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()