chore: import upstream snapshot with attribution

test/legacy_test/test_upsampling_api.py

@@ -0,0 +1,71 @@
+#   Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class TestUpsamplingBilinear2D_Compatibility(unittest.TestCase):
+    def test_upsamplingbilinear2d(self):
+        with paddle.base.dygraph.guard():
+            input = paddle.rand(shape=(2, 3, 6, 10)).astype("float32")
+            upsample_out = paddle.nn.UpsamplingBilinear2D(size=[12, 12])
+            output = upsample_out(x=input)
+            out_np = output.numpy()
+
+            # test func alias
+            upsample_out = paddle.nn.UpsamplingBilinear2d(size=[12, 12])
+            output = upsample_out(x=input)
+            np.testing.assert_allclose(out_np, output.numpy())
+
+            # test @param_one_alias(["x", "input"])
+            upsample_out = paddle.nn.UpsamplingBilinear2D(size=[12, 12])
+            output = upsample_out(input=input)
+            np.testing.assert_allclose(out_np, output.numpy())
+
+            # test both
+            upsample_out = paddle.nn.UpsamplingBilinear2d(size=[12, 12])
+            output = upsample_out(input=input)
+            np.testing.assert_allclose(out_np, output.numpy())
+
+
+class TestUpsamplingNearest2D_Compatibility(unittest.TestCase):
+    def test_upsamplingnearest2d(self):
+        with paddle.base.dygraph.guard():
+            input = paddle.rand(shape=(2, 3, 6, 10)).astype("float32")
+            upsample_out = paddle.nn.UpsamplingNearest2D(size=[12, 12])
+            output = upsample_out(x=input)
+            out_np = output.numpy()
+
+            # test func alias
+            upsample_out = paddle.nn.UpsamplingNearest2d(size=[12, 12])
+            output = upsample_out(x=input)
+            np.testing.assert_allclose(out_np, output.numpy())
+
+            # test @param_one_alias(["x", "input"])
+            upsample_out = paddle.nn.UpsamplingNearest2D(size=[12, 12])
+            output = upsample_out(input=input)
+            np.testing.assert_allclose(out_np, output.numpy())
+
+            # test both
+            upsample_out = paddle.nn.UpsamplingNearest2d(size=[12, 12])
+            output = upsample_out(input=input)
+            np.testing.assert_allclose(out_np, output.numpy())
+
+
+if __name__ == '__main__':
+    unittest.main()