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Support Objects365 pretrain and Adding the DINO++ model can achieve an accuracy of 63.4mAP at a model scale of 200M(Under the same scale, the accuracy is the best)
107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
import os.path as osp
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from xtcocotools.coco import COCO
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from easycv.datasets.detection.data_sources.coco import DetSourceCoco
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from easycv.datasets.registry import DATASOURCES
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objv2_ignore_list = [
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# images exist in annotations but not in image folder.
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'patch16/objects365_v2_00908726.jpg',
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'patch6/objects365_v1_00320532.jpg',
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'patch6/objects365_v1_00320534.jpg',
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]
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@DATASOURCES.register_module
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class DetSourceObjects365(DetSourceCoco):
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"""
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objects365 data source.
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The form of the objects365 dataset folder build:
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|- objects365
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|- annotation
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|- zhiyuan_objv2_train.json
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|- zhiyuan_objv2_val.json
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|- train
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|- patch0
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|- *****(imageID)
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|- patch1
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|- *****(imageID)
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...
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|- patch50
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|- *****(imageID)
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|- val
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|- patch0
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|- *****(imageID)
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|- patch1
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|- *****(imageID)
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...
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|- patch43
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|- *****(imageID)
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"""
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def __init__(self,
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ann_file,
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img_prefix,
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pipeline,
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test_mode=False,
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filter_empty_gt=False,
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classes=[],
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iscrowd=False):
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"""
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Args:
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ann_file: Path of annotation file.
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img_prefix: coco path prefix
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test_mode (bool, optional): If set True, `self._filter_imgs` will not works.
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filter_empty_gt (bool, optional): If set true, images without bounding
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boxes of the dataset's classes will be filtered out. This option
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only works when `test_mode=False`, i.e., we never filter images
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during tests.
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iscrowd: when traing setted as False, when val setted as True
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"""
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super(DetSourceObjects365, self).__init__(
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ann_file=ann_file,
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img_prefix=img_prefix,
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pipeline=pipeline,
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test_mode=test_mode,
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filter_empty_gt=filter_empty_gt,
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classes=classes,
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iscrowd=iscrowd)
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def load_annotations(self, ann_file):
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"""Load annotation from COCO style annotation file.
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Args:
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ann_file (str): Path of annotation file.
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Returns:
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list[dict]: Annotation info from COCO api.
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"""
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self.coco = COCO(ann_file)
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# The order of returned `cat_ids` will not
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# change with the order of the CLASSES
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self.cat_ids = self.coco.getCatIds(catNms=self.CLASSES)
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self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
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self.img_ids = self.coco.getImgIds()
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data_infos = []
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total_ann_ids = []
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for i in self.img_ids:
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info = self.coco.loadImgs([i])[0]
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# rename filename and filter wrong data
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info['patch_name'] = osp.join(
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osp.split(osp.split(info['file_name'])[0])[-1],
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osp.split(info['file_name'])[-1])
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if info['patch_name'] in objv2_ignore_list:
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continue
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info['filename'] = info['patch_name']
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data_infos.append(info)
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ann_ids = self.coco.getAnnIds(imgIds=[i])
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total_ann_ids.extend(ann_ids)
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assert len(set(total_ann_ids)) == len(
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total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
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return data_infos
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