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| # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import numpy as np | |
| import scipy.sparse as sp | |
| import os | |
| def check_and_clean_skinning_weights(file_path, output_path, tolerance=0.1): | |
| """ | |
| Check if all rows in pc_skinning_weights sum to 1 for each item in the NPZ file. | |
| Remove invalid items and save a cleaned version. | |
| Args: | |
| file_path: Path to the input NPZ file | |
| output_path: Path for the cleaned NPZ file | |
| tolerance: Tolerance for floating point comparison | |
| Returns: | |
| tuple: (cleaned_data_list, removed_indices) | |
| """ | |
| data_list = np.load(file_path, allow_pickle=True)['arr_0'] | |
| invalid_indices = [] | |
| valid_data_list = [] | |
| for idx, data in enumerate(data_list): | |
| is_valid = True | |
| weights_data = data['skinning_weights_value'] | |
| weights_row = data['skinning_weights_row'] | |
| weights_col = data['skinning_weights_col'] | |
| weights_shape = data['skinning_weights_shape'] | |
| skinning_sparse = sp.coo_matrix( | |
| (weights_data, (weights_row, weights_col)), | |
| shape=weights_shape | |
| ) | |
| skinning_csr = skinning_sparse.tocsr() | |
| row_sums = np.array(skinning_csr.sum(axis=1)).flatten() | |
| invalid_rows = np.where(np.abs(row_sums - 1.0) > tolerance)[0] | |
| if len(invalid_rows) > 0: | |
| min_sum = np.min(row_sums) | |
| max_sum = np.max(row_sums) | |
| invalid_indices.append((data['uuid'], f"{len(invalid_rows)} rows, range: [{min_sum:.6f}, {max_sum:.6f}]")) | |
| is_valid = False | |
| if is_valid: | |
| valid_data_list.append(data) | |
| # Save the cleaned data | |
| if valid_data_list: | |
| np.savez_compressed(output_path, valid_data_list, allow_pickle=True) | |
| print(f"Saved {len(valid_data_list)} valid items to {output_path}") | |
| return valid_data_list, invalid_indices | |
| def main(): | |
| # File paths | |
| file_path = "articulation_xlv2_train.npz" # "articulation_xlv2_test.npz" | |
| log_file = "invalid_skinning_weights_intrain.txt" # "invalid_skinning_weights_intest.txt" | |
| output_path = "articulation_xlv2_train_updated.npz" # "articulation_xlv2_test_updated.npz" | |
| # Clean the data | |
| valid_data, invalid_indices = check_and_clean_skinning_weights(file_path, output_path) | |
| # Log the results | |
| with open(log_file, "w") as f: | |
| f.write(f"Original file: {file_path}\n") | |
| f.write(f"Cleaned file: {output_path}\n") | |
| f.write(f"Total items: {len(np.load(file_path, allow_pickle=True)['arr_0'])}\n") | |
| f.write(f"Valid items: {len(valid_data)}\n") | |
| f.write(f"Removed items: {len(invalid_indices)}\n\n") | |
| if invalid_indices: | |
| f.write("Details of removed items:\n") | |
| for idx, details in invalid_indices: | |
| f.write(f" Index {idx}: {details}\n") | |
| print(f"Cleaning complete. Results written to {log_file}") | |
| if __name__ == "__main__": | |
| main() |