Delete model_farm_yolov10x_qcs8550_qnn2.31_fp16_aidlite

model_farm_yolov10x_qcs8550_qnn2.31_fp16_aidlite/README.md

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-## Model Information
-
-### Source model
-- Input shape: [1,3,640,640]
-- Number of parameters: 30.34M
-- Model size: 112.73M
-- Output shape: [1,300,6]
-
-Source model repository: [YOLOV10-x](https://github.com/THU-MIG/yolov10)
-
-### Converted model
-
-- Precision: FP16
-- Backend: QNN2.31
-- Target Device: SNM972 QCS8550
-
-## Model Conversion Reference
-User can find model conversion reference at [aimo.aidlux.com](https://aimo.aidlux.com/#/public/47ed290a-394b-4b75-8d25-5fa2058c6c48)
-
-## Inference with AidLite SDK
-
-### SDK installation
-Model Farm uses AidLite SDK as the model inference SDK. For details, please refer to the [AidLite Developer Documentation](https://v2.docs.aidlux.com/en/sdk-api/aidlite-sdk/)
-
-- Install AidLite SDK
-
-```bash
-# Install the appropriate version of the aidlite sdk
-sudo aid-pkg update
-sudo aid-pkg install aidlite-sdk
-# Download the qnn version that matches the above backend. Eg Install QNN2.23 Aidlite: sudo aid-pkg install aidlite-qnn223
-sudo aid-pkg install aidlite-{QNN VERSION}
-```
-
-- Verify AidLite SDK
-
-```bash
-# aidlite sdk c++ check
-python3 -c "import aidlite ; print(aidlite.get_library_version())"
-
-# aidlite sdk python check
-python3 -c "import aidlite ; print(aidlite.get_py_library_version())"
-```
-
-### Run Demo
-
-#### python
- > Note: The ONNX model is used in the post-processing stage, and users' own models can also reuse this ONNX model.
-```bash
-cd model_farm_yolov10x_qcs8550_qnn2.31_fp16_aidlite
-python3  python/run_test.py --target_model ./models/cutoff_yolov10x_fp16.qnn231.ctx.bin.aidem --imgs ./python/bus.jpg  --invoke_nums 10
-```

model_farm_yolov10x_qcs8550_qnn2.31_fp16_aidlite/models/cutoff_yolov10x_fp16.qnn231.ctx.bin.aidem

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model_farm_yolov10x_qcs8550_qnn2.31_fp16_aidlite/models/post_pro.onnx

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model_farm_yolov10x_qcs8550_qnn2.31_fp16_aidlite/python/run_test.py

