Skip to content

[prototype] Switch to spatial_size #6736

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 10 commits into from
Oct 11, 2022
Merged

[prototype] Switch to spatial_size #6736

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
10 commits
Select commit Hold shift + click to select a range
41d6fb4
Change `image_size` to `spatial_size`
datumbox Oct 10, 2022
07e7e25
Fix linter
datumbox Oct 10, 2022
973fe25
Fixing more tests.
datumbox Oct 10, 2022
47ed918
Adding get_num_channels_video and get_spatial_size_* kernels for vide…
datumbox Oct 10, 2022
a21e428
Refactor get_spatial_size
datumbox Oct 10, 2022
6bb1f03
Reduce the usage of `query_chw` where possible
datumbox Oct 10, 2022
319e35a
Rename `query_chw` to `query_spatial_size`
datumbox Oct 11, 2022
173418c
Merge branch 'main' into prototype/image_size
datumbox Oct 11, 2022
c479560
Adding `get_num_frames` dispatcher and kernel.
datumbox Oct 11, 2022
23a78c9
Adding jit-scriptability tests
datumbox Oct 11, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
55 changes: 30 additions & 25 deletions test/prototype_common_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -184,13 +184,18 @@ def load(self, device="cpu"):
return args, kwargs


DEFAULT_SQUARE_IMAGE_SIZE = 15
DEFAULT_LANDSCAPE_IMAGE_SIZE = (7, 33)
DEFAULT_PORTRAIT_IMAGE_SIZE = (31, 9)
DEFAULT_IMAGE_SIZES = (DEFAULT_LANDSCAPE_IMAGE_SIZE, DEFAULT_PORTRAIT_IMAGE_SIZE, DEFAULT_SQUARE_IMAGE_SIZE, "random")
DEFAULT_SQUARE_SPATIAL_SIZE = 15
DEFAULT_LANDSCAPE_SPATIAL_SIZE = (7, 33)
DEFAULT_PORTRAIT_SPATIAL_SIZE = (31, 9)
DEFAULT_SPATIAL_SIZES = (
DEFAULT_LANDSCAPE_SPATIAL_SIZE,
DEFAULT_PORTRAIT_SPATIAL_SIZE,
DEFAULT_SQUARE_SPATIAL_SIZE,
"random",
)


def _parse_image_size(size, *, name="size"):
def _parse_spatial_size(size, *, name="size"):
if size == "random":
return tuple(torch.randint(15, 33, (2,)).tolist())
elif isinstance(size, int) and size > 0:
Expand Down Expand Up @@ -246,11 +251,11 @@ def load(self, device):
@dataclasses.dataclass
class ImageLoader(TensorLoader):
color_space: features.ColorSpace
image_size: Tuple[int, int] = dataclasses.field(init=False)
spatial_size: Tuple[int, int] = dataclasses.field(init=False)
num_channels: int = dataclasses.field(init=False)

def __post_init__(self):
self.image_size = self.shape[-2:]
self.spatial_size = self.shape[-2:]
self.num_channels = self.shape[-3]


Expand All @@ -277,7 +282,7 @@ def make_image_loader(
dtype=torch.float32,
constant_alpha=True,
):
size = _parse_image_size(size)
size = _parse_spatial_size(size)
num_channels = get_num_channels(color_space)

def fn(shape, dtype, device):
Expand All @@ -295,7 +300,7 @@ def fn(shape, dtype, device):

def make_image_loaders(
*,
sizes=DEFAULT_IMAGE_SIZES,
sizes=DEFAULT_SPATIAL_SIZES,
color_spaces=(
features.ColorSpace.GRAY,
features.ColorSpace.GRAY_ALPHA,
Expand All @@ -316,7 +321,7 @@ def make_image_loaders(
@dataclasses.dataclass
class BoundingBoxLoader(TensorLoader):
format: features.BoundingBoxFormat
image_size: Tuple[int, int]
spatial_size: Tuple[int, int]


def randint_with_tensor_bounds(arg1, arg2=None, **kwargs):
Expand All @@ -331,7 +336,7 @@ def randint_with_tensor_bounds(arg1, arg2=None, **kwargs):
).reshape(low.shape)


def make_bounding_box_loader(*, extra_dims=(), format, image_size="random", dtype=torch.float32):
def make_bounding_box_loader(*, extra_dims=(), format, spatial_size="random", dtype=torch.float32):
if isinstance(format, str):
format = features.BoundingBoxFormat[format]
if format not in {
Expand All @@ -341,7 +346,7 @@ def make_bounding_box_loader(*, extra_dims=(), format, image_size="random", dtyp
}:
raise pytest.UsageError(f"Can't make bounding box in format {format}")

image_size = _parse_image_size(image_size, name="image_size")
spatial_size = _parse_spatial_size(spatial_size, name="spatial_size")

