ultralytics 8.0.41 TF SavedModel and EdgeTPU export (#1034)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

tests/test_cli.py

@@ -30,7 +30,7 @@ def test_train_seg():
 
 
 def test_train_cls():
-    run(f'yolo train classify model={CFG}-cls.yaml data=mnist160 imgsz=32 epochs=1')
+    run(f'yolo train classify model={CFG}-cls.yaml data=imagenet10 imgsz=32 epochs=1')
 
 
 # Val checks -----------------------------------------------------------------------------------------------------------
@@ -43,7 +43,7 @@ def test_val_segment():
 
 
 def test_val_classify():
-    run(f'yolo val classify model={MODEL}-cls.pt data=mnist160 imgsz=32')
+    run(f'yolo val classify model={MODEL}-cls.pt data=imagenet10 imgsz=32')
 
 
 # Predict checks -------------------------------------------------------------------------------------------------------

tests/test_engine.py

@@ -74,8 +74,14 @@ def test_segment():
 
 
 def test_classify():
-    overrides = {'data': 'mnist160', 'model': 'yolov8n-cls.yaml', 'imgsz': 32, 'epochs': 1, 'batch': 64, 'save': False}
-    CFG.data = 'mnist160'
+    overrides = {
+        'data': 'imagenet10',
+        'model': 'yolov8n-cls.yaml',
+        'imgsz': 32,
+        'epochs': 1,
+        'batch': 64,
+        'save': False}
+    CFG.data = 'imagenet10'
     CFG.imgsz = 32
     CFG.batch = 64
     # YOLO(CFG_SEG).train(**overrides)  # works

tests/test_python.py

@@ -154,7 +154,7 @@ def test_predict_callback_and_setup():
         bs = [predictor.dataset.bs for _ in range(len(path))]
         predictor.results = zip(predictor.results, im0s, bs)
 
-    model = YOLO('yolov8n.pt')
+    model = YOLO(MODEL)
     model.add_callback('on_predict_batch_end', on_predict_batch_end)
 
     dataset = load_inference_source(source=SOURCE, transforms=model.transforms)
@@ -169,9 +169,16 @@ def test_predict_callback_and_setup():
 
 def test_result():
     model = YOLO('yolov8n-seg.pt')
-    img = str(ROOT / 'assets/bus.jpg')
-    res = model([img, img])
+    res = model([SOURCE, SOURCE])
     res[0].numpy()
     res[0].cpu().numpy()
     resimg = res[0].visualize(show_conf=False)
     print(resimg)
+
+    model = YOLO('yolov8n.pt')
+    res = model(SOURCE)
+    res[0].visualize()
+
+    model = YOLO('yolov8n-cls.pt')
+    res = model(SOURCE)
+    res[0].visualize()