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testLight.py
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testLight.py
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import torch
import numpy as np
from torch.autograd import Variable
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 wrapperBRDFLight as wcg
parser = argparse.ArgumentParser()
# The locationi of training set
parser.add_argument('--dataRoot', default=None, help='path to input images')
parser.add_argument('--experimentBRDF', default=None, help='path to the model for BRDF prediction')
parser.add_argument('--experiment', default=None, help='the path to store samples and models')
parser.add_argument('--testRoot', default=None, help='the path to store the test results')
# The basic training setting
parser.add_argument('--nepochBRDF0', type=int, default=14, help='the number of epochs for BRDF prediction')
parser.add_argument('--nepochBRDF1', type=int, default=7, help='the number of epochs for BRDF prediction')
parser.add_argument('--nepoch0', type=int, default=10, help='the number of epochs for training')
parser.add_argument('--nepoch1', type=int, default=10, help='the number of epochs for training')
parser.add_argument('--batchSize0', type=int, default=4, help='input batch size')
parser.add_argument('--batchSize1', type=int, default=4, 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('--offset', type=float, default=1.0, help='the offset for log error')
parser.add_argument('--envRow', type=int, default=120, help='the number of samples of envmaps in y direction')
parser.add_argument('--envCol', type=int, default=160, help='the number of samples of envmaps in x direction')
parser.add_argument('--envHeight', type=int, default=8, help='the size of envmaps in y direction')
parser.add_argument('--envWidth', type=int, default=16, help='the size of envmaps in x direction')
parser.add_argument('--SGNum', type=int, default=12, help='the number of spherical Gaussian lobe' )
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--deviceIds', type=int, nargs='+', default=[0], help='the gpus used for training network')
# Cascae Level
parser.add_argument('--cascadeLevel', type=int, default=0, help='the casacade level')
# The detail network setting
opt = parser.parse_args()
print(opt)
if opt.offset is None:
offset = 1.0
else:
offset = opt.offset
opt.gpuId = opt.deviceIds[0]
torch.multiprocessing.set_sharing_strategy('file_system')
if opt.experiment is None:
opt.experiment = 'check_cascadeLight%d_sg%d_offset%.1f' \
% (opt.cascadeLevel, opt.SGNum, opt.offset )
opt.testRoot = opt.experiment.replace('check', 'test')
os.system('mkdir {0}'.format(opt.testRoot) )
os.system('cp *.py %s' % opt.testRoot )
if opt.cascadeLevel == 0:
opt.nepoch = opt.nepoch0
opt.batchSize = opt.batchSize0
opt.imHeight, opt.imWidth = opt.imHeight0, opt.imWidth0
opt.nepochBRDF = opt.nepochBRDF0
elif opt.cascadeLevel == 1:
opt.nepoch = opt.nepoch1
opt.batchSize = opt.batchSize1
opt.imHeight, opt.imWidth = opt.imHeight1, opt.imWidth1
opt.nepochBRDF = opt.nepochBRDF1
if opt.experimentBRDF is None:
opt.experimentBRDF = 'check_cascade%d_w%d_h%d' \
% (opt.cascadeLevel, opt.imWidth, opt.imHeight )
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 )
lightEncoder = models.encoderLight(cascadeLevel = opt.cascadeLevel, SGNum =
opt.SGNum )
axisDecoder = models.decoderLight(mode=0, SGNum = opt.SGNum )
lambDecoder = models.decoderLight(mode = 1, SGNum = opt.SGNum )
weightDecoder = models.decoderLight(mode = 2, SGNum = opt.SGNum )
renderLayer = models.renderingLayer(isCuda = opt.cuda,
imWidth=opt.envCol, imHeight=opt.envRow,
envWidth = opt.envWidth, envHeight = opt.envHeight)
output2env = models.output2env(isCuda = opt.cuda,
envWidth = opt.envWidth, envHeight = opt.envHeight, SGNum = opt.SGNum )
####################################################################
