def test_transforms_presets_yolo():
im_fname = gcv.utils.download('https://github.com/dmlc/web-data/blob/master/' +
'gluoncv/detection/biking.jpg?raw=true', path='biking.jpg')
x, orig_img = yolo.load_test(im_fname, short=512)
x1, orig_img1 = yolo.transform_test(mx.image.imread(im_fname), short=512)
np.testing.assert_allclose(x.asnumpy(), x1.asnumpy())
np.testing.assert_allclose(orig_img, orig_img1)
if not osp.isdir(osp.expanduser('~/.mxnet/datasets/voc')):
return
train_dataset = gcv.data.VOCDetection(splits=((2007, 'trainval'), (2012, 'trainval')))
val_dataset = gcv.data.VOCDetection(splits=[(2007, 'test')])
width, height = (512, 512)
net = gcv.model_zoo.get_model('yolo3_darknet53_voc', pretrained=False, pretrained_base=False)
net.initialize()
num_workers = 0
batch_size = 4
batchify_fn = Tuple(*([Stack() for _ in range(6)] + [Pad(axis=0, pad_val=-1) for _ in range(1)]))
train_loader = gluon.data.DataLoader(
train_dataset.transform(yolo.YOLO3DefaultTrainTransform(width, height, net)),
batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(
val_dataset.transform(yolo.YOLO3DefaultValTransform(width, height)),
batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep', num_workers=num_workers)
train_loader2 = gluon.data.DataLoader(
train_dataset.transform(yolo.YOLO3DefaultTrainTransform(width, height)),
batch_size, True, batchify_fn=val_batchify_fn, last_batch='rollover', num_workers=num_workers)
for loader in [train_loader, val_loader, train_loader2]:
for i, batch in enumerate(loader):
if i > 1:
break
pass
def test_transforms_presets_yolo():
im_fname = gcv.utils.download('https://github.com/dmlc/web-data/blob/master/' +
'gluoncv/detection/biking.jpg?raw=true', path='biking.jpg')
x, orig_img = yolo.load_test(im_fname, short=512)
x1, orig_img1 = yolo.transform_test(mx.image.imread(im_fname), short=512)
np.testing.assert_allclose(x.asnumpy(), x1.asnumpy())
np.testing.assert_allclose(orig_img, orig_img1)
if not osp.isdir(osp.expanduser('~/.mxnet/datasets/voc')):
return
train_dataset = VOCDetectionTiny()
val_dataset = VOCDetectionTiny(splits=[('tiny_motorbike', 'test')])
width, height = (512, 512)
net = gcv.model_zoo.get_model('yolo3_darknet53_voc', pretrained=False, pretrained_base=False)
net.initialize()
num_workers = 0
batch_size = 4
batchify_fn = Tuple(*([Stack() for _ in range(6)] + [Pad(axis=0, pad_val=-1) for _ in range(1)]))
train_loader = gluon.data.DataLoader(
train_dataset.transform(yolo.YOLO3DefaultTrainTransform(width, height, net)),
batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(
val_dataset.transform(yolo.YOLO3DefaultValTransform(width, height)),
batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep', num_workers=num_workers)
train_loader2 = gluon.data.DataLoader(
train_dataset.transform(yolo.YOLO3DefaultTrainTransform(width, height)),
batch_size, True, batchify_fn=val_batchify_fn, last_batch='rollover', num_workers=num_workers)
for loader in [train_loader, val_loader, train_loader2]:
for i, batch in enumerate(loader):
if i > 1:
break
pass