def get_dataset(dataset, args): if dataset.lower() == 'voc': train_dataset = gdata.VOCDetection( splits=[(2007, 'trainval'), (2012, 'trainval')]) val_dataset = gdata.VOCDetection(splits=[(2007, 'test')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() in ['clipart', 'comic', 'watercolor']: root = os.path.join('~', '.mxnet', 'datasets', dataset.lower()) train_dataset = gdata.CustomVOCDetection(root=root, splits=[('', 'train')], generate_classes=True) val_dataset = gdata.CustomVOCDetection(root=root, splits=[('', 'test')], generate_classes=True) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False) val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False) val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True) else: raise NotImplementedError( 'Dataset: {} not implemented.'.format(dataset)) if args.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric
def get_dataset(dataset, mixup=False, tclass=None): if dataset.lower() == 'real': train_dataset = RealDataset(mode='train') val_dataset = RealDataset(mode='test') elif dataset.lower() == 'real_with_grasp': train_dataset = RealGraspDataset(mode='train') val_dataset = RealGraspDataset(mode='test') elif dataset.lower() == 'synth_spec': train_dataset = SpecSynthDataset(tclass=tclass, root=pjoin(cfg.dataset_folder, 'synth_small_bg'), mode='all') val_dataset = SpecRealDataset(tclass=tclass, mode='all') elif dataset.lower() == 'synth_small_printer': train_dataset = SynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), mode='all') val_dataset = RealDataset(mode='test') elif dataset.lower() == 'synth_part2': train_dataset = SpecSynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), tclass='part2', mode='all') val_dataset = SpecRealDataset(mode='all', tclass='part2') elif dataset.lower() == 'synth_part3': train_dataset = SpecSynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), tclass='part3', mode='all') val_dataset = SpecRealDataset(mode='all', tclass='part3') elif dataset.lower() == 'synth_dosing_nozzle': train_dataset = SpecSynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), tclass='part3', mode='all') val_dataset = SpecRealDataset(mode='all', tclass='dosing_nozzle') elif dataset.split('_')[0] == 'synth': train_dataset = SynthDataset(root=pjoin(cfg.dataset_folder, dataset), mode='all') val_dataset = RealDataset(mode='test') val_metric = VOCMApMetric(iou_thresh=0.5, class_names=val_dataset.classes) if mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': train_dataset = VOCLike( root="/content/drive/My Drive/Research/Dataset_conversion/Dataset/", splits=[(2007, 'train')]) val_dataset = VOCLike( root="/content/drive/My Drive/Research/Dataset_conversion/Dataset/", splits=[(2007, 'validation')]) print(train_dataset.classes) print(val_dataset.classes) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False) val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False) val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True) else: raise NotImplementedError( 'Dataset: {} not implemented.'.format(dataset)) if args.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': pass # train_dataset = gdata.VOCDetection( # splits=[(2007, 'trainval'), (2012, 'trainval')]) # val_dataset = gdata.VOCDetection( # splits=[(2007, 'test')]) # val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': train_dataset = gdata.COCODetection( root='/home/xcq/PycharmProjects/datasets/coco/', splits='instances_train2017', use_crowd=False) val_dataset = gdata.COCODetection( root='/home/xcq/PycharmProjects/datasets/coco/', splits='instances_val2017', skip_empty=False) val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True) else: raise NotImplementedError( 'Dataset: {} not implemented.'.format(dataset)) if args.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': pass elif dataset.lower() == 'coco': train_dataset = gdata.COCOInstance(root='/home/xcq/PycharmProjects/datasets/coco/',splits='instances_train2017') val_dataset = gdata.COCOInstance(root='/home/xcq/PycharmProjects/datasets/coco/',splits='instances_val2017', skip_empty=False) val_metric = COCOInstanceMetric(val_dataset, args.save_prefix + '_eval', cleanup=True) else: raise NotImplementedError('Dataset: {} not implemented.'.format(dataset)) if args.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric
def _get_dataset(dataset, args): if dataset.lower() == 'voc': train_dataset = gdata.VOCDetection( splits=[(2007, 'trainval'), (2012, 'trainval')]) val_dataset = gdata.VOCDetection(splits=[(2007, 'test')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'voc_tiny': # need to download the dataset and specify the path to store the dataset in # root = os.path.expanduser('~/.mxnet/datasets/') # filename_zip = ag.download('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip', path=root) # filename = ag.unzip(filename_zip, root=root) # data_root = os.path.join(root, filename) train_dataset = gdata.CustomVOCDetectionBase(classes=('motorbike', ), root=args.dataset_root + 'tiny_motorbike', splits=[('', 'trainval')]) val_dataset = gdata.CustomVOCDetectionBase(classes=('motorbike', ), root=args.dataset_root + 'tiny_motorbike', splits=[('', 'test')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() in ['clipart', 'comic', 'watercolor']: root = os.path.join('~', '.mxnet', 'datasets', dataset.lower()) train_dataset = gdata.CustomVOCDetection(root=root, splits=[('', 'train')], generate_classes=True) val_dataset = gdata.CustomVOCDetection(root=root, splits=[('', 'test')], generate_classes=True) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False) val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False) val_metric = COCODetectionMetric(val_dataset, os.path.join( args.logdir, args.save_prefix + '_eval'), cleanup=True) else: raise NotImplementedError( 'Dataset: {} not implemented.'.format(dataset)) if args.train.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric
def get_dataset(args): dataset = args.dataset if dataset.lower() == 'voc': train_dataset = gdata.VOCDetection( splits=[(2007, 'trainval'), (2012, 'trainval')]) val_dataset = gdata.VOCDetection(splits=[(2007, 'test')]) val_metric = VOC07MApMetric(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) if args.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args): if dataset.lower() == 'voc': train_dataset = VOCLike(root='clothes_data', splits=((2018, 'train'), )) print(train_dataset) val_dataset = VOCLike(root='clothes_data', splits=((2018, 'test'), )) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False) val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False) val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True) else: raise NotImplementedError( 'Dataset: {} not implemented.'.format(dataset)) if args.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric