加载在自定义数据集上训练的 Yolov9 模型：AttributeError：“str”对象没有属性“shape”

Question

我已经在自定义数据集上训练了yolov9模型用于实例分割，现在我想在分割后获得分割区域。

类似于下图所示的输出，但针对图像中分割的每个对象。

from pathlib import Path
import numpy as np
import torch
import cv2


model = torch.hub.load('.', 'custom', path='yolov9-inst/runs/train-seg/gelan-c-seg15/weights/best.pt', source='local') 
# Image
img = 'WALL-INSTANCEE-2/test/images/5a243513a69b150001f56c31_emptyroom6_jpeg_jpg.rf.7aa8f6a9aefbb1c76adc60a7b392dcd6.jpg'
# Inference
res = model(img)


#  Iterate detection results (helpful for multiple images)
for r in res:
    img = np.copy(r.orig_img)
    img_name = Path(r.path).stem  # source image base-name

    # Iterate each object contour (multiple detections)
    for ci, c in enumerate(r):
        #  Get detection class name
        label = c.names[c.boxes.cls.tolist().pop()]

# Create binary mask
b_mask = np.zeros(img.shape[:2], np.uint8)

#  Extract contour result
contour = c.masks.xy.pop()
#  Changing the type
contour = contour.astype(np.int32)
#  Reshaping
contour = contour.reshape(-1, 1, 2)


# Draw contour onto mask
_ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)

但是我在仅查找资源时遇到此错误。

YOLO 🚀 v0.1-104-g5b1ea9a Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (NVIDIA RTX A5000, 24248MiB)

Fusing layers... 
gelan-c-seg-custom summary: 414 layers, 27364441 parameters, 0 gradients, 144.2 GFLOPs
WARNING ⚠️ YOLO SegmentationModel is not yet AutoShape compatible. You will not be able to run inference with this model.
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[84], line 6
      4 img = 'WALL-INSTANCEE-2/test/images/5a243513a69b150001f56c31_emptyroom6_jpeg_jpg.rf.7aa8f6a9aefbb1c76adc60a7b392dcd6.jpg'
      5 # Inference
----> 6 results = model(img)

File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File /workspace/yolov9-inst/./models/common.py:868, in DetectMultiBackend.forward(self, im, augment, visualize)
    866 def forward(self, im, augment=False, visualize=False):
    867     # YOLO MultiBackend inference
--> 868     b, ch, h, w = im.shape  # batch, channel, height, width
    869     if self.fp16 and im.dtype != torch.float16:
    870         im = im.half()  # to FP16

AttributeError: 'str' object has no attribute 'shape'

请谁能帮我解决这个问题

Answer 1

模型需要 NumPy 数组或张量形式的图像，但您直接将文件路径作为字符串传递。您需要先从文件中读取图像，然后将其传递给模型。