PackKIEInputs

class mmocr.datasets.transforms.PackKIEInputs(meta_keys=())

Pack the inputs data for key information extraction.

The type of outputs is dict:

• inputs: image converted to tensor, whose shape is (C, H, W).

• data_samples: Two components of TextDetDataSample will be updated:

  • gt_instances (InstanceData): Depending on annotations, a subset of the following keys will be updated:

    • bboxes (torch.Tensor((N, 4), dtype=torch.float32)): The groundtruth of bounding boxes in the form of [x1, y1, x2, y2]. Renamed from ‘gt_bboxes’.

    • labels (torch.LongTensor(N)): The labels of instances. Renamed from ‘gt_bboxes_labels’.

    • edge_labels (torch.LongTensor(N, N)): The edge labels. Renamed from ‘gt_edges_labels’.

    • texts (list[str]): The groundtruth texts. Renamed from ‘gt_texts’.

  • metainfo (dict): ‘metainfo’ is always populated. The contents of the ‘metainfo’ depends on meta_keys. By default it includes:

    • “img_path”: Path to the image file.

    • “img_shape”: Shape of the image input to the network as a tuple (h, w). Note that the image may be zero-padded afterward on the bottom/right if the batch tensor is larger than this shape.

    • “scale_factor”: A tuple indicating the ratio of width and height of the preprocessed image to the original one.

    • “ori_shape”: Shape of the preprocessed image as a tuple (h, w).

Parameters

meta_keys (Sequence[str], optional) – Meta keys to be converted to the metainfo of TextDetSample. Defaults to ('img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction').

transform(results)

Method to pack the input data.

Parameters

results (dict) – Result dict from the data pipeline.

Returns

• ‘inputs’ (obj:torch.Tensor): Data for model forwarding.

• ’data_samples’ (obj:DetDataSample): The annotation info of the sample.

Return type

dict