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TFLite --int8 'flatbuffers==1.12' fix 2 (#6217)

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* TFLite `--int8` 'flatbuffers==1.12' fix 2

Reorganizes #6216 fix to update before `tensorflow` import so no restart required.

* Update export.py
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Glenn Jocher 2022-01-05 13:34:36 -08:00 committed by GitHub
parent 8125ec5d42
commit 00d7b97869
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1 changed files with 2 additions and 1 deletions
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export.py
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@ -287,7 +287,6 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.optimizations = [tf.lite.Optimize.DEFAULT]
if int8: if int8:
from models.tf import representative_dataset_gen from models.tf import representative_dataset_gen
check_requirements(('flatbuffers==1.12',)) # https://github.com/ultralytics/yolov5/issues/5707
dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data 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)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
@ -435,6 +434,8 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
# TensorFlow Exports # TensorFlow Exports
if any(tf_exports): if any(tf_exports):
pb, tflite, edgetpu, tfjs = tf_exports[1:] pb, tflite, edgetpu, tfjs = tf_exports[1:]
if (tflite or edgetpu) and int8: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.' assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
model = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs, model = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class, topk_all=topk_all, agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class, topk_all=topk_all,