转换规则 No. 10

paconvert/api_mapping.json

@@ -9090,6 +9090,25 @@
       "out"
     ]
   },
+  "torch.stft": {
+    "Matcher": "StftMatcher",
+    "paddle_api": "paddle.signal.stft",
+    "args_list": [
+      "input",
+      "n_fft",
+      "hop_length",
+      "win_length",
+      "window",
+      "center",
+      "pad_mode",
+      "normalized",
+      "onesided",
+      "return_complex"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.sub": {
     "Matcher": "SubMatcher",
     "args_list": [

paconvert/api_matcher.py

@@ -3071,6 +3071,67 @@ def generate_code(self, kwargs):
         return code
 
 
+class StftMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        if "hop_length" not in kwargs:
+            kwargs["hop_length"] = None
+        if "win_length" not in kwargs:
+            kwargs["win_length"] = None
+        if "window" not in kwargs:
+            kwargs["window"] = None
+        if "center" not in kwargs:
+            kwargs["center"] = "True"
+        if "pad_mode" not in kwargs:
+            kwargs["pad_mode"] = "'reflect'"
+        if "normalized" not in kwargs:
+            kwargs["normalized"] = "False"
+
+        return_complex_temp = (
+            kwargs.pop("return_complex") if "return_complex" in kwargs else None
+        )
+
+        if return_complex_temp:
+            kwargs["onesided"] = "True" if return_complex_temp != "(False)" else "False"
+
+        if "out" in kwargs and kwargs["out"] is not None:
+            API_TEMPLATE = textwrap.dedent(
+                """
+                paddle.assign((paddle.signal.stft(x={}, n_fft={}, hop_length={}, win_length={}, window={}, center={}, pad_mode={}, normalized={}, onesided={})), output={})
+                """
+            )
+            code = API_TEMPLATE.format(
+                kwargs["input"],
+                kwargs["n_fft"],
+                kwargs["hop_length"],
+                kwargs["win_length"],
+                kwargs["window"],
+                kwargs["center"],
+                kwargs["pad_mode"],
+                kwargs["normalized"],
+                kwargs["onesided"],
+                kwargs["out"],
+            )
+        else:
+            API_TEMPLATE = textwrap.dedent(
+                """
+                paddle.signal.stft(x={}, n_fft={}, hop_length={}, win_length={}, window={}, center={}, pad_mode={}, normalized={}, onesided={})
+                """
+            )
+            n_fft = get_unique_name("n_fft")
+            code = API_TEMPLATE.format(
+                kwargs["input"],
+                kwargs["n_fft"],
+                kwargs["hop_length"],
+                kwargs["win_length"],
+                kwargs["window"],
+                kwargs["center"],
+                kwargs["pad_mode"],
+                kwargs["normalized"],
+                kwargs["onesided"],
+            )
+        return code
+
+
 class CovMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         if "input" not in kwargs:

tests/test_stft.py

@@ -0,0 +1,91 @@
+# 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.
+#
+
+#
+#     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.stft")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1.3398,  0.2663, -0.2686,  0.2450],
+                            [-0.7401, -0.8805, -0.3402, -1.1936],
+                            [ 0.4907, -1.3948, -1.0691, -0.3132],
+                            [-1.6092,  0.5419, -0.2993,  0.3195]])
+        n_fft = 4
+        result = torch.stft(x, n_fft=n_fft, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1.3398,  0.2663, -0.2686,  0.2450],
+                            [-0.7401, -0.8805, -0.3402, -1.1936],
+                            [ 0.4907, -1.3948, -1.0691, -0.3132],
+                            [-1.6092,  0.5419, -0.2993,  0.3195]])
+        n_fft = 4
+        hop_length = 4
+        result = torch.stft(x, n_fft=n_fft, hop_length=hop_length, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1.3398,  0.2663, -0.2686,  0.2450],
+                            [-0.7401, -0.8805, -0.3402, -1.1936],
+                            [ 0.4907, -1.3948, -1.0691, -0.3132],
+                            [-1.6092,  0.5419, -0.2993,  0.3195]])
+        n_fft = 4
+        win_length = 4
+        result = torch.stft(x, n_fft=n_fft, win_length=win_length, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 1.3398,  0.2663, -0.2686,  0.2450],
+                            [-0.7401, -0.8805, -0.3402, -1.1936],
+                            [ 0.4907, -1.3948, -1.0691, -0.3132],
+                            [-1.6092,  0.5419, -0.2993,  0.3195]])
+        n_fft = 4
+        result = torch.stft(x, n_fft=n_fft, center=False, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])