[Example] Merge Download Paddle Model, Paddle->ONNX, ONNX -> MLIR, MLIR -> BModel into infer.py (#1622)

fix infer.py and README

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

examples/vision/classification/paddleclas/sophgo/python/README.md

@@ -15,8 +15,11 @@ cd FastDeploy/examples/vision/classification/paddleclas/sophgo/python
 # Download images.
 wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
 
-# Inference.
+# Inference. Need to manually set the model, configuration file and image path used for inference.
-python3 infer.py --model_file ./bmodel/resnet50_1684x_f32.bmodel  --config_file ResNet50_vd_infer/inference_cls.yaml  --image ILSVRC2012_val_00000010.jpeg
+python3 infer.py --auto False --model_file ./bmodel/resnet50_1684x_f32.bmodel  --config_file ResNet50_vd_infer/inference_cls.yaml  --image ILSVRC2012_val_00000010.jpeg
+
+# Automatic completion of downloading data - model compilation - inference, no need to set up model, configuration file and image paths.
+python3 infer.py --auto True --model '' --config_file '' --image ''
 
 # The returned result.
 ClassifyResult(

examples/vision/classification/paddleclas/sophgo/python/README_CN.md

@@ -15,8 +15,12 @@ cd FastDeploy/examples/vision/classification/paddleclas/sophgo/python
 # 下载图片
 wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
 
-# 推理
+# 手动设置推理使用的模型、配置文件和图片路径
-python3 infer.py --model_file ./bmodel/resnet50_1684x_f32.bmodel  --config_file ResNet50_vd_infer/inference_cls.yaml  --image ILSVRC2012_val_00000010.jpeg
+python3 infer.py --auto False --model_file ./bmodel/resnet50_1684x_f32.bmodel  --config_file ResNet50_vd_infer/inference_cls.yaml  --image ILSVRC2012_val_00000010.jpeg
+
+# 自动完成下载数据-模型编译-推理，不需要设置模型、配置文件和图片路径
+python3 infer.py --auto True --model '' --config_file '' --image ''
+
 
 # 运行完成后返回结果如下所示
 ClassifyResult(

examples/vision/classification/paddleclas/sophgo/python/infer.py

@@ -1,15 +1,15 @@
 import fastdeploy as fd
 import cv2
 import os
-
+from subprocess import run
 
 def parse_arguments():
     import argparse
     import ast
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model", required=True, help="Path of model.")
+    parser.add_argument("--auto", required=True, help="Auto download, convert, compile and infer if True")
-    parser.add_argument(
+    parser.add_argument("--model", required=True, help="Path of bmodel")
-        "--config_file", required=True, help="Path of config file.")
+    parser.add_argument("--config_file", required=True, help="Path of config file")
     parser.add_argument(
         "--image", type=str, required=True, help="Path of test image file.")
     parser.add_argument(
@@ -17,17 +17,86 @@ def parse_arguments():
 
     return parser.parse_args()
 
+def download():
+    cmd_str = 'wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz'
+    jpg_str = 'wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg'
+    tar_str = 'tar xvf ResNet50_vd_infer.tgz'
+    if not os.path.exists('ResNet50_vd_infer.tgz'):
+        run(cmd_str, shell=True)
+    if not os.path.exists('ILSVRC2012_val_00000010.jpeg'):
+        run(jpg_str, shell=True)
+    run(tar_str, shell=True)
+
+def paddle2onnx():
+    cmd_str = 'paddle2onnx --model_dir ResNet50_vd_infer \
+            --model_filename inference.pdmodel \
+            --params_filename inference.pdiparams \
+            --save_file ResNet50_vd_infer.onnx \
+            --enable_dev_version True'
+    print(cmd_str)
+    run(cmd_str, shell=True)
+
+def mlir_prepare():
+    mlir_path = os.getenv("MODEL_ZOO_PATH")
+    mlir_path = mlir_path[:-13]
+    cmd_list = ['mkdir ResNet50',
+                'cp -rf ' + os.path.join(mlir_path, 'regression/dataset/COCO2017/') + ' ./ResNet50',
+                'cp -rf ' + os.path.join(mlir_path, 'regression/image/') + ' ./ResNet50',
+                'cp ResNet50_vd_infer.onnx ./ResNet50/',
+                'mkdir ./ResNet50/workspace']
+    for str in cmd_list:
+        print(str)
+        run(str, shell=True)
+
+def onnx2mlir():
+    cmd_str = 'model_transform.py \
+        --model_name ResNet50_vd_infer \
+        --model_def ../ResNet50_vd_infer.onnx \
+        --input_shapes [[1,3,224,224]] \
+        --mean 0.0,0.0,0.0 \
+        --scale 0.0039216,0.0039216,0.0039216 \
+        --keep_aspect_ratio \
+        --pixel_format rgb \
+        --output_names save_infer_model/scale_0.tmp_1 \
+        --test_input ../image/dog.jpg \
+        --test_result ./ResNet50_vd_infer_top_outputs.npz \
+        --mlir ./ResNet50_vd_infer.mlir'
+    print(cmd_str)
+    os.chdir('./ResNet50/workspace/')
+    run(cmd_str, shell=True)
+    os.chdir('../../')
+
+def mlir2bmodel():
+    cmd_str = 'model_deploy.py \
+        --mlir ./ResNet50_vd_infer.mlir \
+        --quantize F32 \
+        --chip bm1684x \
+        --test_input ./ResNet50_vd_infer_in_f32.npz \
+        --test_reference ./ResNet50_vd_infer_top_outputs.npz \
+        --model ./ResNet50_vd_infer_1684x_f32.bmodel'
+    print(cmd_str)
+    os.chdir('./ResNet50/workspace')
+    run(cmd_str, shell=True)
+    os.chdir('../../')
+
 
 args = parse_arguments()
 
-# 配置runtime，加载模型
+if(args.auto):
+    download()
+    paddle2onnx()
+    mlir_prepare()
+    onnx2mlir()
+    mlir2bmodel()
+
+# config runtime and load the model
 runtime_option = fd.RuntimeOption()
 runtime_option.use_sophgo()
 
-model_file = args.model
+model_file = './ResNet50/workspace/ResNet50_vd_infer_1684x_f32.bmodel' if args.auto else args.model
 params_file = ""
-config_file = args.config_file
+config_file = './ResNet50_vd_infer/inference_cls.yaml' if args.auto else args.config_file
-
+image_file = './ILSVRC2012_val_00000010.jpeg' if args.auto else args.image
 model = fd.vision.classification.PaddleClasModel(
     model_file,
     params_file,
@@ -35,7 +104,7 @@ model = fd.vision.classification.PaddleClasModel(
     runtime_option=runtime_option,
     model_format=fd.ModelFormat.SOPHGO)
 
-# 预测图片分类结果
+# predict the results of image classification
-im = cv2.imread(args.image)
+im = cv2.imread(image_file)
 result = model.predict(im, args.topk)
 print(result)