Python envmapInitial示例

示例#1

文件： trainInitEnv.py 项目： BBBBBbruce/DNNreimplement

imBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imBgBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
SHBatch = Variable(torch.FloatTensor(opt.batchSize, 3, 9) )

imP1Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imP2Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imP3Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imEBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )

# Initial Network
encoderInit = nn.DataParallel(models.encoderInitial(), device_ids = opt.deviceIds)
albedoInit = nn.DataParallel(models.decoderInitial(mode=0), device_ids = opt.deviceIds)
normalInit = nn.DataParallel(models.decoderInitial(mode=1), device_ids = opt.deviceIds)
roughInit = nn.DataParallel(models.decoderInitial(mode=2), device_ids = opt.deviceIds)
depthInit = nn.DataParallel(models.decoderInitial(mode=3), device_ids = opt.deviceIds)
envInit = nn.DataParallel(models.envmapInitial(), device_ids = opt.deviceIds)

renderLayer = models.renderingLayer(gpuId = opt.gpuId, isCuda = opt.cuda)

# Global illumination
globIllu1to2 = models.globalIllumination()
globIllu2to3 = models.globalIllumination()
#########################################

#########################################
# Load weight of network
globIllu1to2.load_state_dict(torch.load('{0}/globIllu1to2_{1}.pth'.format(opt.modelRootGlob, opt.epochIdGlob) ) )
globIllu2to3.load_state_dict(torch.load('{0}/globIllu2to3_{1}.pth'.format(opt.modelRootGlob, opt.epochIdGlob) ) )
globIllu1to2 = globIllu1to2.eval()
globIllu2to3 = globIllu2to3.eval()
for param in globIllu1to2.parameters():

示例#2

文件： outputInitEnv.py 项目： BBBBBbruce/DNNreimplement

imBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imBgBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
SHBatch = Variable(torch.FloatTensor(opt.batchSize, 3, 9) )

imP1Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imP2Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imP3Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imEBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )

# Initial Network
encoderInit = models.encoderInitial()
albedoInit = models.decoderInitial(mode=0)
normalInit = models.decoderInitial(mode=1)
roughInit = models.decoderInitial(mode=2)
depthInit = models.decoderInitial(mode=3)
envInit = models.envmapInitial()

renderLayer = models.renderingLayer(gpuId = opt.gpuId, isCuda = opt.cuda)

# Global illumination
globIllu1to2 = models.globalIllumination()
globIllu2to3 = models.globalIllumination()
#########################################

#########################################
# Load the weight to the network
encoderInit.load_state_dict(torch.load('{0}/encoderInit_{1}.pth'.format(opt.modelRoot, opt.epochId) ) )
albedoInit.load_state_dict(torch.load('{0}/albedoInit_{1}.pth'.format(opt.modelRoot, opt.epochId) ) )
normalInit.load_state_dict(torch.load('{0}/normalInit_{1}.pth'.format(opt.modelRoot, opt.epochId) ) )
roughInit.load_state_dict(torch.load('{0}/roughInit_{1}.pth'.format(opt.modelRoot, opt.epochId) ) )
depthInit.load_state_dict(torch.load('{0}/depthInit_{1}.pth'.format(opt.modelRoot, opt.epochId) ) )