Improved `detect.py` timing (#4741)

* Improved detect.py timing

* Eliminate 1 time_sync() call

* Inference-only time

* dash

* #Save section

* Cleanup

detect.py

 
 import argparse
 import sys
-import time
 from pathlib import Path
 
 import cv2
     # Run inference
     if pt and device.type != 'cpu':
         model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters())))  # run once
-    t0 = time.time()
+    dt, seen = [0.0, 0.0, 0.0], 0
     for path, img, im0s, vid_cap in dataset:
+        t1 = time_sync()
         if onnx:
             img = img.astype('float32')
         else:
         img = img / 255.0  # 0 - 255 to 0.0 - 1.0
         if len(img.shape) == 3:
             img = img[None]  # expand for batch dim
+        t2 = time_sync()
+        dt[0] += t2 - t1
 
         # Inference
-        t1 = time_sync()
         if pt:
             visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
             pred = model(img, augment=augment, visualize=visualize)[0]
             pred[..., 2] *= imgsz[1]  # w
             pred[..., 3] *= imgsz[0]  # h
             pred = torch.tensor(pred)
+        t3 = time_sync()
+        dt[1] += t3 - t2
 
         # NMS
         pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
-        t2 = time_sync()
+        dt[2] += time_sync() - t3
 
         # Second-stage classifier (optional)
         if classify:
             pred = apply_classifier(pred, modelc, img, im0s)
 
         # Process predictions
-        for i, det in enumerate(pred):  # detections per image
+        for i, det in enumerate(pred):  # per image
+            seen += 1
             if webcam:  # batch_size >= 1
                 p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
             else:
                         if save_crop:
                             save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 
-            # Print time (inference + NMS)
-            print(f'{s}Done. ({t2 - t1:.3f}s)')
+            # Print time (inference-only)
+            print(f'{s}Done. ({t3 - t2:.3f}s)')
 
             # Stream results
             im0 = annotator.result()
                         vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                     vid_writer[i].write(im0)
 
+    # Print results
+    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
+    print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
     if save_txt or save_img:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
         print(f"Results saved to {colorstr('bold', save_dir)}{s}")
-
     if update:
         strip_optimizer(weights)  # update model (to fix SourceChangeWarning)
 
-    print(f'Done. ({time.time() - t0:.3f}s)')
-
 
 def parse_opt():
     parser = argparse.ArgumentParser()

val.py

     names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
     class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
     s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
-    p, r, f1, mp, mr, map50, map, t0, t1, t2 = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+    dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
     loss = torch.zeros(3, device=device)
     jdict, stats, ap, ap_class = [], [], [], []
     for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
-        t_ = time_sync()
+        t1 = time_sync()
         img = img.to(device, non_blocking=True)
         img = img.half() if half else img.float()  # uint8 to fp16/32
         img /= 255.0  # 0 - 255 to 0.0 - 1.0
         targets = targets.to(device)
         nb, _, height, width = img.shape  # batch size, channels, height, width
-        t = time_sync()
-        t0 += t - t_
+        t2 = time_sync()
+        dt[0] += t2 - t1
 
         # Run model
         out, train_out = model(img, augment=augment)  # inference and training outputs
-        t1 += time_sync() - t
+        dt[1] += time_sync() - t2
 
         # Compute loss
         if compute_loss:
         # Run NMS
         targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
         lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
-        t = time_sync()
+        t3 = time_sync()
         out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
-        t2 += time_sync() - t
+        dt[2] += time_sync() - t3
 
         # Statistics per image
         for si, pred in enumerate(out):
             print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
 
     # Print speeds
-    t = tuple(x / seen * 1E3 for x in (t0, t1, t2))  # speeds per image
+    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
     if not training:
         shape = (batch_size, 3, imgsz, imgsz)
         print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)