rename eval

eval.py

@@ -5,21 +5,21 @@ from models.experimental import *
 from utils.datasets import *
 
 
-def test(data,
-         weights=None,
-         batch_size=16,
-         imgsz=640,
-         conf_thres=0.001,
-         iou_thres=0.6,  # for NMS
-         save_json=False,
-         single_cls=False,
-         augment=False,
-         verbose=False,
-         model=None,
-         dataloader=None,
-         save_dir='',
-         merge=False,
-         save_txt=False):
+def evaluate(data,
+             weights=None,
+             batch_size=16,
+             imgsz=640,
+             conf_thres=0.001,
+             iou_thres=0.6,  # for NMS
+             save_json=False,
+             single_cls=False,
+             augment=False,
+             verbose=False,
+             model=None,
+             dataloader=None,
+             save_dir='',
+             merge=False,
+             save_txt=False):
     # Initialize/load model and set device
     training = model is not None
     if training:  # called by train.py
@@ -254,16 +254,16 @@ if __name__ == '__main__':
     print(opt)
 
     if opt.task in ['val', 'test']:  # run normally
-        test(opt.data,
-             opt.weights,
-             opt.batch_size,
-             opt.img_size,
-             opt.conf_thres,
-             opt.iou_thres,
-             opt.save_json,
-             opt.single_cls,
-             opt.augment,
-             opt.verbose)
+        evaluate(opt.data,
+                 opt.weights,
+                 opt.batch_size,
+                 opt.img_size,
+                 opt.conf_thres,
+                 opt.iou_thres,
+                 opt.save_json,
+                 opt.single_cls,
+                 opt.augment,
+                 opt.verbose)
 
     elif opt.task == 'study':  # run over a range of settings and save/plot
         for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov3-spp.pt']:
@@ -272,7 +272,7 @@ if __name__ == '__main__':
             y = []  # y axis
             for i in x:  # img-size
                 print('\nRunning %s point %s...' % (f, i))
-                r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
+                r, _, t = evaluate(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
                 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

@@ -291,14 +291,14 @@ def train(hyp):
         ema.update_attr(model, include=['md', 'nc', 'hyp', 'gr', 'names', 'stride'])
         final_epoch = epoch + 1 == epochs
         if not opt.notest or final_epoch:  # Calculate mAP
-            results, maps, times = eval.test(opt.data,
-                                             batch_size=batch_size,
-                                             imgsz=imgsz_test,
-                                             save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
-                                             model=ema.ema,
-                                             single_cls=opt.single_cls,
-                                             dataloader=testloader,
-                                             save_dir=log_dir)
+            results, maps, times = eval.evaluate(opt.data,
+                                                 batch_size=batch_size,
+                                                 imgsz=imgsz_test,
+                                                 save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
+                                                 model=ema.ema,
+                                                 single_cls=opt.single_cls,
+                                                 dataloader=testloader,
+                                                 save_dir=log_dir)
 
         # Write
         with open(results_file, 'a') as f: