EncoderDecoderRecognizer

class mmocr.models.textrecog.EncoderDecoderRecognizer(preprocessor=None, backbone=None, encoder=None, decoder=None, data_preprocessor=None, init_cfg=None)

Base class for encode-decode recognizer.

Parameters

• preprocessor (dict, optional) – Config dict for preprocessor. Defaults to None.

• backbone (dict, optional) – Backbone config. Defaults to None.

• encoder (dict, optional) – Encoder config. If None, the output from backbone will be directly fed into decoder. Defaults to None.

• decoder (dict, optional) – Decoder config. Defaults to None.

• data_preprocessor (dict, optional) – Model preprocessing config for processing the input image data. Keys allowed are ``to_rgb``(bool), ``pad_size_divisor``(int), ``pad_value``(int or float), ``mean``(int or float) and ``std``(int or float). Preprcessing order: 1. to rgb; 2. normalization 3. pad. Defaults to None.

• init_cfg (dict or list[dict], optional) – Initialization configs. Defaults to None.

Return type

None

extract_feat(inputs)

Directly extract features from the backbone.

Parameters

inputs (torch.Tensor) –

Return type

torch.Tensor

loss(inputs, data_samples, **kwargs)

Calculate losses from a batch of inputs and data samples. :param inputs: Input images of shape (N, C, H, W).

Typically these should be mean centered and std scaled.

Parameters

• data_samples (list[TextRecogDataSample]) – A list of N datasamples, containing meta information and gold annotations for each of the images.

• inputs (tensor) –

Returns

A dictionary of loss components.

Return type

dict[str, tensor]

predict(inputs, data_samples, **kwargs)

Predict results from a batch of inputs and data samples with post- processing.

Parameters

• inputs (torch.Tensor) – Image input tensor.

• data_samples (list[TextRecogDataSample]) – A list of N datasamples, containing meta information and gold annotations for each of the images.

Returns

A list of N datasamples of prediction results. Results are stored in pred_text.

Return type

list[TextRecogDataSample]