[TRANS, IMP] Add new max_size parameter to Resize (#3494)

* WIP, still needs tests and docs

* tests

* flake8

* Docs + fixed some tests

* proper error messages

Author: NicolasHug

test/test_functional_tensor.py

@@ -13,7 +13,7 @@
 
 from common_utils import TransformsTester
 
-from typing import Dict, List, Tuple
+from typing import Dict, List, Sequence, Tuple
 
 
 NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
@@ -409,46 +409,58 @@ def test_resize(self):
                 batch_tensors = batch_tensors.to(dt)
 
             for size in [32, 26, [32, ], [32, 32], (32, 32), [26, 35]]:
-                for interpolation in [BILINEAR, BICUBIC, NEAREST]:
-                    resized_tensor = F.resize(tensor, size=size, interpolation=interpolation)
-                    resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation)
-
-                    self.assertEqual(
-                        resized_tensor.size()[1:], resized_pil_img.size[::-1], msg="{}, {}".format(size, interpolation)
-                    )
-
-                    if interpolation not in [NEAREST, ]:
-                        # We can not check values if mode = NEAREST, as results are different
-                        # E.g. resized_tensor  = [[a, a, b, c, d, d, e, ...]]
-                        # E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
-                        resized_tensor_f = resized_tensor
-                        # we need to cast to uint8 to compare with PIL image
-                        if resized_tensor_f.dtype == torch.uint8:
-                            resized_tensor_f = resized_tensor_f.to(torch.float)
-
-                        # Pay attention to high tolerance for MAE
-                        self.approxEqualTensorToPIL(
-                            resized_tensor_f, resized_pil_img, tol=8.0, msg="{}, {}".format(size, interpolation)
+                for max_size in (None, 33, 40, 1000):
+                    if max_size is not None and isinstance(size, Sequence) and len(size) != 1:
+                        continue  # unsupported, see assertRaises below
+                    for interpolation in [BILINEAR, BICUBIC, NEAREST]:
+                        resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, max_size=max_size)
+                        resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation, max_size=max_size)
+
+                        self.assertEqual(
+                            resized_tensor.size()[1:], resized_pil_img.size[::-1],
+                            msg="{}, {}".format(size, interpolation)
                         )
 
-                    if isinstance(size, int):
-                        script_size = [size, ]
-                    else:
-                        script_size = size
+                        if interpolation not in [NEAREST, ]:
+                            # We can not check values if mode = NEAREST, as results are different
+                            # E.g. resized_tensor  = [[a, a, b, c, d, d, e, ...]]
+                            # E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
+                            resized_tensor_f = resized_tensor
+                            # we need to cast to uint8 to compare with PIL image
+                            if resized_tensor_f.dtype == torch.uint8:
+                                resized_tensor_f = resized_tensor_f.to(torch.float)
+
+                            # Pay attention to high tolerance for MAE
+                            self.approxEqualTensorToPIL(
+                                resized_tensor_f, resized_pil_img, tol=8.0, msg="{}, {}".format(size, interpolation)
+                            )
 
-                    resize_result = script_fn(tensor, size=script_size, interpolation=interpolation)
-                    self.assertTrue(resized_tensor.equal(resize_result), msg="{}, {}".format(size, interpolation))
+                        if isinstance(size, int):
+                            script_size = [size, ]
+                        else:
+                            script_size = size
 
-                    self._test_fn_on_batch(
-                        batch_tensors, F.resize, size=script_size, interpolation=interpolation
-                    )
+                        resize_result = script_fn(tensor, size=script_size, interpolation=interpolation,
+                                                  max_size=max_size)
+                        self.assertTrue(resized_tensor.equal(resize_result), msg="{}, {}".format(size, interpolation))
+
+                        self._test_fn_on_batch(
+                            batch_tensors, F.resize, size=script_size, interpolation=interpolation, max_size=max_size
+                        )
 
         # assert changed type warning
         with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
             res1 = F.resize(tensor, size=32, interpolation=2)
             res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
             self.assertTrue(res1.equal(res2))
 
