print train_gt3D.max(), test_gt3D.max(), train_gt3D.min(), test_gt3D.min()
print train_data.max(), test_data.max(), train_data.min(), test_data.min()
imgSizeW = train_data.shape[3]
imgSizeH = train_data.shape[2]
nChannels = train_data.shape[1]
#############################################################################
print("create network")
batchSize = 64
poseNetParams = ScaleNetParams(type=1, nChan=nChannels, wIn=imgSizeW, hIn=imgSizeH, batchSize=batchSize,
resizeFactor=2, numJoints=1, nDims=3)
poseNet = ScaleNet(rng, cfgParams=poseNetParams)
poseNetTrainerParams = ScaleNetTrainerParams()
poseNetTrainerParams.use_early_stopping = False
poseNetTrainerParams.batch_size = batchSize
poseNetTrainerParams.learning_rate = 0.0005
poseNetTrainerParams.weightreg_factor = 0.0001
poseNetTrainerParams.force_macrobatch_reload = True
poseNetTrainerParams.para_augment = True
poseNetTrainerParams.augment_fun_params = {'fun': 'augment_poses', 'args': {'normZeroOne': False,
'di': di,
'aug_modes': aug_modes,
'hd': HandDetector(train_data[0, 0].copy(), abs(di.fx), abs(di.fy), importer=di)}}
print("setup trainer")
poseNetTrainer = ScaleNetTrainer(poseNet, poseNetTrainerParams, rng, './eval/'+eval_prefix)
poseNetTrainer.setData(train_data, train_gt3D[:, di.crop_joint_idx, :], val_data, val_gt3D[:, di.crop_joint_idx, :])
poseNetTrainer.addStaticData({'val_data_x1': val_data2, 'val_data_x2': val_data4})
nChannels = train_data.shape[1]
#############################################################################
print("create network")
batchSize = 64
poseNetParams = ScaleNetParams(type=1,
nChan=nChannels,
wIn=imgSizeW,
hIn=imgSizeH,
batchSize=batchSize,
resizeFactor=2,
numJoints=1,
nDims=3)
poseNet = ScaleNet(rng, cfgParams=poseNetParams)
poseNetTrainerParams = ScaleNetTrainerParams()
poseNetTrainerParams.batch_size = batchSize
poseNetTrainerParams.learning_rate = 0.0005
poseNetTrainerParams.weightreg_factor = 0.0001
poseNetTrainerParams.lr_of_ep = lambda ep: poseNetTrainerParams.learning_rate / (
1 + 0.1 * ep)
print("setup trainer")
poseNetTrainer = ScaleNetTrainer(poseNet, poseNetTrainerParams, rng)
poseNetTrainer.setData(train_data, train_gt3D[:, 13, :], val_data,
val_gt3D[:, 13, :])
poseNetTrainer.addStaticData({
'val_data_x1': val_data2,
'val_data_x2': val_data4
})
poseNetTrainer.addManagedData({