def get_dataset(dataset, args): if dataset.lower() == 'voc': train_dataset = gdata.VOCDetection( splits=[('sbdche', 'train' + '_' + str(args.deg) + '_bboxwh')]) if args.val_2012 == True: val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche', 'val_2012_bboxwh')]) else: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val' + '_' + str(args.deg) + '_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric( iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco_pretrain': train_dataset = gdata.coco_pretrain_Detection( splits=[('_coco_20', 'train' + '_' + str(args.deg) + '_bboxwh')]) if args.val_2012 == True: val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche', 'val_2012_bboxwh')]) else: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val' + '_' + str(args.deg) + '_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric( iou_thresh=0.5, class_names=val_dataset.classes) else: raise NotImplementedError( 'Dataset: {} not implemented.'.format(dataset)) if args.num_samples < 0: args.num_samples = len(train_dataset) return train_dataset, val_dataset, val_metric, val_polygon_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': if args.val_2012 == True: train_dataset = gdata.VOCDetection( splits=[('sbdche', 'train_voc2012_bboxwh')]) val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche', 'val_2012_bboxwh')]) else: train_dataset = gdata.VOCDetection(splits=[('sbdche', 'train' + '_' + '8' + '_bboxwh')]) val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche', 'val' + '_' + '8' + '_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.7, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric( iou_thresh=0.7, class_names=val_dataset.classes) elif dataset.lower() == 'coco_pretrain': train_dataset = gdata.coco_pretrain_Detection( splits=[('_coco_20', 'train' + '_' + '8' + '_bboxwh')]) if args.val_2012 == True: val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche', 'val_2012_bboxwh')]) else: val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche', 'val' + '_' + '8' + '_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.7, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric( iou_thresh=0.7, class_names=val_dataset.classes) elif dataset.lower() == 'coco': train_dataset = gdata.cocoDetection( root='/home/tutian/dataset/coco_to_voc/train', subfolder='./bases_50_xml_each_' + 'var') val_dataset = gdata.cocoDetection( root='/home/tutian/dataset/coco_to_voc/val', subfolder='./bases_50_xml_' + 'raw_coef') val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) # val_polygon_metric = New07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes, root='/home/tutian/dataset/coco_to_voc/val/') val_polygon_metric = None else: raise NotImplementedError( 'Dataset: {} not implemented.'.format(dataset)) if args.num_samples < 0: args.num_samples = len(train_dataset) if args.mixup: from gluoncv.data import MixupDetection train_dataset = MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric, val_polygon_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': if args.val_voc2012: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val_2012_bboxwh')]) else: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val'+'_'+'8'+'_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco':
def get_dataset(dataset, args): if dataset.lower() == 'voc': if args.val_voc2012: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val_2012_bboxwh')]) else: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val' + '_' + str(args.deg) + '_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes) else: raise NotImplementedError('Dataset: {} not implemented.'.format(dataset)) return val_dataset, val_metric, val_polygon_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': if args.val_voc2012: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val_2012_bboxwh')]) else: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val'+'_'+'8'+'_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': val_dataset = gdata.cocoDetection(root='/home/tutian/dataset/coco_to_voc/val', subfolder='./bases_50_xml_'+'raw_coef') val_metric = VOC07MApMetric(iou_thresh=0.75, class_names=val_dataset.classes) val_polygon_metric = New07PolygonMApMetric(iou_thresh=0.75, class_names=val_dataset.classes, root='/home/tutian/dataset/coco_to_voc/val/') else: raise NotImplementedError('Dataset: {} not implemented.'.format(dataset)) return val_dataset, val_metric, val_polygon_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': if args.val_voc2012: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val_2012_bboxwh')]) else: val_dataset = gdata.VOC_Val_Detection( splits=[('sbdche', 'val'+'_'+'8'+'_bboxwh')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': val_dataset = COCOInstance(root='/home/tutian/dataset/', skip_empty=False) val_metric = COCOInstanceMetric(val_dataset, 'test_cocoapi', method='var') val_polygon_metric = None else: raise NotImplementedError('Dataset: {} not implemented.'.format(dataset)) return val_dataset, val_metric, val_polygon_metric