转换规则No64 torch.nn.functional.max_pool1d

paconvert/api_mapping.json

@@ -8732,5 +8732,28 @@
     "kwargs_change": {
       "eps": "epsilon"
     }
+  },
+  "torch.nn.functional.max_pool1d": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.functional.max_pool1d",
+    "args_list": [
+      "input",
+      "kernel_size",
+      "stride",
+      "padding",
+      "dilation",
+      "ceil_mode",
+      "return_indices"
+    ],
+    "unsupport_args": [
+      "dilation",
+      "return_indices"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    },
+    "paddle_default_kwargs": {
+      "return_mask": "False"
+    }
   }
 }

tests/test_nn_functional_max_pool1d.py

@@ -0,0 +1,92 @@
+# Copyright (c) 2023 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.functional.max_pool1d")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        input = torch.tensor([[[ 1.1524,  0.4714,  0.2857],
+            [-1.2533, -0.9829, -1.0981],
+            [ 0.1507, -1.1431, -2.0361]]])
+        result = F.max_pool1d(input , 3)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        input = torch.tensor([[[ 1.1524,  0.4714,  0.2857],
+            [-1.2533, -0.9829, -1.0981],
+            [ 0.1507, -1.1431, -2.0361]]])
+        result = F.max_pool1d(input , 3, stride=2)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        input = torch.tensor([[[ 1.1524,  0.4714,  0.2857, 0.4586, 0.9876, 0.5487],
+            [-1.2533, -0.9829, -1.0981, 0.7655, 0.8541, 0.9873],
+            [ 0.1507, -1.1431, -2.0361, 0.2344, 0.5675, 0.1546]]])
+        result = F.max_pool1d(input , 5, stride=2, padding=2)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        input = torch.tensor([[[ 1.1524,  0.4714,  0.2857, 0.4586, 0.9876, 0.5487],
+            [-1.2533, -0.9829, -1.0981, 0.7655, 0.8541, 0.9873],
+            [ 0.1507, -1.1431, -2.0361, 0.2344, 0.5675, 0.1546]]])
+        result = F.max_pool1d(input , 5, stride=2, padding=2, ceil_mode=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+'''
+# if enable the return_indices, the results of torch and paddle are different
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        input = torch.tensor([[[ 1.1524,  0.4714,  0.2857, 0.4586, 0.9876, 0.5487],
+            [-1.2533, -0.9829, -1.0981, 0.7655, 0.8541, 0.9873],
+            [ 0.1507, -1.1431, -2.0361, 0.2344, 0.5675, 0.1546]]])
+        result = F.max_pool1d(input , 5, stride=2, padding=2, ceil_mode=True, return_indices=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+'''