def test_hsv2rgb(self):
scripted_fn = torch.jit.script(F_t._hsv2rgb)
shape = (3, 100, 150)
for _ in range(10):
hsv_img = torch.rand(*shape, dtype=torch.float, device=self.device)
rgb_img = F_t._hsv2rgb(hsv_img)
ft_img = rgb_img.permute(1, 2, 0).flatten(0, 1)
h, s, v, = hsv_img.unbind(0)
h = h.flatten().cpu().numpy()
s = s.flatten().cpu().numpy()
v = v.flatten().cpu().numpy()
rgb = []
for h1, s1, v1 in zip(h, s, v):
rgb.append(colorsys.hsv_to_rgb(h1, s1, v1))
colorsys_img = torch.tensor(rgb, dtype=torch.float32, device=self.device)
max_diff = (ft_img - colorsys_img).abs().max()
self.assertLess(max_diff, 1e-5)
s_rgb_img = scripted_fn(hsv_img)
self.assertTrue(rgb_img.allclose(s_rgb_img))
batch_tensors = self._create_data_batch(120, 100, num_samples=4, device=self.device).float()
self._test_fn_on_batch(batch_tensors, F_t._hsv2rgb)
def test_hsv2rgb(self):
shape = (3, 100, 150)
for _ in range(20):
img = torch.rand(*shape, dtype=torch.float)
ft_img = F_t._hsv2rgb(img).permute(1, 2, 0).flatten(0, 1)
h, s, v, = img.unbind(0)
h = h.flatten().numpy()
s = s.flatten().numpy()
v = v.flatten().numpy()
rgb = []
for h1, s1, v1 in zip(h, s, v):
rgb.append(colorsys.hsv_to_rgb(h1, s1, v1))
colorsys_img = torch.tensor(rgb, dtype=torch.float32)
max_diff = (ft_img - colorsys_img).abs().max()
self.assertLess(max_diff, 1e-5)
def test_hsv2rgb(device):
scripted_fn = torch.jit.script(F_t._hsv2rgb)
shape = (3, 100, 150)
for _ in range(10):
hsv_img = torch.rand(*shape, dtype=torch.float, device=device)
rgb_img = F_t._hsv2rgb(hsv_img)
ft_img = rgb_img.permute(1, 2, 0).flatten(0, 1)
h, s, v, = hsv_img.unbind(0)
h = h.flatten().cpu().numpy()
s = s.flatten().cpu().numpy()
v = v.flatten().cpu().numpy()
rgb = []
for h1, s1, v1 in zip(h, s, v):
rgb.append(colorsys.hsv_to_rgb(h1, s1, v1))
colorsys_img = torch.tensor(rgb, dtype=torch.float32, device=device)
torch.testing.assert_close(ft_img, colorsys_img, rtol=0.0, atol=1e-5)
s_rgb_img = scripted_fn(hsv_img)
torch.testing.assert_close(rgb_img, s_rgb_img)
batch_tensors = _create_data_batch(120, 100, num_samples=4, device=device).float()
_test_fn_on_batch(batch_tensors, F_t._hsv2rgb)
