[Example] Merge Download Paddle Model, Paddle->ONNX->MLIR->BModel (#1643)

* fix infer.py and README

* [Example] Merge Download Paddle Model, Paddle->Onnx->Mlir->Bmodel and
inference into infer.py. Modify README.md

* modify pp_liteseg sophgo infer.py and README.md

* fix PPOCR,PPYOLOE,PICODET,LITESEG sophgo infer.py and README.md

* fix memory overflow problem while inferring with sophgo backend

* fix memory overflow problem while inferring with sophgo backend

---------

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

examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/infer.py

@@ -1,12 +1,14 @@
 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(
+        "--auto", required=True, help="Auto download, convert, compile and infer if True")
     parser.add_argument("--model", required=True, help="Path of model.")
     parser.add_argument(
         "--config_file", required=True, help="Path of config file.")
@@ -16,15 +18,102 @@ def parse_arguments():
     return parser.parse_args()
 
 
+def download():
+    download_model_str = 'wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz'
+    if not os.path.exists('PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz'):
+        print(download_model_str)
+        run(download_model_str, shell=True)
+    tar_str = 'tar xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz'
+    if not os.path.exists('./PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer'):
+        print(tar_str)
+        run(tar_str, shell=True)
+    download_script_str = 'wget https://raw.githubusercontent.com/PaddlePaddle/Paddle2ONNX/develop/tools/paddle/paddle_infer_shape.py'
+    if not os.path.exists('paddle_infer_shape.py'):
+        print(download_script_str)
+        run(download_script_str, shell=True)
+    download_img_str = 'wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png'
+    if not os.path.exists('cityscapes_demo.png'):
+        print(download_img_str)
+        run(download_img_str, shell=True)
+
+def paddle2onnx():
+    paddle_infer_shape_str = 'python3 paddle_infer_shape.py --model_dir PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer \
+                             --model_filename model.pdmodel \
+                             --params_filename model.pdiparams \
+                             --save_dir pp_liteseg_fix \
+                             --input_shape_dict="{\'x\':[1,3,512,512]}"'
+    print(paddle_infer_shape_str)
+    run(paddle_infer_shape_str, shell=True)
+    pp2onnx_str = 'paddle2onnx --model_dir pp_liteseg_fix \
+                --model_filename model.pdmodel \
+                --params_filename model.pdiparams \
+                --save_file pp_liteseg.onnx \
+                --enable_dev_version True'
+    print(pp2onnx_str)
+    run(pp2onnx_str, shell=True)
+
+def mlir_prepare():
+    mlir_path = os.getenv("MODEL_ZOO_PATH")
+    mlir_path = mlir_path[:-13]
+    regression_path = os.path.join(mlir_path, 'regression')
+    mv_str_list = ['mkdir pp_liteseg', 
+                'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./pp_liteseg', 
+                'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./pp_liteseg',
+                'mv pp_liteseg.onnx ./pp_liteseg',
+                'mkdir ./pp_liteseg/workspace']
+    for str in mv_str_list:
+        print(str)
+        run(str, shell=True)
+
+def onnx2mlir():
+    transform_str = 'model_transform.py \
+        --model_name pp_liteseg \
+        --model_def ../pp_liteseg.onnx \
+        --input_shapes [[1,3,512,512]] \
+        --mean 0.0,0.0,0.0 \
+        --scale 0.0039216,0.0039216,0.0039216 \
+        --keep_aspect_ratio \
+        --pixel_format rgb \
+        --output_names bilinear_interp_v2_6.tmp_0 \
+        --test_input ../image/dog.jpg \
+        --test_result pp_liteseg_top_outputs.npz \
+        --mlir pp_liteseg.mlir'
+    print(transform_str)
+    os.chdir('./pp_liteseg/workspace')
+    run(transform_str, shell=True)
+    os.chdir('../../')
+
+def mlir2bmodel():
+    deploy_str = 'model_deploy.py \
+                --mlir pp_liteseg.mlir \
+                --quantize F32 \
+                --chip bm1684x \
+                --test_input pp_liteseg_in_f32.npz \
+                --test_reference pp_liteseg_top_outputs.npz \
+                --model pp_liteseg_1684x_f32.bmodel'
+    print(deploy_str)
+    os.chdir('./pp_liteseg/workspace')
+    run(deploy_str, shell=True)
+    os.chdir('../../')
+
 args = parse_arguments()
 
+
+if args.auto:
+    download()
+    paddle2onnx()
+    mlir_prepare()
+    onnx2mlir()
+    mlir2bmodel()
+
 # 配置runtime，加载模型
 runtime_option = fd.RuntimeOption()
 runtime_option.use_sophgo()
 
-model_file = args.model
+model_file = './pp_liteseg/workspace/pp_liteseg_1684x_f32.bmodel' if args.auto else args.model
 params_file = ""
-config_file = args.config_file
+config_file = './PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer/deploy.yaml' if args.auto else args.config_file
+img_file = './cityscapes_demo.png' if args.auto else args.image
 
 model = fd.vision.segmentation.PaddleSegModel(
     model_file,
@@ -34,7 +123,7 @@ model = fd.vision.segmentation.PaddleSegModel(
     model_format=fd.ModelFormat.SOPHGO)
 
 # 预测图片分类结果
-im_org = cv2.imread(args.image)
+im_org = cv2.imread(img_file)
 #bmodel 是静态模型，模型输入固定，这里设置为[512, 512]
 im = cv2.resize(im_org, [512, 512], interpolation=cv2.INTER_LINEAR)
 result = model.predict(im)
@@ -42,4 +131,5 @@ print(result)
 
 # 预测结果可视化
 vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
+vis_im = cv2.resize(vis_im, [im_org.shape[1], im_org.shape[0]], interpolation=cv2.INTER_LINEAR)
 cv2.imwrite("sophgo_img.png", vis_im)