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-import time
-import numpy as np
-import cv2
-import os
-import aidlite
-import argparse
-import onnxruntime  
-
-
-
-"""返回 COCO 数据集的类别名称（80 类）。"""
-classes=[
-        "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
-        "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
-        "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
-        "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball",
-        "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket",
-        "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
-        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair",
-        "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
-        "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator",
-        "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"
-    ]
-
-
-def letterbox(
-        im,
-        new_shape,
-        color=(114, 114, 114),
-        auto=False,
-        scaleFill=False,
-        scaleup=True,
-        stride=32,
-):
-    """
-    Resize and pad image while meeting stride-multiple constraints
-    Returns:
-        im (array): (height, width, 3)
-        ratio (array): [w_ratio, h_ratio]
-        (dw, dh) (array): [w_padding h_padding]
-    """
-    shape = im.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):  # [h_rect, w_rect]
-        new_shape = (new_shape, new_shape)
- 
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better val mAP)
-        r = min(r, 1.0)
- 
-    # Compute padding
-    ratio = r, r  # wh ratios
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))  # w h
-    dw, dh = (
-        new_shape[1] - new_unpad[0],
-        new_shape[0] - new_unpad[1],
-    )  # wh padding
- 
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-    elif scaleFill:  # stretch
-        dw, dh = 0.0, 0.0
-        new_unpad = (new_shape[1], new_shape[0])  # [w h]
-        ratio = (
-            new_shape[1] / shape[1],
-            new_shape[0] / shape[0],
-        )  # [w_ratio, h_ratio]
- 
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    im = cv2.copyMakeBorder(
-        im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color
-    )
-    return im, ratio, (dw, dh)
-
-
-class Colors:
-    
-    def __init__(self):
-        hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
-                '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
-        self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
-        self.n = len(self.palette)
-
-    def __call__(self, i, bgr=False):
-        c = self.palette[int(i) % self.n]
-        return (c[2], c[1], c[0]) if bgr else c
-
-    @staticmethod
-    def hex2rgb(h):
-        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
-
-
-def rescale_coords(boxes, image_shape, input_shape):
-    image_height, image_width = image_shape
-    input_height, input_width = input_shape
-    scale = min(input_width / image_width, input_height / image_height)
-    pad_w = (input_width - image_width * scale) / 2
-    pad_h = (input_height - image_height * scale) / 2
-    boxes[:, [0, 2]] = (boxes[:, [0, 2]] - pad_w) / scale
-    boxes[:, [1, 3]] = (boxes[:, [1, 3]] - pad_h) / scale
-    boxes[:, [0, 2]] = np.clip(boxes[:, [0, 2]], 0, image_width)
-    boxes[:, [1, 3]] = np.clip(boxes[:, [1, 3]], 0, image_height)
-    return boxes.astype(int)
-
-def preprocess(image, input_shape):
-    # Resize
-    input_img = letterbox(image, input_shape)[0]
-    # Transpose
-    # input_img = input_img[..., ::-1].transpose(2, 0, 1)
-    input_img = input_img[..., ::-1]
-    # Expand
-    input_img = input_img[np.newaxis, :, :, :].astype(np.float32)
-    # Contiguous
-    input_img = np.ascontiguousarray(input_img)
-    # Norm
-    blob = input_img / 255.0
-    return blob
-
-def postprocess(output_data, conf_thres, image_shape, input_shape):
-    outs = output_data  # test.py 中 output_data 已经是 (8400, 84)
-    outs = outs[outs[:, 4] >= conf_thres]
-    boxes = outs[:, :4]
-    scores = outs[:, -2]
-    labels = outs[:, -1].astype(int)
-    boxes = rescale_coords(boxes, image_shape, input_shape)
-    return boxes, scores, labels
-
-class qnn_yolov10:
-    def __init__(self,model_path,sdk="qnn",backend="npu"):
-        self.config = aidlite.Config.create_instance()
-        if self.config is None:
-            print("Create config failed !")
-            return False
-
-        self.config.implement_type = aidlite.ImplementType.TYPE_LOCAL
-        if sdk.lower()=="qnn":
-            self.config.framework_type = aidlite.FrameworkType.TYPE_QNN
-        else :
-            self.config.framework_type = aidlite.FrameworkType.TYPE_SNPE2
-            
-
-        if backend.lower() =="npu":
