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synced 2025-06-03 21:54:47 +08:00
remove some deprecated files causing import errors
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gaotongxiao
parent
13bd2837ae
commit
3f24e34a5d
@ -1,23 +1,24 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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from . import utils
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from .base_dataset import BaseDataset
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from .builder import DATASETS, LOADERS, PARSERS, TRANSFORMS
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from .builder import LOADERS, PARSERS, TRANSFORMS
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from .icdar_dataset import IcdarDataset
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# TODO: check this
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from .kie_dataset import KIEDataset
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from .ner_dataset import NerDataset
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from .ocr_dataset import OCRDataset
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from .ocr_seg_dataset import OCRSegDataset
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from .openset_kie_dataset import OpensetKIEDataset
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from .pipelines import CustomFormatBundle, DBNetTargets, FCENetTargets
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from .pipelines import DBNetTargets, FCENetTargets
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from .text_det_dataset import TextDetDataset
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from .uniform_concat_dataset import UniformConcatDataset
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from .utils import * # NOQA
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__all__ = [
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'DATASETS', 'IcdarDataset', 'BaseDataset', 'OCRDataset', 'TextDetDataset',
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'CustomFormatBundle', 'DBNetTargets', 'OCRSegDataset', 'KIEDataset',
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'FCENetTargets', 'NerDataset', 'UniformConcatDataset', 'OpensetKIEDataset',
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'TRANSFORMS', 'PARSERS', 'LOADERS'
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'IcdarDataset', 'BaseDataset', 'OCRDataset', 'TextDetDataset',
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'KIEDataset', 'DBNetTargets', 'OCRSegDataset', 'FCENetTargets',
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'NerDataset', 'UniformConcatDataset', 'OpensetKIEDataset', 'TRANSFORMS',
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'PARSERS', 'LOADERS'
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]
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__all__ += utils.__all__
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@ -1,8 +1,7 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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from .box_utils import sort_vertex, sort_vertex8
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from .custom_format_bundle import CustomFormatBundle
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from .dbnet_transforms import EastRandomCrop, ImgAug
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from .kie_transforms import KIEFormatBundle, ResizeNoImg
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from .kie_transforms import ResizeNoImg
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from .loading import (LoadImageFromLMDB, LoadImageFromNdarray,
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LoadTextAnnotations)
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from .ner_transforms import NerTransform, ToTensorNER
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@ -21,14 +20,14 @@ from .transforms import (ColorJitter, PyramidRescale, RandomCropFlip,
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__all__ = [
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'LoadTextAnnotations', 'NormalizeOCR', 'OnlineCropOCR', 'ResizeOCR',
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'ToTensorOCR', 'CustomFormatBundle', 'DBNetTargets', 'PANetTargets',
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'ColorJitter', 'RandomCropInstances', 'RandomRotateTextDet',
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'ScaleAspectJitter', 'MultiRotateAugOCR', 'OCRSegTargets', 'FancyPCA',
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'ToTensorOCR', 'DBNetTargets', 'PANetTargets', 'ColorJitter',
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'RandomCropInstances', 'RandomRotateTextDet', 'ScaleAspectJitter',
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'MultiRotateAugOCR', 'OCRSegTargets', 'FancyPCA',
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'RandomCropPolyInstances', 'RandomRotatePolyInstances', 'RandomPaddingOCR',
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'ImgAug', 'EastRandomCrop', 'RandomRotateImageBox', 'OpencvToPil',
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'PilToOpencv', 'KIEFormatBundle', 'SquareResizePad', 'TextSnakeTargets',
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'sort_vertex', 'LoadImageFromNdarray', 'sort_vertex8', 'FCENetTargets',
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'RandomScaling', 'RandomCropFlip', 'NerTransform', 'ToTensorNER',
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'ResizeNoImg', 'PyramidRescale', 'OneOfWrapper', 'RandomWrapper',
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'TorchVisionWrapper', 'LoadImageFromLMDB'
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'PilToOpencv', 'SquareResizePad', 'TextSnakeTargets', 'sort_vertex',
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'LoadImageFromNdarray', 'sort_vertex8', 'FCENetTargets', 'RandomScaling',
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'RandomCropFlip', 'NerTransform', 'ToTensorNER', 'ResizeNoImg',
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'PyramidRescale', 'OneOfWrapper', 'RandomWrapper', 'TorchVisionWrapper',
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'LoadImageFromLMDB'
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]
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@ -1,66 +0,0 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import numpy as np
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from mmcv.parallel import DataContainer as DC
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from mmdet.datasets.pipelines.formatting import DefaultFormatBundle
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from mmocr.core.visualize import overlay_mask_img, show_feature
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from mmocr.registry import TRANSFORMS
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@TRANSFORMS.register_module()
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class CustomFormatBundle(DefaultFormatBundle):
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"""Custom formatting bundle.
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It formats common fields such as 'img' and 'proposals' as done in
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DefaultFormatBundle, while other fields such as 'gt_kernels' and
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'gt_effective_region_mask' will be formatted to DC as follows:
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- gt_kernels: to DataContainer (cpu_only=True)
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- gt_effective_mask: to DataContainer (cpu_only=True)
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Args:
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keys (list[str]): Fields to be formatted to DC only.
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call_super (bool): If True, format common fields
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by DefaultFormatBundle, else format fields in keys above only.
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visualize (dict): If flag=True, visualize gt mask for debugging.
