Update test.py profiling (#3555)

* Update test.py profiling

* half_precision to half

* inplace

test.py

@@ -38,7 +38,7 @@ def test(data,
          plots=True,
          wandb_logger=None,
          compute_loss=None,
-         half_precision=True,
+         half=True,
          opt=None):
     # Initialize/load model and set device
     training = model is not None
@@ -63,7 +63,7 @@ def test(data,
         #     model = nn.DataParallel(model)
 
     # Half
-    half = device.type != 'cpu' and half_precision  # half precision only supported on CUDA
+    half &= device.type != 'cpu'  # half precision only supported on CUDA
     if half:
         model.half()
 
@@ -95,20 +95,22 @@ def test(data,
     names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
     coco91class = coco80_to_coco91_class()
     s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', '[email protected]', '[email protected]:.95')
-    p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
+    p, r, f1, mp, mr, map50, map, t0, t1, t2 = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
     loss = torch.zeros(3, device=device)
     jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
     for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
+        t_ = time_synchronized()
         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_synchronized()
+        t0 += t - t_
 
         # Run model
-        t = time_synchronized()
         out, train_out = model(img, augment=augment)  # inference and training outputs
-        t0 += time_synchronized() - t
+        t1 += time_synchronized() - t
 
         # Compute loss
         if compute_loss:
@@ -119,7 +121,7 @@ def test(data,
         lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
         t = time_synchronized()
         out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
-        t1 += time_synchronized() - t
+        t2 += time_synchronized() - t
 
         # Statistics per image
         for si, pred in enumerate(out):
@@ -236,9 +238,10 @@ def test(data,
             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, t0 + t1)) + (imgsz, imgsz, batch_size)  # tuple
+    t = tuple(x / seen * 1E3 for x in (t0, t1, t2))  # speeds per image
     if not training:
-        print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
+        shape = (batch_size, 3, imgsz, imgsz)
+        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
 
     # Plots
     if plots:
@@ -327,24 +330,25 @@ if __name__ == '__main__':
              save_txt=opt.save_txt | opt.save_hybrid,
              save_hybrid=opt.save_hybrid,
              save_conf=opt.save_conf,
-             half_precision=opt.half,
+             half=opt.half,
              opt=opt
              )
 
     elif opt.task == 'speed':  # speed benchmarks
-        for w in opt.weights:
-            test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, opt=opt)
+        for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
+            test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, half=True,
+                 opt=opt)
 
     elif opt.task == 'study':  # run over a range of settings and save/plot
         # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
         x = list(range(256, 1536 + 128, 128))  # x axis (image sizes)
-        for w in opt.weights:
+        for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
             f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
             y = []  # y axis
             for i in x:  # img-size
                 print(f'\nRunning {f} point {i}...')
                 r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
-                               plots=False, opt=opt)
+                               plots=False, half=True, opt=opt)
                 y.append(r + t)  # results and times
             np.savetxt(f, y, fmt='%10.4g')  # save
         os.system('zip -r study.zip study_*.txt')

train.py

@@ -74,7 +74,7 @@ def train(hyp, opt, device, tb_writer=None):
         loggers['wandb'] = wandb_logger.wandb
         data_dict = wandb_logger.data_dict
         if wandb_logger.wandb:
-            weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resuming
+            weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # may update weights, epochs if resuming
 
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
@@ -354,18 +354,18 @@ def train(hyp, opt, device, tb_writer=None):
             final_epoch = epoch + 1 == epochs
             if not opt.notest or final_epoch:  # Calculate mAP
                 wandb_logger.current_epoch = epoch + 1
-                results, maps, times = test.test(data_dict,
-                                                 batch_size=batch_size * 2,
-                                                 imgsz=imgsz_test,
-                                                 model=ema.ema,
-                                                 single_cls=single_cls,
-                                                 dataloader=testloader,
-                                                 save_dir=save_dir,
-                                                 save_json=is_coco and final_epoch,
-                                                 verbose=nc < 50 and final_epoch,
-                                                 plots=plots and final_epoch,
-                                                 wandb_logger=wandb_logger,
-                                                 compute_loss=compute_loss)
+                results, maps, _ = test.test(data_dict,
+                                             batch_size=batch_size * 2,
+                                             imgsz=imgsz_test,
+                                             model=ema.ema,
+                                             single_cls=single_cls,
+                                             dataloader=testloader,
+                                             save_dir=save_dir,
+                                             save_json=is_coco and final_epoch,
+                                             verbose=nc < 50 and final_epoch,
+                                             plots=plots and final_epoch,
+                                             wandb_logger=wandb_logger,
+                                             compute_loss=compute_loss)
 
             # Write
             with open(results_file, 'a') as f:

utils/plots.py

@@ -3,7 +3,6 @@
 import glob
 import math
 import os
-import random
 from copy import copy
 from pathlib import Path
 
@@ -252,21 +251,23 @@ def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
 
 def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_txt()
     # Plot study.txt generated by test.py
-    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
-    # ax = ax.ravel()
+    plot2 = False  # plot additional results
+    if plot2:
+        ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
 
     fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
     # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
     for f in sorted(Path(path).glob('study*.txt')):
         y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
         x = np.arange(y.shape[1]) if x is None else np.array(x)
-        s = ['P', 'R', '[email protected]', '[email protected]:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
-        # for i in range(7):
-        #     ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
-        #     ax[i].set_title(s[i])
+        if plot2:
+            s = ['P', 'R', '[email protected]', '[email protected]:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
+            for i in range(7):
+                ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
+                ax[i].set_title(s[i])
 
         j = y[3].argmax() + 1
-        ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
+        ax2.plot(y[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
                  label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
 
     ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],