Update doc for 2.0 API and some callback (#31180) (#31189)

test=document_fix

Author: qingqing01

python/paddle/fluid/layers/detection.py

@@ -2214,17 +2214,18 @@ def multi_box_head(inputs,
     Examples 1: set min_ratio and max_ratio:
         .. code-block:: python
 
-          import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
 
-          images = fluid.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
-          conv1 = fluid.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
-          conv2 = fluid.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
-          conv3 = fluid.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
-          conv4 = fluid.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
-          conv5 = fluid.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
-          conv6 = fluid.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')
+          images = paddle.static.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
+          conv1 = paddle.static.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
+          conv2 = paddle.static.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
+          conv3 = paddle.static.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
+          conv4 = paddle.static.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
+          conv5 = paddle.static.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
+          conv6 = paddle.static.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')
 
-          mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
+          mbox_locs, mbox_confs, box, var = paddle.static.nn.multi_box_head(
             inputs=[conv1, conv2, conv3, conv4, conv5, conv6],
             image=images,
             num_classes=21,
@@ -2239,17 +2240,18 @@ def multi_box_head(inputs,
     Examples 2: set min_sizes and max_sizes:
         .. code-block:: python
 
-          import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
 
-          images = fluid.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
-          conv1 = fluid.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
-          conv2 = fluid.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
-          conv3 = fluid.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
-          conv4 = fluid.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
-          conv5 = fluid.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
-          conv6 = fluid.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')
+          images = paddle.static.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
+          conv1 = paddle.static.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
+          conv2 = paddle.static.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
+          conv3 = paddle.static.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
+          conv4 = paddle.static.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
+          conv5 = paddle.static.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
+          conv6 = paddle.static.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')
 
-          mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
+          mbox_locs, mbox_confs, box, var = paddle.static.nn.multi_box_head(
             inputs=[conv1, conv2, conv3, conv4, conv5, conv6],
             image=images,
             num_classes=21,

python/paddle/hapi/callbacks.py

@@ -298,13 +298,15 @@ def on_predict_batch_end(self, step, logs=None):
 
 class ProgBarLogger(Callback):
     """
-    Logger callback function.
+    Logger callback function to print loss and metrics to stdout. It supports
+    silent mode (not print), progress bar or one line per each printing,
+    see arguments for more detailed.
 
     Args:
         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, 3 = 2 + 
+            0 = silent, 1 = progress bar, 2 = one line each printing, 3 = 2 +
             time counter, such as average reader cost, samples per second. 
             Default: 2.
 
@@ -531,7 +533,9 @@ def on_predict_end(self, logs=None):
 
 class ModelCheckpoint(Callback):
     """
-    Model checkpoint callback function.
+    Model checkpoint callback function to save model weights and optimizer
+    state during training in conjunction with model.fit(). Currently,
+    ModelCheckpoint only supports saving after a fixed number of epochs.
 
     Args:
         save_freq(int): The frequency, in number of epochs, the model checkpoint