| def restore_mean(x, y, mean_x, mean_y): | |
| """ | |
| In GPUDrive, everything is centered at zero by subtracting the mean. | |
| This function reapplies the mean to go back to the original coordinates. | |
| The mean (xyz) is exported per world as world_means_tensor. | |
| Args: | |
| x (torch.Tensor): x coordinates | |
| y (torch.Tensor): y coordinates | |
| mean_x (torch.Tensor): mean of x coordinates. Shape: (num_worlds, 1) | |
| mean_y (torch.Tensor): mean of y coordinates. Shape: (num_worlds, 1) | |
| """ | |
| return x + mean_x, y + mean_y | |
| def normalize_min_max(tensor, min_val, max_val): | |
| """Normalizes an array of values to the range [-1, 1]. | |
| Args: | |
| x (np.array): Array of values to normalize. | |
| min_val (float): Minimum value for normalization. | |
| max_val (float): Maximum value for normalization. | |
| Returns: | |
| np.array: Normalized array of values. | |
| """ | |
| return 2 * ((tensor - min_val) / (max_val - min_val)) - 1 | |
| def normalize_min_max_inplace(tensor, min_val, max_val): | |
| """Normalizes an array of values to the range [-1, 1]. | |
| Args: | |
| x (np.array): Array of values to normalize. | |
| min_val (float): Minimum value for normalization. | |
| max_val (float): Maximum value for normalization. | |
| """ | |
| tensor.sub_(min_val).div_(max_val - min_val).mul_(2).sub_(1) |