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Remove color_space metadata and ConvertColorSpace() transform #7120

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Merged
merged 17 commits into from
Jan 24, 2023
Merged

Remove color_space metadata and ConvertColorSpace() transform #7120

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b9aaf43
Let Normalize() and RandomPhotometricDistort return datapoints instea…
NicolasHug Jan 19, 2023
5a3c51a
cleanup
NicolasHug Jan 19, 2023
cb4c0f4
Address comments
NicolasHug Jan 20, 2023
ac94d72
lint + types
NicolasHug Jan 20, 2023
e52b1b5
Merge branch 'main' of github.com:pytorch/vision into remove_colorspace
NicolasHug Jan 20, 2023
5a407af
Remove color_space meta-data from Image and Video
NicolasHug Jan 20, 2023
07cc5e9
Remove functional part
NicolasHug Jan 20, 2023
e83ad89
Undo changes to Grayscale, will address in another PR
NicolasHug Jan 20, 2023
f9151fa
Updated tests to not use datapoints.ColorSpace
NicolasHug Jan 20, 2023
874b00f
Formatting
NicolasHug Jan 20, 2023
b26a14a
renaming
NicolasHug Jan 24, 2023
51a15be
avoid call to removed datapoints._image._from_tensor_shape
NicolasHug Jan 24, 2023
9651553
Address comments
NicolasHug Jan 24, 2023
7c3019d
formatting
NicolasHug Jan 24, 2023
b973d4a
Merge branch 'main' of github.com:pytorch/vision into remove_colorspace
NicolasHug Jan 24, 2023
b8053f1
Merge
NicolasHug Jan 24, 2023
0693957
lint
NicolasHug Jan 24, 2023
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55 changes: 26 additions & 29 deletions test/prototype_common_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,7 +238,6 @@ def load(self, device):

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

Expand All @@ -248,10 +247,10 @@ def __post_init__(self):


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


Expand All @@ -265,7 +264,7 @@ def get_num_channels(color_space):
def make_image_loader(
size="random",
*,
color_space=datapoints.ColorSpace.RGB,
color_space="RGB",
extra_dims=(),
dtype=torch.float32,
constant_alpha=True,
Expand All @@ -276,11 +275,11 @@ 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 {datapoints.ColorSpace.GRAY_ALPHA, datapoints.ColorSpace.RGB_ALPHA} and constant_alpha:
if color_space in {"GRAY_ALPHA", "RGBA"} and constant_alpha:
data[..., -1, :, :] = max_value
return datapoints.Image(data, color_space=color_space)
return datapoints.Image(data)

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


make_image = from_loader(make_image_loader)
Expand All @@ -290,10 +289,10 @@ def make_image_loaders(
*,
sizes=DEFAULT_SPATIAL_SIZES,
color_spaces=(
datapoints.ColorSpace.GRAY,
datapoints.ColorSpace.GRAY_ALPHA,
datapoints.ColorSpace.RGB,
datapoints.ColorSpace.RGB_ALPHA,
"GRAY",
"GRAY_ALPHA",
"RGB",
"RGBA",
),
extra_dims=DEFAULT_EXTRA_DIMS,
dtypes=(torch.float32, torch.uint8),
Expand All @@ -306,7 +305,7 @@ def make_image_loaders(
make_images = from_loaders(make_image_loaders)


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

Expand All @@ -318,24 +317,24 @@ def fn(shape, dtype, device):
.resize((width, height))
.convert(
{
datapoints.ColorSpace.GRAY: "L",
datapoints.ColorSpace.GRAY_ALPHA: "LA",
datapoints.ColorSpace.RGB: "RGB",
datapoints.ColorSpace.RGB_ALPHA: "RGBA",
"GRAY": "L",
"GRAY_ALPHA": "LA",
"RGB": "RGB",
"RGBA": "RGBA",
}[color_space]
)
)

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

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

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


def make_image_loaders_for_interpolation(
sizes=((233, 147),),
color_spaces=(datapoints.ColorSpace.RGB,),
color_spaces=("RGB",),
dtypes=(torch.uint8,),
):
for params in combinations_grid(size=sizes, color_space=color_spaces, dtype=dtypes):
Expand Down Expand Up @@ -583,7 +582,7 @@ class VideoLoader(ImageLoader):
def make_video_loader(
size="random",
*,
color_space=datapoints.ColorSpace.RGB,
color_space="RGB",
num_frames="random",
extra_dims=(),
dtype=torch.uint8,
Expand All @@ -592,12 +591,10 @@ def make_video_loader(
num_frames = int(torch.randint(1, 5, ())) if num_frames == "random" else num_frames

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

return VideoLoader(
fn, shape=(*extra_dims, num_frames, get_num_channels(color_space), *size), dtype=dtype, color_space=color_space
)
return VideoLoader(fn, shape=(*extra_dims, num_frames, get_num_channels(color_space), *size), dtype=dtype)


make_video = from_loader(make_video_loader)
Expand All @@ -607,8 +604,8 @@ def make_video_loaders(
*,
sizes=DEFAULT_SPATIAL_SIZES,
color_spaces=(
datapoints.ColorSpace.GRAY,
datapoints.ColorSpace.RGB,
"GRAY",
"RGB",
),
num_frames=(1, 0, "random"),
extra_dims=DEFAULT_EXTRA_DIMS,
Expand Down
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