import os # 如果在 "tests" 目录下运行，则切回上一层 pwd = os.getcwd() if pwd.endswith("tests"): os.chdir(os.path.dirname(pwd)) import time import torch import numpy as np from marble.tasks.Chords1217._datamodule_v1 import Chords1217AudioTrain from torch.utils.data import DataLoader def benchmark_get_targets_single_clip( jsonl_path: str, sample_rate: int, channels: int, clip_seconds: float, label_freq: int, mode: str, num_files: int = 20 ): """ 对 Chords1217AudioTrain 在指定 mode 下，测量前 num_files 个文件的第一个 slice (slice_idx=0) 调用 get_targets 的总耗时。仅供对比三种实现（python/numpy/numba）哪种最慢。 """ print(f"\n--- Benchmark get_targets mode='{mode}' (仅前 {num_files} 个文件，每个文件的第 0 号 slice) ---") # 1. 实例化 Dataset（这里只用 Train split） dataset = Chords1217AudioTrain( jsonl=jsonl_path, sample_rate=sample_rate, channels=channels, clip_seconds=clip_seconds, label_freq=label_freq, mode=mode ) # 假设每个文件至少有一个 slice，且 orig_sr == sample_rate，orig_clip_frames == clip_seconds * sample_rate orig_sr = sample_rate orig_clip_frames = int(clip_seconds * sample_rate) t0 = time.perf_counter() for file_idx in range(min(num_files, len(dataset.chords_meta))): # 只测第 0 号 slice 的 get_targets _ = dataset.get_targets( file_idx=file_idx, slice_idx=0, orig_sr=orig_sr, orig_clip_frames=orig_clip_frames ) t1 = time.perf_counter() total_time = t1 - t0 count = min(num_files, len(dataset.chords_meta)) print(f"Mode='{mode}'，总耗时：{total_time:.4f}s，平均每个文件第 0 号 slice get_targets 用时 {total_time/count:.4f}s") def benchmark_get_targets_full_epoch( jsonl_path: str, sample_rate: int, channels: int, clip_seconds: float, label_freq: int, mode: str, batch_size: int = 8, num_workers: int = 4 ): """ 通过 DataLoader 对整个 train split 迭代一遍，测量整个 epoch 调用 get_targets + collate 的时间。 """ print(f"\n--- Benchmark get_targets mode='{mode}' （使用 DataLoader 遍历整个 train split）---") train_ds = Chords1217AudioTrain( jsonl=jsonl_path, sample_rate=sample_rate, channels=channels, clip_seconds=clip_seconds, label_freq=label_freq, mode=mode ) loader = DataLoader( train_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True, prefetch_factor=2, persistent_workers=True ) t0 = time.perf_counter() for batch in loader: # batch 里会调用 get_targets(...) + 其他预处理（如音频 loading、transform 等） pass t1 = time.perf_counter() print(f"Mode='{mode}'，整 train split 迭代一轮 get_targets 耗时：{t1-t0:.4f}s") def benchmark_load_and_preprocess_single_clip( jsonl_path: str, sample_rate: int, channels: int, clip_seconds: float, label_freq: int, mode: str, num_files: int = 20 ): """ 针对 Chords1217AudioTrain，在指定 mode 下，测量前 num_files 个文件的第一个 slice (slice_idx=0) 调用 _load_and_preprocess 的总耗时。仅供对比三种实现（python/numpy/numba）在纯加载+预处理上的差异。 """ print(f"\n--- Benchmark load_and_preprocess mode='{mode}' (仅前 {num_files} 个文件，第 0 号 slice) ---") # 1. 