Calculate various error metrics between reference and real tensors Args: ref_tensor: Reference tensor (PyTorch tensor) real_tensor: Real tensor (PyTorch tensor with the same shape as ref_tensor) eps: Small value to prevent division by zero, default 1e-6 top_
(ref_tensor, real_tensor, eps=1e-6, top_k=10)
| 2 | |
| 3 | |
| 4 | def calculate_errors(ref_tensor, real_tensor, eps=1e-6, top_k=10): |
| 5 | """ |
| 6 | Calculate various error metrics between reference and real tensors |
| 7 | |
| 8 | Args: |
| 9 | ref_tensor: Reference tensor (PyTorch tensor) |
| 10 | real_tensor: Real tensor (PyTorch tensor with the same shape as ref_tensor) |
| 11 | eps: Small value to prevent division by zero, default 1e-6 |
| 12 | top_k: Number of top largest errors to return, default 10 |
| 13 | |
| 14 | Returns: |
| 15 | dict: Dictionary containing the following metrics: |
| 16 | - mean_abs_error: Mean Absolute Error |
| 17 | - max_abs_error: Maximum Absolute Error |
| 18 | - max_abs_error_ref: Reference value at the position of maximum absolute error |
| 19 | - max_abs_error_real: Real value at the position of maximum absolute error |
| 20 | - max_abs_error_pos: Position coordinates of maximum absolute error (as tuple) |
| 21 | - mean_rel_error: Mean Relative Error |
| 22 | - max_rel_error: Maximum Relative Error |
| 23 | - max_rel_error_ref: Reference value at the position of maximum relative error |
| 24 | - max_rel_error_real: Real value at the position of maximum relative error |
| 25 | - max_rel_error_pos: Position coordinates of maximum relative error (as tuple) |
| 26 | """ |
| 27 | # Ensure inputs are PyTorch tensors |
| 28 | if not isinstance(ref_tensor, torch.Tensor) or not isinstance(real_tensor, torch.Tensor): |
| 29 | raise TypeError("Inputs must be PyTorch tensors") |
| 30 | |
| 31 | # Check if tensor shapes match |
| 32 | if ref_tensor.shape != real_tensor.shape: |
| 33 | raise ValueError("Reference and real tensors must have the same shape") |
| 34 | |
| 35 | # Calculate absolute errors |
| 36 | abs_error = torch.abs(ref_tensor - real_tensor) |
| 37 | |
| 38 | # Mean Absolute Error |
| 39 | mae = torch.mean(abs_error).item() |
| 40 | |
| 41 | # Get top K absolute errors and their positions |
| 42 | num_elements = abs_error.numel() |
| 43 | k = min(top_k, num_elements) |
| 44 | |
| 45 | # Flatten the error tensor and obtain the indices of the top k largest values |
| 46 | abs_error_flat = abs_error.flatten() |
| 47 | top_abs_values, top_abs_flat_indices = torch.topk(abs_error_flat, k, largest=True) |
| 48 | |
| 49 | # Convert to multidimensional coordinates and collect corresponding values |
| 50 | top_abs_errors = [] |
| 51 | for val, idx in zip(top_abs_values, top_abs_flat_indices): |
| 52 | pos = tuple(torch.unravel_index(idx, abs_error.shape)) |
| 53 | top_abs_errors.append( |
| 54 | { |
| 55 | "error_value": val.item(), |
| 56 | "ref_value": ref_tensor[pos].item(), |
| 57 | "real_value": real_tensor[pos].item(), |
| 58 | "position": pos, |
| 59 | } |
| 60 | ) |
| 61 |