def test_convert_image_dtype(self):
tensor, _ = self._create_data(26, 34, device=self.device)
batch_tensors = torch.rand(4, 3, 44, 56, device=self.device)
for in_dtype in int_dtypes() + float_dtypes():
in_tensor = tensor.to(in_dtype)
in_batch_tensors = batch_tensors.to(in_dtype)
for out_dtype in int_dtypes() + float_dtypes():
fn = T.ConvertImageDtype(dtype=out_dtype)
scripted_fn = torch.jit.script(fn)
if (in_dtype == torch.float32 and out_dtype in (torch.int32, torch.int64)) or \
(in_dtype == torch.float64 and out_dtype == torch.int64):
with self.assertRaisesRegex(RuntimeError, r"cannot be performed safely"):
self._test_transform_vs_scripted(fn, scripted_fn, in_tensor)
with self.assertRaisesRegex(RuntimeError, r"cannot be performed safely"):
self._test_transform_vs_scripted_on_batch(fn, scripted_fn, in_batch_tensors)
continue
self._test_transform_vs_scripted(fn, scripted_fn, in_tensor)
self._test_transform_vs_scripted_on_batch(fn, scripted_fn, in_batch_tensors)
with get_tmp_dir() as tmp_dir:
scripted_fn.save(os.path.join(tmp_dir, "t_convert_dtype.pt"))
"num_output_channels": 1
}), (T.Grayscale, {
"num_output_channels": 3
}), (T.RandomGrayscale, {})])
def test_to_grayscale(device, Klass, meth_kwargs):
tol = 1.0 + 1e-10
_test_class_op(Klass,
meth_kwargs=meth_kwargs,
test_exact_match=False,
device=device,
tol=tol,
agg_method="max")
@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('in_dtype', int_dtypes() + float_dtypes())
@pytest.mark.parametrize('out_dtype', int_dtypes() + float_dtypes())
def test_convert_image_dtype(device, in_dtype, out_dtype):
tensor, _ = _create_data(26, 34, device=device)
batch_tensors = torch.rand(4, 3, 44, 56, device=device)
in_tensor = tensor.to(in_dtype)
in_batch_tensors = batch_tensors.to(in_dtype)
fn = T.ConvertImageDtype(dtype=out_dtype)
scripted_fn = torch.jit.script(fn)
if (in_dtype == torch.float32 and out_dtype in (torch.int32, torch.int64)) or \
(in_dtype == torch.float64 and out_dtype == torch.int64):
with pytest.raises(RuntimeError, match=r"cannot be performed safely"):
_test_transform_vs_scripted(fn, scripted_fn, in_tensor)
s_transform = torch.jit.script(transform)
s_transform.save(os.path.join(tmpdir, "t_perspective.pt"))
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
"Klass, meth_kwargs",
[(T.Grayscale, {"num_output_channels": 1}), (T.Grayscale, {"num_output_channels": 3}), (T.RandomGrayscale, {})],
)
def test_to_grayscale(device, Klass, meth_kwargs):
tol = 1.0 + 1e-10
_test_class_op(Klass, meth_kwargs=meth_kwargs, test_exact_match=False, device=device, tol=tol, agg_method="max")
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("in_dtype", int_dtypes() + float_dtypes())
@pytest.mark.parametrize("out_dtype", int_dtypes() + float_dtypes())
def test_convert_image_dtype(device, in_dtype, out_dtype):
tensor, _ = _create_data(26, 34, device=device)
batch_tensors = torch.rand(4, 3, 44, 56, device=device)
in_tensor = tensor.to(in_dtype)
in_batch_tensors = batch_tensors.to(in_dtype)
fn = T.ConvertImageDtype(dtype=out_dtype)
scripted_fn = torch.jit.script(fn)
if (in_dtype == torch.float32 and out_dtype in (torch.int32, torch.int64)) or (
in_dtype == torch.float64 and out_dtype == torch.int64
):
with pytest.raises(RuntimeError, match=r"cannot be performed safely"):
