def test_open_images_v4_object_detection():
batch_size = 1
image_size = [256, 256]
dataset = OpenImagesV4BoundingBox(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size))
dataset = DatasetIterator(dataset)
num_max_boxes = dataset.num_max_boxes
assert dataset.num_max_boxes == OpenImagesV4BoundingBox.count_max_boxes()
for _ in range(5):
images, labels = dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
def test_bdd100k_seg():
batch_size = 1
train_dataset = DummyBDD100KSeg(subset="train", batch_size=batch_size)
train_iterator = DatasetIterator(train_dataset)
test_dataset = DummyBDD100KSeg(subset="validation", batch_size=batch_size)
test_iterator = DatasetIterator(test_dataset)
assert train_dataset.num_classes == 41
colors = train_dataset.label_colors
assert len(colors) == 41
train_image_files, train_label_files = train_iterator.feed()
assert train_image_files.shape[0] == batch_size
assert train_label_files.shape[0] == batch_size
test_image_files, test_label_files = test_iterator.feed()
assert test_image_files.shape[0] == batch_size
assert test_label_files.shape[0] == batch_size
def test_hue():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=Hue((-10, 10)),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_filp_top_bottom():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=FlipTopBottom(),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_ssd_random_crop():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=SSDRandomCrop(),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
assert np.all(labels[:, :, 2] <= 512)
assert np.all(labels[:, :, 3] <= 256)
def test_sequence():
batch_size = 3
image_size = [256, 512]
augmentor = Sequence([
FlipLeftRight(),
FlipTopBottom(),
SSDRandomCrop(),
])
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=augmentor,
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_can_iterate(set_test_environment, subset):
batch_size = 1
image_size = (100, 100)
dataset = Div2k(subset, batch_size=batch_size)
iterator = DatasetIterator(dataset)
for _ in range(len(dataset)):
images, labels = iterator.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels[0] is None
def test_open_images_v4_classification():
batch_size = 1
image_size = [256, 256]
dataset = OpenImagesV4Classification(batch_size=batch_size,
pre_processor=Resize(image_size))
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == dataset.num_classes