Python decoderInitial Examples

Programming Language: Python

Namespace/Package Name: models

Method/Function: decoderInitial

Examples at hotexamples.com: 3

Python decoderInitial - 3 examples found. These are the top rated real world Python examples of models.decoderInitial extracted from open source projects. You can rate examples to help us improve the quality of examples.

Example #1

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File: trainInitEnv.py Project: 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) ) )

Example #2

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File: trainInit.py Project: 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)
#########################################

Example #3

Show file

File: outputInitEnv.py Project: 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) ) )