PositionAttentionDecoder

class mmocr.models.textrecog.PositionAttentionDecoder(dictionary, module_loss=None, postprocessor=None, rnn_layers=2, dim_input=512, dim_model=128, max_seq_len=40, mask=True, return_feature=True, encode_value=False, init_cfg=None)

Position attention decoder for RobustScanner.

RobustScanner: RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition

参数

• dictionary (dict or Dictionary) – The config for Dictionary or the instance of Dictionary.

• module_loss (dict, optional) – Config to build module_loss. Defaults to None.

• postprocessor (dict, optional) – Config to build postprocessor. Defaults to None.

• rnn_layers (int) – Number of RNN layers. Defaults to 2.

• dim_input (int) – Dimension \(D_i\) of input vector feat. Defaults to 512.

• dim_model (int) – Dimension \(D_m\) of the model. Should also be the same as encoder output vector out_enc. Defaults to 128.

• max_seq_len (int) – Maximum output sequence length \(T\). Defaults to 40.

• mask (bool) – Whether to mask input features according to img_meta['valid_ratio']. Defaults to True.

• return_feature (bool) – Return feature or logits as the result. Defaults to True.

• encode_value (bool) – Whether to use the output of encoder out_enc as value of attention layer. If False, the original feature feat will be used. Defaults to False.

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

返回类型

None

forward_test(feat, out_enc, img_metas)

参数

• feat (Tensor) – Tensor of shape \((N, D_i, H, W)\).

• out_enc (Tensor) – Encoder output of shape \((N, D_m, H, W)\).

• data_samples (list[TextRecogDataSample], optional) – Batch of TextRecogDataSample, containing gt_text information. Defaults to None.

• img_metas (Sequence[mmocr.structures.textrecog_data_sample.TextRecogDataSample]) –

返回

Character probabilities of shape \((N, T, C)\) if return_feature=False. Otherwise it would be the hidden feature before the prediction projection layer, whose shape is \((N, T, D_m)\).

返回类型

Tensor

forward_train(feat, out_enc, data_samples)

参数

• feat (Tensor) – Tensor of shape \((N, D_i, H, W)\).

• out_enc (Tensor) – Encoder output of shape \((N, D_m, H, W)\).

• data_samples (list[TextRecogDataSample], optional) – Batch of TextRecogDataSample, containing gt_text information. Defaults to None.

返回

A raw logit tensor of shape \((N, T, C)\) if return_feature=False. Otherwise it will be the hidden feature before the prediction projection layer, whose shape is \((N, T, D_m)\).

返回类型

Tensor