Enable TensorFlow ops for `--nms` and `--agnostic-nms` (#7281)

* enable TensorFlow ops if flag --nms or --agnostic-nms is used

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update export.py

* Update export.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <[email protected]>

export.py

@@ -327,7 +327,7 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
         LOGGER.info(f'\n{prefix} export failure: {e}')
 
 
-def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
+def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
     # YOLOv5 TensorFlow Lite export
     try:
         import tensorflow as tf
@@ -343,13 +343,15 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
         if int8:
             from models.tf import representative_dataset_gen
             dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False)  # representative data
-            converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
+            converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
             converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
             converter.target_spec.supported_types = []
             converter.inference_input_type = tf.uint8  # or tf.int8
             converter.inference_output_type = tf.uint8  # or tf.int8
             converter.experimental_new_quantizer = True
             f = str(file).replace('.pt', '-int8.tflite')
+        if nms or agnostic_nms:
+            converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
 
         tflite_model = converter.convert()
         open(f, "wb").write(tflite_model)
@@ -524,7 +526,7 @@ def run(
         if pb or tfjs:  # pb prerequisite to tfjs
             f[6] = export_pb(model, im, file)
         if tflite or edgetpu:
-            f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100)
+            f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
         if edgetpu:
             f[8] = export_edgetpu(model, im, file)
         if tfjs: