Merge pull request ultralytics#21 from Laughing-q/instance_seg

speed up pycocotools ops

segment/val.py

@@ -28,6 +28,7 @@
 import numpy as np
 import torch
 from tqdm import tqdm
+from multiprocessing.pool import ThreadPool
 
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[1]  # YOLOv5 root directory
@@ -39,7 +40,6 @@
 
 from models.common import DetectMultiBackend
 from models.yolo import DetectionModel
-from utils import threaded
 from utils.callbacks import Callbacks
 from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_yaml,
                            coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
@@ -51,6 +51,7 @@
 from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
 from utils.segment.plots import plot_images_and_masks
 from utils.torch_utils import de_parallel, select_device, smart_inference_mode
+from utils.general import NUM_THREADS
 
 
 def save_one_txt(predn, save_conf, shape, file):
@@ -63,17 +64,20 @@ def save_one_txt(predn, save_conf, shape, file):
             f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
 
-@threaded
 def save_one_json(predn, jdict, path, class_map, pred_masks):
     # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
     from pycocotools.mask import encode
+    def single_encode(x):
+        rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
+        rle["counts"] = rle["counts"].decode("utf-8")
+        return rle
+
     image_id = int(path.stem) if path.stem.isnumeric() else path.stem
     box = xyxy2xywh(predn[:, :4])  # xywh
     box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
     pred_masks = np.transpose(pred_masks, (2, 0, 1))
-    rles = [encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0] for x in pred_masks]
-    for rle in rles:
-        rle["counts"] = rle["counts"].decode("utf-8")
+    with ThreadPool(NUM_THREADS) as pool:
+        rles = pool.map(single_encode, pred_masks)
     for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
         jdict.append({
             'image_id': image_id,