def __init__(self):
parser = argparse.ArgumentParser(
description='PyTorch hand pose Training')
args = parser.parse_args()
'''user setting'''
args.gpu_ids = '0'
args.use_net = ['hpe1_orig', 'hpe2'] #hpe1_orig, hig, hpe1, hpe2
forward_list = ['hpe1_orig', 'hpe2'] #hpe1_orig, hig_hpe1, hpe2
trained_modelFile_hpe1_orig = basedir + '..\\HPE\\output\\2020_2_26_11_29\\trainedModel_epoch99.pth.tar'
trained_modelFile_hpe2 = basedir + '..\\HPE2\\output\\2020_4_10_18_46\\trainedModel_epoch99.pth.tar'
'''common setting'''
out_list = {}
args.train_net = []
args.cgan = True
args.is_train = False
args.gan_mode = 'vanilla'
args.trainImageSize = 128
trainImageSize = 128
args.skeleton_pca_dim = 52
args.skeleton_orig_dim = 63
args.discriminator_reconstruction = False
args.test_batch = 1
hpe_numBlocks = 5
#device
#device = torch.device(torch.device("cuda:%s"%args.gpu_ids if torch.cuda.is_available() else "cpu")
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
args.device = device
cudnn.benchmark = True
#--HPE_orig net setting
if 'hpe1_orig' in args.use_net:
if not 'hpe1_orig' in locals():
hpe1_orig = dpnet(args.skeleton_pca_dim, hpe_numBlocks, device)
checkpoint = torch.load(
trained_modelFile_hpe1_orig,
map_location=lambda storage, loc: storage)
hpe1_orig.load_state_dict(checkpoint['state_dict'])
#--HPE2 net setting
if 'hpe2' in args.use_net:
if not 'hpe2' in locals():
hpe2 = dpnet(args.skeleton_pca_dim, hpe_numBlocks, device)
checkpoint = torch.load(
trained_modelFile_hpe2,
map_location=lambda storage, loc: storage)
hpe2.load_state_dict(checkpoint['state_dict_hpe2'])
with open(basedir + '..\\pca_52_by_957032_augmented_X2.pickle',
'rb') as f:
self.pca = pickle.load(f)
#--fusion net setting
#fusionnet=FusionNet(hpe1_orig,pix2pix,hpe1,hpe2,None,args)
fl = []
if 'hpe1_orig' in locals(): fl.append(hpe1_orig)
else: fl.append(None)
if 'pix2pix' in locals(): fl.append(pix2pix)
else: fl.append(None)
if 'hpe1' in locals(): fl.append(hpe1)
else: fl.append(None)
if 'hpe2' in locals(): fl.append(hpe2)
else: fl.append(None)
fusionnet = FusionNet(fl[0], fl[1], fl[2], fl[3], None, args)
self.fusionnet = fusionnet.to(device)
#--dataset of uvr (for utils)
camera_info = {}
camera_info['fx'] = 475.065948
camera_info['fy'] = 475.065857
camera_info['cx'] = 315.944855
camera_info['cy'] = 245.287079
camera_info['camerawidth'] = 640
camera_info['cameraheight'] = 480
camera_info['cube'] = np.asarray([250, 250, 250])
camera_info['trainImageSize'] = 128
self.trainImageSize = camera_info['trainImageSize']
self.cube = camera_info['cube']
camerawidth = camera_info['camerawidth']
cameraheight = camera_info['cameraheight']
jointnum = 21
d_maximum = 500
self.datasetloader_uvr = datasetloader_UVR('..', 0, camera_info, '..',
'..')
self.utils = self.datasetloader_uvr.utils
#--start
self.fusionnet.set_mode('eval')
self.ir_batch = np.zeros(
(1, 1, args.trainImageSize, args.trainImageSize), dtype=np.float32)
self.depth_batch = np.zeros(
(1, 1, args.trainImageSize, args.trainImageSize), dtype=np.float32)
if not 'pix2pix' in locals():
pix2pix=Pix2pix(args)
checkpoint=torch.load(trained_modelFile_hig)
if args.gpu_ids=='all':
pix2pix.netG.load_state_dict(checkpoint['state_dict_hig'])
else:
checkpoint_dict=get_state_dict(checkpoint['state_dict_hig'])
pix2pix.netG.load_state_dict(checkpoint_dict)
pix2pix.netG.eval()
##--select model (HPE)--##
if args.hpe_enabled==True:
if not 'hpe' in locals():
hpe=dpnet(args.skeleton_pca_dim,hpe_numBlocks,device)
device = torch.device("cuda:%s"%args.gpu_ids if torch.cuda.is_available() else "cpu")
hpe=hpe.to(device)
cudnn.benchmark = True
checkpoint=torch.load(trained_modelFile_hpe)
hpe.load_state_dict(checkpoint['state_dict_hpe1'])
hpe.eval()
img_hpe = np.zeros((1,1,trainImageSize,trainImageSize), dtype = np.float32)
with open(basedir+'..\\pca_52_by_957032_augmented_X2.pickle','rb') as f:
pca=pickle.load(f)
##--init realsense--##
if input_type=='sr300':
realsense=Realsense()
if not 'pix2pix' in locals():
pix2pix = Pix2pix(args)
checkpoint = torch.load(trained_modelFile_hig,
map_location=lambda storage, loc: storage)
if 'hig' in args.use_net:
pix2pix.netG.load_state_dict(checkpoint['state_dict_hig'])
if 'hid' in args.use_net:
pix2pix.netD.load_state_dict(checkpoint['state_dict_hid'])
#--HPE net setting
if 'trained_modelFile_hpe1' in locals():
if not 'hpe1' in locals():
hpe_numBlocks = 5
hpe1 = dpnet(args.skeleton_pca_dim, hpe_numBlocks, device)
checkpoint = torch.load(trained_modelFile_hpe1,
map_location=lambda storage, loc: storage)
hpe1.load_state_dict(checkpoint['state_dict_hpe1'])
else:
if not 'hpe1' in locals():
hpe_numBlocks = 5
hpe1 = dpnet(args.skeleton_pca_dim, hpe_numBlocks, device)
checkpoint = torch.load(trained_modelFile_hpe1_orig,
map_location=lambda storage, loc: storage)
hpe1.load_state_dict(checkpoint['state_dict'])
#--HPE_orig net setting
if not 'hpe1_orig' in locals():
hpe_numBlocks = 5
hpe1_orig = dpnet(args.skeleton_pca_dim, hpe_numBlocks, device)
args.trainImageSize = 128
args.skeleton_pca_dim = 52
args.skeleton_orig_dim = 63
args.cgan = True
#device
device = torch.device("cuda:%s" %
args.gpu_ids if torch.cuda.is_available() else "cpu")
args.device = device
#network
pix2pix = Pix2pix(args)
hpe1 = dpnet(52, 5, device)
hpe2 = dpnet(52, 5, device)
net = FusionNet(pix2pix, hpe1, hpe2, args)
w = np.random.rand(args.skeleton_orig_dim, args.skeleton_pca_dim)
b = np.random.rand(args.skeleton_orig_dim)
net.set_reconstruction_net(w, b)
net = net.to(device)
#run
ir = np.random.rand(args.train_batch, 1, 128, 128)
depth = np.random.rand(args.train_batch, 1, 128, 128)
net.set_input(ir, depth)