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器件型号：PROCESSOR-SDK-J722S 主题中讨论的其他器件：AM67A
工具与软件：
嗨、团队：
但我收到以下错误：
*************** EP Error *************** EP Error Unknown Provider Type: TIDLCompilationProvider when using ['TIDLCompilationProvider', 'CPUExecutionProvider'] Falling back to ['CPUExecutionProvider'] and retrying. ****************************************
检查是否设置了 TIDL_TOOLS_PATH、并确保路径具有"tidl_model_import_onnx.SO"
请帮助我修复此症状
感谢您的帮助。
[备份]
下面的代码：
import os
import tqdm
import cv2
import numpy as np
import onnxruntime as rt
import shutil
from scripts.utils import imagenet_class_to_name, download_model
import matplotlib.pyplot as plt
from pathlib import Path
from IPython.display import Markdown as md
from scripts.utils import loggerWritter
from scripts.utils import get_svg_path
import onnx
def preprocess(image_path):
# read the image using openCV
img = cv2.imread(image_path)
# convert to RGB
img = img[:,:,::-1]
# Most of the onnx models are trained using
# 224x224 images. The general rule of thumb
# is to scale the input image while preserving
# the original aspect ratio so that the
# short edge is 256 pixels, and then
# center-crop the scaled image to 224x224
orig_height, orig_width, _ = img.shape
short_edge = min(img.shape[:2])
new_height = (orig_height * 256) // short_edge
new_width = (orig_width * 256) // short_edge
img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
startx = new_width//2 - (224//2)
starty = new_height//2 - (224//2)
img = img[starty:starty+224,startx:startx+224]
# apply scaling and mean subtraction.
# if your model is built with an input
# normalization layer, then you might
# need to skip this
img = img.astype('float32')
for mean, scale, ch in zip([128, 128, 128], [0.0078125, 0.0078125, 0.0078125], range(img.shape[2])):
img[:,:,ch] = ((img.astype('float32')[:,:,ch] - mean) * scale)
img = np.expand_dims(img,axis=0)
img = np.transpose(img, (0, 3, 1, 2))
return img
calib_images = [
'sample-images/elephant.bmp',
'sample-images/bus.bmp',
'sample-images/bicycle.bmp',
'sample-images/zebra.bmp',
]
output_dir = 'custom-artifacts/onnx/resnet18_opset9.onnx'
onnx_model_path = 'models/public/onnx/resnet18_opset9.onnx'
download_model(onnx_model_path)
onnx.shape_inference.infer_shapes_path(onnx_model_path, onnx_model_path)
#compilation options - knobs to tweak
num_bits =8
accuracy =1
log_dir = Path("logs").mkdir(parents=True, exist_ok=True)
# stdout and stderr saved to a *.log file.
with loggerWritter("logs/custon-model-onnx"):
# model compilation options
compile_options = {
'tidl_tools_path' : os.environ['TIDL_TOOLS_PATH'],
'artifacts_folder' : output_dir,
'tensor_bits' : num_bits,
'accuracy_level' : accuracy,
'advanced_options:calibration_frames' : len(calib_images),
'advanced_options:calibration_iterations' : 3, # used if accuracy_level = 1
'advanced_options:add_data_convert_ops' : 1,
'debug_level' : 1,
#'deny_list' : "MaxPool" #Comma separated string of operator types as defined by ONNX runtime, ex "MaxPool, Concat"
}
# create the output dir if not present
# clear the directory
os.makedirs(output_dir, exist_ok=True)
for root, dirs, files in os.walk(output_dir, topdown=False):
[os.remove(os.path.join(root, f)) for f in files]
[os.rmdir(os.path.join(root, d)) for d in dirs]
so = rt.SessionOptions()
EP_list = ['TIDLCompilationProvider','CPUExecutionProvider']
sess = rt.InferenceSession(onnx_model_path ,providers=EP_list, provider_options=[compile_options, {}], sess_options=so)
input_details = sess.get_inputs()
for num in tqdm.trange(len(calib_images)):
output = list(sess.run(None, {input_details[0].name : preprocess(calib_images[num])}))[0]
##optional
#subgraph_link =get_svg_path(output_dir)
#for sg in subgraph_link:
# hl_text = os.path.join(*Path(sg).parts[4:])
# sg_rel = os.path.join('../', sg)
# display(md("[{}]({})".format(hl_text,sg_rel)))
#
#EP_list = ['TIDLExecutionProvider','CPUExecutionProvider']
#print("[ken debug] 5 ")
#sess = rt.InferenceSession(onnx_model_path ,providers=EP_list, provider_options=[compile_options, {}], sess_options=so)
##Running inference several times to get an stable performance output
#for i in range(5):
# output = list(sess.run(None, {input_details[0].name : preprocess('sample-images/elephant.bmp')}))
#
#for idx, cls in enumerate(output[0].squeeze().argsort()[-5:][::-1]):
# print('[%d] %s' % (idx, '/'.join(imagenet_class_to_name(cls))))
#
#from scripts.utils import plot_TI_performance_data, plot_TI_DDRBW_data, get_benchmark_output
#stats = sess.get_TI_benchmark_data()
#fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,5))
#plot_TI_performance_data(stats, axis=ax)
#plt.show()
#print("[ken debug] 6 ")
#tt, st, rb, wb = get_benchmark_output(stats)
#print(f'Statistics : \n Inferences Per Second : {1000.0/tt :7.2f} fps')
#print(f' Inference Time Per Image : {tt :7.2f} ms \n DDR BW Per Image : {rb+ wb : 7.2f} MB')
