转换规则 No. 10

paconvert/api_mapping.json

@@ -12398,6 +12398,26 @@
       "correction": "unbiased"
     }
   },
+  "torch.stft": {
+    "Matcher": "GenericMatcher",
+    "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",
+      "return_complex": ""
+    }
+  },
   "torch.subtract": {
     "Matcher": "Num2TensorBinaryWithAlphaMatcher",
     "paddle_api": "paddle.subtract",

tests/test_stft.py

@@ -0,0 +1,171 @@
+# 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"])
+
+
+def test_case_5():
+    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, center=False, win_length=win_length, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_6():
+    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
+        win_length = 4
+        result = torch.stft(x, n_fft=n_fft, center=False, hop_length=hop_length, win_length=win_length, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    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, normalized=True, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    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, center=False, hop_length=hop_length, normalized=True, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_9():
+    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, center=False, win_length=win_length, normalized=True, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])