转换规则 No.234/236/237

paconvert/api_mapping.json

@@ -9194,6 +9194,13 @@
       "drop_last"
     ]
   },
+  "torch.utils.data.ChainDataset": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.io.ChainDataset",
+    "args_list": [
+      "datasets"
+    ]
+  },
   "torch.utils.data.Dataset": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.io.Dataset"
@@ -9245,6 +9252,18 @@
       "data_source"
     ]
   },
+  "torch.utils.data.Subset": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.io.Subset",
+    "args_list": [
+      "dataset",
+      "indices"
+    ]
+  },
+  "torch.utils.data.TensorDataset": {
+    "Matcher": "TensorDatasetMatcher",
+    "paddle_api": "paddle.io.TensorDataset"
+  },
   "torch.utils.data.default_collate": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.io.dataloader.collate.default_collate_fn",

paconvert/api_matcher.py

@@ -3687,6 +3687,18 @@ def generate_code(self, kwargs):
         return code
 
 
+class TensorDatasetMatcher(BaseMatcher):
+    def get_paddle_nodes(self, args, kwargs):
+        new_args = self.parse_args(args)
+        tensors_v = "[{}".format(new_args[0])
+        for arg in new_args[1:]:
+            tensors_v += ", {}".format(arg)
+        tensors_v += "]"
+        code = "{}({})".format(self.get_paddle_api(), tensors_v)
+        node = ast.parse(code.strip("\n")).body
+        return node
+
+
 class TensorMaxMinMatcher(BaseMatcher):
     def get_paddle_class_nodes(self, func, args, kwargs):
 

tests/test_utils_data_ChainDataset.py

@@ -0,0 +1,106 @@
+# 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.utils.data.ChainDataset")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import numpy as np
+        import math
+        import torch
+        from torch.utils.data import IterableDataset, ChainDataset
+        class MyIterableDataset(torch.utils.data.IterableDataset):
+            def __init__(self, start, end):
+                super(MyIterableDataset).__init__()
+                assert end > start, "this example code only works with end >= start"
+                self.start = start
+                self.end = end
+
+            def __iter__(self):
+                iter_start = self.start
+                iter_end = self.end
+                return iter(range(iter_start, iter_end))
+
+
+        dataset = ChainDataset([MyIterableDataset(start=3, end=7), MyIterableDataset(start=3, end=7)])
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import numpy as np
+        import math
+        import torch
+        from torch.utils.data import IterableDataset, ChainDataset
+        class MyIterableDataset(torch.utils.data.IterableDataset):
+            def __init__(self, start, end):
+                super(MyIterableDataset).__init__()
+                assert end > start, "this example code only works with end >= start"
+                self.start = start
+                self.end = end
+
+            def __iter__(self):
+                iter_start = self.start
+                iter_end = self.end
+                return iter(range(iter_start, iter_end))
+
+
+        dataset = ChainDataset([MyIterableDataset(start=1, end=10), MyIterableDataset(start=1, end=3)])
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import numpy as np
+        import math
+        import torch
+        from torch.utils.data import IterableDataset, ChainDataset
+        class MyIterableDataset(torch.utils.data.IterableDataset):
+            def __init__(self, start, end):
+                super(MyIterableDataset).__init__()
+                assert end > start, "this example code only works with end >= start"
+                self.start = start
+                self.end = end
+
+            def __iter__(self):
+                iter_start = self.start
+                iter_end = self.end
+                return iter(range(iter_start, iter_end))
+
+
+        dataset = ChainDataset([MyIterableDataset(start=1, end=10)])
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_utils_data_Subset.py

@@ -0,0 +1,120 @@
+# 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.utils.data.Subset")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset, Subset
+        class MyDataset(Dataset):
+            def __init__(self, size=10):
+                super(Dataset).__init__()
+                self.data = list(range(size))
+
+            def __getitem__(self, idx):
+                return self.data[idx]
+
+            def __len__(self):
+                return len(self.data)
+
+        dataset = Subset(MyDataset(10),[1, 2, 3, 4, 5, 6])
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset, Subset
+        class MyDataset(Dataset):
+            def __init__(self, size=10):
+                super(Dataset).__init__()
+                self.data = list(range(size))
+
+            def __getitem__(self, idx):
+                return self.data[idx]
+
+            def __len__(self):
+                return len(self.data)
+
+        dataset = Subset(MyDataset(10),[9, 1])
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset, Subset
+        class MyDataset(Dataset):
+            def __init__(self, size=10):
+                super(Dataset).__init__()
+                self.data = list(range(size))
+
+            def __getitem__(self, idx):
+                return self.data[idx]
+
+            def __len__(self):
+                return len(self.data)
+
+        dataset = Subset(MyDataset(10),[9, 1, 3])
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset, Subset
+        class MyDataset(Dataset):
+            def __init__(self, size=10):
+                super(Dataset).__init__()
+                self.data = list(range(size))
+
+            def __getitem__(self, idx):
+                return self.data[idx]
+
+            def __len__(self):
+                return len(self.data)
+        data = MyDataset(10)
+        indices = [9, 1, 3]
+        dataset = Subset(data, indices)
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_utils_data_TensorDataset.py

@@ -0,0 +1,103 @@
+# 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.utils.data.TensorDataset")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import numpy as np
+        import torch
+        from torch.utils.data import TensorDataset
+        np.random.seed(0)
+        input_np = np.random.random([2, 3, 4]).astype('float32')
+        input = torch.from_numpy(input_np)
+        dataset = TensorDataset(input)
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import numpy as np
+        import torch
+        from torch.utils.data import TensorDataset
+        np.random.seed(0)
+        input_np = np.random.random([2, 3, 4]).astype('float32')
+        input = torch.from_numpy(input_np)
+        label_np = np.random.random([2, 1]).astype('int32')
+        label = torch.from_numpy(label_np)
+        dataset = TensorDataset(input, label)
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import numpy as np
+        import torch
+        from torch.utils.data import TensorDataset
+        np.random.seed(0)
+        input_np = np.random.random([2, 3, 4]).astype('float32')
+        input = torch.from_numpy(input_np)
+        input_np2 = np.random.random([2, 5, 5]).astype('float32')
+        input2 = torch.from_numpy(input_np2)
+        label_np = np.random.random([2, 1]).astype('int32')
+        label = torch.from_numpy(label_np)
+        dataset = TensorDataset(input, input2, label)
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import numpy as np
+        import torch
+        from torch.utils.data import TensorDataset
+        np.random.seed(0)
+        input_np = np.random.random([2, 3, 4]).astype('float32')
+        input = torch.from_numpy(input_np)
+        input_np2 = np.random.random([2, 5, 5]).astype('float32')
+        input2 = torch.from_numpy(input_np2)
+        label_np = np.random.random([2, 1]).astype('int32')
+        label = torch.from_numpy(label_np)
+        data = [input, input2, label]
+
+        dataset = TensorDataset(*data)
+        result = []
+        for d in dataset:
+            result.append(d)
+        """
+    )
+    obj.run(pytorch_code, ["result"])