How to use the gluoncv.utils.download function in gluoncv

parser.add_argument('--pretrained', type=str, default='True',
                        help='Load weights from previously saved parameters.')
    parser.add_argument('--thresh', type=float, default=0.5,
                        help='Threshold of object score when visualize the bboxes.')
    args = parser.parse_args()
    return args

if __name__ == '__main__':
    args = parse_args()
    # context list
    ctx = [mx.gpu(int(i)) for i in args.gpus.split(',') if i.strip()]
    ctx = [mx.cpu()] if not ctx else ctx

    # grab some image if not specified
    if not args.images.strip():
        gcv.utils.download("https://cloud.githubusercontent.com/assets/3307514/" +
            "20012568/cbc2d6f6-a27d-11e6-94c3-d35a9cb47609.jpg", 'street.jpg')
        image_list = ['street.jpg']
    else:
        image_list = [x.strip() for x in args.images.split(',') if x.strip()]

    if args.pretrained.lower() in ['true', '1', 'yes', 't']:
        net = gcv.model_zoo.get_model(args.network, pretrained=True)
    else:
        net = gcv.model_zoo.get_model(args.network, pretrained=False, pretrained_base=False)
        net.load_parameters(args.pretrained)
    net.set_nms(0.45, 200)
    net.collect_params().reset_ctx(ctx = ctx)

    for image in image_list:
        ax = None
        x, img = presets.yolo.load_test(image, short=512)

######################################################################
# Load a pretrained model
# -------------------------
#
# Let's get an Faster RCNN model trained on COCO
# dataset with ResNet-50 backbone.

net = model_zoo.get_model('faster_rcnn_resnet50_v1b_coco', pretrained=True)

######################################################################
# Pre-process an image
# --------------------
# Similar to faster rcnn inference tutorial, we grab and preprocess a demo image

im_fname = utils.download('https://github.com/dmlc/web-data/blob/master/' +
                          'gluoncv/detection/biking.jpg?raw=true',
                          path='biking.jpg')
x, orig_img = data.transforms.presets.rcnn.load_test(im_fname)

######################################################################
# Reset classes to exactly what we want
# -------------------------------------
# Original COCO model has 80 classes
print('coco classes: ', net.classes)
net.reset_class(classes=['bicycle', 'backpack'], reuse_weights=['bicycle', 'backpack'])
# now net has 2 classes as desired
print('new classes: ', net.classes)

######################################################################
# Inference and display
# ---------------------

def download_ade(path, overwrite=False):
    _AUG_DOWNLOAD_URLS = [
        ('http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip', '219e1696abb36c8ba3a3afe7fb2f4b4606a897c7'),
        ('http://data.csail.mit.edu/places/ADEchallenge/release_test.zip', 'e05747892219d10e9243933371a497e905a4860c'),]
    download_dir = os.path.join(path, 'downloads')
    makedirs(download_dir)
    for url, checksum in _AUG_DOWNLOAD_URLS:
        filename = download(url, path=download_dir, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with zipfile.ZipFile(filename,"r") as zip_ref:
            zip_ref.extractall(path=path)

def download_aug(path, overwrite=False):
    _AUG_DOWNLOAD_URLS = [
        ('http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz', '7129e0a480c2d6afb02b517bb18ac54283bfaa35')]
    makedirs(path)
    for url, checksum in _AUG_DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with tarfile.open(filename) as tar:
            tar.extractall(path=path)
            shutil.move(os.path.join(path, 'benchmark_RELEASE'),
                        os.path.join(path, 'VOCaug'))
            filenames = ['VOCaug/dataset/train.txt', 'VOCaug/dataset/val.txt']
            # generate trainval.txt
            with open(os.path.join(path, 'VOCaug/dataset/trainval.txt'), 'w') as outfile:
                for fname in filenames:
                    fname = os.path.join(path, fname)
                    with open(fname) as infile:
                        for line in infile:
                            outfile.write(line)

