chore: import upstream snapshot with attribution

python/tvm/topi/testing/get_valid_counts_python.py

@@ -0,0 +1,69 @@
+# 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.
+"""Numpy reference implementation for get_valid_counts."""
+
+import numpy as np
+
+
+def get_valid_counts_python(data, score_threshold=0, id_index=0, score_index=1):
+    """Numpy reference for get_valid_counts.
+
+    Parameters
+    ----------
+    data : numpy.ndarray
+        3-D array with shape [batch_size, num_anchors, elem_length].
+
+    score_threshold : float
+        Lower limit of score for valid bounding boxes.
+
+    id_index : int
+        Index of the class categories, -1 to disable.
+
+    score_index : int
+        Index of the scores/confidence of boxes.
+
+    Returns
+    -------
+    valid_count : numpy.ndarray
+        1-D array, shape [batch_size].
+
+    out_tensor : numpy.ndarray
+        Rearranged data, shape [batch_size, num_anchors, elem_length].
+
+    out_indices : numpy.ndarray
+        Indices mapping, shape [batch_size, num_anchors].
+    """
+    batch_size, num_anchors, box_data_length = data.shape
+    valid_count = np.zeros(batch_size, dtype="int32")
+    out_tensor = np.full_like(data, -1.0)
+    out_indices = np.full((batch_size, num_anchors), -1, dtype="int32")
+
+    for i in range(batch_size):
+        cnt = 0
+        for j in range(num_anchors):
+            score = data[i, j, score_index]
+            if id_index < 0:
+                is_valid = score > score_threshold
+            else:
+                is_valid = score > score_threshold and data[i, j, id_index] >= 0
+            if is_valid:
+                out_tensor[i, cnt, :] = data[i, j, :]
+                out_indices[i, cnt] = j
+                cnt += 1
+        valid_count[i] = cnt
+
+    return valid_count, out_tensor, out_indices