chore: import upstream snapshot with attribution

python/tvm/topi/testing/common.py

@@ -0,0 +1,70 @@
+# 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
+"""Common utility for topi test"""
+
+import numpy as np
+import scipy.signal
+
+
+def _convolve2d(data, weights):
+    """2d convolution operator in HW layout.
+
+    This is intended to be used as a replacement for
+    scipy.signals.convolve2d, with wider support for different dtypes.
+    scipy.signal.convolve2d does not support all TVM-supported
+    dtypes (e.g. float16).  Where possible, this function uses
+    scipy.signal.convolve2d to take advantage of compiled scipy
+    routines, falling back to an explicit loop only where needed.
+
+    Parameters
+    ----------
+    data : numpy.ndarray
+        2-D with shape [in_height, in_width]
+
+    weights : numpy.ndarray
+        2-D with shape [filter_height, filter_width].
+
+    Returns
+    -------
+    b_np : np.ndarray
+        2-D with shape [out_height, out_width]
+
+        Return value and layout conventions are matched to
+        ``scipy.signal.convolve2d(data, weights, mode="valid")``
+    """
+
+    try:
+        return scipy.signal.convolve2d(data, weights, mode="valid")
+    except ValueError:
+        pass
+
+    weights = np.rot90(weights, k=2)
+
+    assert len(data.shape) == len(weights.shape) == 2
+
+    dtype = data.dtype
+    kernel_h, kernel_w = weights.shape
+
+    output_shape = [a_dim - w_dim + 1 for a_dim, w_dim in zip(data.shape, weights.shape)]
+    output = np.zeros(output_shape, dtype=dtype)
+
+    for y in range(output_shape[0]):
+        for x in range(output_shape[1]):
+            output[y][x] = np.sum(data[y : y + kernel_h, x : x + kernel_w] * weights)
+
+    return output