_DOWNLOAD_URLS = [
        ('http://images.cocodataset.org/zips/train2017.zip',
         '10ad623668ab00c62c096f0ed636d6aff41faca5'),
        ('http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
         '8551ee4bb5860311e79dace7e79cb91e432e78b3'),
        ('http://images.cocodataset.org/zips/val2017.zip',
         '4950dc9d00dbe1c933ee0170f5797584351d2a41'),
        # ('http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip',
         # '46cdcf715b6b4f67e980b529534e79c2edffe084'),
        # test2017.zip, for those who want to attend the competition.
        # ('http://images.cocodataset.org/zips/test2017.zip',
        #  '4e443f8a2eca6b1dac8a6c57641b67dd40621a49'),
    ]
    makedirs(path)
    for url, checksum in _DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with zipfile.ZipFile(filename) as zf:
            zf.extractall(path=path)

#
# First, we download the video and sample the video frames at a speed of 1 frame per second.
#
# .. raw:: html
#
#     <div align="center">
#         <img src="../../_static/action_basketball_demo.gif">
#     </div>
#
#     <br>

from gluoncv.utils import try_import_cv2
cv2 = try_import_cv2()

url = 'https://github.com/bryanyzhu/tiny-ucf101/raw/master/v_Basketball_g01_c01.avi'
video_fname = utils.download(url)

cap = cv2.VideoCapture(video_fname)
cnt = 0
video_frames = []
while(cap.isOpened()):
    ret, frame = cap.read()
    cnt += 1
    if ret and cnt % 25 == 0:
        video_frames.append(frame)
    if not ret: break

cap.release()
print('We evenly extract %d frames from the video %s.' % (len(video_frames), video_fname))

################################################################
#

print('image shape:', first_img.shape)
print('Label example:')
print(record_dataset[0][1])


##############################################################################
#
# .. _pascal_voc_like:
#
# 2. Derive from PASCAL VOC format
# --------------------------------
# It you have a custom dataset fully comply with the `Pascal VOC `_ object detection format,
# that could be good news, because it's can be adapted to GluonCV format real quick.
#
# We provide a template for you to peek the structures
fname = utils.download('https://github.com/dmlc/web-data/blob/master/gluoncv/datasets/VOCtemplate.zip?raw=true', 'VOCtemplate.zip')
with zipfile.ZipFile(fname) as zf:
    zf.extractall('.')

##############################################################################
# A VOC-like dataset will have the following structure:
#
"""
VOCtemplate
└── VOC2018
    ├── Annotations
    │   └── 000001.xml
    ├── ImageSets
    │   └── Main
    │       └── train.txt
    └── JPEGImages
        └── 000001.jpg

def build_rec_process(img_dir, train=False, num_thread=1):

    from gluoncv.utils import download, makedirs

    rec_dir = os.path.abspath(os.path.join(img_dir, '../rec'))
    makedirs(rec_dir)
    prefix = 'train' if train else 'val'
    print('Building ImageRecord file for ' + prefix + ' ...')
    # to_path = rec_dir

    # download lst file and im2rec script
    script_path = os.path.join(rec_dir, 'im2rec.py')
    script_url = 'https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/im2rec.py'
    download(script_url, script_path)

    lst_path = os.path.join(rec_dir, prefix + '.lst')
    lst_url = 'http://data.mxnet.io/models/imagenet/resnet/' + prefix + '.lst'
    download(lst_url, lst_path)

    # execution
    import sys
    cmd = [
        sys.executable,
        script_path,
        rec_dir,
        img_dir,
        '--recursive',
        '--pass-through',
        '--pack-label',
        '--num-thread',

def build_rec_process(img_dir, train=False, num_thread=1):
    rec_dir = os.path.abspath(os.path.join(img_dir, '../rec'))
    makedirs(rec_dir)
    prefix = 'train' if train else 'val'
    print('Building ImageRecord file for ' + prefix + ' ...')
    to_path = rec_dir

    # download lst file and im2rec script
    script_path = os.path.join(rec_dir, 'im2rec.py')
    script_url = 'https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/im2rec.py'
    download(script_url, script_path)

    lst_path = os.path.join(rec_dir, prefix + '.lst')
    lst_url = 'http://data.mxnet.io/models/imagenet/resnet/' + prefix + '.lst'
    download(lst_url, lst_path)

    # execution
    import sys
    cmd = [
        sys.executable,
        script_path,
        rec_dir,
        img_dir,
        '--recursive',
        '--pass-through',
        '--pack-label',
        '--num-thread',