val_gt3D_embed = pca.transform(
val_gt3D.reshape((val_gt3D.shape[0], val_gt3D.shape[1] * 3)))
############################################################################
print("create network")
batchSize = 128
poseNetParams = PoseRegNetParams(type=0,
nChan=nChannels,
wIn=imgSizeW,
hIn=imgSizeH,
batchSize=batchSize,
numJoints=1,
nDims=train_gt3D_embed.shape[1])
poseNet = PoseRegNet(rng, cfgParams=poseNetParams)
poseNetTrainerParams = PoseRegNetTrainerParams()
poseNetTrainerParams.batch_size = batchSize
poseNetTrainerParams.learning_rate = 0.01
print("setup trainer")
poseNetTrainer = PoseRegNetTrainer(poseNet, poseNetTrainerParams, rng)
poseNetTrainer.setData(train_data, train_gt3D_embed, val_data,
val_gt3D_embed)
poseNetTrainer.compileFunctions(compileDebugFcts=False)
###################################################################
#
# TRAIN
nEpochs = 100
train_res = poseNetTrainer.train(n_epochs=nEpochs, storeFilters=True)
train_costs = train_res[0]
val_gt3D_embed = pca.transform(
val_gt3D.reshape((val_gt3D.shape[0], val_gt3D.shape[1] * 3)))
############################################################################
print("create network")
batchSize = 128
poseNetParams = PoseRegNetParams(type=0,
nChan=nChannels,
wIn=imgSizeW,
hIn=imgSizeH,
batchSize=batchSize,
numJoints=1,
nDims=train_gt3D_embed.shape[1])
poseNet = PoseRegNet(rng, cfgParams=poseNetParams)
poseNetTrainerParams = PoseRegNetTrainerParams()
poseNetTrainerParams.batch_size = batchSize
poseNetTrainerParams.learning_rate = 0.001
poseNetTrainerParams.weightreg_factor = 0.0
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':