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rename features._Feature to datapoints._Datapoint #7002

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merged 6 commits into from
Dec 5, 2022
Merged

rename features._Feature to datapoints._Datapoint #7002

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64cbedd
rename features._Feature to datapoints.Datapoint
pmeier Dec 2, 2022
8395bf8
_Datapoint to Datapoint
pmeier Dec 2, 2022
074d71f
move is_simple_tensor to transforms.utils
pmeier Dec 2, 2022
5dc222b
fix CI
pmeier Dec 2, 2022
d675ff4
move Datapoint out of public namespace
pmeier Dec 5, 2022
a62022c
Merge branch 'main' into rename-features
pmeier Dec 5, 2022
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76 changes: 38 additions & 38 deletions test/prototype_common_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
from datasets_utils import combinations_grid
from torch.nn.functional import one_hot
from torch.testing._comparison import assert_equal as _assert_equal, BooleanPair, NonePair, NumberPair, TensorLikePair
from torchvision.prototype import features
from torchvision.prototype import datapoints
from torchvision.prototype.transforms.functional import convert_dtype_image_tensor, to_image_tensor
from torchvision.transforms.functional_tensor import _max_value as get_max_value

Expand Down Expand Up @@ -238,7 +238,7 @@ def load(self, device):

@dataclasses.dataclass
class ImageLoader(TensorLoader):
color_space: features.ColorSpace
color_space: datapoints.ColorSpace
spatial_size: Tuple[int, int] = dataclasses.field(init=False)
num_channels: int = dataclasses.field(init=False)

Expand All @@ -248,10 +248,10 @@ def __post_init__(self):


NUM_CHANNELS_MAP = {
features.ColorSpace.GRAY: 1,
features.ColorSpace.GRAY_ALPHA: 2,
features.ColorSpace.RGB: 3,
features.ColorSpace.RGB_ALPHA: 4,
datapoints.ColorSpace.GRAY: 1,
datapoints.ColorSpace.GRAY_ALPHA: 2,
datapoints.ColorSpace.RGB: 3,
datapoints.ColorSpace.RGB_ALPHA: 4,
}


Expand All @@ -265,7 +265,7 @@ def get_num_channels(color_space):
def make_image_loader(
size="random",
*,
color_space=features.ColorSpace.RGB,
color_space=datapoints.ColorSpace.RGB,
extra_dims=(),
dtype=torch.float32,
constant_alpha=True,
Expand All @@ -276,9 +276,9 @@ def make_image_loader(
def fn(shape, dtype, device):
max_value = get_max_value(dtype)
data = torch.testing.make_tensor(shape, low=0, high=max_value, dtype=dtype, device=device)
if color_space in {features.ColorSpace.GRAY_ALPHA, features.ColorSpace.RGB_ALPHA} and constant_alpha:
if color_space in {datapoints.ColorSpace.GRAY_ALPHA, datapoints.ColorSpace.RGB_ALPHA} and constant_alpha:
data[..., -1, :, :] = max_value
return features.Image(data, color_space=color_space)
return datapoints.Image(data, color_space=color_space)

return ImageLoader(fn, shape=(*extra_dims, num_channels, *size), dtype=dtype, color_space=color_space)

Expand All @@ -290,10 +290,10 @@ def make_image_loaders(
*,
sizes=DEFAULT_SPATIAL_SIZES,
color_spaces=(
features.ColorSpace.GRAY,
features.ColorSpace.GRAY_ALPHA,
features.ColorSpace.RGB,
features.ColorSpace.RGB_ALPHA,
datapoints.ColorSpace.GRAY,
datapoints.ColorSpace.GRAY_ALPHA,
datapoints.ColorSpace.RGB,
datapoints.ColorSpace.RGB_ALPHA,
),
extra_dims=DEFAULT_EXTRA_DIMS,
dtypes=(torch.float32, torch.uint8),
Expand All @@ -306,7 +306,7 @@ def make_image_loaders(
make_images = from_loaders(make_image_loaders)


def make_image_loader_for_interpolation(size="random", *, color_space=features.ColorSpace.RGB, dtype=torch.uint8):
def make_image_loader_for_interpolation(size="random", *, color_space=datapoints.ColorSpace.RGB, dtype=torch.uint8):
size = _parse_spatial_size(size)
num_channels = get_num_channels(color_space)

Expand All @@ -318,24 +318,24 @@ def fn(shape, dtype, device):
.resize((width, height))
.convert(
{
features.ColorSpace.GRAY: "L",
features.ColorSpace.GRAY_ALPHA: "LA",
features.ColorSpace.RGB: "RGB",
features.ColorSpace.RGB_ALPHA: "RGBA",
datapoints.ColorSpace.GRAY: "L",
datapoints.ColorSpace.GRAY_ALPHA: "LA",
datapoints.ColorSpace.RGB: "RGB",
datapoints.ColorSpace.RGB_ALPHA: "RGBA",
}[color_space]
)
)

image_tensor = convert_dtype_image_tensor(to_image_tensor(image_pil).to(device=device), dtype=dtype)

return features.Image(image_tensor, color_space=color_space)
return datapoints.Image(image_tensor, color_space=color_space)

return ImageLoader(fn, shape=(num_channels, *size), dtype=dtype, color_space=color_space)


def make_image_loaders_for_interpolation(
sizes=((233, 147),),
color_spaces=(features.ColorSpace.RGB,),
color_spaces=(datapoints.ColorSpace.RGB,),
dtypes=(torch.uint8,),
):
for params in combinations_grid(size=sizes, color_space=color_spaces, dtype=dtypes):
Expand All @@ -344,7 +344,7 @@ def make_image_loaders_for_interpolation(

