File size: 6,009 Bytes
55654c5 57699b7 55654c5 4b236af 55654c5 d33294b 55654c5 d30c3db 55654c5 d33294b d30c3db 55654c5 86cd32d 55654c5 e5b568e 55654c5 d30c3db 55654c5 d30c3db 55654c5 4b236af 55654c5 e5b568e 62917b7 e5b568e 55654c5 e5b568e 55654c5 e5b568e 55654c5 e5b568e 55654c5 e5b568e 55654c5 d30c3db e5b568e 55654c5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.
#
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
# Third party copyrights are property of their respective owners.
import os
import sys
import numpy as np
import cv2 as cv
import onnx
from onnx import version_converter
import onnxruntime
from onnxruntime.quantization import quantize_static, CalibrationDataReader, QuantType, QuantFormat
from transform import Compose, Resize, CenterCrop, Normalize, ColorConvert
class DataReader(CalibrationDataReader):
def __init__(self, model_path, image_dir, transforms):
model = onnx.load(model_path)
self.input_name = model.graph.input[0].name
self.transforms = transforms
self.data = self.get_calibration_data(image_dir)
self.enum_data_dicts = iter([{self.input_name: x} for x in self.data])
def get_next(self):
return next(self.enum_data_dicts, None)
def get_calibration_data(self, image_dir):
blobs = []
supported = ["jpg", "png"] # supported file suffix
for image_name in os.listdir(image_dir):
image_name_suffix = image_name.split('.')[-1].lower()
if image_name_suffix not in supported:
continue
img = cv.imread(os.path.join(image_dir, image_name))
img = self.transforms(img)
blob = cv.dnn.blobFromImage(img)
blobs.append(blob)
return blobs
class Quantize:
def __init__(self, model_path, calibration_image_dir, transforms=Compose(), per_channel=False, act_type='int8', wt_type='int8'):
self.type_dict = {"uint8" : QuantType.QUInt8, "int8" : QuantType.QInt8}
self.model_path = model_path
self.calibration_image_dir = calibration_image_dir
self.transforms = transforms
self.per_channel = per_channel
self.act_type = act_type
self.wt_type = wt_type
# data reader
self.dr = DataReader(self.model_path, self.calibration_image_dir, self.transforms)
def check_opset(self, convert=True):
model = onnx.load(self.model_path)
if model.opset_import[0].version != 13:
print('\tmodel opset version: {}. Converting to opset 13'.format(model.opset_import[0].version))
# convert opset version to 13
model_opset13 = version_converter.convert_version(model, 13)
# save converted model
output_name = '{}-opset.onnx'.format(self.model_path[:-5])
onnx.save_model(model_opset13, output_name)
# update model_path for quantization
self.model_path = output_name
def run(self):
print('Quantizing {}: act_type {}, wt_type {}'.format(self.model_path, self.act_type, self.wt_type))
self.check_opset()
output_name = '{}-act_{}-wt_{}-quantized.onnx'.format(self.model_path[:-5], self.act_type, self.wt_type)
quantize_static(self.model_path, output_name, self.dr,
quant_format=QuantFormat.QOperator, # start from onnxruntime==1.11.0, quant_format is set to QuantFormat.QDQ by default, which performs fake quantization
per_channel=self.per_channel,
weight_type=self.type_dict[self.wt_type],
activation_type=self.type_dict[self.act_type])
os.remove('augmented_model.onnx')
os.remove('{}-opt.onnx'.format(self.model_path[:-5]))
print('\tQuantized model saved to {}'.format(output_name))
models=dict(
yunet=Quantize(model_path='../../models/face_detection_yunet/face_detection_yunet_2022mar.onnx',
calibration_image_dir='../../benchmark/data/face_detection',
transforms=Compose([Resize(size=(160, 120))])),
sface=Quantize(model_path='../../models/face_recognition_sface/face_recognition_sface_2021dec.onnx',
calibration_image_dir='../../benchmark/data/face_recognition',
transforms=Compose([Resize(size=(112, 112))])),
pphumenseg=Quantize(model_path='../../models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2021oct.onnx',
calibration_image_dir='../../benchmark/data/human_segmentation',
transforms=Compose([Resize(size=(192, 192))])),
ppresnet50=Quantize(model_path='../../models/image_classification_ppresnet/image_classification_ppresnet50_2022jan.onnx',
calibration_image_dir='../../benchmark/data/image_classification',
transforms=Compose([Resize(size=(224, 224))])),
# TBD: DaSiamRPN
youtureid=Quantize(model_path='../../models/person_reid_youtureid/person_reid_youtu_2021nov.onnx',
calibration_image_dir='../../benchmark/data/person_reid',
transforms=Compose([Resize(size=(128, 256))])),
# TBD: DB-EN & DB-CN
crnn_en=Quantize(model_path='../../models/text_recognition_crnn/text_recognition_CRNN_EN_2021sep.onnx',
calibration_image_dir='../../benchmark/data/text',
transforms=Compose([Resize(size=(100, 32)), Normalize(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5]), ColorConvert(ctype=cv.COLOR_BGR2GRAY)])),
crnn_cn=Quantize(model_path='../../models/text_recognition_crnn/text_recognition_CRNN_CN_2021nov.onnx',
calibration_image_dir='../../benchmark/data/text',
transforms=Compose([Resize(size=(100, 32))]))
)
if __name__ == '__main__':
selected_models = []
for i in range(1, len(sys.argv)):
selected_models.append(sys.argv[i])
if not selected_models:
selected_models = list(models.keys())
print('Models to be quantized: {}'.format(str(selected_models)))
for selected_model_name in selected_models:
q = models[selected_model_name]
q.run()
|