| import json | |
| from collections import defaultdict | |
| from pathlib import Path | |
| import pandas as pd | |
| from omegaconf import MISSING | |
| from ._hear_util import resample_hear_corpus | |
| from .hear_fsd import HearFSD | |
| ESC50_NUM_FOLDS = 5 | |
| __all__ = ["HearESC50"] | |
| def hear_scene_kfolds( | |
| target_dir: str, | |
| cache_dir: str, | |
| dataset_root: str, | |
| test_fold: int, | |
| num_folds: int, | |
| get_path_only: bool = False, | |
| ): | |
| assert test_fold < num_folds, ( | |
| "test_fold id must be smaller than num_folds. " | |
| f"get test_fold={test_fold} and num_folds={num_folds}" | |
| ) | |
| target_dir = Path(target_dir) | |
| train_csv = target_dir / "train.csv" | |
| valid_csv = target_dir / "valid.csv" | |
| test_csv = target_dir / "test.csv" | |
| if get_path_only: | |
| return train_csv, valid_csv, [test_csv] | |
| resample_hear_corpus(dataset_root, target_sr=16000) | |
| dataset_root = Path(dataset_root) | |
| wav_root: Path = dataset_root / "16000" | |
| def load_json(filepath): | |
| with open(filepath, "r") as fp: | |
| return json.load(fp) | |
| fold_metas = [] | |
| fold_datas = [] | |
| for fold_id in range(num_folds): | |
| meta = load_json(dataset_root / f"fold{fold_id:2d}.json".replace(" ", "0")) | |
| fold_metas.append(meta) | |
| data = defaultdict(list) | |
| for k in list(meta.keys()): | |
| wav_path = wav_root / f"fold{fold_id:2d}".replace(" ", "0") / k | |
| labels = meta[k] | |
| data["id"].append(k) | |
| data["wav_path"].append(wav_path) | |
| data["labels"].append(",".join([str(label).strip() for label in labels])) | |
| df = pd.DataFrame(data=data) | |
| fold_datas.append(df) | |
| test_id = test_fold | |
| valid_id = (test_fold + 1) % num_folds | |
| train_ids = [idx for idx in range(num_folds) if idx not in [test_id, valid_id]] | |
| test_data = fold_datas[test_id] | |
| valid_data = fold_datas[valid_id] | |
| train_data = [] | |
| for idx in train_ids: | |
| train_data.append(fold_datas[idx]) | |
| train_data = pd.concat(train_data) | |
| train_data.to_csv(train_csv, index=False) | |
| valid_data.to_csv(valid_csv, index=False) | |
| test_data.to_csv(test_csv, index=False) | |
| return train_csv, valid_csv, [test_csv] | |
| class HearESC50(HearFSD): | |
| def default_config(self) -> dict: | |
| return dict( | |
| start=0, | |
| stop=None, | |
| target_dir=MISSING, | |
| cache_dir=None, | |
| remove_all_cache=False, | |
| prepare_data=dict( | |
| dataset_root=MISSING, | |
| test_fold=MISSING, | |
| num_folds=ESC50_NUM_FOLDS, | |
| ), | |
| build_batch_sampler=dict( | |
| train=dict( | |
| batch_size=32, | |
| shuffle=True, | |
| ), | |
| valid=dict( | |
| batch_size=1, | |
| ), | |
| test=dict( | |
| batch_size=1, | |
| ), | |
| ), | |
| build_upstream=dict( | |
| name=MISSING, | |
| ), | |
| build_featurizer=dict( | |
| layer_selections=None, | |
| normalize=False, | |
| ), | |
| build_downstream=dict( | |
| hidden_layers=2, | |
| pooling_type="MeanPooling", | |
| ), | |
| build_model=dict( | |
| upstream_trainable=False, | |
| ), | |
| build_task=dict( | |
| prediction_type="multiclass", | |
| scores=["top1_acc", "d_prime", "aucroc", "mAP"], | |
| ), | |
| build_optimizer=dict( | |
| name="Adam", | |
| conf=dict( | |
| lr=1.0e-3, | |
| ), | |
| ), | |
| build_scheduler=dict( | |
| name="ExponentialLR", | |
| gamma=0.9, | |
| ), | |
| save_model=dict(), | |
| save_task=dict(), | |
| train=dict( | |
| total_steps=4000, | |
| log_step=100, | |
| eval_step=500, | |
| save_step=100, | |
| gradient_clipping=1.0, | |
| gradient_accumulate=4, | |
| valid_metric="top1_acc", | |
| valid_higher_better=True, | |
| auto_resume=True, | |
| resume_ckpt_dir=None, | |
| ), | |
| evaluate=dict(), | |
| ) | |
| def prepare_data( | |
| self, | |
| prepare_data: dict, | |
| target_dir: str, | |
| cache_dir: str, | |
| get_path_only: bool = False, | |
| ): | |
| return hear_scene_kfolds( | |
| **self._get_current_arguments(flatten_dict="prepare_data") | |
| ) | |