為了能夠轉(zhuǎn)換包含 TensorFlow 運算符的 TensorFlow Lite 模型，可使用位于?TensorFlow Lite 轉(zhuǎn)換器?中的?target_spec.supported_ops?參數(shù)。target_spec.supported_ops?的可選值如下：
TFLITE_BUILTINS?- 使用 TensorFlow Lite 內(nèi)置運算符轉(zhuǎn)換模型。
注意：target_spec.supported_ops?是之前 Python API 中的?target_ops。
我們優(yōu)先推薦使用?TFLITE_BUILTINS?轉(zhuǎn)換模型，然后是同時使用?TFLITE_BUILTINS,SELECT_TF_OPS?，最后是只使用?SELECT_TF_OPS。同時使用兩個選項（也就是?TFLITE_BUILTINS,SELECT_TF_OPS）會用 TensorFlow Lite 內(nèi)置的運算符去轉(zhuǎn)換支持的運算符。有些 TensorFlow 運算符 TensorFlow Lite 只支持部分用法，這時可以使用?SELECT_TF_OPS?選項來避免這種局限性。
# -*- coding: utf-8 -*-
"""
# --------------------------------------------------------
# @Author : panjq
# @E-mail : pan_jinquan@163.com
# @Date : 2020-02-05 11:01:49
# --------------------------------------------------------
"""
import os
import numpy as np
import glob
import cv2
import argparse
import tensorflow as tfprint("TF version:{}".format(tf.__version__))
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
tf.config.experimental.set_memory_growth(physical_devices[0], True)def converer_keras_to_tflite_v1(keras_path, outputs_layer=None, out_tflite=None):""":param keras_path: keras *.h5 files:param outputs_layer:param out_tflite: output *.tflite file:return:"""model_dir = os.path.dirname(keras_path)model_name = os.path.basename(keras_path)[:-len(".h5")]# 加載keras模型, 結(jié)構(gòu)打印model_keras = tf.keras.models.load_model(keras_path)print(model_keras.summary())# 從keras模型中提取fc1層, 需先保存成新keras模型, 再轉(zhuǎn)換成tflitemodel_embedding = tf.keras.models.Model(inputs=model_keras.input,outputs=model_keras.get_layer(outputs_layer).output)print(model_embedding.summary())keras_file = os.path.join(model_dir, "{}_{}.h5".format(model_name, outputs_layer))tf.keras.models.Model.save(model_embedding, keras_file)# converter = tf.lite.TocoConverter.from_keras_model_file(keras_file)converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file) # tf1.3# converter = tf.lite.TFLiteConverter.from_keras_model(model_keras) # tf2.0tflite_model = converter.convert()if not out_tflite:out_tflite = os.path.join(model_dir, "{}_{}.tflite".format(model_name, outputs_layer))open(out_tflite, "wb").write(tflite_model)print("successfully convert to tflite done")print("save model at: {}".format(out_tflite))def converer_keras_to_tflite_v2(keras_path, outputs_layer=None, out_tflite=None, optimize=False, quantization=False):""":param keras_path: keras *.h5 files:param outputs_layer: default last layer:param out_tflite: output *.tflite file:param optimize:return:"""if not os.path.exists(keras_path):raise Exception("Error:{}".format(keras_path))model_dir = os.path.dirname(keras_path)model_name = os.path.basename(keras_path)[:-len(".h5")]# 加載keras模型, 結(jié)構(gòu)打印# model = tf.keras.models.load_model(keras_path)model = tf.keras.models.load_model(model_path, custom_objects={'tf': tf}, compile=False)print(model.summary())if outputs_layer:# 從keras模型中提取層,轉(zhuǎn)換成tflitemodel = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer(outputs_layer).output)# outputs = [model.output["bbox"],model.output["scores"]]# model = tf.keras.models.Model(inputs=model.input, outputs=outputs)print(model.summary())# converter = tf.lite.TocoConverter.from_keras_model_file(keras_file)# converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file) # tf1.3converter = tf.lite.TFLiteConverter.from_keras_model(model) # tf2.0prefix = [model_name, outputs_layer]# converter.allow_custom_ops = False# converter.experimental_new_converter = True""""https://tensorflow.google.cn/lite/guide/ops_select我們優(yōu)先推薦使用 TFLITE_BUILTINS 轉(zhuǎn)換模型，然后是同時使用 TFLITE_BUILTINS,SELECT_TF_OPS ，最后是只使用 SELECT_TF_OPS。