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Unverified Commit aeafa912 authored by vfdev's avatar vfdev Committed by GitHub
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Refactored and modified private api for resize functional op (#6191) 

* Refactored and modified private api for resize functional op

* Fixed failures

* More updates

* Fixed flake8
parent a5536de9
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Showing with 59 additions and 99 deletions
test/test_transforms_tensor.py
View file @ aeafa912
......@@ -394,9 +394,7 @@ class TestResize:
@pytest.mark.parametrize(
"size",
[
[
32,
],
[32],
[32, 32],
(32, 32),
[34, 35],
......@@ -412,7 +410,7 @@ class TestResize:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
if max_size is not None and len(size) != 1:
pytest.xfail("with max_size, size must be a sequence with 2 elements")
pytest.skip("Size should be an int or a sequence of length 1 if max_size is specified")
transform = T.Resize(size=size, interpolation=interpolation, max_size=max_size)
s_transform = torch.jit.script(transform)
......@@ -420,11 +418,7 @@ class TestResize:
_test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
def test_resize_save(self, tmpdir):
transform = T.Resize(
size=[
32,
]
)
transform = T.Resize(size=[32])
s_transform = torch.jit.script(transform)
s_transform.save(os.path.join(tmpdir, "t_resize.pt"))
......@@ -435,12 +429,8 @@ class TestResize:
"size",
[
(32,),
[
44,
],
[
32,
],
[44],
[32],
[32, 32],
(32, 32),
[44, 55],
......
torchvision/prototype/transforms/functional/_geometry.py
View file @ aeafa912
......@@ -42,6 +42,8 @@ def resize_image_tensor(
max_size: Optional[int] = None,
antialias: Optional[bool] = None,
) -> torch.Tensor:
# TODO: use _compute_output_size to enable max_size option
max_size # ununsed right now
new_height, new_width = size
num_channels, old_height, old_width = get_dimensions_image_tensor(image)
batch_shape = image.shape[:-3]
......@@ -49,7 +51,6 @@ def resize_image_tensor(
image.reshape((-1, num_channels, old_height, old_width)),
size=size,
interpolation=interpolation.value,
max_size=max_size,
antialias=antialias,
).reshape(batch_shape + (num_channels, new_height, new_width))
......@@ -60,7 +61,9 @@ def resize_image_pil(
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
max_size: Optional[int] = None,
) -> PIL.Image.Image:
return _FP.resize(img, size, interpolation=pil_modes_mapping[interpolation], max_size=max_size)
# TODO: use _compute_output_size to enable max_size option
max_size # ununsed right now
return _FP.resize(img, size, interpolation=pil_modes_mapping[interpolation])
def resize_segmentation_mask(
......
torchvision/transforms/functional.py
View file @ aeafa912
......@@ -360,6 +360,29 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
return F_t.normalize(tensor, mean=mean, std=std, inplace=inplace)
def _compute_output_size(image_size: Tuple[int, int], size: List[int], max_size: Optional[int] = None) -> List[int]:
if len(size) == 1: # specified size only for the smallest edge
h, w = image_size
short, long = (w, h) if w <= h else (h, w)
requested_new_short = size if isinstance(size, int) else size[0]
new_short, new_long = requested_new_short, int(requested_new_short * long / short)
if max_size is not None:
if max_size <= requested_new_short:
raise ValueError(
f"max_size = {max_size} must be strictly greater than the requested "
f"size for the smaller edge size = {size}"
)
if new_long > max_size:
new_short, new_long = int(max_size * new_short / new_long), max_size
new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short)
else: # specified both h and w
new_w, new_h = size[1], size[0]
return [new_h, new_w]
def resize(
img: Tensor,
size: List[int],
......@@ -423,13 +446,32 @@ def resize(
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
if isinstance(size, (list, tuple)):
if len(size) not in [1, 2]:
raise ValueError(
f"Size must be an int or a 1 or 2 element tuple/list, not a {len(size)} element tuple/list"
)
if max_size is not None and len(size) != 1:
raise ValueError(
"max_size should only be passed if size specifies the length of the smaller edge, "
"i.e. size should be an int or a sequence of length 1 in torchscript mode."
)
_, image_height, image_width = get_dimensions(img)
if isinstance(size, int):
size = [size]
output_size = _compute_output_size((image_height, image_width), size, max_size)
if (image_height, image_width) == output_size:
return img
if not isinstance(img, torch.Tensor):
if antialias is not None and not antialias:
warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.")
pil_interpolation = pil_modes_mapping[interpolation]
