chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,62 @@
+# 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
+"""Reorg in python"""
+
+import numpy as np
+
+
+def reorg_python(a_np, stride):
+    """Reorg operator
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    stride : int
+        Stride size
+
+    Returns
+    -------
+    b_np : np.ndarray
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+
+    batch, in_channel, in_height, in_width = a_np.shape
+    a_np = np.reshape(a_np, batch * in_channel * in_height * in_width)
+    out_c = int(in_channel / (stride * stride))
+    out_channel = in_channel * stride * stride
+    out_height = int(in_height / stride)
+    out_width = int(in_width / stride)
+    b_np = np.zeros(batch * out_channel * out_height * out_width)
+    cnt = 0
+    for b in range(batch):
+        for k in range(in_channel):
+            for j in range(in_height):
+                for i in range(in_width):
+                    c2 = k % out_c
+                    offset = int(k / out_c)
+                    w2 = int(i * stride + offset % stride)
+                    h2 = int(j * stride + offset / stride)
+                    out_index = int(
+                        w2 + in_width * stride * (h2 + in_height * stride * (c2 + out_c * b))
+                    )
+                    b_np[cnt] = a_np[int(out_index)]
+                    cnt = cnt + 1
+    b_np = np.reshape(b_np, (batch, out_channel, out_height, out_width))
+    return b_np