-
Notifications
You must be signed in to change notification settings - Fork 8
/
trainBRDF.py
245 lines (208 loc) · 12.3 KB
/
trainBRDF.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
import torch
import numpy as np
import torch.optim as optim
import argparse
import random
import os
import models
import torchvision.utils as vutils
import utils
import dataLoader
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import os.path as osp
import lossFunctions
parser = argparse.ArgumentParser()
# The locationi of training set
parser.add_argument('--dataRoot',default='Dataset', help='path to input images')
parser.add_argument('--experiment', default=None, help='the path to store samples and models')
# The basic training setting
parser.add_argument('--nepoch', type=int, default=15, help='the number of epochs for training')
parser.add_argument('--batchSize', type=int, default=4, help='input batch size')
parser.add_argument('--imHeight', type=int, default=240, help='the height / width of the input image to network')
parser.add_argument('--imWidth', type=int, default=320, help='the height / width of the input image to network')
parser.add_argument('--deviceIds', type=int, nargs='+', default=[0], help='the gpus used for training network')
# Fine tune the network
parser.add_argument('--isFineTune', action='store_true', help='fine-tune the network')
parser.add_argument('--epochId', type=int, default = -1, help='the training of epoch of the loaded model')
# The training weight
parser.add_argument('--albedoWeight', type=float, default=1.5, help='the weight for the diffuse component')
parser.add_argument('--normalWeight', type=float, default=1.0, help='the weight for the diffuse component')
parser.add_argument('--roughWeight', type=float, default=0.5, help='the weight for the roughness component')
# The detail network setting
opt = parser.parse_args()
print(opt)
albeW, normW = opt.albedoWeight, opt.normalWeight
rougW = opt.roughWeight
curDir = '/'.join(osp.abspath(__file__).split('/')[0:-1] )
if opt.experiment is None:
opt.experiment = 'check_brdf_w%d_h%d' % (opt.imWidth, opt.imHeight )
opt.experiment = osp.join(curDir, opt.experiment )
os.system('mkdir {0}'.format(opt.experiment) )
os.system('cp *.py %s' % opt.experiment )
opt.seed = 0
print("Random Seed: ", opt.seed )
random.seed(opt.seed )
torch.manual_seed(opt.seed )
# Initial Network
encoder = models.encoder(isGtGeometry = True )
albedoDecoder = models.decoder(mode=0 )
normalDecoder = models.decoder(mode=1 )
roughDecoder = models.decoder(mode=2 )
if opt.isFineTune:
encoder.load_state_dict(
torch.load('{0}/encoder_{1}.pth'.format(opt.experiment, opt.epochId ) ) )
albedoDecoder.load_state_dict(
torch.load('{0}/albedo_{1}.pth'.format(opt.experiment, opt.epochId ) ) )
normalDecoder.load_state_dict(
torch.load('{0}/normal_{1}.pth'.format(opt.experiment, opt.epochId ) ) )
roughDecoder.load_state_dict(
torch.load('{0}/rough_{1}.pth'.format(opt.experiment, opt.epochId ) ) )
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 )
encoder = encoder.cuda()
albedoDecoder = albedoDecoder.cuda()
normalDecoder = normalDecoder.cuda()
roughDecoder = roughDecoder.cuda()
# Optimizer
opEncoder = optim.Adam(encoder.parameters(), lr=1e-4, betas=(0.5, 0.999) )
opAlbedo = optim.Adam(albedoDecoder.parameters(), lr=1e-4, betas=(0.5, 0.999) )
opNormal = optim.Adam(normalDecoder.parameters(), lr=1e-4, betas=(0.5, 0.999) )
opRough = optim.Adam(roughDecoder.parameters(), lr=1e-4, betas=(0.5, 0.999) )
