Fix 6 Flake8 issues (#6541)

* F541

* F821

* F841

* E741

* E302

* E722

* Apply suggestions from code review

* Update general.py

* Update datasets.py

* Update export.py

* Update plots.py

* Update plots.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

export.py

@@ -244,7 +244,7 @@ def export_saved_model(model, im, file, dynamic,
 
         tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
         im = tf.zeros((batch_size, *imgsz, 3))  # BHWC order for TensorFlow
-        y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
+        _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
         inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
         outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
         keras_model = keras.Model(inputs=inputs, outputs=outputs)
@@ -407,16 +407,17 @@ def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
     tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs'))  # TensorFlow exports
     file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
 
-    # Checks
-    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
-    opset = 12 if ('openvino' in include) else opset  # OpenVINO requires opset <= 12
-
     # Load PyTorch model
     device = select_device(device)
     assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
     model = attempt_load(weights, map_location=device, inplace=True, fuse=True)  # load FP32 model
     nc, names = model.nc, model.names  # number of classes, class names
 
+    # Checks
+    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
+    opset = 12 if ('openvino' in include) else opset  # OpenVINO requires opset <= 12
+    assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}'
+
     # Input
     gs = int(max(model.stride))  # grid size (max stride)
     imgsz = [check_img_size(x, gs) for x in imgsz]  # verify img_size are gs-multiples
@@ -438,7 +439,8 @@ def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
 
     for _ in range(2):
         y = model(im)  # dry runs
-    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
+    shape = tuple(y[0].shape)  # model output shape
+    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
 
     # Exports
     f = [''] * 10  # exported filenames