Automatic TFLite uint8 determination (#4515)

* Auto TFLite uint8 detection

This PR automatically determines if TFLite models are uint8 quantized rather than accepting a manual argument.

The quantization determination is based on

@zldrobit
comment https://github.com/ultralytics/yolov5/pull/1127#issuecomment-901713847

* Cleanup

detect.py

@@ -52,7 +52,6 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
         hide_labels=False,  # hide labels
         hide_conf=False,  # hide confidences
         half=False,  # use FP16 half-precision inference
-        tfl_int8=False,  # INT8 quantized TFLite model
         ):
     save_img = not nosave and not source.endswith('.txt')  # save inference images
     webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
@@ -104,6 +103,7 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
             interpreter.allocate_tensors()  # allocate
             input_details = interpreter.get_input_details()  # inputs
             output_details = interpreter.get_output_details()  # outputs
+            int8 = input_details[0]['dtype'] == np.uint8  # is TFLite quantized uint8 model
     imgsz = check_img_size(imgsz, s=stride)  # check image size
 
     # Dataloader
@@ -145,15 +145,15 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
             elif saved_model:
                 pred = model(imn, training=False).numpy()
             elif tflite:
-                if tfl_int8:
+                if int8:
                     scale, zero_point = input_details[0]['quantization']
-                    imn = (imn / scale + zero_point).astype(np.uint8)
+                    imn = (imn / scale + zero_point).astype(np.uint8)  # de-scale
                 interpreter.set_tensor(input_details[0]['index'], imn)
                 interpreter.invoke()
                 pred = interpreter.get_tensor(output_details[0]['index'])
-                if tfl_int8:
+                if int8:
                     scale, zero_point = output_details[0]['quantization']
-                    pred = (pred.astype(np.float32) - zero_point) * scale
+                    pred = (pred.astype(np.float32) - zero_point) * scale  # re-scale
             pred[..., 0] *= imgsz[1]  # x
             pred[..., 1] *= imgsz[0]  # y
             pred[..., 2] *= imgsz[1]  # w
@@ -268,7 +268,6 @@ def parse_opt():
     parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
     parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
     parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    parser.add_argument('--tfl-int8', action='store_true', help='INT8 quantized TFLite model')
     opt = parser.parse_args()
     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
     return opt