Unified input for F.affine (#2444)

* [WIP] F.affine

* [WIP] F.affine + tests

* Unified input for F.affine

* Removed commented code

* Removed unused imports

test/test_functional_tensor.py

@@ -348,6 +348,95 @@ class Tester(unittest.TestCase):
             msg="{} vs {}".format(expected_out_tensor[0, :10, :10], out_tensor[0, :10, :10])
         )
 
+    def test_affine(self):
+        # Tests on square image
+        tensor, pil_img = self._create_data(26, 26)
+
+        scripted_affine = torch.jit.script(F.affine)
+        # 1) identity map
+        out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
+        self.assertTrue(
+            tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
+        )
+        out_tensor = scripted_affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
+        self.assertTrue(
+            tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
+        )
+
+        # 2) Test rotation
+        test_configs = [
+            (90, torch.rot90(tensor, k=1, dims=(-1, -2))),
+            (45, None),
+            (30, None),
+            (-30, None),
+            (-45, None),
+            (-90, torch.rot90(tensor, k=-1, dims=(-1, -2))),
+            (180, torch.rot90(tensor, k=2, dims=(-1, -2))),
+        ]
+        for a, true_tensor in test_configs:
+            for fn in [F.affine, scripted_affine]:
+                out_tensor = fn(tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
+                if true_tensor is not None:
+                    self.assertTrue(
+                        true_tensor.equal(out_tensor),
+                        msg="{}\n{} vs \n{}".format(a, out_tensor[0, :5, :5], true_tensor[0, :5, :5])
+                    )
+                else:
+                    true_tensor = out_tensor
+
+                out_pil_img = F.affine(pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=0)
+                out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
+
+                num_diff_pixels = (true_tensor != out_pil_tensor).sum().item() / 3.0
+                ratio_diff_pixels = num_diff_pixels / true_tensor.shape[-1] / true_tensor.shape[-2]
+                # Tolerance : less than 6% of different pixels
+                self.assertLess(
+                    ratio_diff_pixels,
+                    0.06,
+                    msg="{}\n{} vs \n{}".format(
+                        ratio_diff_pixels, true_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
+                    )
+                )
+        # 3) Test translation
+        test_configs = [
+            [10, 12], (12, 13)
+        ]
+        for t in test_configs:
+            for fn in [F.affine, scripted_affine]:
+                out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
+                out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], resample=0)
+                self.compareTensorToPIL(out_tensor, out_pil_img)
+
+        # 3) Test rotation + translation + scale + share
+        test_configs = [
+            (45, [5, 6], 1.0, [0.0, 0.0]),
+            (33, (5, -4), 1.0, [0.0, 0.0]),
+            (45, [5, 4], 1.2, [0.0, 0.0]),
+            (33, (4, 8), 2.0, [0.0, 0.0]),
+            (85, (10, -10), 0.7, [0.0, 0.0]),
+            (0, [0, 0], 1.0, [35.0, ]),
+            (25, [0, 0], 1.2, [0.0, 15.0]),
+            (45, [10, 0], 0.7, [2.0, 5.0]),
+            (45, [10, -10], 1.2, [4.0, 5.0]),
+        ]
+        for r in [0, ]:
+            for a, t, s, sh in test_configs:
+                for fn in [F.affine, scripted_affine]:
+                    out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, resample=r)
+                    out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, resample=r)
+                    out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
+
+                    num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
+                    ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
+                    # Tolerance : less than 5% of different pixels
+                    self.assertLess(
+                        ratio_diff_pixels,
+                        0.05,
+                        msg="{}: {}\n{} vs \n{}".format(
+                            (r, a, t, s, sh), ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
+                        )
+                    )
+
 
 if __name__ == '__main__':
     unittest.main()

test/test_transforms.py

@@ -1317,8 +1317,8 @@ class Tester(unittest.TestCase):
                 for j in range(-5, 5):
                     input_img[pt[0] + i, pt[1] + j, :] = [255, 155, 55]
 
-        with self.assertRaises(TypeError):
-            F.affine(input_img, 10)
+        with self.assertRaises(TypeError, msg="Argument translate should be a sequence"):
+            F.affine(input_img, 10, translate=0, scale=1, shear=1)
 
         pil_img = F.to_pil_image(input_img)
 

torchvision/transforms/functional.py

 import math
 import numbers
 import warnings
-from typing import Any
+from typing import Any, Optional
 
 import numpy as np
-from numpy import sin, cos, tan
-from PIL import Image, __version__ as PILLOW_VERSION
+from PIL import Image
 
 import torch
 from torch import Tensor
@@ -21,6 +20,7 @@ from . import functional_tensor as F_t
 
