Fix doc format for callbacks, metrics and Model (#28638)

* Fix doc format for callbacks, metrics and Model
* Fix code sample and doc

python/paddle/fluid/dataloader/batch_sampler.py

@@ -180,11 +180,11 @@ class DistributedBatchSampler(BatchSampler):
         batch_size(int): sample indice number in a mini-batch indices.
         num_replicas(int, optional): porcess number in distributed training.
             If :attr:`num_replicas` is None, :attr:`num_replicas` will be
-            retrieved from :code:`paddle.fluid.dygraph.parallel.ParallenEnv`.
+            retrieved from :code:`paddle.distributed.ParallenEnv`.
             Default None.
         rank(int, optional): the rank of the current process among :attr:`num_replicas`
             processes. If :attr:`rank` is None, :attr:`rank` is retrieved from
-            :code:`paddle.fluid.dygraph.parallel.ParallenEnv`. Default None.
+            :code:`paddle.distributed.ParallenEnv`. Default None.
         shuffle(bool): whther to shuffle indices order before genrating
             batch indices. Default False.
         drop_last(bool): whether drop the last incomplete batch dataset size

python/paddle/hapi/callbacks.py

@@ -161,10 +161,8 @@ class Callback(object):
           - 'batch_size': an integer. Number of samples per batch.
           - 'epochs': an integer. Number of epochs.
           - 'steps': an integer. Number of steps of one epoch.
-          - 'verbose': an integer. Verbose mode is 0, 1 or 2.
-             0 = silent, 1 = progress bar, 2 = one line per epoch.
-          - 'metrics': a list of str. Names of metrics, including 'loss'
-              and the names of paddle.metric.Metric.
+          - 'verbose': an integer. Verbose mode is 0, 1 or 2. 0 = silent, 1 = progress bar, 2 = one line per epoch.
+          - 'metrics': a list of str. Names of metrics, including 'loss' and the names of paddle.metric.Metric.
         """
         self.params = params
 
@@ -298,10 +296,12 @@ class Callback(object):
 
 
 class ProgBarLogger(Callback):
-    """Logger callback function
+    """
+    Logger callback function.
+
     Args:
-        log_freq (int): The frequency, in number of steps, the logs such as `loss`, 
-                `metrics` are printed. Default: 1.
+        log_freq (int): The frequency, in number of steps,
+            the logs such as loss, metrics are printed. Default: 1.
         verbose (int): The verbosity mode, should be 0, 1, or 2.
             0 = silent, 1 = progress bar, 2 = one line per epoch. Default: 2.
 
@@ -310,6 +310,7 @@ class ProgBarLogger(Callback):
 
             import paddle
             import paddle.vision.transforms as T
+            from paddle.vision.datasets import MNIST
             from paddle.static import InputSpec
 
             inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
@@ -319,7 +320,7 @@ class ProgBarLogger(Callback):
                 T.Transpose(),
                 T.Normalize([127.5], [127.5])
             ])
-            train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
+            train_dataset = MNIST(mode='train', transform=transform)
 
             lenet = paddle.vision.LeNet()
             model = paddle.Model(lenet,
@@ -439,7 +440,9 @@ class ProgBarLogger(Callback):
 
 
 class ModelCheckpoint(Callback):
-    """Model checkpoint callback function
+    """
+    Model checkpoint callback function.
+
     Args:
         save_freq(int): The frequency, in number of epochs, the model checkpoint
             are saved. Default: 1.
@@ -451,6 +454,7 @@ class ModelCheckpoint(Callback):
 
             import paddle
             import paddle.vision.transforms as T
+            from paddle.vision.datasets import MNIST
             from paddle.static import InputSpec
 
             inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
@@ -460,7 +464,7 @@ class ModelCheckpoint(Callback):
                 T.Transpose(),
                 T.Normalize([127.5], [127.5])
             ])
-            train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
+            train_dataset = MNIST(mode='train', transform=transform)
 
             lenet = paddle.vision.LeNet()
             model = paddle.Model(lenet,
@@ -740,7 +744,9 @@ class EarlyStopping(Callback):
 
 
 class VisualDL(Callback):
-    """VisualDL callback function
+    """
+    VisualDL callback function.
+
     Args:
         log_dir (str): The directory to save visualdl log file.
 

python/paddle/hapi/model.py

@@ -808,7 +808,7 @@ class Model(object):
     """
     An Model object is network with training and inference features.
     Dynamic graph and static graph are supported at the same time,
-    switched by `paddle.disable_static()`. The usage is as follows.
+    switched by `paddle.enable_static()`. The usage is as follows.
     But note, the switching between dynamic and static should be before
     instantiating a Model. The input description, i.e, paddle.static.InputSpec,
     must be required for static graph.
@@ -1052,9 +1052,9 @@ class Model(object):
         If `training` is set to False, only inference model will be saved.
 
         Args:
-            path (str): The file prefix to save model. The format is
-                'dirname/file_prefix' or 'file_prefix'. if empty str. A exception
-                 will be raised.
+            path (str): The file prefix to save model. The format
+                is 'dirname/file_prefix' or 'file_prefix'. if empty str.
+                A exception will be raised.
             training (bool, optional): Whether to save for training. If not, save
                 for inference only. Default: True.
 