def fn(shape, dtype, device):
*extra_dims, num_coordinates = shape
Expand All @@ -350,10 +355,10 @@ def fn(shape, dtype, device):

if any(dim == 0 for dim in extra_dims):
return features.BoundingBox(
torch.empty(*extra_dims, 4, dtype=dtype, device=device), format=format, image_size=image_size
torch.empty(*extra_dims, 4, dtype=dtype, device=device), format=format, spatial_size=spatial_size
)

height, width = image_size
height, width = spatial_size

if format == features.BoundingBoxFormat.XYXY:
x1 = torch.randint(0, width // 2, extra_dims)
Expand All @@ -375,10 +380,10 @@ def fn(shape, dtype, device):
parts = (cx, cy, w, h)

return features.BoundingBox(
torch.stack(parts, dim=-1).to(dtype=dtype, device=device), format=format, image_size=image_size
torch.stack(parts, dim=-1).to(dtype=dtype, device=device), format=format, spatial_size=spatial_size
)

return BoundingBoxLoader(fn, shape=(*extra_dims, 4), dtype=dtype, format=format, image_size=image_size)
return BoundingBoxLoader(fn, shape=(*extra_dims, 4), dtype=dtype, format=format, spatial_size=spatial_size)


make_bounding_box = from_loader(make_bounding_box_loader)
Expand All @@ -388,11 +393,11 @@ def make_bounding_box_loaders(
*,
extra_dims=DEFAULT_EXTRA_DIMS,
formats=tuple(features.BoundingBoxFormat),
image_size="random",
spatial_size="random",
dtypes=(torch.float32, torch.int64),
):
for params in combinations_grid(extra_dims=extra_dims, format=formats, dtype=dtypes):
yield make_bounding_box_loader(**params, image_size=image_size)
yield make_bounding_box_loader(**params, spatial_size=spatial_size)


make_bounding_boxes = from_loaders(make_bounding_box_loaders)
Expand Down Expand Up @@ -475,7 +480,7 @@ class MaskLoader(TensorLoader):

def make_detection_mask_loader(size="random", *, num_objects="random", extra_dims=(), dtype=torch.uint8):
# This produces "detection" masks, i.e. `(*, N, H, W)`, where `N` denotes the number of objects
size = _parse_image_size(size)
size = _parse_spatial_size(size)
num_objects = int(torch.randint(1, 11, ())) if num_objects == "random" else num_objects

def fn(shape, dtype, device):
Expand All @@ -489,7 +494,7 @@ def fn(shape, dtype, device):


def make_detection_mask_loaders(
sizes=DEFAULT_IMAGE_SIZES,
sizes=DEFAULT_SPATIAL_SIZES,
num_objects=(1, 0, "random"),
extra_dims=DEFAULT_EXTRA_DIMS,
dtypes=(torch.uint8,),
Expand All @@ -503,7 +508,7 @@ def make_detection_mask_loaders(

def make_segmentation_mask_loader(size="random", *, num_categories="random", extra_dims=(), dtype=torch.uint8):
# This produces "segmentation" masks, i.e. `(*, H, W)`, where the category is encoded in the values
size = _parse_image_size(size)
size = _parse_spatial_size(size)
num_categories = int(torch.randint(1, 11, ())) if num_categories == "random" else num_categories

def fn(shape, dtype, device):
Expand All @@ -518,7 +523,7 @@ def fn(shape, dtype, device):

def make_segmentation_mask_loaders(
*,
sizes=DEFAULT_IMAGE_SIZES,
sizes=DEFAULT_SPATIAL_SIZES,
num_categories=(1, 2, "random"),
extra_dims=DEFAULT_EXTRA_DIMS,
dtypes=(torch.uint8,),
Expand All @@ -532,7 +537,7 @@ def make_segmentation_mask_loaders(

def make_mask_loaders(
*,
sizes=DEFAULT_IMAGE_SIZES,
sizes=DEFAULT_SPATIAL_SIZES,
num_objects=(1, 0, "random"),
num_categories=(1, 2, "random"),
extra_dims=DEFAULT_EXTRA_DIMS,
Expand All @@ -559,7 +564,7 @@ def make_video_loader(
extra_dims=(),
dtype=torch.uint8,
):
size = _parse_image_size(size)
size = _parse_spatial_size(size)
num_frames = int(torch.randint(1, 5, ())) if num_frames == "random" else num_frames

def fn(shape, dtype, device):
Expand All @@ -576,7 +581,7 @@ def fn(shape, dtype, device):

def make_video_loaders(
*,
sizes=DEFAULT_IMAGE_SIZES,
sizes=DEFAULT_SPATIAL_SIZES,
color_spaces=(
features.ColorSpace.GRAY,
features.ColorSpace.RGB,
Expand Down
Loading