【基于yolo转onnx 量化测试】

1、 训练模型转onnx 和量化

from ultralytics import YOLO

model_path = "yolov10/runs/train8/weights/best.pt"
model = YOLO(model_path)    # 载入官方模型
# 导出模型
model.export(format='onnx',half=True)

2、量化，减少了三分之一的存储空间从100M到30M

from onnxruntime.quantization import quantize_dynamic, QuantType

# 动态量化模型
quantize_dynamic("/train8/weights/best.onnx", "best_quantized.onnx", weight_type=QuantType.QUInt8)

3、利用onnx 模型推理

import math
import time
import cv2
import numpy as np
import onnxruntime

class_names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', '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', 'couch',
               'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
               'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
               'scissors', 'teddy bear', 'hair drier', 'toothbrush']

# Create a list of colors for each class where each color is a tuple of 3 integer values
rng = np.random.default_rng(3)
colors = rng.uniform(0, 255, size=(len(class_names), 3))


def nms(boxes, scores, iou_threshold):
    # Sort by score
    sorted_indices = np.argsort(scores)[::-1]

    keep_boxes = []
    while sorted_indices.size > 0:
        # Pick the last box
        box_id = sorted_indices[0]
        keep_boxes.append(box_id)

        # Compute IoU of the picked box with the rest
        ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])

        # Remove boxes with IoU over the threshold
        keep_indices = np.where(ious < iou_threshold)[0]

        # print(keep_indices.shape, sorted_indices.shape)
        sorted_indices = sorted_indices[keep_indices + 1]

    return keep_boxes


def compute_iou(box, boxes):
    # Compute xmin, ymin, xmax, ymax for both boxes
    xmin = np.maximum(box[0], boxes[:, 0])
    ymin = np.maximum(box[1], boxes[:, 1])
    xmax = np.minimum(box[2], boxes[:, 2])
    ymax = np.minimum(box[3], boxes[:, 3])

    # Compute intersection area
    intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)

    # Compute union area
    box_area = (box[2] - box[0]) * (box[3] - box[1])
    boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    union_area = box_area + boxes_area - intersection_area

    # Compute IoU
    iou = intersection_area / union_area

    return iou


def xywh2xyxy(x):
    # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
    y = np.copy(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2
    y[..., 1] = x[..., 1] - x[..., 3] / 2
    y[..., 2] = x[..., 0] + x[..., 2] / 2
    y[..., 3] = x[..., 1] + x[..., 3] / 2
    return y


def sigmoid(x):
    return 1 / (1 + np.exp(-x))


def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3, mask_maps=None):
    img_height, img_width = image.shape[:2]
    size = min([img_height, img_width]) * 0.0006
    text_thickness = int(min([img_height, img_width]) * 0.001)

    mask_img = draw_masks(image, boxes, class_ids, mask_alpha, mask_maps)

    # Draw bounding boxes and labels of detections
    for box, score, class_id in zip(boxes, scores, class_ids):
        color = colors[class_id]

        x1, y1, x2, y2 = box.astype(int)

        # Draw rectangle
        cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, 2)

        label = class_names[class_id]
        caption = f'{label} {int(score * 100)}%'
        (tw, th), _ = cv2.getTextSize(text=caption, fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                                      fontScale=size, thickness=text_thickness)
        th = int(th * 1.2)

        cv2.rectangle(mask_img, (x1, y1),
                      (x1 + tw, y1 - th), color, -1)

        cv2.putText(mask_img, caption, (x1, y1),
                    cv2.FONT_HERSHEY_SIMPLEX, size, (255, 255, 255), text_thickness, cv2.LINE_AA)

    return mask_img


def draw_masks(image, boxes, class_ids, mask_alpha=0.3, mask_maps=None):
    mask_img = image.copy()

    # Draw bounding boxes and labels of detections
    for i, (box, class_id) in enumerate(zip(boxes, class_ids)):
        color = colors[class_id]

        x1, y1, x2, y2 = box.astype(int)

