feat(ops): Add keras.ops.numpy.rot90 operation (#20723)

keras/src/backend/jax/numpy.py

@@ -15,6 +15,19 @@
 from keras.src.backend.jax.core import convert_to_tensor
 
 
+def rot90(array, k=1, axes=(0, 1)):
+    """Rotate an array by 90 degrees in the specified plane."""
+    if array.ndim < 2:
+        raise ValueError(
+            f"Input array must have at least 2 dimensions. Received: array.ndim={array.ndim}"
+        )
+    if len(axes) != 2 or axes[0] == axes[1]:
+        raise ValueError(
+            f"Invalid axes: {axes}. Axes must be a tuple of two different dimensions."
+        )
+    return jnp.rot90(array, k=k, axes=axes)
+
+
 @sparse.elementwise_binary_union(linear=True, use_sparsify=True)
 def add(x1, x2):
     x1 = convert_to_tensor(x1)

keras/src/backend/numpy/numpy.py

@@ -8,6 +8,19 @@
 from keras.src.backend.numpy.core import convert_to_tensor
 
 
+def rot90(array, k=1, axes=(0, 1)):
+    """Rotate an array by 90 degrees in the specified plane."""
+    if array.ndim < 2:
+        raise ValueError(
+            f"Input array must have at least 2 dimensions. Received: array.ndim={array.ndim}"
+        )
+    if len(axes) != 2 or axes[0] == axes[1]:
+        raise ValueError(
+            f"Invalid axes: {axes}. Axes must be a tuple of two different dimensions."
+        )
+    return np.rot90(array, k=k, axes=axes)
+
+
 def add(x1, x2):
     if not isinstance(x1, (int, float)):
         x1 = convert_to_tensor(x1)

keras/src/backend/tensorflow/numpy.py

@@ -23,6 +23,49 @@
 from keras.src.backend.tensorflow.core import shape as shape_op
 
 
+def rot90(array, k=1, axes=(0, 1)):
+    """Rotate an array by 90 degrees in the specified plane."""
+    array = convert_to_tensor(array)
+
+    if array.shape.rank < 2:
+        raise ValueError(
+            f"Input array must have at least 2 dimensions. Received: array.ndim={array.shape.rank}"
+        )
+
+    if len(axes) != 2 or axes[0] == axes[1]:
+        raise ValueError(
+            f"Invalid axes: {axes}. Axes must be a tuple of two different dimensions."
+        )
+
+    k = k % 4
+    if k == 0:
+        return array
+
+    axes = tuple(axis if axis >= 0 else array.shape.rank + axis for axis in axes)
+
+    perm = [i for i in range(array.shape.rank) if i not in axes]
+    perm.extend(axes)
+    array = tf.transpose(array, perm)
+
+    shape = tf.shape(array)
+    non_rot_shape = shape[:-2]
+    rot_shape = shape[-2:]
+
+    array = tf.reshape(array, tf.concat([[-1], rot_shape], axis=0))
+
+    for _ in range(k):
+        array = tf.transpose(array, [0, 2, 1])
+        array = tf.reverse(array, axis=[1])
+    array = tf.reshape(array, tf.concat([non_rot_shape, rot_shape], axis=0))
+
+    inv_perm = [0] * len(perm)
+    for i, p in enumerate(perm):
+        inv_perm[p] = i
+    array = tf.transpose(array, inv_perm)
+
+    return array
+
+
 @sparse.elementwise_binary_union(tf.sparse.add)
 def add(x1, x2):
     if not isinstance(x1, (int, float)):

keras/src/backend/torch/numpy.py

@@ -25,6 +25,40 @@
 )
 
 
+def rot90(array, k=1, axes=(0, 1)):
+    """Rotate an array by 90 degrees in the specified plane using PyTorch.
+
+    Args:
+        array: Input tensor
+        k: Number of 90-degree rotations (default=1)
+        axes: Tuple of two axes that define the plane of rotation (default=(0,1))
+
+    Returns:
+        Rotated tensor
+    """
+    array = convert_to_tensor(array)
+
+    if array.ndim < 2:
+        raise ValueError(
+            f"Input array must have at least 2 dimensions. Received: array.ndim={array.ndim}"
+        )
+    if len(axes) != 2 or axes[0] == axes[1]:
+        raise ValueError(
+            f"Invalid axes: {axes}. Axes must be a tuple of two different dimensions."
+        )
+
+    axes = tuple(axis if axis >= 0 else array.ndim + axis for axis in axes)
+
+    if not builtins.all(0 <= axis < array.ndim for axis in axes):
+        raise ValueError(f"Invalid axes {axes} for tensor with {array.ndim} dimensions")
+
+    rotated = torch.rot90(array, k=k, dims=axes)
+    if isinstance(array, np.ndarray):
+        rotated = rotated.cpu().numpy()
+
+    return rotated
+
+
 def add(x1, x2):
     x1 = convert_to_tensor(x1)
     x2 = convert_to_tensor(x2)

