsNpList = np.ones( [1, 1], dtype = np.float32 )
epoch = opt.nepoch
testingLog = open('{0}/testingLog_{1}.txt'.format(opt.testRoot, epoch), 'w')
for i, dataBatch in enumerate(brdfLoader):
j += 1
# Load data from cpu to gpu
albedo_cpu = dataBatch['albedo']
albedoBatch = Variable(albedo_cpu ).cuda()
normal_cpu = dataBatch['normal']
normalBatch = Variable(normal_cpu ).cuda()
rough_cpu = dataBatch['rough']
roughBatch = Variable(rough_cpu ).cuda()
depth_cpu = dataBatch['depth']
depthBatch = Variable(depth_cpu ).cuda()
segArea_cpu = dataBatch['segArea']
segEnv_cpu = dataBatch['segEnv']
segObj_cpu = dataBatch['segObj']
seg_cpu = torch.cat([segArea_cpu, segEnv_cpu, segObj_cpu], dim=1 )
segBatch = Variable(seg_cpu ).cuda()
segBRDFBatch = segBatch[:, 2:3, :, :]
segAllBatch = segBatch[:, 0:1, :, :] + segBatch[:, 2:3, :, :]
# Load the image from cpu to gpu
im_cpu = (dataBatch['im'] )
imBatch = Variable(im_cpu ).cuda()
if opt.cascadeLevel > 0:
albedoPre_cpu = dataBatch['albedoPre']
albedoPreBatch = Variable(albedoPre_cpu ).cuda()
normalPre_cpu = dataBatch['normalPre']
normalPreBatch = Variable(normalPre_cpu ).cuda()
roughPre_cpu = dataBatch['roughPre']
roughPreBatch = Variable(roughPre_cpu ).cuda()
depthPre_cpu = dataBatch['depthPre']
depthPreBatch = Variable(depthPre_cpu ).cuda()
diffusePre_cpu = dataBatch['diffusePre']
diffusePreBatch = Variable(diffusePre_cpu ).cuda()
specularPre_cpu = dataBatch['specularPre']
specularPreBatch = Variable(specularPre_cpu ).cuda()
if albedoPreBatch.size(2) < opt.imHeight or albedoPreBatch.size(3) < opt.imWidth:
albedoPreBatch = F.interpolate(albedoPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if normalPreBatch.size(2) < opt.imHeight or normalPreBatch.size(3) < opt.imWidth:
normalPreBatch = F.interpolate(normalPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if roughPreBatch.size(2) < opt.imHeight or roughPreBatch.size(3) < opt.imWidth:
roughPreBatch = F.interpolate(roughPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if depthPreBatch.size(2) < opt.imHeight or depthPreBatch.size(3) < opt.imWidth:
depthPreBatch = F.interpolate(depthPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
# Regress the diffusePred and specular Pred
envRow, envCol = diffusePreBatch.size(2), diffusePreBatch.size(3)
imBatchSmall = F.adaptive_avg_pool2d(imBatch, (envRow, envCol) )
diffusePreBatch, specularPreBatch = models.LSregressDiffSpec(
diffusePreBatch, specularPreBatch,
imBatchSmall, diffusePreBatch, specularPreBatch )
if diffusePreBatch.size(2) < opt.imHeight or diffusePreBatch.size(3) < opt.imWidth:
diffusePreBatch = F.interpolate(diffusePreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if specularPreBatch.size(2) < opt.imHeight or specularPreBatch.size(3) < opt.imWidth:
specularPreBatch = F.interpolate(specularPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
########################################################
# Build the cascade network architecture #
albedoPreds = []
normalPreds = []
roughPreds = []
depthPreds = []
# Initial Prediction
if opt.cascadeLevel == 0:
inputBatch = imBatch
elif opt.cascadeLevel > 0:
inputBatch = torch.cat([imBatch, albedoPreBatch,
normalPreBatch, roughPreBatch, depthPreBatch,
diffusePreBatch, specularPreBatch ], dim=1 )
x1, x2, x3, x4, x5, x6 = encoder(inputBatch )
albedoPred = 0.5 * (albedoDecoder(imBatch, x1, x2, x3, x4, x5, x6) + 1)
normalPred = normalDecoder(imBatch, x1, x2, x3, x4, x5, x6)
roughPred = roughDecoder(imBatch, x1, x2, x3, x4, x5, x6)
depthPred = 0.5 * (depthDecoder(imBatch, x1, x2, x3, x4, x5, x6) + 1)
albedoPred = models.LSregress( albedoPred * segBRDFBatch.expand_as(albedoPred),
albedoBatch * segBRDFBatch.expand_as(albedoPred), albedoPred )
albedoPred = torch.clamp(albedoPred, 0, 1 )
depthPred = models.LSregress(depthPred * segAllBatch.expand_as(depthPred),
depthBatch * segAllBatch.expand_as(depthPred), depthPred)
albedoPreds.append(albedoPred)
normalPreds.append(normalPred)
roughPreds.append(roughPred)
depthPreds.append(depthPred )
########################################################
# Compute the error
albedoErrs = []
normalErrs = []
roughErrs = []
depthErrs = []
pixelObjNum = (torch.sum(segBRDFBatch ).cpu().data).item()
pixelAllNum = (torch.sum(segAllBatch ).cpu().data).item()
for m in range(0, len(albedoPreds) ):