同時使用兩個選項（也就是 TFLITE_BUILTINS,SELECT_TF_OPS）會用 TensorFlow Lite 內(nèi)置的運算符去轉(zhuǎn)換支持的運算符。有些 TensorFlow 運算符 TensorFlow Lite 只支持部分用法，這時可以使用 SELECT_TF_OPS 選項來避免這種局限性。"""# converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,# tf.lite.OpsSet.SELECT_TF_OPS]# converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]# converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]if optimize:print("weight quantization")# Enforce full integer quantization for all ops and use int input/outputconverter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]prefix += ["optimize"]else:converter.optimizations = [tf.lite.Optimize.DEFAULT]if quantization == "int8":converter.representative_dataset = representative_dataset_gen# converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]# converter.inference_input_type = tf.int8 # or tf.uint8# converter.inference_output_type = tf.int8 # or tf.uint8converter.target_spec.supported_types = [tf.int8]elif quantization == "float16":converter.target_spec.supported_types = [tf.float16]prefix += [quantization]if not out_tflite:prefix = [str(n) for n in prefix if n]prefix = "_".join(prefix)out_tflite = os.path.join(model_dir, "{}.tflite".format(prefix))tflite_model = converter.convert()open(out_tflite, "wb").write(tflite_model)print("successfully convert to tflite done")print("save model at: {}".format(out_tflite))def representative_dataset_gen():"""# 生成代表性數(shù)據(jù)集:return:"""image_dir = '/home/dm/panjinquan3/FaceDetector/tf-yolov3-detection/data/finger_images/'input_size = [320, 320]imgSet = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f))]for img_path in imgSet:orig_image = cv2.imread(img_path)rgb_image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)image_tensor = cv2.resize(rgb_image, dsize=tuple(input_size))image_tensor = np.asarray(image_tensor / 255.0, dtype=np.float32)image_tensor = image_tensor[np.newaxis, :]yield [image_tensor]def parse_args():# weights_path = "../yolov3-micro.h5"weights_path = "../yolov3-micro_freeze_head.h5"parser = argparse.ArgumentParser()parser.add_argument("-c", "--model_path", help="model_path", default=weights_path, type=str)parser.add_argument("--outputs_layer", help="outputs_layer", default=None, type=str)parser.add_argument("-o", "--out_tflite", help="out tflite model path", default=None, type=str)parser.add_argument("-opt", "--optimize", help="optimize model", default=True, type=bool)parser.add_argument("-q", "--quantization", help="quantization model", default="float16", type=str)return parser.parse_args()def get_model(model_path):if not model_path:model_path = os.path.join(os.getcwd(), "*.h5")model_list = glob.glob(model_path)else:model_list = [model_path]return model_listdef unsupport_tflite_op():"""tf.shape,tf.Sizeerror: 'tf.Size' op is neither a custom op nor a flex operror: 'tf.Softmax' op is neither a custom op nor a flex op===========================================================tf.Softmax-->tf.nn.softmax"""passif __name__ == '__main__':args = parse_args()# outputs_layer = "fc1"outputs_layer = args.outputs_layermodel_list = get_model(args.model_path)out_tflite = args.out_tfliteoptimize = args.optimizequantization = args.quantizationfor model_path in model_list:converer_keras_to_tflite_v2(model_path, outputs_layer, out_tflite=out_tflite, optimize=optimize,quantization=quantization)# converer_keras_to_tflite_v1(keras_model, outputs_layer, out_tflite=None)
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3. 常見問題解決方法
'tf.ResizeNearestNeighbor' op is neither a custom op nor a flex op <unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag): ResizeNearestNeighbor.