 
 _is_pil_image = F_pil._is_pil_image
+_parse_fill = F_pil._parse_fill
 
 
 def _get_image_size(img: Tensor) -> List[int]:
@@ -485,43 +485,6 @@ def hflip(img: Tensor) -> Tensor:
     return F_t.hflip(img)
 
 
-def _parse_fill(fill, img, min_pil_version):
-    """Helper function to get the fill color for rotate and perspective transforms.
-
-    Args:
-        fill (n-tuple or int or float): Pixel fill value for area outside the transformed
-            image. If int or float, the value is used for all bands respectively.
-            Defaults to 0 for all bands.
-        img (PIL Image): Image to be filled.
-        min_pil_version (str): The minimum PILLOW version for when the ``fillcolor`` option
-            was first introduced in the calling function. (e.g. rotate->5.2.0, perspective->5.0.0)
-
-    Returns:
-        dict: kwarg for ``fillcolor``
-    """
-    major_found, minor_found = (int(v) for v in PILLOW_VERSION.split('.')[:2])
-    major_required, minor_required = (int(v) for v in min_pil_version.split('.')[:2])
-    if major_found < major_required or (major_found == major_required and minor_found < minor_required):
-        if fill is None:
-            return {}
-        else:
-            msg = ("The option to fill background area of the transformed image, "
-                   "requires pillow>={}")
-            raise RuntimeError(msg.format(min_pil_version))
-
-    num_bands = len(img.getbands())
-    if fill is None:
-        fill = 0
-    if isinstance(fill, (int, float)) and num_bands > 1:
-        fill = tuple([fill] * num_bands)
-    if not isinstance(fill, (int, float)) and len(fill) != num_bands:
-        msg = ("The number of elements in 'fill' does not match the number of "
-               "bands of the image ({} != {})")
-        raise ValueError(msg.format(len(fill), num_bands))
-
-    return {"fillcolor": fill}
-
-
 def _get_perspective_coeffs(startpoints, endpoints):
     """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.
 
@@ -827,7 +790,9 @@ def rotate(img, angle, resample=False, expand=False, center=None, fill=None):
     return img.rotate(angle, resample, expand, center, **opts)
 
 
-def _get_inverse_affine_matrix(center, angle, translate, scale, shear):
+def _get_inverse_affine_matrix(
+        center: List[int], angle: float, translate: List[float], scale: float, shear: List[float]
+) -> List[float]:
     # Helper method to compute inverse matrix for affine transformation
 
     # As it is explained in PIL.Image.rotate
@@ -847,14 +812,6 @@ def _get_inverse_affine_matrix(center, angle, translate, scale, shear):
     #
     # Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1
 
-    if isinstance(shear, numbers.Number):
-        shear = [shear, 0]
-
-    if not isinstance(shear, (tuple, list)) and len(shear) == 2:
-        raise ValueError(
-            "Shear should be a single value or a tuple/list containing " +
-            "two values. Got {}".format(shear))
-
     rot = math.radians(angle)
     sx, sy = [math.radians(s) for s in shear]
 
@@ -862,32 +819,37 @@ def _get_inverse_affine_matrix(center, angle, translate, scale, shear):
     tx, ty = translate
 
     # RSS without scaling
-    a = cos(rot - sy) / cos(sy)
-    b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
-    c = sin(rot - sy) / cos(sy)
-    d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)
+    a = math.cos(rot - sy) / math.cos(sy)
+    b = -math.cos(rot - sy) * math.tan(sx) / math.cos(sy) - math.sin(rot)
+    c = math.sin(rot - sy) / math.cos(sy)
+    d = -math.sin(rot - sy) * math.tan(sx) / math.cos(sy) + math.cos(rot)
 
     # Inverted rotation matrix with scale and shear
     # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
-    M = [d, -b, 0,
-         -c, a, 0]
-    M = [x / scale for x in M]
+    matrix = [d, -b, 0.0, -c, a, 0.0]
+    matrix = [x / scale for x in matrix]
 
     # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
-    M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
-    M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)
+    matrix[2] += matrix[0] * (-cx - tx) + matrix[1] * (-cy - ty)
+    matrix[5] += matrix[3] * (-cx - tx) + matrix[4] * (-cy - ty)
 
     # Apply center translation: C * RSS^-1 * C^-1 * T^-1
-    M[2] += cx
-    M[5] += cy
-    return M
+    matrix[2] += cx
+    matrix[5] += cy
 
+    return matrix
 
-def affine(img, angle, translate, scale, shear, resample=0, fillcolor=None):
-    """Apply affine transformation on the image keeping image center invariant
+
+def affine(
+        img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float],
+        resample: int = 0, fillcolor: Optional[int] = None
+) -> Tensor:
+    """Apply affine transformation on the image keeping image center invariant.
+    The image can be a PIL Image or a Tensor, in which case it is expected
+    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
 