实例化 Dataset（这里只用 Train split） dataset = Chords1217AudioTrain( jsonl=jsonl_path, sample_rate=sample_rate, channels=channels, clip_seconds=clip_seconds, label_freq=label_freq, mode=mode ) # 假设 orig_sr == sample_rate，orig_clip_frames == clip_seconds * sample_rate orig_sr = sample_rate orig_clip_frames = int(clip_seconds * sample_rate) t0 = time.perf_counter() for file_idx in range(min(num_files, len(dataset.meta))): info = dataset.meta[file_idx] audio_path = info["audio_path"] # 测量 _load_and_preprocess 的耗时 _ = dataset._load_and_preprocess( path=audio_path, slice_idx=0, orig_sr=orig_sr, orig_clip_frames=orig_clip_frames ) t1 = time.perf_counter() total_time = t1 - t0 count = min(num_files, len(dataset.meta)) print(f"Mode='{mode}'，总耗时：{total_time:.4f}s，平均每个文件第 0 号 slice _load_and_preprocess 用时 {total_time/count:.4f}s") def benchmark_load_and_preprocess_full_epoch( jsonl_path: str, sample_rate: int, channels: int, clip_seconds: float, label_freq: int, mode: str, batch_size: int = 8, num_workers: int = 4 ): """ 通过 DataLoader 遍历整个 train split，但在 collate 阶段只收集 waveform，不调用 get_targets， 仅测 _load_and_preprocess 的耗时。 """ print(f"\n--- Benchmark load_and_preprocess mode='{mode}' （使用 DataLoader 遍历 entire train split，仅 _load_and_preprocess） ---") train_ds = Chords1217AudioTrain( jsonl=jsonl_path, sample_rate=sample_rate, channels=channels, clip_seconds=clip_seconds, label_freq=label_freq, mode=mode ) # 定义一个只做加载 + 预处理的 collate_fn def collate_waveforms(batch): # batch 是 [(waveform, targets, audio_path), ...] # waveform 已经包含了 _load_and_preprocess 的结果 waveforms = [item[0] for item in batch] return torch.stack(waveforms, dim=0) loader = DataLoader( train_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, collate_fn=collate_waveforms, pin_memory=True, prefetch_factor=2, persistent_workers=True ) t0 = time.perf_counter() for _ in loader: # 只负责跑 _load_and_preprocess，然后被 collate_fn 收集 pass t1 = time.perf_counter() print(f"Mode='{mode}'，整 train split 迭代一轮 _load_and_preprocess 耗时：{t1-t0:.4f}s") if __name__ == '__main__': # ====== 以下是需要根据实际情况修改的参数 ====== JSONL_PATH = 'data/Chords1217/Chords1217.train.jsonl' # 你的 Chords1217 metadata jsonl 文件 SAMPLE_RATE = 24000 CHANNELS = 1 CLIP_SECONDS = 15.0 # 每个 clip 时长 LABEL_FREQ = 75 # 每秒多少帧标签（对 get_targets 有用，对 load_and_preprocess 无影响） NUM_FILES = 500 # 单文件测试时用到的文件数量 BATCH_SIZE = 64 # DataLoader 的 batch_size NUM_WORKERS = 4 # DataLoader 的 num_workers # ------------------------------- # 1) 先跑 get_targets 的基准测试 # ------------------------------- for m in ['python', 'numpy', 'numba']: benchmark_get_targets_single_clip( jsonl_path=JSONL_PATH, sample_rate=SAMPLE_RATE, channels=CHANNELS, clip_seconds=CLIP_SECONDS, label_freq=LABEL_FREQ, mode=m, num_files=NUM_FILES ) # for m in ['python', 'numpy', 'numba']: # benchmark_get_targets_full_epoch( # jsonl_path=JSONL_PATH, # sample_rate=SAMPLE_RATE, # channels=CHANNELS, # clip_seconds=CLIP_SECONDS, # label_freq=LABEL_FREQ, # mode=m, # batch_size=BATCH_SIZE, # num_workers=NUM_WORKERS # ) # ----------------------------------------- # 2) 再跑 _load_and_preprocess 的基准测试 # ----------------------------------------- for m in ['python', 'numpy', 'numba']: benchmark_load_and_preprocess_single_clip( jsonl_path=JSONL_PATH, sample_rate=SAMPLE_RATE, channels=CHANNELS, clip_seconds=CLIP_SECONDS, label_freq=LABEL_FREQ, mode=m, num_files=NUM_FILES ) # for m in ['python', 'numpy', 'numba']: # benchmark_load_and_preprocess_full_epoch( # jsonl_path=JSONL_PATH, # sample_rate=SAMPLE_RATE, # channels=CHANNELS, # clip_seconds=CLIP_SECONDS, # label_freq=LABEL_FREQ, # mode=m, # batch_size=BATCH_SIZE, # num_workers=NUM_WORKERS # )