def __call__(self, original_image):
    '\n        Args:\n            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n\n        Returns:\n            predictions (dict):\n                the output of the model for one image only.\n                See :doc:`/tutorials/models` for details about the format.\n        '
    with torch.no_grad():
        if (self.input_format == 'RGB'):
            original_image = original_image[:, :, ::(- 1)]
        (height, width) = original_image.shape[:2]
        image = original_image
        for tfm_gen in self.transform_gen:
            image = tfm_gen.get_transform(image).apply_image(image)
        image = torch.as_tensor(image.astype('float32').transpose(2, 0, 1))
        inputs = {'image': image, 'height': height, 'width': width}
        predictions = self.model([inputs])[0]
        return predictions
Args:
    original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).

Returns:
    predictions (dict):
        the output of the model for one image only.
        See :doc:`/tutorials/models` for details about the format.
def __call__(self, original_image):
    '\n        Args:\n            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n\n        Returns:\n            predictions (dict):\n                the output of the model for one image only.\n                See :doc:`/tutorials/models` for details about the format.\n        '
    with torch.no_grad():
        if (self.input_format == 'RGB'):
            original_image = original_image[:, :, ::(- 1)]
        (height, width) = original_image.shape[:2]
        image = original_image
        for tfm_gen in self.transform_gen:
            image = tfm_gen.get_transform(image).apply_image(image)
        image = torch.as_tensor(image.astype('float32').transpose(2, 0, 1))
        inputs = {'image': image, 'height': height, 'width': width}
        predictions = self.model([inputs])[0]
        return predictions