keras/src/ops/numpy.py

@@ -16,6 +16,69 @@
 from keras.src.ops.operation_utils import reduce_shape
 
 
+class Rot90(Operation):
+    def __init__(self, k=1, axes=(0, 1)):
+        super().__init__()
+        self.k = k
+        self.axes = axes
+
+    def call(self, array):
+        return backend.numpy.rot90(array, k=self.k, axes=self.axes)
+
+    def compute_output_spec(self, array):
+        array_shape = list(array.shape)
+        if len(array_shape) < 2:
+            raise ValueError(
+                "Input array must have at least 2 dimensions. "
+                f"Received: array.shape={array_shape}"
+            )
+        if len(self.axes) != 2 or self.axes[0] == self.axes[1]:
+            raise ValueError(
+                f"Invalid axes: {self.axes}. Axes must be a tuple of two different dimensions."
+            )
+        axis1, axis2 = self.axes
+        array_shape[axis1], array_shape[axis2] = array_shape[axis2], array_shape[axis1]
+        return KerasTensor(shape=array_shape, dtype=array.dtype)
+
+
+@keras_export(["keras.ops.rot90", "keras.ops.numpy.rot90"])
+def rot90(array, k=1, axes=(0, 1)):
+    """Rotate an array by 90 degrees in the plane specified by axes.
+
+    This function rotates an array counterclockwise by 90 degrees `k` times
+    in the plane specified by `axes`. Supports arrays of two or more dimensions.
+
+    Args:
+        array: Input array to rotate.
+        k: Number of times the array is rotated by 90 degrees.
+        axes: A tuple of two integers specifying the plane for rotation.
+
+    Returns:
+        Rotated array.
+
+    Examples:
+
+    >>> import numpy as np
+    >>> from keras import ops
+    >>> m = np.array([[1, 2], [3, 4]])
+    >>> rotated = ops.rot90(m)
+    >>> rotated
+    array([[2, 4],
+           [1, 3]])
+
+    >>> m = np.arange(8).reshape((2, 2, 2))
+    >>> rotated = ops.rot90(m, k=1, axes=(1, 2))
+    >>> rotated
+    array([[[1, 3],
+            [0, 2]],
+           [[5, 7],
+            [4, 6]]])
+    """
+    if any_symbolic_tensors((array,)):
+        return Rot90(k=k, axes=axes).symbolic_call(array)
+    return backend.numpy.rot90(array, k=k, axes=axes)
+
+
 def shape_equal(shape1, shape2, axis=None, allow_none=True):
     """Check if two shapes are equal.
 

keras/src/ops/numpy_test.py

@@ -19,6 +19,73 @@
 from keras.src.testing.test_utils import named_product
 
 
+class NumPyTestRot90(testing.TestCase):
+    def test_basic(self):
+        array = np.array([[1, 2], [3, 4]])
+        rotated = knp.rot90(array)
+        expected = np.array([[2, 4], [1, 3]])
+        assert np.array_equal(rotated, expected), f"Failed basic 2D test: {rotated}"
+
+    def test_multiple_k(self):
+        array = np.array([[1, 2], [3, 4]])
+
+        # k=2 (180 degrees rotation)
+        rotated = knp.rot90(array, k=2)
+        expected = np.array([[4, 3], [2, 1]])
+        assert np.array_equal(rotated, expected), f"Failed k=2 test: {rotated}"
+
+        # k=3 (270 degrees rotation)
+        rotated = knp.rot90(array, k=3)
+        expected = np.array([[3, 1], [4, 2]])
+        assert np.array_equal(rotated, expected), f"Failed k=3 test: {rotated}"
+
+        # k=4 (full rotation)
+        rotated = knp.rot90(array, k=4)
+        expected = array
+        assert np.array_equal(rotated, expected), f"Failed k=4 test: {rotated}"
+
+    def test_axes(self):
+        array = np.arange(8).reshape((2, 2, 2))
+        rotated = knp.rot90(array, k=1, axes=(1, 2))
+        expected = np.array([[[1, 3], [0, 2]], [[5, 7], [4, 6]]])
+        assert np.array_equal(rotated, expected), f"Failed custom axes test: {rotated}"
+
+    def test_single_image(self):
+        array = np.random.random((4, 4, 3))
+        rotated = knp.rot90(array, k=1, axes=(0, 1))
+        expected = np.rot90(array, k=1, axes=(0, 1))
+        assert np.allclose(rotated, expected), "Failed single image test"
+
+    def test_batch_images(self):
+        array = np.random.random((2, 4, 4, 3))
+        rotated = knp.rot90(array, k=1, axes=(1, 2))
+        expected = np.rot90(array, k=1, axes=(1, 2))
+        assert np.allclose(rotated, expected), "Failed batch images test"
+
+    def test_invalid_axes(self):
+        array = np.array([[1, 2], [3, 4]])
+        try:
+            knp.rot90(array, axes=(0, 0))
+        except ValueError as e:
+            assert (
+                "Invalid axes: (0, 0). Axes must be a tuple of two different dimensions."
+                in str(e)
+            ), f"Failed invalid axes test: {e}"
+        else:
+            raise AssertionError("Failed to raise error for invalid axes")
+
+    def test_invalid_rank(self):
+        array = np.array([1, 2, 3])  # 1D array
+        try:
+            knp.rot90(array)
+        except ValueError as e:
+            assert (
+                "Input array must have at least 2 dimensions." in str(e)
+            ), f"Failed invalid rank test: {e}"
+        else:
+            raise AssertionError("Failed to raise error for invalid input rank")
+
+
 class NumpyTwoInputOpsDynamicShapeTest(testing.TestCase):
     def test_add(self):
         x = KerasTensor((None, 3))