@@ -1084,9 +1084,9 @@ class Model(object):
                         return self.net(x)
 
                 dynamic = True  # False
-                device = paddle.set_device('cpu')
                 # if use static graph, do not set
-                paddle.disable_static(device) if dynamic else None
+                if not dynamic:
+                    paddle.enable_static()
 
                 input = InputSpec([None, 784], 'float32', 'x')
                 label = InputSpec([None, 1], 'int64', 'label')
@@ -1361,18 +1361,19 @@ class Model(object):
 
               import paddle
               import paddle.vision.transforms as T
+              from paddle.vision.datasets import MNIST
               from paddle.static import InputSpec
 
               dynamic = True
-              device = paddle.set_device('cpu') # or 'gpu'
-              paddle.disable_static(device) if dynamic else None
+              if not dynamic:
+                  paddle.enable_static()
 
               transform = T.Compose([
                   T.Transpose(),
                   T.Normalize([127.5], [127.5])
               ])
-              train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
-              val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
+              train_dataset = MNIST(mode='train', transform=transform)
+              val_dataset = MNIST(mode='test', transform=transform)
            
               input = InputSpec([None, 1, 28, 28], 'float32', 'image')
               label = InputSpec([None, 1], 'int64', 'label')
@@ -1399,22 +1400,23 @@ class Model(object):
 
               import paddle
               import paddle.vision.transforms as T
+              from paddle.vision.datasets import MNIST
               from paddle.static import InputSpec
 
               dynamic = True
-              device = paddle.set_device('cpu') # or 'gpu'
-              paddle.disable_static(device) if dynamic else None
+              if not dynamic:
+                  paddle.enable_static()
               
               transform = T.Compose([
                     T.Transpose(),
                     T.Normalize([127.5], [127.5])
                 ])
-              train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
+              train_dataset = MNIST(mode='train', transform=transform)
               train_loader = paddle.io.DataLoader(train_dataset,
-                  places=device, batch_size=64)
-              val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
+                  batch_size=64)
+              val_dataset = MNIST(mode='test', transform=transform)
               val_loader = paddle.io.DataLoader(val_dataset,
-                  places=device, batch_size=64)
+                  batch_size=64)
            
               input = InputSpec([None, 1, 28, 28], 'float32', 'image')
               label = InputSpec([None, 1], 'int64', 'label')
@@ -1540,6 +1542,7 @@ class Model(object):
                 value is a scalar or numpy.array.
 
         Examples:
+
           .. code-block:: python
 
             import paddle
@@ -1559,14 +1562,6 @@ class Model(object):
             model.prepare(metrics=paddle.metric.Accuracy())
             result = model.evaluate(val_dataset, batch_size=64)
             print(result)
-
-            # imperative mode
-            paddle.disable_static()
-            model = paddle.Model(paddle.vision.models.LeNet(), input, label)
-            model.prepare(metrics=paddle.metric.Accuracy())
-            result = model.evaluate(val_dataset, batch_size=64)
-            print(result)
-                
         """
 
         if eval_data is not None and isinstance(eval_data, Dataset):
@@ -1637,6 +1632,7 @@ class Model(object):
             list: output of models.
 
         Examples:
+
           .. code-block:: python
 
             import numpy as np

python/paddle/metric/metrics.py

@@ -39,6 +39,8 @@ class Metric(object):
     Base class for metric, encapsulates metric logic and APIs
     Usage:
 
+        .. code-block:: text
+
             m = SomeMetric()
             for prediction, label in ...:
                 m.update(prediction, label)
@@ -52,6 +54,9 @@ class Metric(object):
     call :code:`update` with states in NumPy format.
     Metric calculated as follows (operations in Model and Metric are
     indicated with curly brackets, while data nodes not):
+
+        .. code-block:: text
+
                  inputs & labels              || ------------------
                        |                      ||
                     {model}                   ||
@@ -67,6 +72,7 @@ class Metric(object):
               metric states(numpy)            ||    numpy data
                        |                      ||
                 {Metric.update}               \/ ------------------
+
     Examples:
 
         For :code:`Accuracy` metric, which takes :code:`pred` and :code:`label`
@@ -79,7 +85,8 @@ class Metric(object):
         prediction of each sample like follows, while the correct prediction
         matrix shape is [N, 5].
 
-        .. code-block:: python
+          .. code-block:: text
+
               def compute(pred, label):
                   # sort prediction and slice the top-5 scores
                   pred = paddle.argsort(pred, descending=True)[:, :5]
@@ -92,7 +99,8 @@ class Metric(object):
         shape as [N, 5] instead of 2 tensors with shapes as [N, 10] and [N, 1].
         :code:`update` can be define as follows:
 
-        .. code-block:: python
+          .. code-block:: text
+
               def update(self, correct):
                   accs = []
                   for i, k in enumerate(self.topk):
@@ -206,10 +214,13 @@ class Accuracy(Metric):
 
           import paddle
           from paddle.static import InputSpec
+          import paddle.vision.transforms as T
+          from paddle.vision.datasets import MNIST
              
           input = InputSpec([None, 1, 28, 28], 'float32', 'image')
           label = InputSpec([None, 1], 'int64', 'label')
-        train_dataset = paddle.vision.datasets.MNIST(mode='train')
+          transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
+          train_dataset = MNIST(mode='train', transform=transform)
 
           model = paddle.Model(paddle.vision.LeNet(), input, label)
           optim = paddle.optimizer.Adam(
@@ -355,7 +366,6 @@ class Precision(Metric):
               def __len__(self):
                   return self.n
   
-        paddle.disable_static()
           model = paddle.Model(nn.Sequential(
               nn.Linear(10, 1),
               nn.Sigmoid()
@@ -489,7 +499,6 @@ class Recall(Metric):
               def __len__(self):
                   return self.n
           
-        paddle.disable_static()
           model = paddle.Model(nn.Sequential(
               nn.Linear(10, 1),
               nn.Sigmoid()
@@ -634,7 +643,6 @@ class Auc(Metric):
               def __len__(self):
                   return self.n
           
-        paddle.disable_static()
           model = paddle.Model(nn.Sequential(
               nn.Linear(10, 2), nn.Softmax())
           )