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Add PaddleClas infer.py (#107)
* Update README.md * Update README.md * Update README.md * Create README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add evaluation calculate time and fix some bugs * Update classification __init__ * Move to ppseg * Add segmentation doc * Add PaddleClas infer.py * Update PaddleClas infer.py * Delete .infer.py.swp Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
@@ -1,5 +1,6 @@
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import fastdeploy as fd
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import fastdeploy as fd
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import cv2
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import cv2
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import os
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def parse_arguments():
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def parse_arguments():
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@@ -9,7 +10,9 @@ def parse_arguments():
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parser.add_argument(
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parser.add_argument(
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"--model", required=True, help="Path of PaddleClas model.")
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"--model", required=True, help="Path of PaddleClas model.")
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parser.add_argument(
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parser.add_argument(
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"--image", required=True, help="Path of test image file.")
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--topk", type=int, default=1, help="Return topk results.")
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parser.add_argument(
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parser.add_argument(
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"--device",
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"--device",
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type=str,
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type=str,
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@@ -31,7 +34,8 @@ def build_option(args):
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if args.use_trt:
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if args.use_trt:
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option.use_trt_backend()
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option.use_trt_backend()
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option.set_trt_input_shape("images", [1, 3, 640, 640])
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option.set_trt_input_shape("inputs", [1, 3, 224, 224],
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[1, 3, 224, 224], [1, 3, 224, 224])
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return option
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return option
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@@ -39,9 +43,13 @@ args = parse_arguments()
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# 配置runtime，加载模型
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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runtime_option = build_option(args)
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model = fd.vision.classification.PaddleClasModel(args.model, runtime_option=runtime_option)
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model_file = os.path.join(args.model, "inference.pdmodel")
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params_file = os.path.join(args.model, "inference.pdiparams")
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config_file = os.path.join(args.model, "inference_cls.yaml")
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model = fd.vision.classification.PaddleClasModel(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片分类结果
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# 预测图片分类结果
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im = cv2.imread(args.image)
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im = cv2.imread(args.image)
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result = model.predict(im)
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result = model.predict(im, args.topk)
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print(result)
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print(result)
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56
examples/vision/segmentation/paddleseg/README.md
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56
examples/vision/segmentation/paddleseg/README.md
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@@ -0,0 +1,56 @@
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# PaddleClas 模型部署
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## 模型版本说明
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- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
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目前FastDeploy支持如下模型的部署
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- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
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- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
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- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
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- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
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- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
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- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
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- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
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- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
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- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
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- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/ResNet_and_vd.md)
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## 准备PaddleClas部署模型
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PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
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注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文档，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
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## 下载预训练模型
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为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
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| 模型 | 参数文件大小 |输入Shape | Top1 | Top5 |
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|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
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| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
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| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz) | 26MB | 224x224 |77.04% | 93.27% |
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| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) | 255MB | 600x600 | 84.3% | 96.9% |
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| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)| 18MB | 224x224 | 75.8% | 75.8% |
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| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) | 29MB | 224x224 | 75.7% | 92.5% |
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| [GhostNet_x0_5_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) | 10MB | 224x224 | 66.8% | 86.9% |
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| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) | 1.9MB | 224x224 | 51.4% | 75.5% |
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| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) | 17MB | 224x224 | 77.9% | 93.9% |
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| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) | 5.9MB | 224x224 | 53.2% | 76.5% |
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| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) | 14MB | 224x224 | 76.74% | 93.39% |
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| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) | 6.4MB | 224x224 | 55.55% | 77.71% |
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| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) | 22MB | 224x224 | 78.96% | 94.48% |
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| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) | 2.4MB | 224x224 | 49.9% | 73.79% |
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| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) | 29MB | 224x224 | 73.15% | 91.2% |
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| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) | 4.8MB | 224x224 | 60.1% | 81.9% |
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| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) | 92MB | 299x299 | 79.14% | 94.59% |
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| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) | 57MB | 224x224 | 81.95% | 96.12% |
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| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) | 274MB | 224x224 | 85.0% | 97.35% |
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| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) | 98MB | 224x224 | 79.12% | 94.44% |
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## 详细部署文档
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- [Python部署](python)
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- [C++部署](cpp)
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14
examples/vision/segmentation/paddleseg/cpp/CMakeLists.txt
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14
examples/vision/segmentation/paddleseg/cpp/CMakeLists.txt
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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77
examples/vision/segmentation/paddleseg/cpp/README.md
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77
examples/vision/segmentation/paddleseg/cpp/README.md
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# YOLOv7 C++部署示例
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本目录下提供`infer.cc`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/compile/prebuilt_libraries.md)
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以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试
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```
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mkdir build
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cd build
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wget https://xxx.tgz
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tar xvf fastdeploy-linux-x64-0.2.0.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.2.0
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make -j
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#下载官方转换好的yolov7模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000087038.jpg
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# CPU推理
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./infer_demo yolov7.onnx 000000087038.jpg 0
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# GPU推理
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./infer_demo yolov7.onnx 000000087038.jpg 1
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# GPU上TensorRT推理
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./infer_demo yolov7.onnx 000000087038.jpg 2
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```
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## YOLOv7 C++接口
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### YOLOv7类
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```
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fastdeploy::vision::detection::YOLOv7(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const Frontend& model_format = Frontend::ONNX)
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```
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YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式。
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(Frontend): 模型格式，默认为ONNX格式
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#### Predict函数
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> ```
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> YOLOv7::Predict(cv::Mat* im, DetectionResult* result,
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> float conf_threshold = 0.25,
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> float nms_iou_threshold = 0.5)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > * **conf_threshold**: 检测框置信度过滤阈值
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> > * **nms_iou_threshold**: NMS处理过程中iou阈值
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### 类成员变量
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> > * **size**(vector<int>): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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114
examples/vision/segmentation/paddleseg/cpp/infer.cc
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114
examples/vision/segmentation/paddleseg/cpp/infer.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision.h"
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void CpuInfer(const std::string& model_file, const std::string& params_file,
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const std::string& config_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseCpu() auto model =
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fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::ClassifyResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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// print res
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res.Str();
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}
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void GpuInfer(const std::string& model_file, const std::string& params_file,
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const std::string& config_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto model = fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::ClassifyResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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// print res
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res.Str();
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}
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|
||||||
|
void TrtInfer(const std::string& model_file, const std::string& params_file,
|
||||||
|
const std::string& config_file, const std::string& image_file) {
|
||||||
|
auto option = fastdeploy::RuntimeOption();
|
||||||
|
option.UseGpu();
|
||||||
|
option.UseTrtBackend();
|
||||||
|
option.SetTrtInputShape("inputs", [ 1, 3, 224, 224 ], [ 1, 3, 224, 224 ],
|
||||||
|
[ 1, 3, 224, 224 ]);
|
||||||
|
auto model = fastdeploy::vision::classification::PaddleClasModel(
|
||||||
|
model_file, params_file, config_file, option);
|
||||||
|
if (!model.Initialized()) {
|
||||||
|
std::cerr << "Failed to initialize." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
auto im = cv::imread(image_file);
|
||||||
|
|
||||||
|
fastdeploy::vision::ClassifyResult res;
|
||||||
|
if (!model.Predict(&im, &res)) {
|
||||||
|
std::cerr << "Failed to predict." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
// print res
|
||||||
|
res.Str();
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char* argv[]) {
|
||||||
|
if (argc < 4) {
|
||||||
|
std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
|
||||||
|
"e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0"
|
||||||
|
<< std::endl;
|
||||||
|
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
||||||
|
"with gpu; 2: run with gpu and use tensorrt backend."
|
||||||
|
<< std::endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string model_file =
|
||||||
|
argv[1] + "/" + "model.pdmodel" std::string params_file =
|
||||||
|
argv[1] + "/" + "model.pdiparams" std::string config_file =
|
||||||
|
argv[1] + "/" + "inference_cls.yaml" std::string image_file =
|
||||||
|
argv[2] if (std::atoi(argv[3]) == 0) {
|
||||||
|
CpuInfer(model_file, params_file, config_file, image_file);
|
||||||
|
}
|
||||||
|
else if (std::atoi(argv[3]) == 1) {
|
||||||
|
GpuInfer(model_file, params_file, config_file, image_file);
|
||||||
|
}
|
||||||
|
else if (std::atoi(argv[3]) == 2) {
|
||||||
|
TrtInfer(model_file, params_file, config_file, image_file);
|
||||||
|
}
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
75
examples/vision/segmentation/paddleseg/python/README.md
Normal file
75
examples/vision/segmentation/paddleseg/python/README.md
Normal file
@@ -0,0 +1,75 @@
|
|||||||
|
# PaddleClas模型 Python部署示例
|
||||||
|
|
||||||
|
在部署前，需确认以下两个步骤
|
||||||
|
|
||||||
|
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)
|
||||||
|
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start/install.md)
|
||||||
|
|
||||||
|
本目录下提供`infer.py`快速完成ResNet50_vd在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
|
||||||
|
|
||||||
|
```
|
||||||
|
# 下载ResNet50_vd模型文件和测试图片
|
||||||
|
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
|
||||||
|
tar -xvf ResNet50_vd_infer.tgz
|
||||||
|
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||||
|
|
||||||
|
|
||||||
|
#下载部署示例代码
|
||||||
|
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||||
|
cd examples/vision/classification/paddleclas/python
|
||||||
|
|
||||||
|
# CPU推理
|
||||||
|
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu
|
||||||
|
# GPU推理
|
||||||
|
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu
|
||||||
|
# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
|
||||||
|
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True
|
||||||
|
```
|
||||||
|
|
||||||
|
运行完成后返回结果如下所示
|
||||||
|
```
|
||||||
|
ClassifyResult(
|
||||||
|
label_ids: 153,
|
||||||
|
scores: 0.686229,
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
## PaddleClasModel Python接口
|
||||||
|
|
||||||
|
```
|
||||||
|
fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
|
||||||
|
```
|
||||||
|
|
||||||
|
PaddleClas模型加载和初始化，其中model_file, params_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
|
||||||
|
|
||||||
|
**参数**
|
||||||
|
|
||||||
|
> * **model_file**(str): 模型文件路径
|
||||||
|
> * **params_file**(str): 参数文件路径
|
||||||
|
> * **config_file**(str): 推理部署配置文件
|
||||||
|
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||||
|
> * **model_format**(Frontend): 模型格式，默认为Paddle格式
|
||||||
|
|
||||||
|
### predict函数
|
||||||
|
|
||||||
|
> ```
|
||||||
|
> PaddleClasModel.predict(input_image, topk=1)
|
||||||
|
> ```
|
||||||
|
>
|
||||||
|
> 模型预测结口，输入图像直接输出检测结果。
|
||||||
|
>
|
||||||
|
> **参数**
|
||||||
|
>
|
||||||
|
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
|
||||||
|
> > * **topk**(int):返回预测概率最高的topk个分类结果
|
||||||
|
|
||||||
|
> **返回**
|
||||||
|
>
|
||||||
|
> > 返回`fastdeploy.vision.ClassifyResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||||
|
|
||||||
|
|
||||||
|
## 其它文档
|
||||||
|
|
||||||
|
- [PaddleClas 模型介绍](..)
|
||||||
|
- [PaddleClas C++部署](../cpp)
|
||||||
|
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||||
56
examples/vision/segmentation/paddleseg/python/infer.py
Normal file
56
examples/vision/segmentation/paddleseg/python/infer.py
Normal file
@@ -0,0 +1,56 @@
|
|||||||
|
import fastdeploy as fd
|
||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
|
||||||
|
|
||||||
|
def parse_arguments():
|
||||||
|
import argparse
|
||||||
|
import ast
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument(
|
||||||
|
"--model", required=True, help="Path of PaddleClas model.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--image", type=str, required=True, help="Path of test image file.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--topk", type=int, default=1, help="Return topk results.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--device",
|
||||||
|
type=str,
|
||||||
|
default='cpu',
|
||||||
|
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--use_trt",
|
||||||
|
type=ast.literal_eval,
|
||||||
|
default=False,
|
||||||
|
help="Wether to use tensorrt.")
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
def build_option(args):
|
||||||
|
option = fd.RuntimeOption()
|
||||||
|
|
||||||
|
if args.device.lower() == "gpu":
|
||||||
|
option.use_gpu()
|
||||||
|
|
||||||
|
if args.use_trt:
|
||||||
|
option.use_trt_backend()
|
||||||
|
option.set_trt_input_shape("inputs", [1, 3, 224, 224],
|
||||||
|
[1, 3, 224, 224], [1, 3, 224, 224])
|
||||||
|
return option
|
||||||
|
|
||||||
|
|
||||||
|
args = parse_arguments()
|
||||||
|
|
||||||
|
# 配置runtime，加载模型
|
||||||
|
runtime_option = build_option(args)
|
||||||
|
model_file = os.path.join(args.model, "inference.pdmodel")
|
||||||
|
params_file = os.path.join(args.model, "inference.pdiparams")
|
||||||
|
config_file = os.path.join(args.model, "inference_cls.yaml")
|
||||||
|
#model = fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=runtime_option)
|
||||||
|
model = fd.vision.classification.ResNet50vd(
|
||||||
|
model_file, params_file, config_file, runtime_option=runtime_option)
|
||||||
|
|
||||||
|
# 预测图片分类结果
|
||||||
|
im = cv2.imread(args.image)
|
||||||
|
result = model.predict(im, args.topk)
|
||||||
|
print(result)
|
||||||
Reference in New Issue
Block a user