Add benchmark_processing script

tools/benchmark_processing.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+"""This file is for benchmark data loading process. It can also be used to
+refresh the memcached cache. The command line to run this file is:
+
+$ python -m cProfile -o program.prof tools/analysis/benchmark_processing.py
+configs/task/method/[config filename]
+
+Note: When debugging, the `workers_per_gpu` in the config should be set to 0
+during benchmark.
+
+It use cProfile to record cpu running time and output to program.prof
+To visualize cProfile output program.prof, use Snakeviz and run:
+$ snakeviz program.prof
+"""
+import argparse
+
+import mmcv
+from mmcv import Config
+
+from mmdet.datasets import build_dataloader
+from mmocr.datasets import build_dataset
+
+assert build_dataset is not None
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Benchmark data loading')
+    parser.add_argument('config', help='Train config file path.')
+    args = parser.parse_args()
+    cfg = Config.fromfile(args.config)
+
+    dataset = build_dataset(cfg.data.train)
+
+    # prepare data loaders
+    if 'imgs_per_gpu' in cfg.data:
+        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
+
+    data_loader = build_dataloader(
+        dataset,
+        cfg.data.samples_per_gpu,
+        cfg.data.workers_per_gpu,
+        1,
+        dist=False,
+        seed=None)
+
+    # Start progress bar after first 5 batches
+    prog_bar = mmcv.ProgressBar(
+        len(dataset) - 5 * cfg.data.samples_per_gpu, start=False)
+    for i, data in enumerate(data_loader):
+        if i == 5:
+            prog_bar.start()
+        for _ in range(len(data['img'])):
+            if i < 5:
+                continue
+            prog_bar.update()
+
+
+if __name__ == '__main__':
+    main()