def export(est, export_dir, input_image_size=None):
"""Export graph to SavedModel and TensorFlow Lite.
Args:
est: estimator instance.
export_dir: string, exporting directory.
input_image_size: int, input image size.
Raises:
ValueError: the export directory path is not specified.
"""
if not export_dir:
raise ValueError('The export directory path is not specified.')
if not input_image_size:
input_image_size = FLAGS.input_image_size
is_cond_conv = FLAGS.model_name.startswith('efficientnet-condconv')
batch_size = 1 if is_cond_conv else None # Use fixed batch size for condconv.
logging.info('Starting to export model.')
if (FLAGS.model_name.startswith('efficientnet-lite')
or FLAGS.model_name.startswith('efficientnet-edgetpu')):
# lite or edgetpu use binlinear for easier post-quantization.
resize_method = tf.image.ResizeMethod.BILINEAR
else:
resize_method = None
image_serving_input_fn = imagenet_input.build_image_serving_input_fn(
input_image_size, batch_size=batch_size, resize_method=resize_method)
est.export_saved_model(export_dir_base=export_dir,
serving_input_receiver_fn=image_serving_input_fn)
def export(est, export_dir, post_quantize=True):
"""Export graph to SavedModel and TensorFlow Lite.
Args:
est: estimator instance.
export_dir: string, exporting directory.
post_quantize: boolean, whether to quantize model checkpoint after training.
Raises:
ValueError: the export directory path is not specified.
"""
if not export_dir:
raise ValueError('The export directory path is not specified.')
# The guide to serve a exported TensorFlow model is at:
# https://www.tensorflow.org/serving/serving_basic
image_serving_input_fn = imagenet_input.build_image_serving_input_fn(
FLAGS.input_image_size)
tf.logging.info('Starting to export model.')
subfolder = est.export_saved_model(
export_dir_base=export_dir,
serving_input_receiver_fn=image_serving_input_fn)
tf.logging.info('Starting to export TFLite.')
converter = tf.lite.TFLiteConverter.from_saved_model(
# os.path.join(export_dir, str(subfolder)),
subfolder.decode(),
input_arrays=['truediv'],
output_arrays=['logits'])
tflite_model = converter.convert()
tflite_file = os.path.join(export_dir, FLAGS.model_name + '.tflite')
tf.gfile.GFile(tflite_file, 'wb').write(tflite_model)
if post_quantize:
tf.logging.info('Starting to export quantized TFLite.')
converter = tf.lite.TFLiteConverter.from_saved_model(
# os.path.join(export_dir, subfolder),
subfolder.decode(),
input_arrays=['truediv'],
output_arrays=['logits'])
converter.post_training_quantize = True
quant_tflite_model = converter.convert()
quant_tflite_file = os.path.join(
export_dir, FLAGS.model_name + '_postquant.tflite')
tf.gfile.GFile(quant_tflite_file, 'wb').write(quant_tflite_model)
def export(est, export_dir, params, post_quantize=True):
"""Export graph to SavedModel and TensorFlow Lite.
Args:
est: estimator instance.
export_dir: string, exporting directory.
params: `ParamsDict` passed to the model from the TPUEstimator.
post_quantize: boolean, whether to quantize model checkpoint after training.
Raises:
ValueError: the export directory path is not specified.
"""
if not export_dir:
raise ValueError('The export directory path is not specified.')
# The guide to serve a exported TensorFlow model is at:
# https://www.tensorflow.org/serving/serving_basic
image_serving_input_fn = imagenet_input.build_image_serving_input_fn(
params.input_image_size)
tf.logging.info('Starting to export model.')
subfolder = est.export_saved_model(
export_dir_base=export_dir,
serving_input_receiver_fn=image_serving_input_fn)
tf.logging.info('Starting to export TFLite.')
converter = tf.lite.TFLiteConverter.from_saved_model(
subfolder, input_arrays=['truediv'], output_arrays=['logits'])
if params.quantized_training:
# Export quantized tflite if it is trained with quantized ops.
converter.inference_type = tf.uint8
converter.quantized_input_stats = {'truediv': (0., 2.)}
tflite_model = converter.convert()
tflite_file = os.path.join(export_dir, params.model_name + '.tflite')
tf.gfile.GFile(tflite_file, 'wb').write(tflite_model)
if post_quantize:
tf.logging.info('Starting to export quantized TFLite.')
converter = tf.lite.TFLiteConverter.from_saved_model(
subfolder, input_arrays=['truediv'], output_arrays=['logits'])
converter.post_training_quantize = True
quant_tflite_model = converter.convert()
quant_tflite_file = os.path.join(
export_dir, params.model_name + '_postquant.tflite')
tf.gfile.GFile(quant_tflite_file, 'wb').write(quant_tflite_model)
def export(est, export_dir, input_image_size=None):
"""Export graph to SavedModel and TensorFlow Lite.
Args:
est: estimator instance.
export_dir: string, exporting directory.
input_image_size: int, input image size.
Raises:
ValueError: the export directory path is not specified.
"""
if not export_dir:
raise ValueError('The export directory path is not specified.')
if not input_image_size:
input_image_size = FLAGS.input_image_size
tf.logging.info('Starting to export model.')
image_serving_input_fn = imagenet_input.build_image_serving_input_fn(
input_image_size)
est.export_saved_model(export_dir_base=export_dir,
serving_input_receiver_fn=image_serving_input_fn)
def export(est, export_dir, input_image_size=None):
"""Export graph to SavedModel and TensorFlow Lite.
Args:
est: estimator instance.
export_dir: string, exporting directory.
input_image_size: int, input image size.
Raises:
ValueError: the export directory path is not specified.
"""
if not export_dir:
raise ValueError('The export directory path is not specified.')
if not input_image_size:
input_image_size = FLAGS.input_image_size
is_cond_conv = FLAGS.model_name.startswith('efficientnet-condconv')
batch_size = 1 if is_cond_conv else None # Use fixed batch size for condconv.
logging.info('Starting to export model.')
image_serving_input_fn = imagenet_input.build_image_serving_input_fn(
input_image_size, batch_size=batch_size)
est.export_saved_model(export_dir_base=export_dir,
serving_input_receiver_fn=image_serving_input_fn)