     Args:
-        img (PIL Image): PIL Image to be rotated.
+        img (PIL Image or Tensor): image to be rotated.
         angle (float or int): rotation angle in degrees between -180 and 180, clockwise direction.
         translate (list or tuple of integers): horizontal and vertical translations (post-rotation translation)
         scale (float): overall scale
@@ -895,27 +857,62 @@ def affine(img, angle, translate, scale, shear, resample=0, fillcolor=None):
             If a tuple of list is specified, the first value corresponds to a shear parallel to the x axis, while
             the second value corresponds to a shear parallel to the y axis.
         resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
-            An optional resampling filter.
-            See `filters`_ for more information.
-            If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
+            An optional resampling filter. See `filters`_ for more information.
+            If omitted, or if the image is PIL Image and has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
+            If input is Tensor, only ``PIL.Image.NEAREST`` and ``PIL.Image.BILINEAR`` are supported.
         fillcolor (int): Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)
+
+    Returns:
+        PIL Image or Tensor: Transformed image.
     """
-    if not F_pil._is_pil_image(img):
-        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    if not isinstance(angle, (int, float)):
+        raise TypeError("Argument angle should be int or float")
+
+    if not isinstance(translate, (list, tuple)):
+        raise TypeError("Argument translate should be a sequence")
+
+    if len(translate) != 2:
+        raise ValueError("Argument translate should be a sequence of length 2")
+
+    if scale <= 0.0:
+        raise ValueError("Argument scale should be positive")
+
+    if not isinstance(shear, (numbers.Number, (list, tuple))):
+        raise TypeError("Shear should be either a single value or a sequence of two values")
 
-    assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
-        "Argument translate should be a list or tuple of length 2"
+    if isinstance(angle, int):
+        angle = float(angle)
 
-    assert scale > 0.0, "Argument scale should be positive"
+    if isinstance(translate, tuple):
+        translate = list(translate)
 
-    output_size = img.size
-    # center = (img.size[0] * 0.5 + 0.5, img.size[1] * 0.5 + 0.5)
+    if isinstance(shear, numbers.Number):
+        shear = [shear, 0.0]
+
+    if isinstance(shear, tuple):
+        shear = list(shear)
+
+    if len(shear) == 1:
+        shear = [shear[0], shear[0]]
+
+    if len(shear) != 2:
+        raise ValueError("Shear should be a sequence containing two values. Got {}".format(shear))
+
+    img_size = _get_image_size(img)
+    if not isinstance(img, torch.Tensor):
+        # center = (img_size[0] * 0.5 + 0.5, img_size[1] * 0.5 + 0.5)
         # it is visually better to estimate the center without 0.5 offset
-    # otherwise image rotated by 90 degrees is shifted 1 pixel
-    center = (img.size[0] * 0.5, img.size[1] * 0.5)
+        # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine
+        center = [img_size[0] * 0.5, img_size[1] * 0.5]
         matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
-    kwargs = {"fillcolor": fillcolor} if int(PILLOW_VERSION.split('.')[0]) >= 5 else {}
-    return img.transform(output_size, Image.AFFINE, matrix, resample, **kwargs)
+
+        return F_pil.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)
+
+    # we need to rescale translate by image size / 2 as its values can be between -1 and 1
+    translate = [2.0 * t / s for s, t in zip(img_size, translate)]
+
+    matrix = _get_inverse_affine_matrix([0, 0], angle, translate, scale, shear)
+    return F_t.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)
 
 
 def to_grayscale(img, num_output_channels=1):

torchvision/transforms/functional_pil.py

 import numbers
 from typing import Any, List, Sequence
 
+import numpy as np
 import torch
+from PIL import Image, ImageOps, ImageEnhance, __version__ as PILLOW_VERSION
+
 try:
     import accimage
 except ImportError:
     accimage = None
-from PIL import Image, ImageOps, ImageEnhance
-import numpy as np
 
