SingleStageTextDetector¶
- class mmocr.models.textdet.SingleStageTextDetector(backbone, det_head, neck=None, data_preprocessor=None, init_cfg=None)[source]¶
The class for implementing single stage text detector.
Single-stage text detectors directly and densely predict bounding boxes or polygons on the output features of the backbone + neck (optional).
- Parameters
backbone (dict) – Backbone config.
neck (dict, optional) – Neck config. If None, the output from backbone will be directly fed into
det_head
.det_head (dict) – Head config.
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
- extract_feat(inputs)[source]¶
Extract features.
- Parameters
inputs (Tensor) – Image tensor with shape (N, C, H ,W).
- Returns
Multi-level features that may have different resolutions.
- Return type
Tensor or tuple[Tensor]
- loss(inputs, data_samples)[source]¶
Calculate losses from a batch of inputs and data samples.
- Parameters
inputs (torch.Tensor) – Input images of shape (N, C, H, W). Typically these should be mean centered and std scaled.
data_samples (list[TextDetDataSample]) – A list of N datasamples, containing meta information and gold annotations for each of the images.
- Returns
A dictionary of loss components.
- Return type
- predict(inputs, data_samples)[source]¶
Predict results from a batch of inputs and data samples with post- processing.
- Parameters
inputs (torch.Tensor) – Images of shape (N, C, H, W).
data_samples (list[TextDetDataSample]) – 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. Each DetDataSample usually contain ‘pred_instances’. And the
pred_instances
usually contains following keys.- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- polygons (list[np.ndarray]): The length is num_instances.
Each element represents the polygon of the instance, in (xn, yn) order.
- Return type