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Feb 13, 2023
Merged

only use plain tensors in kernel tests #7230

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c700e9a
only use plain tensors in kernel tests
pmeier Feb 13, 2023
6138329
remove skips hybrids
pmeier Feb 13, 2023
99dd341
add DispatcherInfos for hybrids
pmeier Feb 13, 2023
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7 changes: 0 additions & 7 deletions test/prototype_common_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -237,13 +237,6 @@ class TensorLoader:
def load(self, device):
return self.fn(self.shape, self.dtype, device)

def unwrap(self):
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This just undoes the change in #7228 since we no longer need this.

return TensorLoader(
fn=lambda shape, dtype, device: self.fn(shape, dtype, device).as_subclass(torch.Tensor),
shape=self.shape,
dtype=self.dtype,
)


@dataclasses.dataclass
class ImageLoader(TensorLoader):
Expand Down
34 changes: 28 additions & 6 deletions test/prototype_transforms_dispatcher_infos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,13 +64,21 @@ def sample_inputs(self, *datapoint_types, filter_metadata=True):

if not filter_metadata:
yield from sample_inputs
else:
for args_kwargs in sample_inputs:
for attribute in datapoint_type.__annotations__.keys():
if attribute in args_kwargs.kwargs:
del args_kwargs.kwargs[attribute]
return

yield args_kwargs
import itertools

for args_kwargs in sample_inputs:
for name in itertools.chain(
datapoint_type.__annotations__.keys(),
# FIXME: this seems ok for conversion dispatchers, but we should probably handle this on a
# per-dispatcher level. However, so far there is no option for that.
(f"old_{name}" for name in datapoint_type.__annotations__.keys()),
):
if name in args_kwargs.kwargs:
del args_kwargs.kwargs[name]

yield args_kwargs


def xfail_jit(reason, *, condition=None):
Expand Down Expand Up @@ -458,4 +466,18 @@ def fill_sequence_needs_broadcast(args_kwargs):
skip_dispatch_datapoint,
],
),
DispatcherInfo(
F.clamp_bounding_box,
kernels={datapoints.BoundingBox: F.clamp_bounding_box},
test_marks=[
skip_dispatch_datapoint,
],
),
DispatcherInfo(
F.convert_format_bounding_box,
kernels={datapoints.BoundingBox: F.convert_format_bounding_box},
test_marks=[
skip_dispatch_datapoint,
],
),
]
50 changes: 8 additions & 42 deletions test/prototype_transforms_kernel_infos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@
from datasets_utils import combinations_grid
from prototype_common_utils import (
ArgsKwargs,
BoundingBoxLoader,
get_num_channels,
ImageLoader,
InfoBase,
Expand Down Expand Up @@ -337,7 +336,6 @@ def sample_inputs_resize_video():


def reference_resize_bounding_box(bounding_box, *, spatial_size, size, max_size=None):

old_height, old_width = spatial_size
new_height, new_width = F._geometry._compute_resized_output_size(spatial_size, size=size, max_size=max_size)

Expand All @@ -350,13 +348,15 @@ def reference_resize_bounding_box(bounding_box, *, spatial_size, size, max_size=
)

expected_bboxes = reference_affine_bounding_box_helper(
bounding_box, format=bounding_box.format, affine_matrix=affine_matrix
bounding_box, format=datapoints.BoundingBoxFormat.XYXY, affine_matrix=affine_matrix
)
return expected_bboxes, (new_height, new_width)


def reference_inputs_resize_bounding_box():
for bounding_box_loader in make_bounding_box_loaders(extra_dims=((), (4,))):
for bounding_box_loader in make_bounding_box_loaders(
formats=[datapoints.BoundingBoxFormat.XYXY], extra_dims=((), (4,))
):
for size in _get_resize_sizes(bounding_box_loader.spatial_size):
yield ArgsKwargs(bounding_box_loader, size=size, spatial_size=bounding_box_loader.spatial_size)