#########################################
encoder.load_state_dict( torch.load('{0}/encoder{1}_{2}.pth'.format(opt.experimentBRDF,
opt.cascadeLevel, opt.nepochBRDF-1) ).state_dict() )
for param in encoder.parameters():
param.requires_grad = False
albedoDecoder.load_state_dict( torch.load('{0}/albedo{1}_{2}.pth'.format(opt.experimentBRDF,
opt.cascadeLevel, opt.nepochBRDF-1) ).state_dict() )
for param in albedoDecoder.parameters():
param.requires_grad = False
normalDecoder.load_state_dict( torch.load('{0}/normal{1}_{2}.pth'.format(opt.experimentBRDF,
opt.cascadeLevel, opt.nepochBRDF-1) ).state_dict() )
for param in normalDecoder.parameters():
param.requires_grad = False
roughDecoder.load_state_dict(torch.load('{0}/rough{1}_{2}.pth'.format(opt.experimentBRDF,
opt.cascadeLevel, opt.nepochBRDF-1) ).state_dict() )
for param in roughDecoder.parameters():
param.requires_grad = False
depthDecoder.load_state_dict(torch.load('{0}/depth{1}_{2}.pth'.format(opt.experimentBRDF,
opt.cascadeLevel, opt.nepochBRDF-1) ).state_dict() )
for param in depthDecoder.parameters():
param.requires_grad = False
lightEncoder.load_state_dict(torch.load('{0}/lightEncoder{1}_{2}.pth'.format(opt.experiment,
opt.cascadeLevel, opt.nepoch-1) ).state_dict() )
for param in lightEncoder.parameters():
param.requires_grad = False
axisDecoder.load_state_dict(torch.load('{0}/axisDecoder{1}_{2}.pth'.format(opt.experiment,
opt.cascadeLevel, opt.nepoch-1) ).state_dict() )
for param in axisDecoder.parameters():
param.requires_grad = False
lambDecoder.load_state_dict(
torch.load('{0}/lambDecoder{1}_{2}.pth'.format(opt.experiment,
opt.cascadeLevel, opt.nepoch-1) ).state_dict() )
for param in lambDecoder.parameters():
param.requires_grad = False
weightDecoder.load_state_dict(torch.load('{0}/weightDecoder{1}_{2}.pth'.format(opt.experiment,
opt.cascadeLevel, opt.nepoch-1) ).state_dict() )
for param in weightDecoder.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 )
lightEncoder = nn.DataParallel(lightEncoder, device_ids = opt.deviceIds )
axisDecoder = nn.DataParallel(axisDecoder, device_ids = opt.deviceIds )
lambDecoder = nn.DataParallel(lambDecoder, device_ids = opt.deviceIds )
weightDecoder = nn.DataParallel(weightDecoder, 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 )
lightEncoder = lightEncoder.cuda(opt.gpuId )
axisDecoder = axisDecoder.cuda(opt.gpuId )
lambDecoder = lambDecoder.cuda(opt.gpuId )
weightDecoder = weightDecoder.cuda(opt.gpuId )
####################################
####################################
brdfDataset = dataLoader.BatchLoader( opt.dataRoot, isAllLight = True,
imWidth = opt.imWidth, imHeight = opt.imHeight, isLight = True,
cascadeLevel = opt.cascadeLevel, phase = 'TEST' )
brdfLoader = DataLoader(brdfDataset, batch_size = opt.batchSize, num_workers =
6, shuffle = True )
j = 0
# BRDFLost
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 )
reconstErrsNpList = np.ones( [1, 1], dtype = np.float32 )
renderErrsNpList = 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
#####################################################################################################################
############################################# Test with CGBRDF dataset #############################################
#####################################################################################################################
# Clear the gradient in optimizer
albedoPair, normalPair, roughPair, depthPair, \
envmapsPair, renderPair \
= wcg.wrapperBRDFLight(dataBatch, opt, encoder, \
albedoDecoder, normalDecoder, roughDecoder, depthDecoder, \
lightEncoder, axisDecoder, lambDecoder, weightDecoder, \
output2env, renderLayer, 1e-3 )
albedoPred, albedoErr = albedoPair[0], albedoPair[1]
albedoBatch = albedoPair[2]
normalPred, normalErr = normalPair[0], normalPair[1]
normalBatch = normalPair[2]
