How to use the gluoncv.data function in gluoncv

def get_dataset(dataset, args):
    if dataset.lower() == 'coco':
        train_dataset = gdata.COCOInstance(splits='instances_train2017')
        val_dataset = gdata.COCOInstance(splits='instances_val2017', skip_empty=False)
        starting_id = 0
        if args.horovod and MPI:
            length = len(val_dataset)
            shard_len = length // hvd.size()
            rest = length % hvd.size()
            # Compute the start index for this partition
            starting_id = shard_len * hvd.rank() + min(hvd.rank(), rest)
        val_metric = COCOInstanceMetric(val_dataset, args.save_prefix + '_eval',
                                        use_ext=args.use_ext, starting_id=starting_id)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    if args.horovod and MPI:
        val_dataset = val_dataset.shard(hvd.size(), hvd.rank())
    return train_dataset, val_dataset, val_metric

epoch, i, batch_size/(time.time()-btic), name1, loss1, name2, loss2))
        btic = time.time()

#############################################################################################
# Save finetuned weights to disk
net.save_parameters('ssd_512_mobilenet1.0_pikachu.params')

#############################################################################################
# Predict with finetuned model
# ----------------------------
# We can test the performance using finetuned weights
test_url = 'https://raw.githubusercontent.com/zackchase/mxnet-the-straight-dope/master/img/pikachu.jpg'
download(test_url, 'pikachu_test.jpg')
net = gcv.model_zoo.get_model('ssd_512_mobilenet1.0_custom', classes=classes, pretrained_base=False)
net.load_parameters('ssd_512_mobilenet1.0_pikachu.params')
x, image = gcv.data.transforms.presets.ssd.load_test('pikachu_test.jpg', 512)
cid, score, bbox = net(x)
ax = viz.plot_bbox(image, bbox[0], score[0], cid[0], class_names=classes)
plt.show()

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval',
                                         cleanup=not args.save_json)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return 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() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017')
        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))
    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() == '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':
        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(
            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,
            data_shape=(args.data_shape, args.data_shape))
    else:
        raise NotImplementedError(
            'Dataset: {} not implemented.'.format(dataset))

def get_dali_dataset(dataset_name, devices, args):
    if dataset_name.lower() == "coco":
        # training
        expanded_file_root = os.path.expanduser(args.dataset_root)
        coco_root = os.path.join(expanded_file_root,
                                 'coco',
                                 'train2017')
        coco_annotations = os.path.join(expanded_file_root,
                                        'coco',
                                        'annotations',
                                        'instances_train2017.json')
        if args.horovod:
            train_dataset = [gdata.COCODetectionDALI(num_shards=hvd.size(), shard_id=hvd.rank(), file_root=coco_root,
                                                     annotations_file=coco_annotations, device_id=hvd.local_rank())]
        else:
            train_dataset = [gdata.COCODetectionDALI(num_shards= len(devices), shard_id=i, file_root=coco_root,
                                                     annotations_file=coco_annotations, device_id=i) for i, _ in enumerate(devices)]

        # validation
        if (not args.horovod or hvd.rank() == 0):
            val_dataset = gdata.COCODetection(root=os.path.join(args.dataset_root + '/coco'),
                                              splits='instances_val2017',
                                              skip_empty=False)
            val_metric = COCODetectionMetric(
                val_dataset, args.save_prefix + '_eval', cleanup=True,
                data_shape=(args.data_shape, args.data_shape))
        else:
            val_dataset = None
            val_metric = None
    else:
        raise NotImplementedError('Dataset: {} not implemented with DALI.'.format(dataset_name))

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = VOCLike(root='D:\VOCdevkit', splits=[(2028, 'trainval')])
        val_dataset = VOCLike(root='D:\VOCdevkit', splits=[(2028, '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,
            data_shape=(args.data_shape, args.data_shape))
    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

det_score = det_score[i].asnumpy()
                det_mask = det_mask[i].asnumpy()
                det_info = det_info[i].asnumpy()
                # filter by conf threshold
                im_height, im_width, im_scale = det_info
                valid = np.where(((det_id >= 0) & (det_score >= 0.001)))[0]
                det_id = det_id[valid]
                det_score = det_score[valid]
                det_bbox = det_bbox[valid] / im_scale
                det_mask = det_mask[valid]
                # fill full mask
                im_height, im_width = int(round(im_height / im_scale)), int(
                    round(im_width / im_scale))
                full_masks = []
                for bbox, mask in zip(det_bbox, det_mask):
                    full_masks.append(gdata.transforms.mask.fill(mask, bbox, (im_width, im_height)))
                full_masks = np.array(full_masks)
                eval_metric.update(det_bbox, det_id, det_score, full_masks)
    return eval_metric.get()

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.75, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval',
                                         cleanup=not args.save_json)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric