怎么用训练好的模型批量预测呀

[yycrycry]

No description provided.

[Fafa-DL]

模型评估

[yycrycry]

谢谢大神，批量预测时一直报错： File "tools/batch_test.py", line 66, in
main()
File "tools/batch_test.py", line 50, in main
result = inference_model(model, img, val_pipeline, classes_names, label_names)
File "E:\lyy\Awesome-Backbones-main (1)\Awesome-Backbones-main\utils\inference.py", line 97, in inference_model
processed_data = pipeline(data)
File "E:\lyy\Awesome-Backbones-main (1)\Awesome-Backbones-main\core\datasets\compose.py", line 40, in call
data = t(data)
File "E:\lyy\Awesome-Backbones-main (1)\Awesome-Backbones-main\core\datasets\loading.py", line 92, in call
if results['img_prefix'] is not None:
KeyError: 'img_prefix
我的输入数据是二通道的numpy数组，所以我将数据加载方式改成了这样：
class LoadNumpyFromArray(object):
"""Load an image from file.

Required keys are "img_prefix" and "img_info" (a dict that must contain the
key "filename"). Added or updated keys are "filename", "img", "img_shape",
"ori_shape" (same as `img_shape`) and "img_norm_cfg" (means=0 and stds=1).

Args:
    to_float32 (bool): Whether to convert the loaded image to a float32
        numpy array. If set to False, the loaded image is an uint8 array.
        Defaults to False.
    color_type (str): The flag argument for :func:`mmcv.imfrombytes()`.
        Defaults to 'color'.
    
"""

def __init__(self,
             to_float32=False,
             color_type='grayscale',
             file_client_args=dict(backend='disk')):
    self.to_float32 = to_float32
    self.color_type = color_type
    
def get(self, filepath):
    """Read image data from a given ``filepath``.

    Args:
        filepath (str or Path): Path to read image.

    Returns:
        np.ndarray: Expected numpy array representing the image.
    """
    try:
        img = np.load(filepath)
        if img is None:
            print(f"Error: Failed to read image from {filepath}")
        return img
    except Exception as e:
        print(f"Error: {str(e)}")
        return None


def __call__(self, results):
    if results['img_prefix'] is not None:
        filename = osp.join(results['img_prefix'], results['img_info']['filename'])
    else:
        filename = results['img_info']['filename']

    img = np.load(filename, allow_pickle=True)  # 读取时指定 allow_pickle=True

    if self.to_float32:
        img = img.astype(np.float32)

    results['filename'] = filename
    results['ori_filename'] = results['img_info']['filename']
    results['img'] = img
    results['img_shape'] = img.shape
    results['ori_shape'] = img.shape
    num_channels = img.shape[2] if len(img.shape) == 3 else 1  # 调整通道数
    results['img_norm_cfg'] = dict(
        mean=np.zeros(num_channels, dtype=np.float32),
        std=np.ones(num_channels, dtype=np.float32),
        to_rgb=False)
    return results

训练的时候没有报错，但预测的时候报错了，请问是什么原因呀

[Fafa-DL]

训练的时候没问题然后预测有问题，是不是自己更换了测试集

[gepaohhh]

这个为啥会不匹配