chore: import upstream snapshot with attribution

python/tvm/topi/testing/depth_to_space.py

@@ -0,0 +1,53 @@
+# 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, line-too-long, unused-variable, too-many-locals
+"""Depth to space in python"""
+
+import numpy as np
+
+
+def depth_to_space_python(data, block_size, mode="DCR"):
+    """Depth to Space operator in python for NCHW layout.
+
+    Parameters
+    ----------
+    data : np.ndarray
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    block_size : int
+        Size of blocks to convert channel pixels into.
+
+    Returns
+    -------
+    d2s_out : np.ndarray
+        4-D with shape [batch, in_channel / (block_size * block_size),
+                        out_height * block_size, out_width * block_size]
+    """
+    in_n, in_c, in_h, in_w = data.shape
+    new_h = int(in_h * block_size)
+    new_w = int(in_h * block_size)
+    new_c = int(in_c / (block_size * block_size))
+
+    if mode == "DCR":
+        expanded = np.reshape(data, newshape=[in_n, block_size, block_size, new_c, in_h, in_w])
+        transposed = np.transpose(expanded, axes=[0, 3, 4, 1, 5, 2])
+    else:
+        expanded = np.reshape(data, newshape=(in_n, new_c, block_size, block_size, in_h, in_w))
+        transposed = np.transpose(expanded, axes=(0, 1, 4, 2, 5, 3))
+    newshape = [in_n, new_c, new_h, new_w]
+    d2s_out = np.reshape(transposed, newshape=newshape)
+    return d2s_out