add some loss api

paconvert/api_mapping.json

@@ -4941,6 +4941,15 @@
       "target": "label"
     }
   },
+  "torch.nn.MSELoss": {
+    "Matcher": "SizeAverageMatcher",
+    "paddle_api": "paddle.nn.MSELoss",
+    "args_list": [
+      "size_average",
+      "reduce",
+      "reduction"
+    ]
+  },
   "torch.nn.functional.margin_ranking_loss": {
     "Matcher": "SizeAverageMatcher",
     "paddle_api": "paddle.nn.functional.margin_ranking_loss",
@@ -8593,6 +8602,21 @@
       "pos_weight"
     ]
   },
+  "torch.nn.functional.binary_cross_entropy": {
+    "Matcher": "SizeAverageMatcher",
+    "paddle_api": "paddle.nn.functional.binary_cross_entropy",
+    "args_list": [
+      "input",
+      "target",
+      "weight",
+      "size_average",
+      "reduce",
+      "reduction"
+    ],
+    "kwargs_change": {
+      "target": "label"
+    }
+  },
   "torch.nn.functional.max_pool2d": {
     "Matcher": "FunctionalMaxPool2DMatcher",
     "paddle_api": "paddle.nn.functional.max_pool2d",
@@ -8670,6 +8694,16 @@
       "pos_weight"
     ]
   },
+  "torch.nn.BCELoss": {
+    "Matcher": "SizeAverageMatcher",
+    "paddle_api": "paddle.nn.BCELoss",
+    "args_list": [
+      "weight",
+      "size_average",
+      "reduce",
+      "reduction"
+    ]
+  },
   "torch.utils.data.BatchSampler": {
     "Matcher": "TorchUtilDataBatchSampler",
     "args_list": [
@@ -8700,6 +8734,49 @@
       "input": "x"
     }
   },
+  "torch.nn.L1Loss": {
+    "Matcher": "SizeAverageMatcher",
+    "paddle_api": "paddle.nn.L1Loss",
+    "args_list": [
+      "size_average",
+      "reduce",
+      "reduction"
+    ]
+  },
+  "torch.nn.Unfold": {
+    "Matcher": "UnfoldMatcher",
+    "paddle_api": "paddle.nn.Unfold",
+    "args_list": [
+      "kernel_size",
+      "dilation",
+      "padding",
+      "stride"
+    ],
+    "kwargs_change": {
+      "kernel_size": "kernel_sizes",
+      "dilation": "dilations",
+      "padding": "paddings",
+      "stride": "strides"
+    }
+  },
+  "torch.nn.functional.unfold": {
+    "Matcher": "UnfoldMatcher",
+    "paddle_api": "paddle.nn.functional.unfold",
+    "args_list": [
+      "input",
+      "kernel_size",
+      "dilation",
+      "padding",
+      "stride"
+    ],
+    "kwargs_change": {
+      "input": "x",
+      "kernel_size": "kernel_sizes",
+      "dilation": "dilations",
+      "padding": "paddings",
+      "stride": "strides"
+    }
+  },
   "torch.nn.modules.batchnorm._BatchNorm": {
     "Matcher": "Modules_BatchNormBaseMatcher",
     "paddle_api": "paddle.nn.layer.norm._BatchNormBase",

paconvert/api_matcher.py

@@ -3359,6 +3359,30 @@ def generate_code(self, kwargs):
         return GenericMatcher.generate_code(self, kwargs)
 
 
+class UnfoldMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        if "kwargs_change" in self.api_mapping:
+            kwargs_change = self.api_mapping["kwargs_change"]
+            for key in list(kwargs_change.keys()):
+                if key in kwargs:
+                    if "input" not in key:
+                        if "(" in kwargs[key]:
+                            value = ast.literal_eval(kwargs[key])
+                            if isinstance(value, tuple):
+                                kwargs[key] = list(ast.literal_eval(kwargs[key]))
+                        else:
+                            kwargs[key] = "list({})".format(kwargs[key])
+                    kwargs[kwargs_change[key]] = kwargs[key]
+                    kwargs.pop(key)
+
+        if "paddings" not in kwargs:
+            kwargs["paddings"] = 0
+
+        code = "{}({})".format(self.get_paddle_api(), self.kwargs_to_str(kwargs))
+
+        return code
+
+
 class ParameterMatcher(BaseMatcher):
     def get_paddle_nodes(self, args, kwargs):
         kwargs = self.parse_args_and_kwargs(args, kwargs)

tests/test_nn_BCELoss.py

@@ -0,0 +1,146 @@
+# 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.BCELoss")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        weight = torch.tensor([0.5,0.2,0.3])
+        loss = torch.nn.BCELoss(weight=weight,size_average=True)
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        weight = torch.tensor([0.5,0.2,0.3])
+        loss = torch.nn.BCELoss(weight=weight,size_average=False)
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        weight = torch.tensor([0.5,0.2,0.3])
+        loss = torch.nn.BCELoss(weight=weight,reduction='none')
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        weight = torch.tensor([0.5,0.2,0.3])
+        loss = torch.nn.BCELoss(weight=weight,reduction='mean')
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        weight = torch.tensor([0.5,0.2,0.3])
+        loss = torch.nn.BCELoss(weight=weight,reduction='sum')
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        weight = torch.tensor([0.5,0.2,0.3])
+        loss = torch.nn.BCELoss(weight=weight,reduce=True)
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        weight = torch.tensor([0.5,0.2,0.3])
+        loss = torch.nn.BCELoss(weight=weight,reduce=False)
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[0.2837, 0.0297,  0.0355],
+            [ 0.9112, 0.7526, 0.4061]])
+        target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
+        loss = torch.nn.BCELoss()
+        result = loss(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["result"])