return F_pil.resize(img, size=size, interpolation=pil_interpolation, max_size=max_size)
return F_pil.resize(img, size=output_size, interpolation=pil_interpolation)
return F_t.resize(img, size=size, interpolation=interpolation.value, max_size=max_size, antialias=antialias)
return F_t.resize(img, size=output_size, interpolation=interpolation.value, antialias=antialias)
def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
......
torchvision/transforms/functional_pil.py
View file @ aeafa912
import numbers
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
......@@ -240,46 +240,16 @@ def crop(
@torch.jit.unused
def resize(
img: Image.Image,
size: Union[Sequence[int], int],
size: Union[List[int], int],
interpolation: int = _pil_constants.BILINEAR,
max_size: Optional[int] = None,
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError(f"img should be PIL Image. Got {type(img)}")
if not (isinstance(size, int) or (isinstance(size, Sequence) and len(size) in (1, 2))):
if not (isinstance(size, list) and len(size) == 2):
raise TypeError(f"Got inappropriate size arg: {size}")
if isinstance(size, Sequence) and len(size) == 1:
size = size[0]
if isinstance(size, int):
w, h = img.size
short, long = (w, h) if w <= h else (h, w)
new_short, new_long = size, int(size * long / short)
if max_size is not None:
if max_size <= size:
raise ValueError(
f"max_size = {max_size} must be strictly greater than the requested "
f"size for the smaller edge size = {size}"
)
if new_long > max_size:
new_short, new_long = int(max_size * new_short / new_long), max_size
new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short)
if (w, h) == (new_w, new_h):
return img
else:
return img.resize((new_w, new_h), interpolation)
else:
if max_size is not None:
raise ValueError(
"max_size should only be passed if size specifies the length of the smaller edge, "
"i.e. size should be an int or a sequence of length 1 in torchscript mode."
)
return img.resize(size[::-1], interpolation)
return img.resize(size[::-1], interpolation)
@torch.jit.unused
......
torchvision/transforms/functional_tensor.py
View file @ aeafa912
......@@ -430,70 +430,25 @@ def resize(
img: Tensor,
size: List[int],
interpolation: str = "bilinear",
max_size: Optional[int] = None,
antialias: Optional[bool] = None,
) -> Tensor:
_assert_image_tensor(img)
if not isinstance(size, (int, tuple, list)):
raise TypeError("Got inappropriate size arg")
if not isinstance(interpolation, str):
raise TypeError("Got inappropriate interpolation arg")
if interpolation not in ["nearest", "bilinear", "bicubic"]:
raise ValueError("This interpolation mode is unsupported with Tensor input")
if isinstance(size, tuple):
size = list(size)
if isinstance(size, list):
if len(size) not in [1, 2]:
raise ValueError(
f"Size must be an int or a 1 or 2 element tuple/list, not a {len(size)} element tuple/list"
)
if max_size is not None and len(size) != 1:
raise ValueError(
"max_size should only be passed if size specifies the length of the smaller edge, "
"i.e. size should be an int or a sequence of length 1 in torchscript mode."
)
if antialias is None:
antialias = False
if antialias and interpolation not in ["bilinear", "bicubic"]:
raise ValueError("Antialias option is supported for bilinear and bicubic interpolation modes only")
_, h, w = get_dimensions(img)
if isinstance(size, int) or len(size) == 1: # specified size only for the smallest edge
short, long = (w, h) if w <= h else (h, w)
requested_new_short = size if isinstance(size, int) else size[0]
new_short, new_long = requested_new_short, int(requested_new_short * long / short)
if max_size is not None:
if max_size <= requested_new_short:
raise ValueError(
f"max_size = {max_size} must be strictly greater than the requested "
f"size for the smaller edge size = {size}"
)
if new_long > max_size:
new_short, new_long = int(max_size * new_short / new_long), max_size
new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short)
if (w, h) == (new_w, new_h):
return img
else: # specified both h and w
new_w, new_h = size[1], size[0]
img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [torch.float32, torch.float64])
# Define align_corners to avoid warnings
align_corners = False if interpolation in ["bilinear", "bicubic"] else None
img = interpolate(img, size=[new_h, new_w], mode=interpolation, align_corners=align_corners, antialias=antialias)
img = interpolate(img, size=size, mode=interpolation, align_corners=align_corners, antialias=antialias)
if interpolation == "bicubic" and out_dtype == torch.uint8:
img = img.clamp(min=0, max=255)
......
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