-            self.config.accelerate_type = aidlite.AccelerateType.TYPE_DSP
-        elif backend.lower() =="gpu":
-            self.config.accelerate_type = aidlite.AccelerateType.TYPE_GPU
-        else:
-            self.config.accelerate_type = aidlite.AccelerateType.TYPE_CPU
-        self.config.is_quantify_model = 1
-               
-        self.model = aidlite.Model.create_instance(model_path)
-        if self.model is None:
-            print("Create model failed !")
-            return False
-        self.interpreter = aidlite.InterpreterBuilder.build_interpretper_from_model_and_config(self.model, self.config)
-        if self.interpreter is None:
-            print("build_interpretper_from_model_and_config failed !")
-            return None
-        result = self.interpreter.init()
-        if result != 0:
-            print(f"interpreter init failed !")
-            return False
-        result = self.interpreter.load_model()
-        if result != 0:
-            print("interpreter load model failed !")
-            return False
-        print("detect model load success!")
-    
-    def __del__(self):
-        self.interpreter.destory()
-    
-    def __call__(self, img_input,invoke_nums):
-        result = self.interpreter.set_input_tensor(0, img_input.data)
-        if result != 0:
-            print("interpreter set_input_tensor() failed")
-        invoke_time=[]
-        for i in range(invoke_nums):
-            t1=time.time()
-            result = self.interpreter.invoke()
-            if result != 0:
-                print("interpreter set_input_tensor() failed")
-            cost_time = (time.time()-t1)*1000
-            invoke_time.append(cost_time)
-
-        max_invoke_time = max(invoke_time)
-        min_invoke_time = min(invoke_time)
-        mean_invoke_time = sum(invoke_time)/invoke_nums
-        var_invoketime=np.var(invoke_time)
-        print("====================================")
-        print(f"QNN invoke {invoke_nums} times:\n --mean_invoke_time is {mean_invoke_time} \n --max_invoke_time is {max_invoke_time} \n --min_invoke_time is {min_invoke_time} \n --var_invoketime is {var_invoketime}")
-        print("====================================")
-
-        output1 = self.interpreter.get_output_tensor(0)
-        return output1
-        
-    
-class onnx_yolov10:
-    def __init__(self,model_path):
-        self.sess_options = onnxruntime.SessionOptions()
-        self.sess_options.intra_op_num_threads = 1
-        self.sess = onnxruntime.InferenceSession(model_path,sess_options=self.sess_options)
-        self.outname = [i.name for i in self.sess.get_outputs()]
-        self.inname = [i.name for i in self.sess.get_inputs()]
-    def __call__(self,img_input):
-        inp = {self.inname[0]:img_input}
-        t1=time.time()
-        out_put = self.sess.run(self.outname,inp)[0]
-        cost_time = (time.time()-t1)*1000
-        return out_put
-    
-    
-def main(args):
-    input_shape = (640, 640)
-    conf_thres = 0.25
-    img_path = args.imgs
-    invoke_nums = args.invoke_nums
-    qnn_path = args.target_model
-    
-    # qnn +onnx推理
-    qnn_model1 = qnn_yolov10(qnn_path)
-    
-    onnx_model_path = 'models/post_pro.onnx'
-    onnx_model = onnx_yolov10(onnx_model_path)
-
-    print("Begin to run qnn...")
-    im0 = cv2.imread(img_path)
-    image_shape = im0.shape[:2]
-    img_qnn = preprocess(im0, input_shape) 
-    qnn_out_shape = (1,8400,84)
-    out1 = qnn_model1(img_qnn,invoke_nums)
-    out1 = out1.reshape(*qnn_out_shape)
-    out2 = onnx_model(out1)[0]
-       
-    boxes, scores, labels = postprocess(out2, conf_thres, image_shape, input_shape)
-    print(f"Detect {len(boxes)} targets")
-    
-    colors = Colors()
-    for label, score, box in zip(labels, scores, boxes):
-        label_text = f'{classes[label]}: {score:.2f}'
-        color = colors(label, True)
-        cv2.rectangle(im0, (box[0], box[1]), (box[2], box[3]), color, 2, lineType=cv2.LINE_AA)
-        cv2.putText(im0, label_text, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-    output_image_path = "python/detected_results.jpg"
-    cv2.imwrite(output_image_path, im0)
-    print(f"Saved detected result to {output_image_path}")
-    
-
-
-def parser_args():
-    parser = argparse.ArgumentParser(description="Inferrence yolov10 model")
-    parser.add_argument('--target_model',type=str,default='./models/cutoff_yolov10x_fp16.qnn231.ctx.bin.aidem',help="Predict images path")
-    parser.add_argument('--imgs',type=str,default='./python/bus.jpg',help="Predict images path")
-    parser.add_argument('--invoke_nums',type=int,default=10,help="Inference nums")
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == "__main__":
-    args = parser_args()
-    main(args)