Add ops.map_coordinates

keras_core/backend/jax/image.py

@@ -162,3 +162,31 @@ def affine_transform(
     if need_squeeze:
         affined = jnp.squeeze(affined, axis=0)
     return affined
+
+
+MAP_COORDINATES_FILL_MODES = {
+    "constant",
+    "nearest",
+    "wrap",
+    "mirror",
+    "reflect",
+}
+
+
+def map_coordinates(
+    input, coordinates, order, fill_mode="constant", fill_value=0.0
+):
+    if fill_mode not in MAP_COORDINATES_FILL_MODES:
+        raise ValueError(
+            "Invalid value for argument `fill_mode`. Expected one of "
+            f"{set(MAP_COORDINATES_FILL_MODES)}. Received: "
+            f"fill_mode={fill_mode}"
+        )
+    if order not in range(2):
+        raise ValueError(
+            "Invalid value for argument `order`. Expected one of "
+            f"{[0, 1]}. Received: order={order}"
+        )
+    return jax.scipy.ndimage.map_coordinates(
+        input, coordinates, order, fill_mode, fill_value
+    )

keras_core/backend/numpy/image.py

@@ -173,3 +173,55 @@ def affine_transform(
     if input_dtype == "float16":
         affined = affined.astype(input_dtype)
     return affined
+
+
+MAP_COORDINATES_FILL_MODES = {
+    "constant",
+    "nearest",
+    "wrap",
+    "mirror",
+    "reflect",
+}
+
+
+def map_coordinates(
+    input, coordinates, order, fill_mode="constant", fill_value=0.0
+):
+    if fill_mode not in MAP_COORDINATES_FILL_MODES:
+        raise ValueError(
+            "Invalid value for argument `fill_mode`. Expected one of "
+            f"{set(MAP_COORDINATES_FILL_MODES.keys())}. Received: "
+            f"fill_mode={fill_mode}"
+        )
+    if order not in range(2):
+        raise ValueError(
+            "Invalid value for argument `order`. Expected one of "
+            f"{[0, 1]}. Received: order={order}"
+        )
+    # SciPy's implementation of map_coordinates handles boundaries incorrectly,
+    # unless mode='reflect'. For order=1, this only affects interpolation
+    # outside the bounds of the original array.
+    # https://github.com/scipy/scipy/issues/2640
+    padding = [
+        (
+            max(-np.floor(c.min()).astype(int) + 1, 0),
+            max(np.ceil(c.max()).astype(int) + 1 - size, 0),
+        )
+        for c, size in zip(coordinates, input.shape)
+    ]
+    shifted_coords = [c + p[0] for p, c in zip(padding, coordinates)]
+    pad_mode = {
+        "nearest": "edge",
+        "mirror": "reflect",
+        "reflect": "symmetric",
+    }.get(fill_mode, fill_mode)
+    if fill_mode == "constant":
+        padded = np.pad(
+            input, padding, mode=pad_mode, constant_values=fill_value
+        )
+    else:
+        padded = np.pad(input, padding, mode=pad_mode)
+    result = scipy.ndimage.map_coordinates(
+        padded, shifted_coords, order=order, mode=fill_mode, cval=fill_value
+    )
+    return result

keras_core/backend/tensorflow/image.py

@@ -1,5 +1,11 @@
+import functools
+import itertools
+import operator
+
 import tensorflow as tf
 
