if not os.path.exists(out_dir): os.makedirs(out_dir) shutil.copyfile(sys.argv[0], out_dir + '/training_script.py') sys.stdout = mutil.Logger(out_dir) gpu = 3 torch.cuda.set_device(gpu) mb_size = 600 # mini-batch_size mode_num = 3 distance = 10 R = np.array([1, 2, 3]) * 2 theta = np.array([[-15, 15], [75, 105], [165, 195]]) # theta[:, 0] = theta[:, 0] - 15 # theta[:, 1] = theta[:, 1] + 15 data = data_prepare.Data_2D_Curve(mb_size, theta, R) grid_num = 100 data_mesh = data_prepare.Data_2D_Curve(grid_num * grid_num, theta, R) Z_dim = 2 X_dim = 2 h_dim = 128 c_dim = mode_num cnt = 0 num = '0' # else: # print("you have already creat one.") # exit(1)
if not os.path.exists(out_dir): os.makedirs(out_dir) shutil.copyfile(sys.argv[0], out_dir + '/training_script.py') sys.stdout = mutil.Logger(out_dir) gpu = 0 torch.cuda.set_device(gpu) mb_size = 600 # mini-batch_size mode_num = 3 distance = 10 R = np.array([1, 2, 3])*2 theta = np.array([[-15, 15], [75, 105], [165, 195]]) # theta[:, 0] = theta[:, 0] - 15 # theta[:, 1] = theta[:, 1] + 15 data = data_prepare.Data_2D_Curve(mb_size, theta, R) Z_dim = 2 X_dim = 2 h_dim = 128 c_dim = mode_num cnt = 0 num = '0' # else: # print("you have already creat one.") # exit(1) G = model.G_Net(Z_dim+c_dim, X_dim, h_dim).cuda() D = model.D_Net(X_dim, 1, h_dim).cuda()