chore: import upstream snapshot with attribution

python/tvm/topi/testing/space_to_batch_nd.py

@@ -0,0 +1,94 @@
+# 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
+"""Space to batch ND in python"""
+
+import numpy as np
+
+
+def space_to_batch_nd_python(data, block_shape, pad_before, pad_after, pad_value=0):
+    """Space to Batch operator in python for NHWC layout.
+
+    Parameters
+    ----------
+    data : np.ndarray
+        N-D with shape [batch, spatial_shape, remaining_shapes],
+        where spatial_shape has M dimensions.
+
+    block_shape : list of ints
+        1-D array of size [M] where M is number of spatial dims, specifies block
+        size for each spatial dimension.
+
+    pad_before : list of ints
+        list of shape [M] where M is number of spatial dims, specifies
+        zero-padding size before each spatial dimension.
+
+    pad_after : list of ints
+        list of shape [M] where M is number of spatial dims, specifies
+        zero-padding size after each spatial dimension.
+
+    pad_value : float, optional
+        the value used for padding. Defaults to 0.
+
+    Returns
+    -------
+    s2b_out : np.ndarray
+        N-D with shape [batch * prod(block_shape),
+                        padded_data[1] / block_shape[0], ..., padded_data[M] / block_shape[M-1],
+                        remaining_shape]
+    """
+    M = len(block_shape)
+    in_batch = data.shape[0]
+    block_shape_prod = np.prod(block_shape)
+
+    # Apply padding to input data
+    input_shape = data.shape
+    # Add the paddings for batch and remaining dims
+    paddings = map(list, zip(pad_before, pad_after))
+    paddings = [[0, 0]] + list(paddings) + [[0, 0]] * (data.ndim - 1 - M)
+    padded_data = np.pad(data, paddings, mode="constant", constant_values=pad_value)
+    padded_shape = padded_data.shape
+
+    # Get the reshape shape and transpose axes
+    r_shape = []
+    trans_axis = []
+    r_shape.append(in_batch)
+    for i in range(1, M + 1):
+        r_shape.append(int(padded_shape[i] // block_shape[i - 1]))
+        r_shape.append(block_shape[i - 1])
+        trans_axis.append(len(r_shape) - 1)
+
+    axis_len = len(trans_axis)
+    trans_axis.append(0)
+    for i in range(axis_len):
+        trans_axis.append(trans_axis[i] - 1)
+
+    out_shape = []
+    out_shape.append(int(in_batch * block_shape_prod))
+    for i in range(1, M + 1):
+        out_shape.append(int(padded_shape[i] // block_shape[i - 1]))
+
+    for i in range(M + 1, len(input_shape)):
+        r_shape.append(input_shape[i])
+        trans_axis.append(len(r_shape) - 1)
+        out_shape.append(input_shape[i])
+
+    s2b_out = np.reshape(padded_data, newshape=r_shape)
+    s2b_out = np.transpose(s2b_out, axes=trans_axis)
+    s2b_out = np.reshape(s2b_out, newshape=out_shape)
+
+    return s2b_out