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前面系列博客中有用tensorRT、OpenVINO加速模型推理
TensorRT加速方法介绍(python pytorch模型)_竹叶青lvye的博客-CSDN博客_tensorrt加速
OpenVINO使用介绍_竹叶青lvye的博客-CSDN博客_openvino resnet在
这边再简单提下ONNX Runtime的使用(微软推出),上面博客中只是将ONNX模型作为一个中间转换模型用,可能不怎么去接触更为原生态的推理框架ONNX Runtime
ONNX Runtime | Homehttps://onnxruntime.ai/从官网介绍看,其也是提供训练用API调用的,可以看到微软也是有一些想和tensorflow,pytorch在深度学习领域分一杯羹的想法的,大公司毕竟是有强有力的资本的,何愁招兵买马呢。
此时cuda、cuddn、python版本同前面博客时的配置
这边还是按照官方配置,去推断一张图片,pip安装下如下库
pip install onnxruntime-gpu
博主这边还是拿tensorflow来示例
pip install tf2onnx
代码如下:
- import tensorflow as tf
- import numpy as np
- from tensorflow.keras.preprocessing import image
- from tensorflow.keras.applications import resnet50
-
- from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
- from PIL import Image
- import time
- import tf2onnx
- import onnxruntime as rt
- import cv2
- import time
-
- physical_devices = tf.config.list_physical_devices('GPU')
- tf.config.experimental.set_memory_growth(physical_devices[0], True)
-
- #加载预训练模型
- model = resnet50.ResNet50(weights='imagenet')
-
- spec = (tf.TensorSpec((None, 224, 224, 3), tf.float32, name="input"),)
- output_path = "test.onnx"
-
- model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13, output_path=output_path)
- output_names = [n.name for n in model_proto.graph.output]
-
- providers = ['CPUExecutionProvider']
- m = rt.InferenceSession(output_path, providers=providers)
-
- img = cv2.imread('2008_002682.jpg')
- img = cv2.resize(img,(224,224))
- img_np = np.array(img, dtype=np.float32) / 255.
-
- img_np = np.expand_dims(img_np, axis=0)
- print(img_np.shape)
-
- t_model = time.perf_counter()
- onnx_pred = m.run(output_names, {"input": img_np})
- print(f'do inference cost:{time.perf_counter() - t_model:.8f}s')
- print('ONNX Predicted:', decode_predictions(onnx_pred[0], top=3)[0])
-
-
测试图片,还是前面常用的小猫图片
部分执行结果如下:
- 2022-05-01 22:41:51.165567: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1144] Optimization results for grappler item: graph_to_optimize
- constant_folding: Graph size after: 556 nodes (-320), 891 edges (-320), time = 253.008ms.
- function_optimizer: function_optimizer did nothing. time = 0.565ms.
- constant_folding: Graph size after: 556 nodes (0), 891 edges (0), time = 111.51ms.
- function_optimizer: function_optimizer did nothing. time = 0.847ms.
-
- (1, 224, 224, 3)
- do inference cost:0.01735498s
- ONNX Predicted: [('n01930112', 'nematode', 0.13559905), ('n03041632', 'cleaver', 0.04139605), ('n03838899', 'oboe', 0.03445778)]
-
- Process finished with exit code 0
预测结果同OpenVINO下的结果,耗时更少。暂时不再深入了解了,官网上也提供了一些方面资料,后面需要时再用吧。
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