[xdoctest] reformat example code with google style No.186-190 (#56166)

* fix: updated code examples. * fix: added paddle.seed * fix: updated code style * Apply suggestions from code review * refactor: refine detail of code examples * Update python/paddle/distributed/auto_parallel/static/process_mesh_v2.py * fix: refine detail * fix: refine detail * Update python/paddle/distributed/auto_parallel/static/process_mesh_v2.py Co-authored-by: Nyakku Shigure <sigure.qaq@gmail.com> * refactor: refine detail * refactor: refine detail * fix: refine doc --------- Co-authored-by: Nyakku Shigure <sigure.qaq@gmail.com>
PaddlePaddle · Aug 22, 2023 · 17d6da6 · 17d6da6
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diff --git a/python/paddle/distributed/auto_parallel/static/engine.py b/python/paddle/distributed/auto_parallel/static/engine.py
@@ -79,39 +79,39 @@ class Engine:
 
         .. code-block:: python
 
-            import paddle
-            import paddle.vision.transforms as T
-            from paddle.distributed.fleet import auto
-            from paddle.vision.datasets import MNIST
-
-            transform = T.Compose([
-                T.Transpose(),
-                T.Normalize([127.5], [127.5])
-            ])
-            train_dataset = MNIST(mode='train', transform=transform)
-            valid_dataset = MNIST(mode='test', transform=transform)
-
-            model = paddle.vision.models.LeNet()
-            loss = paddle.nn.CrossEntropyLoss()
-            optimizer = paddle.optimizer.Adam(
-                learning_rate=0.001, parameters=model.parameters())
-            metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-            engine = auto.Engine(model, loss, optimizer, metrics)
-            # fit
-            engine.fit(train_dataset,
-                       epochs=2,
-                       batch_size=64)
-            # evaluate
-            engine.evaluate(valid_dataset,
-                            batch_size=64)
-            # predict
-            engine.predict(valid_dataset,
-                           batch_size=64)
-            # save
-            engine.save("./my_model")
-            # load
-            engine.load("./my_model")
+            >>> import paddle
+            >>> import paddle.vision.transforms as T
+            >>> from paddle.distributed.fleet import auto
+            >>> from paddle.vision.datasets import MNIST
+
+            >>> transform = T.Compose([
+            ...     T.Transpose(),
+            ...     T.Normalize([127.5], [127.5])
+            >>> ])
+            >>> train_dataset = MNIST(mode='train', transform=transform)
+            >>> valid_dataset = MNIST(mode='test', transform=transform)
+
+            >>> model = paddle.vision.models.LeNet()
+            >>> loss = paddle.nn.CrossEntropyLoss()
+            >>> optimizer = paddle.optimizer.Adam(
+            ...     learning_rate=0.001, parameters=model.parameters())
+            >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+            >>> engine = auto.Engine(model, loss, optimizer, metrics)
+            >>> # fit
+            >>> engine.fit(train_dataset,
+            ...            epochs=2,
+            ...            batch_size=64)
+            >>> # evaluate
+            >>> engine.evaluate(valid_dataset,
+            ...                 batch_size=64)
+            >>> # predict
+            >>> engine.predict(valid_dataset,
+            ...                batch_size=64)
+            >>> # save
+            >>> engine.save("./my_model")
+            >>> # load
+            >>> engine.load("./my_model")
 
     """
 
@@ -918,27 +918,27 @@ def fit(
 
             .. code-block:: python
 
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
-
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                train_dataset = MNIST(mode='train', transform=transform)
-
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                optimizer = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=model.parameters())
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-                engine = auto.Engine(model, loss, optimizer, metrics)
-                engine.fit(train_dataset,
-                           epochs=2,
-                           batch_size=64)
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
+
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> train_dataset = MNIST(mode='train', transform=transform)
+
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> optimizer = paddle.optimizer.Adam(
+                ...     learning_rate=0.001, parameters=model.parameters())
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+                >>> engine = auto.Engine(model, loss, optimizer, metrics)
+                >>> engine.fit(train_dataset,
+                ...             epochs=2,
+                ...             batch_size=64)
         """
         self._mode = 'train'
         self._inputs_spec, self._labels_spec = self._prepare_data_spec(
@@ -1071,23 +1071,23 @@ def evaluate(
 
