leeflix pre-commit-ci[bot] glenn-jocher commited on
Commit
8d0291f
1 Parent(s): 2da6866

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

Browse files

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

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

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* Update export.py

* Update export.py

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

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

Files changed (1) hide show
  1. export.py +5 -3
export.py CHANGED
@@ -327,7 +327,7 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
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  LOGGER.info(f'\n{prefix} export failure: {e}')
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329
 
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- def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
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  # YOLOv5 TensorFlow Lite export
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  try:
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  import tensorflow as tf
@@ -343,13 +343,15 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
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  if int8:
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  from models.tf import representative_dataset_gen
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  dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data
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- converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
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  converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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  converter.target_spec.supported_types = []
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  converter.inference_input_type = tf.uint8 # or tf.int8
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  converter.inference_output_type = tf.uint8 # or tf.int8
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  converter.experimental_new_quantizer = True
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  f = str(file).replace('.pt', '-int8.tflite')
 
 
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  tflite_model = converter.convert()
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  open(f, "wb").write(tflite_model)
@@ -524,7 +526,7 @@ def run(
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  if pb or tfjs: # pb prerequisite to tfjs
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  f[6] = export_pb(model, im, file)
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  if tflite or edgetpu:
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- f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100)
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  if edgetpu:
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  f[8] = export_edgetpu(model, im, file)
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  if tfjs:
 
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  LOGGER.info(f'\n{prefix} export failure: {e}')
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+ def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
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  # YOLOv5 TensorFlow Lite export
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  try:
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  import tensorflow as tf
 
343
  if int8:
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  from models.tf import representative_dataset_gen
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  dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data
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+ converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
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  converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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  converter.target_spec.supported_types = []
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  converter.inference_input_type = tf.uint8 # or tf.int8
350
  converter.inference_output_type = tf.uint8 # or tf.int8
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  converter.experimental_new_quantizer = True
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  f = str(file).replace('.pt', '-int8.tflite')
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+ if nms or agnostic_nms:
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+ converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
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  tflite_model = converter.convert()
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  open(f, "wb").write(tflite_model)
 
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  if pb or tfjs: # pb prerequisite to tfjs
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  f[6] = export_pb(model, im, file)
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  if tflite or edgetpu:
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+ f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
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  if edgetpu:
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  f[8] = export_edgetpu(model, im, file)
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  if tfjs: