mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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c91e99b5f5
* Adjust folders structures in paddleclas * remove useless files * Update sophgo * improve readme
126 lines
3.9 KiB
Python
126 lines
3.9 KiB
Python
import fastdeploy as fd
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import cv2
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import os
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from subprocess import run
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--auto",
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required=True,
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help="Auto download, convert, compile and infer if True")
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parser.add_argument("--model", required=True, help="Path of bmodel")
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parser.add_argument(
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"--config_file", required=True, help="Path of config file")
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parser.add_argument(
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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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return parser.parse_args()
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def download():
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cmd_str = 'wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz'
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jpg_str = 'wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg'
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tar_str = 'tar xvf ResNet50_vd_infer.tgz'
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if not os.path.exists('ResNet50_vd_infer.tgz'):
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run(cmd_str, shell=True)
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if not os.path.exists('ILSVRC2012_val_00000010.jpeg'):
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run(jpg_str, shell=True)
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run(tar_str, shell=True)
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def paddle2onnx():
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cmd_str = 'paddle2onnx --model_dir ResNet50_vd_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ResNet50_vd_infer.onnx \
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--enable_dev_version True'
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print(cmd_str)
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run(cmd_str, shell=True)
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def mlir_prepare():
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mlir_path = os.getenv("MODEL_ZOO_PATH")
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mlir_path = mlir_path[:-13]
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cmd_list = [
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'mkdir ResNet50', 'cp -rf ' + os.path.join(
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mlir_path, 'regression/dataset/COCO2017/') + ' ./ResNet50',
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'cp -rf ' + os.path.join(mlir_path,
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'regression/image/') + ' ./ResNet50',
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'cp ResNet50_vd_infer.onnx ./ResNet50/', 'mkdir ./ResNet50/workspace'
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]
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for str in cmd_list:
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print(str)
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run(str, shell=True)
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def onnx2mlir():
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cmd_str = 'model_transform.py \
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--model_name ResNet50_vd_infer \
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--model_def ../ResNet50_vd_infer.onnx \
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--input_shapes [[1,3,224,224]] \
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--mean 0.0,0.0,0.0 \
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--scale 0.0039216,0.0039216,0.0039216 \
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--keep_aspect_ratio \
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--pixel_format rgb \
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--output_names save_infer_model/scale_0.tmp_1 \
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--test_input ../image/dog.jpg \
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--test_result ./ResNet50_vd_infer_top_outputs.npz \
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--mlir ./ResNet50_vd_infer.mlir'
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print(cmd_str)
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os.chdir('./ResNet50/workspace/')
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run(cmd_str, shell=True)
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os.chdir('../../')
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def mlir2bmodel():
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cmd_str = 'model_deploy.py \
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--mlir ./ResNet50_vd_infer.mlir \
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--quantize F32 \
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--chip bm1684x \
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--test_input ./ResNet50_vd_infer_in_f32.npz \
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--test_reference ./ResNet50_vd_infer_top_outputs.npz \
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--model ./ResNet50_vd_infer_1684x_f32.bmodel'
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print(cmd_str)
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os.chdir('./ResNet50/workspace')
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run(cmd_str, shell=True)
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os.chdir('../../')
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args = parse_arguments()
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if (args.auto):
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download()
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paddle2onnx()
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mlir_prepare()
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onnx2mlir()
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mlir2bmodel()
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# config runtime and load the model
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runtime_option = fd.RuntimeOption()
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runtime_option.use_sophgo()
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model_file = './ResNet50/workspace/ResNet50_vd_infer_1684x_f32.bmodel' if args.auto else args.model
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params_file = ""
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config_file = './ResNet50_vd_infer/inference_cls.yaml' if args.auto else args.config_file
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image_file = './ILSVRC2012_val_00000010.jpeg' if args.auto else args.image
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model = fd.vision.classification.PaddleClasModel(
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model_file,
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params_file,
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config_file,
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runtime_option=runtime_option,
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model_format=fd.ModelFormat.SOPHGO)
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# predict the results of image classification
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im = cv2.imread(image_file)
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result = model.predict(im, args.topk)
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print(result)
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