+        for img in (tensor, pil_img):
+            exp_msg = "max_size should only be passed if size specifies the length of the smaller edge"
+            with self.assertRaisesRegex(ValueError, exp_msg):
+                F.resize(img, size=(32, 34), max_size=35)
+            with self.assertRaisesRegex(ValueError, "max_size = 32 must be strictly greater"):
+                F.resize(img, size=32, max_size=32)
+
     def test_resized_crop(self):
         # test values of F.resized_crop in several cases:
         # 1) resize to the same size, crop to the same size => should be identity

test/test_transforms.py

@@ -312,23 +312,30 @@ def test_resize(self):
             img = Image.new("RGB", size=(width, height), color=127)
 
             for osize in test_output_sizes_1:
-
-                t = transforms.Resize(osize)
-                result = t(img)
-
-                msg = "{}, {} - {}".format(height, width, osize)
-                osize = osize[0] if isinstance(osize, (list, tuple)) else osize
-                # If size is an int, smaller edge of the image will be matched to this number.
-                # i.e, if height > width, then image will be rescaled to (size * height / width, size).
-                if height < width:
-                    expected_size = (int(osize * width / height), osize)  # (w, h)
-                    self.assertEqual(result.size, expected_size, msg=msg)
-                elif width < height:
-                    expected_size = (osize, int(osize * height / width))  # (w, h)
-                    self.assertEqual(result.size, expected_size, msg=msg)
-                else:
-                    expected_size = (osize, osize)  # (w, h)
-                    self.assertEqual(result.size, expected_size, msg=msg)
+                for max_size in (None, 37, 1000):
+
+                    t = transforms.Resize(osize, max_size=max_size)
+                    result = t(img)
+
+                    msg = "{}, {} - {} - {}".format(height, width, osize, max_size)
+                    osize = osize[0] if isinstance(osize, (list, tuple)) else osize
+                    # If size is an int, smaller edge of the image will be matched to this number.
+                    # i.e, if height > width, then image will be rescaled to (size * height / width, size).
+                    if height < width:
+                        exp_w, exp_h = (int(osize * width / height), osize)  # (w, h)
+                        if max_size is not None and max_size < exp_w:
+                            exp_w, exp_h = max_size, int(max_size * exp_h / exp_w)
+                        self.assertEqual(result.size, (exp_w, exp_h), msg=msg)
+                    elif width < height:
+                        exp_w, exp_h = (osize, int(osize * height / width))  # (w, h)
+                        if max_size is not None and max_size < exp_h:
+                            exp_w, exp_h = int(max_size * exp_w / exp_h), max_size
+                        self.assertEqual(result.size, (exp_w, exp_h), msg=msg)
+                    else:
+                        exp_w, exp_h = (osize, osize)  # (w, h)
+                        if max_size is not None and max_size < osize:
+                            exp_w, exp_h = max_size, max_size
+                        self.assertEqual(result.size, (exp_w, exp_h), msg=msg)
 
         for height, width in input_sizes:
             img = Image.new("RGB", size=(width, height), color=127)

test/test_transforms_tensor.py

@@ -7,6 +7,7 @@
 import numpy as np
 
 import unittest
+from typing import Sequence
 
 from common_utils import TransformsTester, get_tmp_dir, int_dtypes, float_dtypes
 
@@ -322,32 +323,29 @@ def test_resize(self):
 
         tensor, _ = self._create_data(height=34, width=36, device=self.device)
         batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
-        script_fn = torch.jit.script(F.resize)
 
         for dt in [None, torch.float32, torch.float64]:
             if dt is not None:
                 # This is a trivial cast to float of uint8 data to test all cases
                 tensor = tensor.to(dt)
             for size in [32, 34, [32, ], [32, 32], (32, 32), [34, 35]]:
-                for interpolation in [BILINEAR, BICUBIC, NEAREST]:
+                for max_size in (None, 35, 1000):
+                    if max_size is not None and isinstance(size, Sequence) and len(size) != 1:
+                        continue  # Not supported
+                    for interpolation in [BILINEAR, BICUBIC, NEAREST]:
 
-                    resized_tensor = F.resize(tensor, size=size, interpolation=interpolation)
+                        if isinstance(size, int):
+                            script_size = [size, ]
+                        else:
+                            script_size = size
 
-                    if isinstance(size, int):
-                        script_size = [size, ]
-                    else:
-                        script_size = size
-
-                    s_resized_tensor = script_fn(tensor, size=script_size, interpolation=interpolation)
-                    self.assertTrue(s_resized_tensor.equal(resized_tensor))
-
-                    transform = T.Resize(size=script_size, interpolation=interpolation)
-                    s_transform = torch.jit.script(transform)
-                    self._test_transform_vs_scripted(transform, s_transform, tensor)
-                    self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
+                        transform = T.Resize(size=script_size, interpolation=interpolation, max_size=max_size)
+                        s_transform = torch.jit.script(transform)
+                        self._test_transform_vs_scripted(transform, s_transform, tensor)
+                        self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
 
         with get_tmp_dir() as tmp_dir:
-            script_fn.save(os.path.join(tmp_dir, "t_resize.pt"))
+            s_transform.save(os.path.join(tmp_dir, "t_resize.pt"))
 
     def test_resized_crop(self):
         tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=self.device)

torchvision/transforms/functional.py

@@ -337,7 +337,8 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
     return tensor
 
