chore: import upstream snapshot with attribution

python/tvm/topi/testing/batch_to_space_nd.py

@@ -0,0 +1,99 @@
+# 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
+"""Batch to space ND in python"""
+
+import numpy as np
+
+from . import strided_slice_python
+
+
+def batch_to_space_nd_python(data, block_shape, crop_begin_list, crop_end_list):
+    """Batch to Space 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.
+
+    crop_begin_list : list of ints
+        list of shape [M] where M is number of spatial dims, specifies
+        begin crop size for each spatial dimension.
+
+    crop_end_list : list of ints
+        list of shape [M] where M is number of spatial dims, specifies
+        end crop size for each spatial dimension.
+
+    Returns
+    -------
+    b2s_out : np.ndarray
+        N-D with shape
+        [batch / prod(block_shape),
+        in_shape[1] * block_shape[0] - crop_begin_list[0] - crop_end_list[0], ...,
+        in_shape[M] * block_shape[M-1] - crop_begin_list[M-1] - crop_end_list[M-1],
+        remaining_shape]
+    """
+    in_shape = data.shape
+    N = len(in_shape)
+    M = len(block_shape)
+    block_shape_prod = np.prod(block_shape)
+    in_batch = data.shape[0]
+    axis = []
+    r_p_shape = []
+
+    r_shape = [block_shape[i] for i in range(0, M)]
+    axis.append(len(r_shape))
+    r_shape.append(in_batch // block_shape_prod)
+
+    for i in range(1, N):
+        axis.append(len(r_shape))
+        if len(axis) < (M + N):
+            axis.append(len(r_shape) - (M + 1))
+        r_shape.append(in_shape[i])
+
+    r_p_shape.append(int(in_batch / block_shape_prod))
+    for i in range(1, M + 1):
+        r_p_shape.append(in_shape[i] * block_shape[i - 1])
+    for i in range(M + 1, N):
+        r_p_shape.append(in_shape[i])
+
+    b2s_out = np.reshape(data, newshape=r_shape)
+    b2s_out = np.transpose(b2s_out, axes=axis)
+    b2s_out = np.reshape(b2s_out, newshape=r_p_shape)
+
+    # Crop the start and end of dimensions of b2s_out
+    begin_idx = []
+    end_idx = []
+    strides = []
+
+    for i, _ in enumerate(r_p_shape):
+        strides.append(1)
+        if 0 < i <= M:
+            # begin and end index for spatial dimensions
+            begin_idx.append(crop_begin_list[i - 1])
+            end_idx.append(r_p_shape[i] - crop_end_list[i - 1])
+        else:
+            begin_idx.append(0)
+            end_idx.append(r_p_shape[i])
+
+    b2s_out = strided_slice_python(b2s_out, begin_idx, end_idx, strides)
+    return b2s_out