brdfDataset = dataLoader.BatchLoader( opt.dataRoot,
imWidth = opt.imWidth, imHeight = opt.imHeight )
brdfLoader = DataLoader(brdfDataset, batch_size = opt.batchSize,
num_workers = 8, shuffle = True )
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 )
albedoGradErrsNpList = np.ones( [1, 1], dtype = np.float32 )
roughGradErrsNpList= np.ones( [1, 1], dtype = np.float32 )
nrow = opt.batchSize * 2
for epoch in list(range(opt.epochId + 1, opt.nepoch ) ):
trainingLog = open('{0}/trainingLog_{1}.txt'.format(opt.experiment, epoch), 'w' )
for i, dataBatch in enumerate(brdfLoader):
j += 1
# Load data from cpu to gpu
albedoBatch, normalBatch, roughBatch, \
depthBatch, depthOriginBatch, depthScaleBatch, \
segBRDFBatch, segAllBatch \
= dataLoader.getBRDF(dataBatch )
# Load the image from cpu to gpu
im_cpu = dataBatch['im']
imBatch = im_cpu.cuda()
imDl_cpu = dataBatch['imDl']
imDlBatch = imDl_cpu.cuda()
imDm_cpu = dataBatch['imDm']
imDmBatch = imDm_cpu.cuda()
imBatch = torch.cat([imBatch, imDlBatch, imDmBatch], dim=0 )
# Clear the gradient in optimizer
opEncoder.zero_grad()
opAlbedo.zero_grad()
opNormal.zero_grad()
opRough.zero_grad()
# Build the cascade network architecture #
depthMax = torch.max(torch.max(depthBatch, dim=2, keepdim=True )[0], dim=3, keepdim=True )[0]
depthBatch = depthBatch * segAllBatch + (1 - segAllBatch ) * depthMax
inputBatch = torch.cat([imBatch, depthBatch], dim=1 )
# Initial Prediction
x1, x2, x3, x4, x5, x6 = encoder(inputBatch )
albedoPred, _ = albedoDecoder(x1, x2, x3,
x4, x5, x6, [opt.imHeight, opt.imWidth ] )
normalPred, _ = normalDecoder(x1, x2, x3,
x4, x5, x6, [opt.imHeight, opt.imWidth] )
roughPred, _ = roughDecoder(x1, x2, x3,
x4, x5, x6, [opt.imHeight, opt.imWidth] )
# Compute the error
albedoErrs = []
normalErrs = []
roughErrs = []
albedoGradErrs = []
roughGradErrs = []
albedoErrs.append( torch.mean( torch.pow(albedoPred - albedoBatch, 2) * segBRDFBatch ) )
normalErrs.append( torch.mean( torch.pow(normalPred - normalBatch, 2) * segBRDFBatch ) )
roughErrs.append( torch.mean( torch.pow(roughPred - roughBatch, 2) * segBRDFBatch ) )
albedoGradErrs.append(lossFunctions.gradLoss(albedoPred, albedoBatch, segBRDFBatch ) )
roughGradErrs.append(lossFunctions.gradLoss(roughPred, roughBatch, segBRDFBatch ) )
# Back propagate the gradients
totalErr = albeW * sum(albedoErrs ) + normW * sum(normalErrs ) \
+ rougW * sum(roughErrs )
totalErr += albeW * sum(albedoGradErrs ) + rougW * sum(roughGradErrs )
totalErr.backward()
# Update the network parameter
opEncoder.step()
opAlbedo.step()
opNormal.step()
opRough.step()
# Output training error
utils.writeErrToScreen('albedo', albedoErrs, epoch, j )
utils.writeErrToScreen('normal', normalErrs, epoch, j )
utils.writeErrToScreen('rough', roughErrs, epoch, j )
utils.writeErrToFile('albedo', albedoErrs, trainingLog, epoch, j )
utils.writeErrToFile('normal', normalErrs, trainingLog, epoch, j )
utils.writeErrToFile('rough', roughErrs, trainingLog, epoch, j )
utils.writeErrToFile('albedoGrad', albedoGradErrs, trainingLog, epoch, j )
utils.writeErrToFile('roughGrad', roughGradErrs, trainingLog, 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 )
albedoGradErrsNpList = np.concatenate( [albedoGradErrsNpList, utils.turnErrorIntoNumpy(albedoGradErrs)], axis=0 )
roughGradErrsNpList = np.concatenate( [roughGradErrsNpList, utils.turnErrorIntoNumpy(roughGradErrs)], axis=0 )
if j < 1000:
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('albedoGradAccu', np.mean(albedoGradErrsNpList[1:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('roughGradAccu', np.mean(roughGradErrsNpList[1:j+1, :], axis=0), epoch, j )
utils.writeNpErrToFile('albedoAccu', np.mean(albedoErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('normalAccu', np.mean(normalErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('roughAccu', np.mean(roughErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('albedoGradAccu', np.mean(albedoGradErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('roughGradAccu', np.mean(roughGradErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j )
else:
utils.writeNpErrToScreen('albedoAccu', np.mean(albedoErrsNpList[j-999:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('normalAccu', np.mean(normalErrsNpList[j-999:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('roughAccu', np.mean(roughErrsNpList[j-999:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('albedoGradAccu', np.mean(albedoGradErrsNpList[j-999:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('roughGradAccu', np.mean(roughGradErrsNpList[j-999:j+1, :], axis=0), epoch, j )
utils.writeNpErrToFile('albedoAccu', np.mean(albedoErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('normalAccu', np.mean(normalErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('roughAccu', np.mean(roughErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('albedoGradAccu', np.mean(albedoGradErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j )
utils.writeNpErrToFile('roughGradAccu', np.mean(roughGradErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j )
if j == 1 or j% 2000 == 0:
# Save the ground truth and the input
vutils.save_image( albedoBatch ** (1.0/2.2),
'{0}/{1}_albedoGt.png'.format(opt.experiment, j), nrow=nrow )
vutils.save_image( 0.5*(normalBatch + 1),
'{0}/{1}_normalGt.png'.format(opt.experiment, j), nrow=nrow )
vutils.save_image( roughBatch,
'{0}/{1}_roughGt.png'.format(opt.experiment, j), nrow=nrow )
vutils.save_image( imBatch**(1.0/2.2),
'{0}/{1}_im.png'.format(opt.experiment, j), nrow=nrow )
depthOut = 1 / torch.clamp(depthBatch + 1, 1e-6, 10) * segAllBatch
vutils.save_image( depthOut*segAllBatch,
'{0}/{1}_depthGt.png'.format(opt.experiment, j), nrow=nrow )
# Save the predicted results
vutils.save_image( albedoPred ** (1.0/2.2),
'{0}/{1}_albedoPred.png'.format(opt.experiment, j), nrow=nrow )
vutils.save_image( 0.5*(normalPred + 1),
'{0}/{1}_normalPred.png'.format(opt.experiment, j), nrow=nrow )
vutils.save_image(roughPred,
'{0}/{1}_roughPred.png'.format(opt.experiment, j), nrow=nrow )
trainingLog.close()
# Save the error record
np.save('{0}/albedoError_{1}.npy'.format(opt.experiment, epoch), albedoErrsNpList )
np.save('{0}/normalError_{1}.npy'.format(opt.experiment, epoch), normalErrsNpList )
np.save('{0}/roughError_{1}.npy'.format(opt.experiment, epoch), roughErrsNpList )
np.save('{0}/albedoGradError_{1}.npy'.format(opt.experiment, epoch), albedoGradErrsNpList )
np.save('{0}/roughGradError_{1}.npy'.format(opt.experiment, epoch), roughGradErrsNpList )
# save the models
torch.save(encoder.module.state_dict(), '{0}/encoder_{1}.pth'.format(opt.experiment, epoch ) )
torch.save(albedoDecoder.module.state_dict(), '{0}/albedo_{1}.pth'.format(opt.experiment, epoch ) )
torch.save(normalDecoder.module.state_dict(), '{0}/normal_{1}.pth'.format(opt.experiment, epoch ) )
torch.save(roughDecoder.module.state_dict(), '{0}/rough_{1}.pth'.format(opt.experiment, epoch ) )