damoyolo_tinynasL25_S_cigarette.pt convert onnx

[mrlihellohorld]

Before Asking

• I have read the README carefully. 我已经仔细阅读了README上的操作指引。

• I want to train my custom dataset, and I have read the tutorials for finetune on your data carefully and organize my dataset correctly; 我想训练自定义数据集，我已经仔细阅读了训练自定义数据的教程，以及按照正确的目录结构存放数据集。

• I have pulled the latest code of main branch to run again and the problem still existed. 我已经拉取了主分支上最新的代码，重新运行之后，问题仍不能解决。

Search before asking

• I have searched the DAMO-YOLO issues and found no similar questions.

Question

请问damoyolo微调的抽烟模型，如何转到onnx呢 目录结构是这样的

Additional

No response

[mrlihellohorld]

class DamoYoloONNXDetector:
def init(self, config_path, onnx_model_path, device='cuda:0', infer_size=(640, 640), conf_threshold=0.5):
self.config = parse_config(config_path)
self.infer_size = infer_size
self.conf_threshold = conf_threshold
self.device = device if torch.cuda.is_available() and 'cuda' in device else 'cpu'
self.class_names = self._get_class_names()

    # Load ONNX model
    self.onnx_session, self.input_name = self._load_onnx_model(onnx_model_path)

def _get_class_names(self):
    if "class_names" in self.config.dataset:
        return self.config.dataset.class_names
    else:
        return [str(i) for i in range(self.config.model.head.num_classes)]

def _load_onnx_model(self, onnx_path):
    providers = [('CUDAExecutionProvider', {'device_id': int(self.device.split(':')[-1])})
                 ] if 'cuda' in self.device else ['CPUExecutionProvider']
    session = ort.InferenceSession(onnx_path, providers=providers)
    input_name = session.get_inputs()[0].name
    return session, input_name

def _pad_image(self, img_tensor, target_size):
    n, c, h, w = img_tensor.shape
    assert n == 1 and h <= target_size[0] and w <= target_size[1]
    padded = torch.zeros((n, c, target_size[0], target_size[1]))
    padded[:, :, :h, :w] = img_tensor
    return ImageList(padded, [img_tensor.shape[-2:]], [padded.shape[-2:]])

def _preprocess(self, pil_img):
    np_img = np.array(pil_img.convert("RGB"))
    img = transform_img(np_img, 0,
                        **self.config.test.augment.transform,
                        infer_size=self.infer_size)
    padded = self._pad_image(img.tensors, self.infer_size)
    return padded, np_img.shape[:2]

def _postprocess(self, output, image_size, origin_shape):
    scores = torch.tensor(output[0])
    bboxes = torch.tensor(output[1])
    result = postprocess(
        scores, bboxes,
        self.config.model.head.num_classes,
        self.config.model.head.nms_conf_thre,
        self.config.model.head.nms_iou_thre,
        image_size
    )
    result = result[0].resize(origin_shape)
    bboxes = result.bbox
    scores = result.get_field('scores')
    labels = result.get_field('labels')
    return bboxes, scores, labels

def infer(self, pil_img):
    image_tensor, origin_shape = self._preprocess(pil_img)
    input_np = np.array(image_tensor.tensors.cpu())
    start_time = time.time()
    output = self.onnx_session.run(None, {self.input_name: input_np})
    inference_time = time.time() - start_time
    bboxes, scores, labels = self._postprocess(output, image_tensor, origin_shape)
    #print(bboxes, scores, labels)
    results = []
    for bbox, score, label in zip(bboxes, scores, labels):
        if score >= self.conf_threshold:
            results.append({
                'bbox': bbox.tolist(),
                'score': float(score),
                'label': self.class_names[int(label.item())]
            })

    return results, inference_time

我转onnx进行推理，发现效果和使用modelscope进行推理效果差了很多

[mrlihellohorld]

请问有人能帮忙回答一下吗 谢谢