        # Draw fill mask image
        if mask_maps is None:
            cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1)
        else:
            crop_mask = mask_maps[i][y1:y2, x1:x2, np.newaxis]
            crop_mask_img = mask_img[y1:y2, x1:x2]
            crop_mask_img = crop_mask_img * (1 - crop_mask) + crop_mask * color
            mask_img[y1:y2, x1:x2] = crop_mask_img

    return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)


def draw_comparison(img1, img2, name1, name2, fontsize=2.6, text_thickness=3):
    (tw, th), _ = cv2.getTextSize(text=name1, fontFace=cv2.FONT_HERSHEY_DUPLEX,
                                  fontScale=fontsize, thickness=text_thickness)
    x1 = img1.shape[1] // 3
    y1 = th
    offset = th // 5
    cv2.rectangle(img1, (x1 - offset * 2, y1 + offset),
                  (x1 + tw + offset * 2, y1 - th - offset), (0, 115, 255), -1)
    cv2.putText(img1, name1,
                (x1, y1),
                cv2.FONT_HERSHEY_DUPLEX, fontsize,
                (255, 255, 255), text_thickness)

    (tw, th), _ = cv2.getTextSize(text=name2, fontFace=cv2.FONT_HERSHEY_DUPLEX,
                                  fontScale=fontsize, thickness=text_thickness)
    x1 = img2.shape[1] // 3
    y1 = th
    offset = th // 5
    cv2.rectangle(img2, (x1 - offset * 2, y1 + offset),
                  (x1 + tw + offset * 2, y1 - th - offset), (94, 23, 235), -1)

    cv2.putText(img2, name2,
                (x1, y1),
                cv2.FONT_HERSHEY_DUPLEX, fontsize,
                (255, 255, 255), text_thickness)

    combined_img = cv2.hconcat([img1, img2])
    if combined_img.shape[1] > 3840:
        combined_img = cv2.resize(combined_img, (3840, 2160))

    return combined_img

class YOLOSeg:

    def __init__(self, path, conf_thres=0.7, iou_thres=0.5, num_masks=32):
        self.conf_threshold = conf_thres
        self.iou_threshold = iou_thres
        self.num_masks = num_masks

        # Initialize model
        self.initialize_model(path)

    def __call__(self, image):
        return self.segment_objects(image)

    def initialize_model(self, path):
        self.session = onnxruntime.InferenceSession(path,
                                                    providers=['CUDAExecutionProvider',
                                                               'CPUExecutionProvider'])
        # Get model info
        self.get_input_details()
        self.get_output_details()

    def segment_objects(self, image):
        input_tensor = self.prepare_input(image)

        # Perform inference on the image   # 注意用dfl回归bbox的话，bbox的维度就是80+16*4+32
        outputs = self.inference(input_tensor)  # output[0] (1,4+80+32=116,80*80+40*40+20*20=8400)  //  output[1] (1,32,160,160)
        # 对输出0进行处理，返回bbox和对应的mask_feature
        self.boxes, self.scores, self.class_ids, mask_pred = self.process_box_output(outputs[0])
        self.mask_maps = self.process_mask_output(mask_pred, outputs[1])

        return self.boxes, self.scores, self.class_ids, self.mask_maps

    def prepare_input(self, image):
        self.img_height, self.img_width = image.shape[:2]

        input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # Resize input image
        input_img = cv2.resize(input_img, (self.input_width, self.input_height))

        # Scale input pixel values to 0 to 1
        input_img = input_img / 255.0
        input_img = input_img.transpose(2, 0, 1)
        input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)

        return input_tensor

    def inference(self, input_tensor):
        start = time.perf_counter()
        outputs = self.session.run(self.output_names, {self.input_names[0]: input_tensor})

        # print(f"Inference time: {(time.perf_counter() - start)*1000:.2f} ms")
        return outputs

    def process_box_output(self, box_output):

        predictions = np.squeeze(box_output).T
        num_classes = box_output.shape[1] - self.num_masks - 4

        # Filter out object confidence scores below threshold
        scores = np.max(predictions[:, 4:4+num_classes], axis=1)
        predictions = predictions[scores > self.conf_threshold, :]
        scores = scores[scores > self.conf_threshold]

        if len(scores) == 0:
            return [], [], [], np.array([])

        box_predictions = predictions[..., :num_classes+4]
        mask_predictions = predictions[..., num_classes+4:]

        # Get the class with the highest confidence
        class_ids = np.argmax(box_predictions[:, 4:], axis=1)

        # Get bounding boxes for each object
        boxes = self.extract_boxes(box_predictions)

        # Apply non-maxima suppression to suppress weak, overlapping bounding boxes
        indices = nms(boxes, scores, self.iou_threshold)

        return boxes[indices], scores[indices], class_ids[indices], mask_predictions[indices]

    def process_mask_output(self, mask_predictions, mask_output):

        if mask_predictions.shape[0] == 0:
            return []

        mask_output = np.squeeze(mask_output)

