转换规则No 72 torch.nn.functional.soft_margin_loss (#130)

* torch.nn.functional.soft_margin_loss

* torch.nn.functional.soft_margin_loss

* torch.nn.functional.soft_margin_loss

paconvert/api_mapping.json

@@ -2747,6 +2747,20 @@
       "input": "x"
     }
   },
+  "torch.nn.functional.soft_margin_loss": {
+    "Matcher": "SizeAverageMatcher",
+    "paddle_api": "paddle.nn.functional.soft_margin_loss",
+    "args_list": [
+      "input",
+      "target",
+      "size_average",
+      "reduce",
+      "reduction"
+    ],
+    "kwargs_change": {
+      "target": "label"
+    }
+  },
   "torch.vander": {},
   "torch.cross": {
     "Matcher": "GenericMatcher",

tests/test_nn_functional_soft_margin_loss.py

@@ -0,0 +1,84 @@
+# 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.soft_margin_loss")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[-1.2837, -0.0297,  0.0355],
+            [ 0.9112, -1.7526, -0.4061]])
+        target = torch.tensor([[1., 0., 1.],[0., 1., 1.]])
+        result = torch.nn.functional.soft_margin_loss(input, target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[-1.2837, -0.0297,  0.0355],
+            [ 0.9112, -1.7526, -0.4061]])
+        target = torch.tensor([[1., 0., 1.],[0., 1., 1.]])
+        result = torch.nn.functional.soft_margin_loss(input, target, reduction='sum')
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[-1.2837, -0.0297,  0.0355],
+            [ 0.9112, -1.7526, -0.4061]])
+        target = torch.tensor([[1., 0., 1.],[0., 1., 1.]])
+        result = torch.nn.functional.soft_margin_loss(input, target, reduction='none')
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[-1.2837, -0.0297,  0.0355],
+            [ 0.9112, -1.7526, -0.4061]])
+        target = torch.tensor([[1., 0., 1.],[0., 1., 1.]])
+        result = torch.nn.functional.soft_margin_loss(input, target, size_average=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[-1.2837, -0.0297,  0.0355],
+            [ 0.9112, -1.7526, -0.4061]])
+        target = torch.tensor([[1., 0., 1.],[0., 1., 1.]])
+        result = torch.nn.functional.soft_margin_loss(input, target, reduce=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"])