def test_image_resize_1(self):
images_batch = torch.ones((3, 100, 100, 3), dtype=torch.uint8) * 100
transform = ImageResizeTransform()
images_transformed = transform(images_batch)
IMAGES_GT = torch.ones((3, 3, 800, 800), dtype=torch.float) * 100
self.assertEqual(images_transformed.size(), IMAGES_GT.size())
self.assertAlmostEqual(torch.abs(IMAGES_GT - images_transformed).max().item(), 0.0)
def test_image_list_dataset_with_transform(self):
height, width = 720, 1280
with temp_image(height, width) as image_fpath:
image_list = [image_fpath]
category_list = [None]
transform = ImageResizeTransform()
dataset = ImageListDataset(image_list, category_list, transform)
self.assertEqual(len(dataset), 1)
data1, categories1 = dataset[0]["images"], dataset[0]["categories"]
self.assertEqual(data1.shape, torch.Size((1, 3, 749, 1333)))
self.assertEqual(data1.dtype, torch.float32)
self.assertIsNone(categories1[0])
def test_read_keyframes_with_selector_with_transform(self):
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
video_list = [fname]
random.seed(0)
frame_selector = RandomKFramesSelector(1)
transform = ImageResizeTransform()
dataset = VideoKeyframeDataset(video_list, frame_selector,
transform)
data1 = dataset[0]
self.assertEqual(len(dataset), 1)
self.assertEqual(data1.shape, torch.Size((1, 3, 800, 800)))
self.assertEqual(data1.dtype, torch.float32)
return
self.assertTrue(False)