albedoErrs.append( torch.sum( (albedoPreds[m] - albedoBatch)
* (albedoPreds[m] - albedoBatch) * segBRDFBatch.expand_as(albedoBatch) ) / pixelObjNum / 3.0 )
for m in range(0, len(normalPreds) ):
normalErrs.append( torch.sum( (normalPreds[m] - normalBatch)
* (normalPreds[m] - normalBatch) * segAllBatch.expand_as(normalBatch) ) / pixelAllNum / 3.0)
for m in range(0, len(roughPreds) ):
roughErrs.append( torch.sum( (roughPreds[m] - roughBatch)
* (roughPreds[m] - roughBatch) * segBRDFBatch ) / pixelObjNum / 4.0 )
for n in range(0, len(depthPreds ) ):
depthErrs.append( torch.sum( (torch.log(depthPreds[n] + 1e-3) - torch.log(depthBatch + 1e-3 ) )
* ( torch.log(depthPreds[n] + 1e-3 ) - torch.log(depthBatch + 1e-3 ) ) * segAllBatch.expand_as(depthBatch ) ) / pixelAllNum )
# Output testing error
utils.writeErrToScreen('albedo', albedoErrs, epoch, j)
utils.writeErrToScreen('normal', normalErrs, epoch, j)
utils.writeErrToScreen('rough', roughErrs, epoch, j)
utils.writeErrToScreen('depth', depthErrs, epoch, j)
utils.writeErrToFile('albedo', albedoErrs, testingLog, epoch, j)
utils.writeErrToFile('normal', normalErrs, testingLog, epoch, j)
utils.writeErrToFile('rough', roughErrs, testingLog, epoch, j)
utils.writeErrToFile('depth', depthErrs, testingLog, epoch, j)
albedoErrsNpList = np.concatenate( [albedoErrsNpList, utils.turnErrorIntoNumpy(albedoErrs)], axis=0 )
normalErrsNpList = np.concatenate( [normalErrsNpList, utils.turnErrorIntoNumpy(normalErrs)], axis=0 )
roughErrsNpList = np.concatenate( [roughErrsNpList, utils.turnErrorIntoNumpy(roughErrs)], axis=0 )
depthErrsNpList = np.concatenate( [depthErrsNpList, utils.turnErrorIntoNumpy(depthErrs)], axis=0 )
utils.writeNpErrToScreen('albedoAccu', np.mean(albedoErrsNpList[1:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('normalAccu', np.mean(normalErrsNpList[1:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('roughAccu', np.mean(roughErrsNpList[1:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('depthAccu', np.mean(depthErrsNpList[1:j+1, :], axis=0), epoch, j )
utils.writeNpErrToFile('albedoAccu', np.mean(albedoErrsNpList[1:j+1, :], axis=0), testingLog, epoch, j )
utils.writeNpErrToFile('normalAccu', np.mean(normalErrsNpList[1:j+1, :], axis=0), testingLog, epoch, j )
utils.writeNpErrToFile('roughAccu', np.mean(roughErrsNpList[1:j+1, :], axis=0), testingLog, epoch, j )
utils.writeNpErrToFile('depthAccu', np.mean(depthErrsNpList[1:j+1, :], axis=0), testingLog, epoch, j )
if j == 1 or j% 2000 == 0:
# Save the ground truth and the input
vutils.save_image(( (albedoBatch ) ** (1.0/2.2) ).data,
'{0}/{1}_albedoGt.png'.format(opt.testRoot, j) )
vutils.save_image( (0.5*(normalBatch + 1) ).data,
'{0}/{1}_normalGt.png'.format(opt.testRoot, j) )
vutils.save_image( (0.5*(roughBatch + 1) ).data,
'{0}/{1}_roughGt.png'.format(opt.testRoot, j) )
vutils.save_image( ( (imBatch )**(1.0/2.2) ).data,
'{0}/{1}_im.png'.format(opt.testRoot, j) )
depthOut = 1 / torch.clamp(depthBatch + 1, 1e-6, 10) * segAllBatch.expand_as(depthBatch)
vutils.save_image( ( depthOut*segAllBatch.expand_as(depthBatch) ).data,
'{0}/{1}_depthGt.png'.format(opt.testRoot, j) )
# Save the predicted results
for n in range(0, len(albedoPreds) ):
vutils.save_image( ( (albedoPreds[n] ) ** (1.0/2.2) ).data,
'{0}/{1}_albedoPred_{2}.png'.format(opt.testRoot, j, opt.cascadeLevel) )
for n in range(0, len(normalPreds) ):
vutils.save_image( ( 0.5*(normalPreds[n] + 1) ).data,
'{0}/{1}_normalPred_{2}.png'.format(opt.testRoot, j, opt.cascadeLevel) )
for n in range(0, len(roughPreds) ):
vutils.save_image( ( 0.5*(roughPreds[n] + 1) ).data,
'{0}/{1}_roughPred_{2}.png'.format(opt.testRoot, j, opt.cascadeLevel) )
for n in range(0, len(depthPreds) ):
depthOut = 1 / torch.clamp(depthPreds[n] + 1, 1e-6, 10) * segAllBatch.expand_as(depthPreds[n])
vutils.save_image( ( depthOut * segAllBatch.expand_as(depthPreds[n]) ).data,
'{0}/{1}_depthPred_{2}.png'.format(opt.testRoot, j, n) )
testingLog.close()
# Save the error record
np.save('{0}/albedoError_{1}.npy'.format(opt.testRoot, epoch), albedoErrsNpList )
np.save('{0}/normalError_{1}.npy'.format(opt.testRoot, epoch), normalErrsNpList )
np.save('{0}/roughError_{1}.npy'.format(opt.testRoot, epoch), roughErrsNpList )
np.save('{0}/depthError_{1}.npy'.format(opt.testRoot, epoch), depthErrsNpList )