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"""
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def __init__(self,
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keys=[],
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call_super=True,
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visualize=dict(flag=False, boundary_key=None)):
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super().__init__()
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self.visualize = visualize
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self.keys = keys
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self.call_super = call_super
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def __call__(self, results):
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if self.visualize['flag']:
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img = results['img'].astype(np.uint8)
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boundary_key = self.visualize['boundary_key']
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if boundary_key is not None:
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img = overlay_mask_img(img, results[boundary_key].masks[0])
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features = [img]
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names = ['img']
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to_uint8 = [1]
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for k in results['mask_fields']:
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for iter in range(len(results[k].masks)):
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features.append(results[k].masks[iter])
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names.append(k + str(iter))
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to_uint8.append(0)
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show_feature(features, names, to_uint8)
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if self.call_super:
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results = super().__call__(results)
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for k in self.keys:
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results[k] = DC(results[k], cpu_only=True)
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return results
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def __repr__(self):
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return self.__class__.__name__
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@ -1,8 +1,6 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import numpy as np
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from mmcv import rescale_size
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from mmcv.parallel import DataContainer as DC
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from mmdet.datasets.pipelines.formatting import DefaultFormatBundle, to_tensor
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from mmocr.registry import TRANSFORMS
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@ -40,52 +38,53 @@ class ResizeNoImg:
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return results
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@TRANSFORMS.register_module()
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class KIEFormatBundle(DefaultFormatBundle):
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"""Key information extraction formatting bundle.
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# @TRANSFORMS.register_module()
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# class KIEFormatBundle(DefaultFormatBundle):
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# """Key information extraction formatting bundle.
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Based on the DefaultFormatBundle, itt simplifies the pipeline of formatting
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common fields, including "img", "proposals", "gt_bboxes", "gt_labels",
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"gt_masks", "gt_semantic_seg", "relations" and "texts".
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These fields are formatted as follows.
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# Based on the DefaultFormatBundle, itt simplifies the pipeline of
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# formatting
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# common fields, including "img", "proposals", "gt_bboxes", "gt_labels",
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# "gt_masks", "gt_semantic_seg", "relations" and "texts".
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# These fields are formatted as follows.
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- img: (1) transpose, (2) to tensor, (3) to DataContainer (stack=True)
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- proposals: (1) to tensor, (2) to DataContainer
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- gt_bboxes: (1) to tensor, (2) to DataContainer
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- gt_bboxes_ignore: (1) to tensor, (2) to DataContainer
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- gt_labels: (1) to tensor, (2) to DataContainer
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- gt_masks: (1) to tensor, (2) to DataContainer (cpu_only=True)
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- gt_semantic_seg: (1) unsqueeze dim-0 (2) to tensor,
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(3) to DataContainer (stack=True)
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- relations: (1) scale, (2) to tensor, (3) to DataContainer
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- texts: (1) to tensor, (2) to DataContainer
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"""
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# - img: (1) transpose, (2) to tensor, (3) to DataContainer (stack=True)
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# - proposals: (1) to tensor, (2) to DataContainer
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# - gt_bboxes: (1) to tensor, (2) to DataContainer
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# - gt_bboxes_ignore: (1) to tensor, (2) to DataContainer
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# - gt_labels: (1) to tensor, (2) to DataContainer
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# - gt_masks: (1) to tensor, (2) to DataContainer (cpu_only=True)
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# - gt_semantic_seg: (1) unsqueeze dim-0 (2) to tensor,
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# (3) to DataContainer (stack=True)
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# - relations: (1) scale, (2) to tensor, (3) to DataContainer
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# - texts: (1) to tensor, (2) to DataContainer
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# """
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def __call__(self, results):
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"""Call function to transform and format common fields in results.
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# def __call__(self, results):
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# """Call function to transform and format common fields in results.
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Args:
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results (dict): Result dict contains the data to convert.
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# Args:
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# results (dict): Result dict contains the data to convert.
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Returns:
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dict: The result dict contains the data that is formatted with
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default bundle.
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"""
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super().__call__(results)
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if 'ann_info' in results:
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for key in ['relations', 'texts']:
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value = results['ann_info'][key]
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if key == 'relations' and 'scale_factor' in results:
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scale_factor = results['scale_factor']
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if isinstance(scale_factor, float):
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sx = sy = scale_factor
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else:
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sx, sy = results['scale_factor'][:2]
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r = sx / sy
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factor = np.array([sx, sy, r, 1, r]).astype(np.float32)
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value = value * factor[None, None]
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results[key] = DC(to_tensor(value))
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return results
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# Returns:
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# dict: The result dict contains the data that is formatted with
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# default bundle.
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# """
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# super().__call__(results)
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# if 'ann_info' in results:
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# for key in ['relations', 'texts']:
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# value = results['ann_info'][key]
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# if key == 'relations' and 'scale_factor' in results:
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# scale_factor = results['scale_factor']
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# if isinstance(scale_factor, float):
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# sx = sy = scale_factor
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# else:
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# sx, sy = results['scale_factor'][:2]
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# r = sx / sy
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# factor = np.array([sx, sy, r, 1, r]).astype(np.float32)
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# value = value * factor[None, None]
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# results[key] = DC(to_tensor(value))
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# return results
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def __repr__(self):
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return self.__class__.__name__
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# def __repr__(self):
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# return self.__class__.__name__
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