Expand Down Expand Up @@ -668,8 +668,7 @@ def sample_inputs_affine_video():
def sample_inputs_convert_format_bounding_box():
formats = list(datapoints.BoundingBoxFormat)
for bounding_box_loader, new_format in itertools.product(make_bounding_box_loaders(formats=formats), formats):
yield ArgsKwargs(bounding_box_loader, new_format=new_format)
yield ArgsKwargs(bounding_box_loader.unwrap(), old_format=bounding_box_loader.format, new_format=new_format)
Comment on lines -671 to -672
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We no longer need to pass two things here, since the datapoint will be unwrapped by the test automatically.

yield ArgsKwargs(bounding_box_loader, old_format=bounding_box_loader.format, new_format=new_format)
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Don't we still want to test both pure-tensors and BoundingBoxes, at least for those "hybrid" kernels ? (perhaps that's still covered?)

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It wasn't until 99dd341, but now it is 😇 So the kernel tests actually only test plain tensors, but the dispatcher tests take over the other part.



def reference_convert_format_bounding_box(bounding_box, old_format, new_format):
Expand All @@ -680,14 +679,8 @@ def reference_convert_format_bounding_box(bounding_box, old_format, new_format):

def reference_inputs_convert_format_bounding_box():
for args_kwargs in sample_inputs_convert_format_bounding_box():
if len(args_kwargs.args[0].shape) != 2:
continue

(loader, *other_args), kwargs = args_kwargs
if isinstance(loader, BoundingBoxLoader):
kwargs["old_format"] = loader.format
loader = loader.unwrap()
yield ArgsKwargs(loader, *other_args, **kwargs)
if len(args_kwargs.args[0].shape) == 2:
yield args_kwargs


KERNEL_INFOS.append(
Expand All @@ -697,18 +690,6 @@ def reference_inputs_convert_format_bounding_box():
reference_fn=reference_convert_format_bounding_box,
reference_inputs_fn=reference_inputs_convert_format_bounding_box,
logs_usage=True,
test_marks=[
mark_framework_limitation(
("TestKernels", "test_scripted_vs_eager"),
reason=(
"The function is hybrid kernel / dispatcher. JIT unwraps a `datapoints.BoundingBox` into a "
"`torch.Tensor`, but then the kernel (rightfully) complains that neither `format` nor "
"`spatial_size` was passed"
),
condition=lambda arg_kwargs: isinstance(arg_kwargs.args[0], BoundingBoxLoader)
and arg_kwargs.kwargs.get("old_format") is None,
)
],
),
)

Expand Down Expand Up @@ -2049,10 +2030,8 @@ def sample_inputs_adjust_saturation_video():

def sample_inputs_clamp_bounding_box():
for bounding_box_loader in make_bounding_box_loaders():
yield ArgsKwargs(bounding_box_loader)

yield ArgsKwargs(
bounding_box_loader.unwrap(),
bounding_box_loader,
format=bounding_box_loader.format,
spatial_size=bounding_box_loader.spatial_size,
)
Expand All @@ -2063,19 +2042,6 @@ def sample_inputs_clamp_bounding_box():
F.clamp_bounding_box,
sample_inputs_fn=sample_inputs_clamp_bounding_box,
logs_usage=True,
test_marks=[
mark_framework_limitation(
("TestKernels", "test_scripted_vs_eager"),
reason=(
"The function is hybrid kernel / dispatcher. JIT unwraps a `datapoints.BoundingBox` into a "
"`torch.Tensor`, but then the kernel (rightfully) complains that neither `format` nor "
"`spatial_size` was passed"
),
condition=lambda arg_kwargs: isinstance(arg_kwargs.args[0], BoundingBoxLoader)
and arg_kwargs.kwargs.get("format") is None
and arg_kwargs.kwargs.get("spatial_size") is None,
)
],
)
)

Expand Down
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