【Hackathon 7th No.36】为 Paddle 代码转换工具新增 API 转换规则（第 3 组）

paconvert/api_mapping.json

@@ -14666,6 +14666,195 @@
       "input": "x"
     }
   },
+  "torch.signal.windows.blackman": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'",
+      "window": "'blackman'"
+    }
+  },
+  "torch.signal.windows.cosine": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'",
+      "window": "'cosine'"
+    }
+  },
+  "torch.signal.windows.exponential": {
+    "Matcher": "SignalWindowsWatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "center",
+      "tau",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "unsupport_args": [
+      "center"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'"
+    }
+  },
+  "torch.signal.windows.gaussian": {
+    "Matcher": "SignalWindowsWatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "std",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'"
+    }
+  },
+  "torch.signal.windows.general_cosine": {
+    "Matcher": "SignalWindowsWatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "a",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "a": "",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'"
+    }
+  },
+  "torch.signal.windows.general_hamming": {
+    "Matcher": "SignalWindowsWatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "alpha",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'"
+    }
+  },
+  "torch.signal.windows.hamming": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'",
+      "window": "'hamming'"
+    }
+  },
+  "torch.signal.windows.hann": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.audio.functional.get_window",
+    "min_input_args": 1,
+    "args_list": [
+      "M",
+      "*",
+      "sym",
+      "dtype",
+      "layout",
+      "device",
+      "requires_grad"
+    ],
+    "kwargs_change": {
+      "M": "win_length",
+      "sym": "fftbins",
+      "dtype": "dtype"
+    },
+    "paddle_default_kwargs": {
+      "dtype": "'float32'",
+      "window": "'hann'"
+    }
+  },
   "torch.signbit": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.signbit",

paconvert/api_matcher.py

@@ -519,6 +519,40 @@ def generate_code(self, kwargs):
         return super().generate_code(kwargs)
 
 
+class SignalWindowsWatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        new_kwargs = {}
+        if "exponential" in self.torch_api:
+            if "sym" in kwargs and kwargs["sym"] == "(False)":
+                if "tau" in kwargs:
+                    tau_value = float(str(kwargs.pop("tau")).strip("()"))
+                    new_kwargs["window"] = ("exponential", None, tau_value)
+                else:
+                    new_kwargs["window"] = ("exponential", None, 1.0)
+            else:
+                if "tau" in kwargs:
+                    tau_value = float(str(kwargs.pop("tau")).strip("()"))
+                    new_kwargs["window"] = ("exponential", tau_value)
+                else:
+                    new_kwargs["window"] = ("exponential", 1.0)
+        if "gaussian" in self.torch_api:
+            if "std" in kwargs:
+                std_value = float(str(kwargs.pop("std")).strip("()"))
+                new_kwargs["window"] = ("gaussian", std_value)
+            else:
+                new_kwargs["window"] = ("gaussian", 1.0)
+        if "general_hamming" in self.torch_api:
+            if "alpha" in kwargs:
+                alpha_value = float(str(kwargs.pop("alpha")).strip("()"))
+                new_kwargs["window"] = ("general_hamming", alpha_value)
+            else:
+                new_kwargs["window"] = ("general_hamming", 0.54)
+        if "general_cosine" in self.torch_api:
+            new_kwargs["window"] = ("general_cosine", kwargs.pop("a"))
+        new_kwargs.update(kwargs)
+        return GenericMatcher.generate_code(self, new_kwargs)
+
+
 class Num2TensorBinaryWithAlphaMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         kwargs_change = self.api_mapping.get("kwargs_change", {})

tests/test_signal_windows_blackman.py

@@ -0,0 +1,151 @@
+# Copyright (c) 2024 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.signal.windows.blackman")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(5)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(5, dtype=torch.float64)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = 5
+        result = torch.signal.windows.blackman(a)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = 5
+        result = torch.signal.windows.blackman(M = a, dtype=torch.float32)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(5, dtype=torch.float64, requires_grad=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(5, dtype=torch.float64, layout=torch.strided, requires_grad=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(5, dtype=torch.float64, layout=torch.strided, device=torch.device('cpu'), requires_grad=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(5, sym=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(M=5, sym=False, dtype=torch.float64)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_10():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(M=5, sym=False, dtype=torch.float64, requires_grad=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_11():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(M=5, sym=False, dtype=torch.float64, layout=torch.strided, requires_grad=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_12():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(M=5, sym=False, dtype=torch.float64, layout=torch.strided, device=torch.device('cpu'), requires_grad=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_13():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.signal.windows.blackman(dtype=torch.float64, M=5, layout=torch.strided, device=torch.device('cpu'), requires_grad=True, sym=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)