parser.add_argument('--ADAM_BETA_D',
type=float,
default=0.5,
help='could be changed')
parser.add_argument('--ADAM_BETA_G',
type=float,
default=0.5,
help='could be changed')
FLAGS = parser.parse_args()
# 这个函数把两个参数对应的数据集放进来
Train_examples = ioUtil.load_examples(FLAGS.train_hdf5, 'names')
Test_examples = ioUtil.load_examples(FLAGS.test_hdf5, 'names')
'''重新排列数据'''
Train_data = ioUtil.arrange_datas(Train_examples)
Test_data = ioUtil.arrange_datas(Test_examples)
############# FALG things #################################
FLAGS.point_num_in = Train_examples.skeleton_in.shape[
1] # shape gives nums in every dims
FLAGS.point_num_out = Train_examples.pointSet_out.shape[1]
FLAGS.example_num = Train_examples.skeleton_in.shape[0]
EXAMPLE_NUM = FLAGS.example_num
TRAINING_EPOCHES = FLAGS.epoch
batch_size = FLAGS.batch_size
##################### output data #######################
## 取出来狗
idx_dog_train = [
i for i in range(Train_examples.names.shape[0])
if 'mesh_20' in Train_examples.names[i]
]
skeleton_in_d = Train_examples.skeleton_in[idx_dog_train, :]
pointSet_out_d = Train_examples.pointSet_out[idx_dog_train, :]
names_d = Train_examples.names[idx_dog_train]
Dog_Train_examples = Examples(
names=names_d,
skeleton_in=skeleton_in_d,
pointSet_out=pointSet_out_d,
)
'''重新排列数据'''
Horse_Train_examples = ioUtil.arrange_datas(Horse_Train_examples)
Lioness_Train_examples = ioUtil.arrange_datas(Lioness_Train_examples)
Dog_Train_examples = ioUtil.arrange_datas(Dog_Train_examples)
skeleton_in_m = np.append(Lioness_Train_examples.skeleton_in,
Horse_Train_examples.skeleton_in,
axis=0)
pointSet_out_m = np.append(Lioness_Train_examples.pointSet_out,
Horse_Train_examples.pointSet_out,
axis=0)
pointSet_in_m = np.append(Lioness_Train_examples.pointSet_in,
Horse_Train_examples.pointSet_in,
axis=0)
names_m = np.append(Lioness_Train_examples.names,
Horse_Train_examples.names,
axis=0)