mirror of
https://gitee.com/Lyon1998/pikapython.git
synced 2025-01-22 17:12:55 +08:00
110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
# Copyright 2022 Sipeed Technology Co., Ltd. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
|
|
import os,sys
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
import time
|
|
from PIL import Image
|
|
|
|
from os import environ
|
|
# environ['CUDA_VISIBLE_DEVICES'] = '0'
|
|
|
|
# norm_type: "0to1", "n1to1"
|
|
def h5_to_tflite(h5_name, tflite_name, is_quant, quant_dir, norm_type = None, mean = 0.0, std = 0.0):
|
|
def representative_data_gen():
|
|
files = os.listdir(quant_dir)
|
|
valid_files = []
|
|
valid_format = [".jpg", ".jpeg", ".png", ".bmp", ".ppm", ".pgm"]
|
|
for name in files:
|
|
ext = os.path.splitext(name)[1].lower()
|
|
if ext not in valid_format:
|
|
continue
|
|
valid_files.append(os.path.join(quant_dir, name))
|
|
if len(valid_files) == 0:
|
|
raise Exception("No valid files in quant_input dir {}, support format: ".format(quant_dir, valid_format))
|
|
for path in valid_files:
|
|
img = Image.open(path)
|
|
img = np.array(img).astype(np.float32)
|
|
shape = img.shape
|
|
if len(shape) == 2:
|
|
shape = (1, shape[0], shape[1], 1)
|
|
else:
|
|
shape = (1, shape[0], shape[1], shape[2])
|
|
img = img.reshape(shape)
|
|
if norm_type is not None:
|
|
if norm_type == "0to1":
|
|
img = img/255.0
|
|
elif norm_type == "n1to1":
|
|
img = (img-128)/128
|
|
else:
|
|
raise Exception("Unsupported norm_type: {}".format(norm_type))
|
|
else:
|
|
img = (img - mean) / std
|
|
yield [img]
|
|
|
|
if is_quant==0:
|
|
tf.compat.v1.disable_eager_execution()
|
|
converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file(h5_name)
|
|
tflite_model = converter.convert()
|
|
open(tflite_name, "wb").write(tflite_model)
|
|
print("Done")
|
|
else:
|
|
quant_type = tf.int8 #tf2 only support int8 quant
|
|
converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file(h5_name)
|
|
converter._experimental_disable_per_channel = False #True
|
|
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
|
converter.representative_dataset = representative_data_gen
|
|
# Ensure that if any ops can't be quantized, the converter throws an error
|
|
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
|
# Set the input and output tensors to int8 (APIs added in r2.3)
|
|
converter.inference_input_type = quant_type
|
|
converter.inference_output_type = quant_type
|
|
|
|
start_time = time.time()
|
|
tflite_model_quant = converter.convert()
|
|
used_time = time.time() - start_time
|
|
|
|
open(tflite_name, "wb").write(tflite_model_quant)
|
|
print('Done, quant used time:{}'.format(used_time))
|
|
|
|
|
|
def print_usage():
|
|
print("Usage: python3 h5_to_tflite.py h5_name tflite_name is_quant quant_dir norm_type")
|
|
print(" norm_type: 0to1, n1to1")
|
|
|
|
|
|
# python3 h5_to_tflite.py h5/mnist_dw.h5 tflite/mnist_dw_f.tflite 0
|
|
# python3 h5_to_tflite.py h5/mnist_dw.h5 tflite/mnist_dw_q.tflite 1 quant_img_mnist/ 0to1
|
|
# python3 h5_to_tflite.py h5/mbnet96_0.125.h5 tflite/mbnet96_0.125_q.tflite 1 quant_img96/ 0to1
|
|
if __name__ == '__main__':
|
|
if len(sys.argv) != 4 and len(sys.argv) != 6:
|
|
print_usage()
|
|
exit()
|
|
|
|
h5_name = sys.argv[1]
|
|
tflite_name = sys.argv[2]
|
|
is_quant = int(sys.argv[3])
|
|
quant_dir = None
|
|
norm_type = None
|
|
if is_quant == 1:
|
|
if len(sys.argv) != 6:
|
|
print_usage()
|
|
exit()
|
|
quant_dir = sys.argv[4]
|
|
norm_type = sys.argv[5]
|
|
h5_to_tflite(h5_name, tflite_name, is_quant, quant_dir, norm_type = norm_type)
|
|
|