 
-def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR) -> Tensor:
+def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+           max_size: Optional[int] = None) -> Tensor:
     r"""Resize the input image to the given size.
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
@@ -355,6 +356,14 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = Inte
             Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
             ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
             For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
+        max_size (int, optional): The maximum allowed for the longer edge of
+            the resized image: if the longer edge of the image is greater
+            than ``max_size`` after being resized according to ``size``, then
+            the image is resized again so that the longer edge is equal to
+            ``max_size``. As a result, ```size` might be overruled, i.e the
+            smaller edge may be shorter than ``size``. This is only supported
+            if ``size`` is an int (or a sequence of length 1 in torchscript
+            mode).
 
     Returns:
         PIL Image or Tensor: Resized image.
@@ -372,9 +381,9 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = Inte
 
     if not isinstance(img, torch.Tensor):
         pil_interpolation = pil_modes_mapping[interpolation]
-        return F_pil.resize(img, size=size, interpolation=pil_interpolation)
+        return F_pil.resize(img, size=size, interpolation=pil_interpolation, max_size=max_size)
 
-    return F_t.resize(img, size=size, interpolation=interpolation.value)
+    return F_t.resize(img, size=size, interpolation=interpolation.value, max_size=max_size)
 
 
 def scale(*args, **kwargs):

torchvision/transforms/functional_pil.py

@@ -204,27 +204,40 @@ def crop(img: Image.Image, top: int, left: int, height: int, width: int) -> Imag
 
 
 @torch.jit.unused
-def resize(img, size, interpolation=Image.BILINEAR):
+def resize(img, size, interpolation=Image.BILINEAR, max_size=None):
     if not _is_pil_image(img):
         raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
     if not (isinstance(size, int) or (isinstance(size, Sequence) and len(size) in (1, 2))):
         raise TypeError('Got inappropriate size arg: {}'.format(size))
 
-    if isinstance(size, int) or len(size) == 1:
-        if isinstance(size, Sequence):
-            size = size[0]
+    if isinstance(size, Sequence) and len(size) == 1:
+        size = size[0]
+    if isinstance(size, int):
         w, h = img.size
-        if (w <= h and w == size) or (h <= w and h == size):
+
+        short, long = (w, h) if w <= h else (h, w)
+        if short == size:
             return img
-        if w < h:
-            ow = size
-            oh = int(size * h / w)
-            return img.resize((ow, oh), interpolation)
-        else:
-            oh = size
-            ow = int(size * w / h)
-            return img.resize((ow, oh), interpolation)
+
+        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)
+        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)
 
 

torchvision/transforms/functional_tensor.py

@@ -470,7 +470,7 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
     return img
 
 
-def resize(img: Tensor, size: List[int], interpolation: str = "bilinear") -> Tensor:
+def resize(img: Tensor, size: List[int], interpolation: str = "bilinear", max_size: Optional[int] = None) -> Tensor:
     _assert_image_tensor(img)
 
     if not isinstance(size, (int, tuple, list)):
@@ -484,34 +484,51 @@ def resize(img: Tensor, size: List[int], interpolation: str = "bilinear") -> Ten
     if isinstance(size, tuple):
         size = list(size)
 
-    if isinstance(size, list) and len(size) not in [1, 2]:
-        raise ValueError("Size must be an int or a 1 or 2 element tuple/list, not a "
-                         "{} element tuple/list".format(len(size)))
+    if isinstance(size, list):
+        if len(size) not in [1, 2]:
+            raise ValueError("Size must be an int or a 1 or 2 element tuple/list, not a "
+                             "{} element tuple/list".format(len(size)))
+        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."
+            )
 
     w, h = _get_image_size(img)
 
-    if isinstance(size, int):
-        size_w, size_h = size, size
-    elif len(size) < 2:
-        size_w, size_h = size[0], size[0]
-    else:
-        size_w, size_h = size[1], size[0]  # Convention (h, w)
+    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)
 
-    if isinstance(size, int) or len(size) < 2:
-        if w < h:
-            size_h = int(size_w * h / w)
+        if isinstance(size, int):
+            requested_new_short = size
         else:
-            size_w = int(size_h * w / h)
+            requested_new_short = size[0]
 
-        if (w <= h and w == size_w) or (h <= w and h == size_h):
+        if short == requested_new_short:
             return img
 
+        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]
+
     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=[size_h, size_w], mode=interpolation, align_corners=align_corners)
+    img = interpolate(img, size=[new_h, new_w], mode=interpolation, align_corners=align_corners)
 
     if interpolation == "bicubic" and out_dtype == torch.uint8:
         img = img.clamp(min=0, max=255)