chore: import upstream snapshot with attribution

python/tvm/topi/testing/dilate_python.py

@@ -0,0 +1,64 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name
+"""Dilate operation in python"""
+
+import numpy as np
+
+
+def dilate_python(input_np, strides, dilation_value=0.0, out_dtype=None):
+    """Dilate operation.
+
+    Parameters
+    ----------
+    input_np : numpy.ndarray
+        n-D, can be any layout.
+
+    strides : list / tuple of n ints
+        Dilation stride on each dimension, 1 means no dilation.
+
+    dilation_value : int/float, optional
+        Value used to dilate the input.
+
+    out_dtype : Option[str]
+        The datatype of the dilated array.  If unspecified, will use
+        the same dtype as the input array.
+
+    Returns
+    -------
+    output_np : numpy.ndarray
+        n-D, the same layout as Input.
+
+    """
+    assert len(input_np.shape) == len(strides), (
+        f"Input dimension and strides size dismatch : {len(input_np.shape)} vs {len(strides)}"
+    )
+
+    if out_dtype is None:
+        out_dtype = input_np.dtype
+
+    output_size = [
+        (input_dim - 1) * stride + 1 for input_dim, stride in zip(input_np.shape, strides)
+    ]
+    non_zero_elements = np.ix_(
+        *[range(0, output_dim, stride) for output_dim, stride in zip(output_size, strides)]
+    )
+
+    output_np = np.full(shape=output_size, fill_value=dilation_value, dtype=out_dtype)
+    output_np[non_zero_elements] = input_np
+
+    return output_np