@dataclasses.dataclass
class BoundingBoxLoader(TensorLoader):
format: features.BoundingBoxFormat
format: datapoints.BoundingBoxFormat
spatial_size: Tuple[int, int]


Expand All @@ -362,11 +362,11 @@ def randint_with_tensor_bounds(arg1, arg2=None, **kwargs):

def make_bounding_box_loader(*, extra_dims=(), format, spatial_size="random", dtype=torch.float32):
if isinstance(format, str):
format = features.BoundingBoxFormat[format]
format = datapoints.BoundingBoxFormat[format]
if format not in {
features.BoundingBoxFormat.XYXY,
features.BoundingBoxFormat.XYWH,
features.BoundingBoxFormat.CXCYWH,
datapoints.BoundingBoxFormat.XYXY,
datapoints.BoundingBoxFormat.XYWH,
datapoints.BoundingBoxFormat.CXCYWH,
}:
raise pytest.UsageError(f"Can't make bounding box in format {format}")

Expand All @@ -378,19 +378,19 @@ def fn(shape, dtype, device):
raise pytest.UsageError()

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

height, width = spatial_size

if format == features.BoundingBoxFormat.XYXY:
if format == datapoints.BoundingBoxFormat.XYXY:
x1 = torch.randint(0, width // 2, extra_dims)
y1 = torch.randint(0, height // 2, extra_dims)
x2 = randint_with_tensor_bounds(x1 + 1, width - x1) + x1
y2 = randint_with_tensor_bounds(y1 + 1, height - y1) + y1
parts = (x1, y1, x2, y2)
elif format == features.BoundingBoxFormat.XYWH:
elif format == datapoints.BoundingBoxFormat.XYWH:
x = torch.randint(0, width // 2, extra_dims)
y = torch.randint(0, height // 2, extra_dims)
w = randint_with_tensor_bounds(1, width - x)
Expand All @@ -403,7 +403,7 @@ def fn(shape, dtype, device):
h = randint_with_tensor_bounds(1, torch.minimum(cy, height - cy) + 1)
parts = (cx, cy, w, h)

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

Expand All @@ -416,7 +416,7 @@ def fn(shape, dtype, device):
def make_bounding_box_loaders(
*,
extra_dims=DEFAULT_EXTRA_DIMS,
formats=tuple(features.BoundingBoxFormat),
formats=tuple(datapoints.BoundingBoxFormat),
spatial_size="random",
dtypes=(torch.float32, torch.int64),
):
Expand Down Expand Up @@ -456,7 +456,7 @@ def fn(shape, dtype, device):
# The idiom `make_tensor(..., dtype=torch.int64).to(dtype)` is intentional to only get integer values,
# regardless of the requested dtype, e.g. 0 or 0.0 rather than 0 or 0.123
data = torch.testing.make_tensor(shape, low=0, high=num_categories, dtype=torch.int64, device=device).to(dtype)
return features.Label(data, categories=categories)
return datapoints.Label(data, categories=categories)

return LabelLoader(fn, shape=extra_dims, dtype=dtype, categories=categories)

Expand All @@ -480,7 +480,7 @@ def fn(shape, dtype, device):
# since `one_hot` only supports int64
label = make_label_loader(extra_dims=extra_dims, categories=num_categories, dtype=torch.int64).load(device)
data = one_hot(label, num_classes=num_categories).to(dtype)
return features.OneHotLabel(data, categories=categories)
return datapoints.OneHotLabel(data, categories=categories)

return OneHotLabelLoader(fn, shape=(*extra_dims, num_categories), dtype=dtype, categories=categories)

Expand Down Expand Up @@ -509,7 +509,7 @@ def make_detection_mask_loader(size="random", *, num_objects="random", extra_dim

def fn(shape, dtype, device):
data = torch.testing.make_tensor(shape, low=0, high=2, dtype=dtype, device=device)
return features.Mask(data)
return datapoints.Mask(data)

return MaskLoader(fn, shape=(*extra_dims, num_objects, *size), dtype=dtype)

Expand Down Expand Up @@ -537,7 +537,7 @@ def make_segmentation_mask_loader(size="random", *, num_categories="random", ext

def fn(shape, dtype, device):
data = torch.testing.make_tensor(shape, low=0, high=num_categories, dtype=dtype, device=device)
return features.Mask(data)
return datapoints.Mask(data)

return MaskLoader(fn, shape=(*extra_dims, *size), dtype=dtype)

Expand Down Expand Up @@ -583,7 +583,7 @@ class VideoLoader(ImageLoader):
def make_video_loader(
size="random",
*,
color_space=features.ColorSpace.RGB,
color_space=datapoints.ColorSpace.RGB,
num_frames="random",
extra_dims=(),
dtype=torch.uint8,
Expand All @@ -593,7 +593,7 @@ def make_video_loader(

def fn(shape, dtype, device):
video = make_image(size=shape[-2:], color_space=color_space, extra_dims=shape[:-3], dtype=dtype, device=device)
return features.Video(video, color_space=color_space)
return datapoints.Video(video, color_space=color_space)

return VideoLoader(
fn, shape=(*extra_dims, num_frames, get_num_channels(color_space), *size), dtype=dtype, color_space=color_space
Expand All @@ -607,8 +607,8 @@ def make_video_loaders(
*,
sizes=DEFAULT_SPATIAL_SIZES,
color_spaces=(
features.ColorSpace.GRAY,
features.ColorSpace.RGB,
datapoints.ColorSpace.GRAY,
datapoints.ColorSpace.RGB,
),
num_frames=(1, 0, "random"),
extra_dims=DEFAULT_EXTRA_DIMS,
Expand Down
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