def test_decode_png(self):
for img_path in get_images(IMAGE_DIR, "png"):
img_pil = torch.from_numpy(np.array(Image.open(img_path)))
size = os.path.getsize(img_path)
img_lpng = decode_png(
torch.from_file(img_path, dtype=torch.uint8, size=size))
self.assertEqual(img_lpng, img_pil)
self.assertEqual(decode_png(torch.empty()), torch.empty())
self.assertEqual(decode_png(torch.randint(3, 5, (300, ))),
torch.empty())
def test_decode_png(self):
for img_path in get_images(IMAGE_DIR, ".png"):
img_pil = torch.from_numpy(np.array(Image.open(img_path)))
size = os.path.getsize(img_path)
img_lpng = decode_png(torch.from_file(img_path, dtype=torch.uint8, size=size))
self.assertTrue(img_lpng.equal(img_pil))
with self.assertRaises(ValueError):
decode_png(torch.empty((), dtype=torch.uint8))
with self.assertRaises(RuntimeError):
decode_png(torch.randint(3, 5, (300,), dtype=torch.uint8))
def test_decode_png(self):
for img_path in get_images(IMAGE_DIR, ".png"):
img_pil = torch.from_numpy(np.array(Image.open(img_path)))
img_pil = img_pil.permute(2, 0, 1)
data = read_file(img_path)
img_lpng = decode_png(data)
self.assertTrue(img_lpng.equal(img_pil))
with self.assertRaises(RuntimeError):
decode_png(torch.empty((), dtype=torch.uint8))
with self.assertRaises(RuntimeError):
decode_png(torch.randint(3, 5, (300,), dtype=torch.uint8))
def test_decode_png(self):
conversion = [(None, 0), ("L", 1), ("LA", 2), ("RGB", 3), ("RGBA", 4)]
for img_path in get_images(FAKEDATA_DIR, ".png"):
for pil_mode, channels in conversion:
with Image.open(img_path) as img:
if pil_mode is not None:
img = img.convert(pil_mode)
img_pil = torch.from_numpy(np.array(img))
img_pil = normalize_dimensions(img_pil)
data = read_file(img_path)
img_lpng = decode_image(data, channels=channels)
tol = 0 if conversion is None else 1
self.assertTrue(img_lpng.allclose(img_pil, atol=tol))
with self.assertRaises(RuntimeError):
decode_png(torch.empty((), dtype=torch.uint8))
with self.assertRaises(RuntimeError):
decode_png(torch.randint(3, 5, (300,), dtype=torch.uint8))
def test_decode_png_errors():
with pytest.raises(RuntimeError,
match="Expected a non empty 1-dimensional tensor"):
decode_png(torch.empty((), dtype=torch.uint8))
with pytest.raises(RuntimeError, match="Content is not png"):
decode_png(torch.randint(3, 5, (300, ), dtype=torch.uint8))