        # Calculate the mask maps for each box
        num_mask, mask_height, mask_width = mask_output.shape  # CHW
        masks = sigmoid(mask_predictions @ mask_output.reshape((num_mask, -1)))
        masks = masks.reshape((-1, mask_height, mask_width))

        # Downscale the boxes to match the mask size
        scale_boxes = self.rescale_boxes(self.boxes,
                                   (self.img_height, self.img_width),
                                   (mask_height, mask_width))

        # For every box/mask pair, get the mask map
        mask_maps = np.zeros((len(scale_boxes), self.img_height, self.img_width))
        blur_size = (int(self.img_width / mask_width), int(self.img_height / mask_height))
        for i in range(len(scale_boxes)):

            scale_x1 = int(math.floor(scale_boxes[i][0]))
            scale_y1 = int(math.floor(scale_boxes[i][1]))
            scale_x2 = int(math.ceil(scale_boxes[i][2]))
            scale_y2 = int(math.ceil(scale_boxes[i][3]))

            x1 = int(math.floor(self.boxes[i][0]))
            y1 = int(math.floor(self.boxes[i][1]))
            x2 = int(math.ceil(self.boxes[i][2]))
            y2 = int(math.ceil(self.boxes[i][3]))

            scale_crop_mask = masks[i][scale_y1:scale_y2, scale_x1:scale_x2]
            crop_mask = cv2.resize(scale_crop_mask,
                              (x2 - x1, y2 - y1),
                              interpolation=cv2.INTER_CUBIC)

            crop_mask = cv2.blur(crop_mask, blur_size)

            crop_mask = (crop_mask > 0.5).astype(np.uint8)
            mask_maps[i, y1:y2, x1:x2] = crop_mask

        return mask_maps

    def extract_boxes(self, box_predictions):
        # Extract boxes from predictions
        boxes = box_predictions[:, :4]

        # Scale boxes to original image dimensions
        boxes = self.rescale_boxes(boxes,
                                   (self.input_height, self.input_width),
                                   (self.img_height, self.img_width))

        # Convert boxes to xyxy format
        boxes = xywh2xyxy(boxes)

        # Check the boxes are within the image
        boxes[:, 0] = np.clip(boxes[:, 0], 0, self.img_width)
        boxes[:, 1] = np.clip(boxes[:, 1], 0, self.img_height)
        boxes[:, 2] = np.clip(boxes[:, 2], 0, self.img_width)
        boxes[:, 3] = np.clip(boxes[:, 3], 0, self.img_height)

        return boxes

    def draw_detections(self, image, draw_scores=True, mask_alpha=0.4):
        return draw_detections(image, self.boxes, self.scores,
                               self.class_ids, mask_alpha)

    def draw_masks(self, image, draw_scores=True, mask_alpha=0.5):
        return draw_detections(image, self.boxes, self.scores,
                               self.class_ids, mask_alpha, mask_maps=self.mask_maps)

    def get_input_details(self):
        model_inputs = self.session.get_inputs()
        self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]

        self.input_shape = model_inputs[0].shape
        self.input_height = self.input_shape[2]
        self.input_width = self.input_shape[3]

    def get_output_details(self):
        model_outputs = self.session.get_outputs()
        self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]

    @staticmethod
    def rescale_boxes(boxes, input_shape, image_shape):
        # Rescale boxes to original image dimensions
        input_shape = np.array([input_shape[1], input_shape[0], input_shape[1], input_shape[0]])
        boxes = np.divide(boxes, input_shape, dtype=np.float32)
        boxes *= np.array([image_shape[1], image_shape[0], image_shape[1], image_shape[0]])

        return boxes


if __name__ == '__main__':

    model_path = "yolov8n-seg.onnx"
    model_path = "best_quantized.onnx"
    # model_path = "train8/weights/best.onnx"

    # Initialize YOLOv8 Instance Segmentator
    yoloseg = YOLOSeg(model_path, conf_thres=0.3, iou_thres=0.5)
    img = cv2.imread('bus.jpg')
    img = cv2.imread('street.jpeg')

    # Detect Objects
    yoloseg(img)

    # Draw detections
    combined_img = yoloseg.draw_masks(img)
    # cv2.imwrite("Output_yolov10_seg_quantized.jpg", combined_img)
    cv2.imwrite("street_Output_yolov10_seg_quantized.jpg", combined_img)
    # cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
    # cv2.imshow("Output", combined_img)
    # cv2.waitKey(0)

输入图片：

原文地址：https://blog.csdn.net/liuchenbaidu/article/details/140668283

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