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# -- coding: utf-8 --
# @Time : 2021/11/29
# @Author : ykk648
# @Project : https://github.com/ykk648/AI_power
"""
todo: io_binding https://onnxruntime.ai/docs/api/python/api_summary.html
"""
import onnxruntime
import numpy as np
from cv2box import MyFpsCounter
def get_output_info(onnx_session):
output_name = []
output_shape = []
for node in onnx_session.get_outputs():
output_name.append(node.name)
output_shape.append(node.shape)
return output_name, output_shape
def get_input_info(onnx_session):
input_name = []
input_shape = []
for node in onnx_session.get_inputs():
input_name.append(node.name)
input_shape.append(node.shape)
return input_name, input_shape
def get_input_feed(input_name, image_tensor):
"""
Args:
input_name:
image_tensor: [image tensor, ...]
Returns:
"""
input_feed = {}
for index, name in enumerate(input_name):
input_feed[name] = image_tensor[index]
return input_feed
class ONNXModel:
def __init__(self, onnx_path, provider='gpu', debug=False, input_dynamic_shape=None):
self.provider = provider
if self.provider == 'gpu':
self.providers = (
"CUDAExecutionProvider",
{'device_id': 0, }
)
elif self.provider == 'trt':
self.providers = (
'TensorrtExecutionProvider',
{'trt_engine_cache_enable': True, 'trt_engine_cache_path': './cache/trt', 'trt_fp16_enable': False, }
)
elif self.provider == 'trt16':
self.providers = (
'TensorrtExecutionProvider',
{'trt_engine_cache_enable': True, 'trt_engine_cache_path': './cache/trt', 'trt_fp16_enable': True,
'trt_dla_enable': False, }
)
elif self.provider == 'trt8':
self.providers = (
'TensorrtExecutionProvider',
{'trt_engine_cache_enable': True, 'trt_int8_enable': 1, }
)
else:
self.providers = "CPUExecutionProvider"
# onnxruntime.set_default_logger_severity(3)
session_options = onnxruntime.SessionOptions()
session_options.log_severity_level = 3
self.onnx_session = onnxruntime.InferenceSession(onnx_path, session_options, providers=[self.providers])
# sessionOptions.intra_op_num_threads = 3
self.input_name, self.input_shape = get_input_info(self.onnx_session)
self.output_name, self.output_shape = get_output_info(self.onnx_session)
self.input_dynamic_shape = input_dynamic_shape
if self.input_dynamic_shape is not None:
self.input_dynamic_shape = self.input_dynamic_shape if isinstance(self.input_dynamic_shape, list) else [
self.input_dynamic_shape]
if debug:
print('onnx version: {}'.format(onnxruntime.__version__))
print("input_name:{}, shape:{}".format(self.input_name, self.input_shape))
print("output_name:{}, shape:{}".format(self.output_name, self.output_shape))
self.warm_up()
def warm_up(self):
if not self.input_dynamic_shape:
try:
self.forward([np.random.rand(*self.input_shape[i]).astype(np.float32)
for i in range(len(self.input_shape))])
except TypeError:
print('Model may be dynamic, plz name the \'input_dynamic_shape\' !')
else:
self.forward([np.random.rand(*self.input_dynamic_shape[i]).astype(np.float32)
for i in range(len(self.input_shape))])
print('Model warm up done !')
def speed_test(self):
if not self.input_dynamic_shape:
input_tensor = [np.random.rand(*self.input_shape[i]).astype(np.float32)
for i in range(len(self.input_shape))]
else:
input_tensor = [np.random.rand(*self.input_dynamic_shape[i]).astype(np.float32)
for i in range(len(self.input_shape))]
with MyFpsCounter('[{}] onnx 10 times'.format(self.provider)) as mfc:
for i in range(10):
_ = self.forward(input_tensor)
def forward(self, image_tensor_in, trans=False):
"""
Args:
image_tensor_in: image_tensor [image_tensor] [image_tensor_1, image_tensor_2]
trans: apply trans for image_tensor or first image_tensor(list)
Returns:
model output
"""
if not isinstance(image_tensor_in, list) or len(image_tensor_in) == 1:
image_tensor_in = image_tensor_in[0] if isinstance(image_tensor_in, list) else image_tensor_in
if trans:
image_tensor_in = image_tensor_in.transpose(2, 0, 1)[np.newaxis, :]
image_tensor_in = [np.ascontiguousarray(image_tensor_in)]
else:
# for multi input, only trans first tensor
if trans:
image_tensor_in[0] = image_tensor_in[0].transpose(2, 0, 1)[np.newaxis, :]
image_tensor_in = [np.ascontiguousarray(image_tensor) for image_tensor in image_tensor_in]
input_feed = get_input_feed(self.input_name, image_tensor_in)
return self.onnx_session.run(self.output_name, input_feed=input_feed)
def batch_forward(self, bach_image_tensor, trans=False):
if trans:
bach_image_tensor = bach_image_tensor.transpose(0, 3, 1, 2)
input_feed = get_input_feed(self.input_name, bach_image_tensor)
return self.onnx_session.run(self.output_name, input_feed=input_feed)
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