 
 @torch.jit.unused
@@ -327,3 +328,65 @@ def resize(img, size, interpolation=Image.BILINEAR):
             return img.resize((ow, oh), interpolation)
     else:
         return img.resize(size[::-1], interpolation)
+
+
+@torch.jit.unused
+def _parse_fill(fill, img, min_pil_version):
+    """Helper function to get the fill color for rotate and perspective transforms.
+
+    Args:
+        fill (n-tuple or int or float): Pixel fill value for area outside the transformed
+            image. If int or float, the value is used for all bands respectively.
+            Defaults to 0 for all bands.
+        img (PIL Image): Image to be filled.
+        min_pil_version (str): The minimum PILLOW version for when the ``fillcolor`` option
+            was first introduced in the calling function. (e.g. rotate->5.2.0, perspective->5.0.0)
+
+    Returns:
+        dict: kwarg for ``fillcolor``
+    """
+    major_found, minor_found = (int(v) for v in PILLOW_VERSION.split('.')[:2])
+    major_required, minor_required = (int(v) for v in min_pil_version.split('.')[:2])
+    if major_found < major_required or (major_found == major_required and minor_found < minor_required):
+        if fill is None:
+            return {}
+        else:
+            msg = ("The option to fill background area of the transformed image, "
+                   "requires pillow>={}")
+            raise RuntimeError(msg.format(min_pil_version))
+
+    num_bands = len(img.getbands())
+    if fill is None:
+        fill = 0
+    if isinstance(fill, (int, float)) and num_bands > 1:
+        fill = tuple([fill] * num_bands)
+    if not isinstance(fill, (int, float)) and len(fill) != num_bands:
+        msg = ("The number of elements in 'fill' does not match the number of "
+               "bands of the image ({} != {})")
+        raise ValueError(msg.format(len(fill), num_bands))
+
+    return {"fillcolor": fill}
+
+
+@torch.jit.unused
+def affine(img, matrix, resample=0, fillcolor=None):
+    """Apply affine transformation on the PIL Image keeping image center invariant.
+
+    Args:
+        img (PIL Image): image to be rotated.
+        matrix (list of floats): list of 6 float values representing inverse matrix for affine transformation.
+        resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
+            An optional resampling filter.
+            See `filters`_ for more information.
+            If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
+        fillcolor (int): Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)
+
+    Returns:
+        PIL Image: Transformed image.
+    """
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    output_size = img.size
+    opts = _parse_fill(fillcolor, img, '5.0.0')
+    return img.transform(output_size, Image.AFFINE, matrix, resample, **opts)

torchvision/transforms/functional_tensor.py

+import warnings
+from typing import Optional
+
 import torch
 from torch import Tensor
+from torch.nn.functional import affine_grid, grid_sample
 from torch.jit.annotations import List, BroadcastingList2
 
 
@@ -496,7 +500,8 @@ def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
             :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
             In torchscript mode padding as a single int is not supported, use a tuple or
             list of length 1: ``[size, ]``.
-        interpolation (int, optional): Desired interpolation. Default is bilinear.
+        interpolation (int, optional): Desired interpolation. Default is bilinear (=2). Other supported values:
+            nearest(=0) and bicubic(=3).
 
     Returns:
         Tensor: Resized image.
@@ -571,3 +576,63 @@ def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
         img = img.to(out_dtype)
 
     return img
+
+
+def affine(
+        img: Tensor, matrix: List[float], resample: int = 0, fillcolor: Optional[int] = None
+) -> Tensor:
+    """Apply affine transformation on the Tensor image keeping image center invariant.
+
+    Args:
+        img (Tensor): image to be rotated.
+        matrix (list of floats): list of 6 float values representing inverse matrix for affine transformation.
+        resample (int, optional): An optional resampling filter. Default is nearest (=2). Other supported values:
+            bilinear(=2).
+        fillcolor (int, optional): this option is not supported for Tensor input. Fill value for the area outside the
+            transform in the output image is always 0.
+
+    Returns:
+        Tensor: Transformed image.
+    """
+    if not (isinstance(img, torch.Tensor) and _is_tensor_a_torch_image(img)):
+        raise TypeError('img should be Tensor Image. Got {}'.format(type(img)))
+
+    if fillcolor is not None:
+        warnings.warn("Argument fillcolor is not supported for Tensor input. Fill value is zero")
+
+    _interpolation_modes = {
+        0: "nearest",
+        2: "bilinear",
+    }
+
+    if resample not in _interpolation_modes:
+        raise ValueError("This resampling mode is unsupported with Tensor input")
+
+    theta = torch.tensor(matrix, dtype=torch.float).reshape(1, 2, 3)
+    shape = img.shape
+    grid = affine_grid(theta, size=(1, shape[-3], shape[-2], shape[-1]), align_corners=False)
+
+    # make image NCHW
+    need_squeeze = False
+    if img.ndim < 4:
+        img = img.unsqueeze(dim=0)
+        need_squeeze = True
+
+    mode = _interpolation_modes[resample]
+
+    out_dtype = img.dtype
+    need_cast = False
+    if img.dtype not in (torch.float32, torch.float64):
+        need_cast = True
+        img = img.to(torch.float32)
+
+    img = grid_sample(img, grid, mode=mode, padding_mode="zeros", align_corners=False)
+
+    if need_squeeze:
+        img = img.squeeze(dim=0)
+
+    if need_cast:
+        # it is better to round before cast
+        img = torch.round(img).to(out_dtype)
+
+    return img