+from keras_core.backend.tensorflow.core import convert_to_tensor
+
 RESIZE_INTERPOLATIONS = (
     "bilinear",
     "nearest",
@@ -119,3 +125,114 @@ def affine_transform(
     if need_squeeze:
         affined = tf.squeeze(affined, axis=0)
     return affined
+
+
+def _mirror_index_fixer(index, size):
+    s = size - 1  # Half-wavelength of triangular wave
+    # Scaled, integer-valued version of the triangular wave |x - round(x)|
+    return tf.abs((index + s) % (2 * s) - s)
+
+
+def _reflect_index_fixer(index, size):
+    return tf.math.floordiv(
+        _mirror_index_fixer(2 * index + 1, 2 * size + 1) - 1, 2
+    )
+
+
+_INDEX_FIXERS = {
+    "constant": lambda index, size: index,
+    "nearest": lambda index, size: tf.clip_by_value(index, 0, size - 1),
+    "wrap": lambda index, size: index % size,
+    "mirror": _mirror_index_fixer,
+    "reflect": _reflect_index_fixer,
+}
+
+
+def _nearest_indices_and_weights(coordinate):
+    coordinate = (
+        coordinate if coordinate.dtype.is_integer else tf.round(coordinate)
+    )
+    index = tf.cast(coordinate, tf.int32)
+    weight = tf.constant(1, coordinate.dtype)
+    return [(index, weight)]
+
+
+def _linear_indices_and_weights(coordinate):
+    lower = tf.floor(coordinate)
+    upper_weight = coordinate - lower
+    lower_weight = 1 - upper_weight
+    index = tf.cast(lower, tf.int32)
+    return [(index, lower_weight), (index + 1, upper_weight)]
+
+
+def map_coordinates(
+    input, coordinates, order, fill_mode="constant", fill_value=0.0
+):
+    input_arr = convert_to_tensor(input)
+    coordinate_arrs = convert_to_tensor(coordinates)
+    # unstack into a list of tensors for following operations
+    coordinate_arrs = tf.unstack(coordinate_arrs, axis=0)
+    fill_value = convert_to_tensor(tf.cast(fill_value, input_arr.dtype))
+
+    if len(coordinates) != len(input_arr.shape):
+        raise ValueError(
+            "coordinates must be a sequence of length input.shape, but "
+            f"{len(coordinates)} != {len(input_arr.shape)}"
+        )
+
+    index_fixer = _INDEX_FIXERS.get(fill_mode)
+    if index_fixer is None:
+        raise ValueError(
+            "Invalid value for argument `fill_mode`. Expected one of "
+            f"{set(_INDEX_FIXERS.keys())}. Received: "
+            f"fill_mode={fill_mode}"
+        )
+
+    def is_valid(index, size):
+        if fill_mode == "constant":
+            return (0 <= index) & (index < size)
+        else:
+            return True
+
+    if order == 0:
+        interp_fun = _nearest_indices_and_weights
+    elif order == 1:
+        interp_fun = _linear_indices_and_weights
+    else:
+        raise NotImplementedError("map_coordinates currently requires order<=1")
+
+    valid_1d_interpolations = []
+    for coordinate, size in zip(coordinate_arrs, input_arr.shape):
+        interp_nodes = interp_fun(coordinate)
+        valid_interp = []
+        for index, weight in interp_nodes:
+            fixed_index = index_fixer(index, size)
+            valid = is_valid(index, size)
+            valid_interp.append((fixed_index, valid, weight))
+        valid_1d_interpolations.append(valid_interp)
+
+    outputs = []
+    for items in itertools.product(*valid_1d_interpolations):
+        indices, validities, weights = zip(*items)
+        indices = tf.transpose(tf.stack(indices))
+
+        def fast_path():
+            return tf.transpose(tf.gather_nd(input_arr, indices))
+
+        def slow_path():
+            all_valid = functools.reduce(operator.and_, validities)
+            return tf.where(
+                all_valid,
+                tf.transpose(tf.gather_nd(input_arr, indices)),
+                fill_value,
+            )
+
+        contribution = tf.cond(tf.reduce_all(validities), fast_path, slow_path)
+        outputs.append(
+            functools.reduce(operator.mul, weights)
+            * tf.cast(contribution, weights[0].dtype)
+        )
+    result = functools.reduce(operator.add, outputs)
+    if input_arr.dtype.is_integer:
+        result = result if result.dtype.is_integer else tf.round(result)
+    return tf.cast(result, input_arr.dtype)

keras_core/backend/torch/image.py

@@ -1,3 +1,7 @@
+import functools
+import itertools
+import operator
+
 import torch
 import torch.nn.functional as tnn
 
