转换规则No65 torch.nn.functional.threshold

paconvert/api_mapping.json

@@ -7905,6 +7905,23 @@
       "input": "x"
     }
   },
+  "torch.nn.functional.threshold": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.functional.thresholded_relu",
+    "args_list": [
+      "input",
+      "threshold",
+      "value",
+      "inplace"
+    ],
+    "kwargs_change": {
+      "input": "x",
+      "inplace": ""
+    },
+    "unsupport_args": [
+      "value"
+    ]
+  },
   "torch.nn.functional.triplet_margin_with_distance_loss": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.nn.functional.triplet_margin_with_distance_loss",

tests/test_nn_functional_threshold.py

@@ -0,0 +1,71 @@
+# 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.threshold")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        result = F.threshold(x, 0, 0, inplace=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        result = F.threshold(x, 1, 0, inplace=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        result = F.threshold(x, 0, 1, inplace=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn.functional as F
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        result = F.threshold(x, 0, 0, inplace=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])