[Backend]Add stable_diffusion and detection models support for KunlunXin XPU (#954)

* [FlyCV] Bump up FlyCV -> official release 1.0.0

* add valid_xpu for detection

* add paddledetection model support for xpu

* support all detection model in c++ and python

* fix code

* add python stable_diffusion support

Co-authored-by: DefTruth <qiustudent_r@163.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

examples/vision/detection/yolov7/python/README.md

@@ -14,6 +14,19 @@
 git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd examples/vision/detection/yolov7/python/
 
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_infer.tar
+tar -xf yolov7_infer.tar
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
+# CPU推理
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device cpu
+# GPU推理
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
+# XPU推理
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device xpu
+```
+如果想要验证ONNX模型的推理，可以参考如下命令：
+```bash
 #下载yolov7模型文件和测试图片
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

examples/vision/detection/yolov7/python/README_EN.md

@@ -14,7 +14,19 @@ This doc provides a quick `infer.py` demo of YOLOv7 deployment on CPU/GPU, and a
 # Download sample deployment code
 git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd examples/vision/detection/yolov7/python/
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_infer.tar
+tar -xf yolov7_infer.tar
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 
+# CPU
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device cpu
+# GPU
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
+# XPU
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device xpu
+```
+If you want to test ONNX model:
+```bash
 # Download yolov7 model files and test images
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
@@ -23,7 +35,7 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 python infer.py --model yolov7.onnx --image 000000014439.jpg --device cpu
 # GPU
 python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu
-# GPU上使用TensorRT推理
+# Infer with TensorRT on GPU
 python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_trt True
 ```
 

examples/vision/detection/yolov7/python/infer_paddle_model.py

@@ -0,0 +1,54 @@
+import fastdeploy as fd
+import cv2
+import os
+from fastdeploy import ModelFormat
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of yolov7 paddle model.")
+    parser.add_argument(
+        "--image", required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+    if args.device.lower() == "gpu":
+        option.use_gpu(0)
+
+    if args.device.lower() == "xpu":
+        option.use_xpu()
+
+    return option
+
+
+args = parse_arguments()
+
+model_file = os.path.join(args.model, "model.pdmodel")
+params_file = os.path.join(args.model, "model.pdiparams")
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+model = fd.vision.detection.YOLOv7(
+    model_file,
+    params_file,
+    runtime_option=runtime_option,
+    model_format=ModelFormat.PADDLE)
+
+# 预测图片检测结果
+im = cv2.imread(args.image)
+result = model.predict(im)
+print(result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_detection(im, result)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")