def test_rgb2hsv(self):
shape = (3, 150, 100)
for _ in range(20):
img = torch.rand(*shape, dtype=torch.float)
ft_hsv_img = F_t._rgb2hsv(img).permute(1, 2, 0).flatten(0, 1)
r, g, b, = img.unbind(0)
r = r.flatten().numpy()
g = g.flatten().numpy()
b = b.flatten().numpy()
hsv = []
for r1, g1, b1 in zip(r, g, b):
hsv.append(colorsys.rgb_to_hsv(r1, g1, b1))
colorsys_img = torch.tensor(hsv, dtype=torch.float32)
ft_hsv_img_h, ft_hsv_img_sv = torch.split(ft_hsv_img, [1, 2],
dim=1)
colorsys_img_h, colorsys_img_sv = torch.split(colorsys_img, [1, 2],
dim=1)
max_diff_h = ((colorsys_img_h * 2 * math.pi).sin() -
(ft_hsv_img_h * 2 * math.pi).sin()).abs().max()
max_diff_sv = (colorsys_img_sv - ft_hsv_img_sv).abs().max()
max_diff = max(max_diff_h, max_diff_sv)
self.assertLess(max_diff, 1e-5)
def test_rgb2hsv(device):
scripted_fn = torch.jit.script(F_t._rgb2hsv)
shape = (3, 150, 100)
for _ in range(10):
rgb_img = torch.rand(*shape, dtype=torch.float, device=device)
hsv_img = F_t._rgb2hsv(rgb_img)
ft_hsv_img = hsv_img.permute(1, 2, 0).flatten(0, 1)
r, g, b, = rgb_img.unbind(dim=-3)
r = r.flatten().cpu().numpy()
g = g.flatten().cpu().numpy()
b = b.flatten().cpu().numpy()
hsv = []
for r1, g1, b1 in zip(r, g, b):
hsv.append(colorsys.rgb_to_hsv(r1, g1, b1))
colorsys_img = torch.tensor(hsv, dtype=torch.float32, device=device)
ft_hsv_img_h, ft_hsv_img_sv = torch.split(ft_hsv_img, [1, 2], dim=1)
colorsys_img_h, colorsys_img_sv = torch.split(colorsys_img, [1, 2], dim=1)
max_diff_h = ((colorsys_img_h * 2 * math.pi).sin() - (ft_hsv_img_h * 2 * math.pi).sin()).abs().max()
max_diff_sv = (colorsys_img_sv - ft_hsv_img_sv).abs().max()
max_diff = max(max_diff_h, max_diff_sv)
assert max_diff < 1e-5
s_hsv_img = scripted_fn(rgb_img)
torch.testing.assert_close(hsv_img, s_hsv_img, rtol=1e-5, atol=1e-7)
batch_tensors = _create_data_batch(120, 100, num_samples=4, device=device).float()
_test_fn_on_batch(batch_tensors, F_t._rgb2hsv)
def test_rgb2hsv(self):
scripted_fn = torch.jit.script(F_t._rgb2hsv)
shape = (3, 150, 100)
for _ in range(10):
rgb_img = torch.rand(*shape, dtype=torch.float, device=self.device)
hsv_img = F_t._rgb2hsv(rgb_img)
ft_hsv_img = hsv_img.permute(1, 2, 0).flatten(0, 1)
r, g, b, = rgb_img.unbind(0)
r = r.flatten().cpu().numpy()
g = g.flatten().cpu().numpy()
b = b.flatten().cpu().numpy()
hsv = []
for r1, g1, b1 in zip(r, g, b):
hsv.append(colorsys.rgb_to_hsv(r1, g1, b1))
colorsys_img = torch.tensor(hsv,
dtype=torch.float32,
device=self.device)
ft_hsv_img_h, ft_hsv_img_sv = torch.split(ft_hsv_img, [1, 2],
dim=1)
colorsys_img_h, colorsys_img_sv = torch.split(colorsys_img, [1, 2],
dim=1)
max_diff_h = ((colorsys_img_h * 2 * math.pi).sin() -
(ft_hsv_img_h * 2 * math.pi).sin()).abs().max()
max_diff_sv = (colorsys_img_sv - ft_hsv_img_sv).abs().max()
max_diff = max(max_diff_h, max_diff_sv)
self.assertLess(max_diff, 1e-5)
s_hsv_img = scripted_fn(rgb_img)
self.assertTrue(hsv_img.allclose(s_hsv_img))
def test_rgb2hsv(self):
shape = (3, 150, 100)
for _ in range(20):
img = torch.rand(*shape, dtype=torch.float)
ft_hsv_img = F_t._rgb2hsv(img).permute(1, 2, 0).flatten(0, 1)
r, g, b, = img.unbind(0)
r = r.flatten().numpy()
g = g.flatten().numpy()
b = b.flatten().numpy()
hsv = []
for r1, g1, b1 in zip(r, g, b):
hsv.append(colorsys.rgb_to_hsv(r1, g1, b1))
colorsys_img = torch.tensor(hsv, dtype=torch.float32)
max_diff = (colorsys_img - ft_hsv_img).abs().max()
self.assertLess(max_diff, 1e-5)
def __call__(self, sample):
return {"rgb": sample, "hsv": _rgb2hsv(sample)}