roughPred, roughErr = roughPair[0], roughPair[1]
roughBatch = roughPair[2]
depthPred, depthErr = depthPair[0], depthPair[1]
depthBatch = depthPair[2]
envmapsPredScaledImage, reconstErr = envmapsPair[0], envmapsPair[1]
envmapsBatch = envmapsPair[2]
renderedImPred, renderErr = renderPair[0], renderPair[1]
imBatch = renderPair[2]
# Output training error
utils.writeErrToScreen('albedo', [albedoErr], epoch, j)
utils.writeErrToScreen('normal', [normalErr], epoch, j)
utils.writeErrToScreen('rough', [roughErr], epoch, j)
utils.writeErrToScreen('depth', [depthErr], epoch, j)
utils.writeErrToScreen('reconstErrors', [reconstErr], epoch, j)
utils.writeErrToScreen('renderErrors', [renderErr], epoch, j)
utils.writeErrToFile('albedo', [albedoErr], testingLog, epoch, j)
utils.writeErrToFile('normal', [normalErr], testingLog, epoch, j)
utils.writeErrToFile('rough', [roughErr], testingLog, epoch, j)
utils.writeErrToFile('depth', [depthErr], testingLog, epoch, j)
utils.writeErrToFile('reconstErrors', [reconstErr], testingLog, epoch, j)
utils.writeErrToFile('renderErrors', [renderErr], testingLog, epoch, j)
albedoErrsNpList = np.concatenate( [albedoErrsNpList, utils.turnErrorIntoNumpy( [albedoErr] )], axis=0)
normalErrsNpList = np.concatenate( [normalErrsNpList, utils.turnErrorIntoNumpy( [normalErr] )], axis=0)
roughErrsNpList = np.concatenate( [roughErrsNpList, utils.turnErrorIntoNumpy( [roughErr] )], axis=0)
depthErrsNpList = np.concatenate( [depthErrsNpList, utils.turnErrorIntoNumpy( [depthErr] )], axis=0)
reconstErrsNpList = np.concatenate( [reconstErrsNpList, utils.turnErrorIntoNumpy( [reconstErr] )], axis=0 )
renderErrsNpList = np.concatenate( [renderErrsNpList, utils.turnErrorIntoNumpy( [renderErr] )], 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.writeNpErrToScreen('reconstAccu', np.mean(reconstErrsNpList[1:j+1, :], axis=0), epoch, j )
utils.writeNpErrToScreen('renderAccu', np.mean(renderErrsNpList[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)
utils.writeNpErrToFile('reconstAccu', np.mean(reconstErrsNpList[1:j+1, :], axis=0), testingLog, epoch, j )
utils.writeNpErrToFile('renderAccu', np.mean(renderErrsNpList[1:j+1, :], axis=0), testingLog, epoch, j )
if j == 1 or j% 2000 == 0:
# Save the groundtruth results
vutils.save_image( ( (albedoBatch ) ** (1.0/2.2) ).data,
'{0}/{1}_albedoGt_{2}.png'.format(opt.experiment, j, 0) )
vutils.save_image( ( 0.5*(normalBatch + 1) ).data,
'{0}/{1}_normalGt_{2}.png'.format(opt.experiment, j, 0) )
vutils.save_image( ( 0.5*(roughBatch + 1) ).data,
'{0}/{1}_roughGt_{2}.png'.format(opt.experiment, j, 0) )
depthOutGt = 1 / torch.clamp(depthBatch + 1, 1e-6, 10)
vutils.save_image( ( depthOutGt ).data,
'{0}/{1}_depthGt_{2}.png'.format(opt.experiment, j, 0) )
vutils.save_image( ( (imBatch )**(1.0/2.2) ).data,
'{0}/{1}_im.png'.format(opt.experiment, j) )
utils.writeEnvToFile(envmapsBatch, 0, '{0}/{1}_envmapPred.png'.format(opt.experiment, j) )
# Save the predicted results
vutils.save_image( ( (albedoPred ) ** (1.0/2.2) ).data,
'{0}/{1}_albedoPred_{2}.png'.format(opt.testRoot, j, 0) )
vutils.save_image( ( 0.5*(normalPred + 1) ).data,
'{0}/{1}_normalPred_{2}.png'.format(opt.testRoot, j, 0) )
vutils.save_image( ( 0.5*(roughPred + 1) ).data,
'{0}/{1}_roughPred_{2}.png'.format(opt.testRoot, j, 0) )
depthOut = 1 / torch.clamp(depthPred + 1, 1e-6, 10 )
vutils.save_image( ( depthOut ).data,
'{0}/{1}_depthPred_{2}.png'.format(opt.testRoot, j, 0) )
vutils.save_image( ( (renderedImPred )**(1.0/2.2) ).data,
'{0}/{1}_imRendered.png'.format(opt.testRoot, j) )
utils.writeEnvToFile(envmapsPredScaledImage, 0, '{0}/{1}_envmapPred.png'.format(opt.testRoot, j) )
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 )
np.save('{0}/reconstError_{1}.npy'.format(opt.testRoot, epoch), reconstErrsNpList )
np.save('{0}/renderError_{1}.npy'.format(opt.testRoot, epoch), renderErrsNpList )