             .. code-block:: python
 
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
 
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                valid_dataset = MNIST(mode='test', transform=transform)
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> valid_dataset = MNIST(mode='test', transform=transform)
 
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
 
-                engine = auto.Engine(model, loss, metrics=metrics)
-                engine.evaluate(valid_dataset, batch_size=64)
+                >>> engine = auto.Engine(model, loss, metrics=metrics)
+                >>> engine.evaluate(valid_dataset, batch_size=64)
 
         """
         self._mode = 'eval'
@@ -1181,21 +1181,21 @@ def predict(
 
             .. code-block:: python
 
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
 
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                valid_dataset = MNIST(mode='test', transform=transform)
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> valid_dataset = MNIST(mode='test', transform=transform)
 
-                model = paddle.vision.models.LeNet()
+                >>> model = paddle.vision.models.LeNet()
 
-                engine = auto.Engine(model)
-                engine.predict(valid_dataset, batch_size=64)
+                >>> engine = auto.Engine(model)
+                >>> engine.predict(valid_dataset, batch_size=64)
         """
         self._mode = 'predict'
         self._inputs_spec, self._labels_spec = self._prepare_data_spec(
@@ -1650,28 +1650,29 @@ def save(self, path, training=True):
         Examples:
 
             .. code-block:: python
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
-
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                train_dataset = MNIST(mode='train', transform=transform)
-
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                optimizer = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=model.parameters())
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-                engine = auto.Engine(model, loss, optimizer, metrics)
-                engine.fit(train_dataset,
-                           epochs=1,
-                           batch_size=64)
-                engine.save("./my_model")
+
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
+
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> train_dataset = MNIST(mode='train', transform=transform)
+
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> optimizer = paddle.optimizer.Adam(
+                ...     learning_rate=0.001, parameters=model.parameters())
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+                >>> engine = auto.Engine(model, loss, optimizer, metrics)
+                >>> engine.fit(train_dataset,
+                ...             epochs=1,
+                ...             batch_size=64)
+                >>> engine.save("./my_model")
 
         """
         if training:
@@ -1734,29 +1735,30 @@ def load(self, path, strict=True, load_optimizer=True):
         Examples:
 
             .. code-block:: python
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
-
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                train_dataset = MNIST(mode='train', transform=transform)
-
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                optimizer = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=model.parameters())
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-                engine = auto.Engine(model, loss, optimizer, metrics)
-                engine.fit(train_dataset,
-                           epochs=1,
-                           batch_size=64)
-                engine.save("./my_model")
-                engine.load("./my_model")
+
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
+
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> train_dataset = MNIST(mode='train', transform=transform)
+
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> optimizer = paddle.optimizer.Adam(
+                ...     learning_rate=0.001, parameters=model.parameters())
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+                >>> engine = auto.Engine(model, loss, optimizer, metrics)
+                >>> engine.fit(train_dataset,
+                ...             epochs=1,
+                ...             batch_size=64)
+                >>> engine.save("./my_model")
+                >>> engine.load("./my_model")
 
         """
         self._strict = strict

diff --git a/python/paddle/distributed/auto_parallel/static/process_mesh_v2.py b/python/paddle/distributed/auto_parallel/static/process_mesh_v2.py
@@ -33,14 +33,13 @@ class ProcessMesh(core.ProcessMesh):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.distributed as dist
+            >>> import paddle
+            >>> import paddle.distributed as dist
+            >>> paddle.enable_static()
 
-            paddle.enable_static()
-
-            mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]])
-            assert mesh.shape == [2, 3]
-            assert mesh.processe_ids == [2, 4, 5, 0, 1, 3]
+            >>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]])
+            >>> assert mesh.shape == [2, 3]
+            >>> assert mesh.process_ids == [2, 4, 5, 0, 1, 3]
 
     """