Python decoderInitial Beispiele

Beispiel #1

Datei: trainInitEnv.py Projekt: BBBBBbruce/DNNreimplement

normalBatch  = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
roughBatch = Variable(torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize) )
segBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
depthBatch = Variable(torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize) )
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) ) )

Beispiel #2

Datei: trainInit.py Projekt: BBBBBbruce/DNNreimplement

    torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize))
normalBatch = Variable(
    torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize))
roughBatch = Variable(
    torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize))
segBatch = Variable(
    torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize))
depthBatch = Variable(
    torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize))
imBatch = Variable(
    torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize))

# Initial Network
encoderInit = nn.DataParallel(models.encoderInitial_point(),
                              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)

# Refine Network
encoderRefs, albedoRefs = [], []
normalRefs, roughRefs = [], []
depthRefs = []

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

Beispiel #3

Datei: outputInitEnv.py Projekt: BBBBBbruce/DNNreimplement

normalBatch  = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
roughBatch = Variable(torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize) )
segBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
depthBatch = Variable(torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize) )
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) ) )