get_vr20data = True
else:
get_noblurdata = True
else:
get_noblurdata = True
#load data from the selected dataset
if get_blurdata == True:
img_depth, img_ir = datasetloader_uvr['train_blur'].load_data()
elif get_vr20data == True:
img_depth, img_ir = datasetloader_uvr['train_vr20'].load_data()
elif get_noblurdata == True:
img_depth, img_ir = datasetloader_uvr['train'].load_data()
#optimize
fusionnet.set_input(img_ir, img_depth) #0.0005 sec
fusionnet.optimize_parameters_hpe2() #0.25
if 'hpd' in args.train_net:
for j in range(10):
img_icvl, hpose_icvl, _, _ = next(generator_train_icvl)
fusionnet.set_input_icvl(img_icvl, hpose_icvl)
fusionnet.optimize_parameters_bighand()
#forward and backward
fusionnet.calculateloss_hpe2() #0.08 sec
loss_ = fusionnet.getloss(loss_names)
progress_train.append_local(loss_)
#print progress
#--preprocess depth/ir
train_imgs = np.load(load_filepath_preprocess + '%d.npy' % frame)
depth_train = np.copy(train_imgs[:, 0:trainImageSize])
ir_train = np.copy(train_imgs[:, trainImageSize:2 * trainImageSize])
com = dataset_load['com'][frame]
window = dataset_load['window'][frame]
depth_crop = datasetloader_uvr.utils.crop(depth_orig, window[0],
window[1], window[2],
window[3], window[4],
window[5])
# set input
ir_batch[0, 0, :, :] = ir_train.copy()
depth_batch[0, 0, :, :] = depth_train.copy()
fusionnet.set_input(ir_batch, depth_batch)
#forward
#TIME=time.time()
fusionnet.forward_('hpe1_orig')
fusionnet.forward_('hig_hpe1')
fusionnet.forward_('hpe2')
#fusionnet.forward_('hpe1_blur')
#print(time.time()-TIME)
#reconstruct
com3d = utils.unproject2Dto3D(com)
out1 = fusionnet.reconstruct_joints(pca, com3d, cube, 'hpe1_orig',
'tocpu')
out2 = fusionnet.reconstruct_joints(pca, com3d, cube, 'hig_hpe1',
'tocpu')
else:
get_icvldata=True
'''
elif traindataNum_vr20 == 0:
if i % 2 == 0:
get_uvrdata = True
else:
get_icvldata = True
#input
if get_icvldata == True:
img_icvl, hpose_icvl, _, _ = next(generator_train_icvl)
fusionnet.set_input_icvl(img_icvl, hpose_icvl)
elif get_uvrdata == True:
img_depth, img_ir = datasetloader_uvr['train'].load_data()
fusionnet.set_input(img_ir, img_depth)
else:
img_depth, img_ir = datasetloader_uvr['train_vr20'].load_data()
fusionnet.set_input(img_ir, img_depth)
#optimize
if get_icvldata == True:
fusionnet.optimize_parameters_hpe1_bighand()
else:
fusionnet.optimize_parameters_hpe1_depthIR()
#forward and backward
if get_icvldata == True:
fusionnet.calculateloss_hpe1_bighand()
else:
fusionnet.calculateloss_hpe1_depthIR()