@@ -263,3 +267,109 @@ def affine_transform(
     if need_squeeze:
         affined = affined.squeeze(dim=0)
     return affined
+
+
+def _mirror_index_fixer(index, size):
+    s = size - 1  # Half-wavelength of triangular wave
+    # Scaled, integer-valued version of the triangular wave |x - round(x)|
+    return torch.abs((index + s) % (2 * s) - s)
+
+
+def _reflect_index_fixer(index, size):
+    return torch.floor_divide(
+        _mirror_index_fixer(2 * index + 1, 2 * size + 1) - 1, 2
+    )
+
+
+_INDEX_FIXERS = {
+    "constant": lambda index, size: index,
+    "nearest": lambda index, size: torch.clip(index, 0, size - 1),
+    "wrap": lambda index, size: index % size,
+    "mirror": _mirror_index_fixer,
+    "reflect": _reflect_index_fixer,
+}
+
+
+def _is_integer(a):
+    if not torch.is_floating_point(a) and not torch.is_complex(a):
+        return True
+    return False
+
+
+def _nearest_indices_and_weights(coordinate):
+    coordinate = (
+        coordinate if _is_integer(coordinate) else torch.round(coordinate)
+    )
+    index = coordinate.to(torch.int32)
+    weight = torch.tensor(1).to(torch.int32)
+    return [(index, weight)]
+
+
+def _linear_indices_and_weights(coordinate):
+    lower = torch.floor(coordinate)
+    upper_weight = coordinate - lower
+    lower_weight = 1 - upper_weight
+    index = lower.to(torch.int32)
+    return [(index, lower_weight), (index + 1, upper_weight)]
+
+
+def map_coordinates(
+    input, coordinates, order, fill_mode="constant", fill_value=0.0
+):
+    input_arr = convert_to_tensor(input)
+    coordinate_arrs = [convert_to_tensor(c) for c in coordinates]
+    fill_value = convert_to_tensor(fill_value, input_arr.dtype)
+
+    if len(coordinates) != len(input_arr.shape):
+        raise ValueError(
+            "coordinates must be a sequence of length input.shape, but "
+            f"{len(coordinates)} != {len(input_arr.shape)}"
+        )
+
+    index_fixer = _INDEX_FIXERS.get(fill_mode)
+    if index_fixer is None:
+        raise ValueError(
+            "Invalid value for argument `fill_mode`. Expected one of "
+            f"{set(_INDEX_FIXERS.keys())}. Received: "
+            f"fill_mode={fill_mode}"
+        )
+
+    def is_valid(index, size):
+        if fill_mode == "constant":
+            return (0 <= index) & (index < size)
+        else:
+            return True
+
+    if order == 0:
+        interp_fun = _nearest_indices_and_weights
+    elif order == 1:
+        interp_fun = _linear_indices_and_weights
+    else:
+        raise NotImplementedError("map_coordinates currently requires order<=1")
+
+    valid_1d_interpolations = []
+    for coordinate, size in zip(coordinate_arrs, input_arr.shape):
+        interp_nodes = interp_fun(coordinate)
+        valid_interp = []
+        for index, weight in interp_nodes:
+            fixed_index = index_fixer(index, size)
+            valid = is_valid(index, size)
+            valid_interp.append((fixed_index, valid, weight))
+        valid_1d_interpolations.append(valid_interp)
+
+    outputs = []
+    for items in itertools.product(*valid_1d_interpolations):
+        indices, validities, weights = zip(*items)
+        if all(valid is True for valid in validities):
+            # fast path
+            contribution = input_arr[indices]
+        else:
+            all_valid = functools.reduce(operator.and_, validities)
+            contribution = torch.where(
+                all_valid, input_arr[indices], fill_value
+            )
+        outputs.append(functools.reduce(operator.mul, weights) * contribution)
+    result = functools.reduce(operator.add, outputs)
+    if _is_integer(input_arr):
+        result = result if _is_integer(result) else torch.round(result)
+    return result.to(input_arr.dtype)