add model.fit and refactor callbacks

docs/api/api_python/mindspore/mindspore.Callback.rst

@@ -6,8 +6,7 @@ mindspore.Callback
     用于构建Callback函数的基类。Callback函数是一个上下文管理器，在运行模型时被调用。
     可以使用此机制进行一些自定义操作。
 
-    Callback类的每个方法对应了训练或推理过程的不同阶段，这些方法有相同的入参 `run_context`，用于保存模型
-    训练或推理过程模型的相关信息。定义Callback子类或自定义Callback时，请根据需要重写对应的方法。
+    Callback类的每个方法对应了训练或推理过程的不同阶段，这些方法有相同的入参 `run_context`，用于保存训练或推理过程中模型的相关信息。定义Callback子类或自定义Callback时，请根据需要重写名称前缀为“on_train”或“on_eval”的方法，否则自定义的Callback在 `model.fit` 中使用时会产生错误。
 
     自定义Callback场景下，在类方法中通过 `RunContext.original_args()` 方法可以获取模型训练或推理过程中已有
     的上下文信息，此信息为一个存储了已有属性的字典型变量；用户也可以在此信息中添加其他的自定义属性；此外，
@@ -16,7 +15,7 @@ mindspore.Callback
 
     .. py:method:: begin(run_context)
 
-        在网络执行之前被调用一次。
+        在网络执行之前被调用一次。与 `on_train_begin` 和 `on_eval_begin` 方法具有兼容性。
 
         **参数：**
 
@@ -24,7 +23,7 @@ mindspore.Callback
 
     .. py:method:: end(run_context)
 
-        网络执行后被调用一次。
+        网络执行后被调用一次。与 `on_train_end` 和 `on_eval_end` 方法具有兼容性。
 
         **参数：**
 
@@ -32,7 +31,7 @@ mindspore.Callback
 
     .. py:method:: epoch_begin(run_context)
 
-        在每个epoch开始之前被调用。
+        在每个epoch开始之前被调用。与 `on_train_epoch_begin` 和 `on_eval_epoch_begin` 方法具有兼容性。
 
         **参数：**
 
@@ -40,7 +39,7 @@ mindspore.Callback
 
     .. py:method:: epoch_end(run_context)
 
-        在每个epoch结束后被调用。
+        在每个epoch结束后被调用。与 `on_train_epoch_end` 和 `on_eval_epoch_end` 方法具有兼容性。
 
         **参数：**
 
@@ -52,7 +51,7 @@ mindspore.Callback
 
         **参数：**
 
-        - **run_context** (RunContext) - 包含模型的一些基本信息。
+        - **run_context** (RunContext) - 包含模型的一些基本信息。与 `on_train_step_begin` 和 `on_eval_step_begin` 方法具有兼容性。
 
     .. py:method:: step_end(run_context)
 
@@ -60,4 +59,100 @@ mindspore.Callback
 
         **参数：**
 
+        - **run_context** (RunContext) - 包含模型的一些基本信息。与 `on_train_step_end` 和 `on_eval_step_end` 方法具有兼容性。
+
+    .. py:method:: on_train_begin(run_context)
+
+        在网络执行训练之前调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method::on_train_end(run_context)
+
+        网络训练执行结束时调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_train_epoch_begin(run_context)
+
+        在训练的每个epoch开始之前被调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_train_epoch_end(run_context)
+
+        在训练的每个epoch结束后被调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_train_step_begin(run_context)
+
+        在训练的每个step开始之前被调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_train_step_end(run_context)
+
+        在训练的每个step完成后被调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_eval_begin(run_context)
+
+        在网络执行推理之前调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_eval_end(run_context)
+
+        网络执行推理之后调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_eval_epoch_begin(run_context)
+
+        在推理的epoch开始之前被调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_evalepoch_end(run_context)
+
+        在推理的epoch结束后被调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_eval_step_begin(run_context)
+
+        在推理的每个step开始之前被调用。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的一些基本信息。
+
+    .. py:method:: on_eval_step_end(run_context)
+
+        在推理的每个step完成后被调用。
+
+        **参数：**
+
         - **run_context** (RunContext) - 包含模型的一些基本信息。

docs/api/api_python/mindspore/mindspore.History.rst

@@ -3,12 +3,12 @@ mindspore.History
 
 .. py:class:: mindspore.History
 
-    将网络输出的相关信息记录到 `History` 对象中。
+    将网络输出和评估指标的相关信息记录到 `History` 对象中。
 
     用户不自定义训练网络或评估网络情况下，记录的内容将为损失值；用户自定义了训练网络/评估网络的情况下，如果定义的网络返回 `Tensor` 或 `numpy.ndarray`，则记录此返回值均值，如果返回 `tuple` 或 `list`，则记录第一个元素。
 
     .. note::
-        通常使用在 `mindspore.Model.train` 中。
+        通常使用在 `mindspore.Model.train` 和 `mindspore.Model.fit` 中。
 
     .. py:method:: begin(run_context)
 
@@ -20,7 +20,7 @@ mindspore.History
 
     .. py:method:: epoch_end(run_context)
 
-        epoch结束时记录网络输出的相关信息。
+        epoch结束时记录网络输出和评估指标的相关信息。
 
         **参数：**
 

docs/api/api_python/mindspore/mindspore.LambdaCallback.rst

@@ -5,7 +5,7 @@ mindspore.LambdaCallback
 
     用于自定义简单的callback。
 
-    使用匿名函数构建callback，定义的匿名函数将在 `mindspore.Model.{train | eval}` 的对应阶段被调用。
+    使用匿名函数构建callback，定义的匿名函数将在 `mindspore.Model.{train | eval | fit}` 的对应阶段被调用。
 
     请注意，callback的每个阶段都需要一个位置参数：`run_context`。
 
@@ -14,9 +14,15 @@ mindspore.LambdaCallback
 
     **参数：**
 
-    - **epoch_begin** (Function) - 每个epoch开始时被调用。
-    - **epoch_end** (Function) - 每个epoch结束时被调用。
-    - **step_begin** (Function) - 每个step开始时被调用。
-    - **step_end** (Function) - 每个step结束时被调用。
-    - **begin** (Function) - 模型训练、评估开始时被调用。
-    - **end** (Function) - 模型训练、评估结束时被调用。
+    - **on_train_epoch_begin** (Function) - 训练每个epoch开始时被调用。
+    - **on_train_epoch_end** (Function) - 训练每个epoch结束时被调用。
+    - **on_train_step_begin** (Function) - 训练每个step开始时被调用。
+    - **on_train_step_end** (Function) - 训练每个step结束时被调用。
+    - **on_train_begin** (Function) - 模型训练开始时被调用。
+    - **on_train_end** (Function) - 模型训练结束时被调用。
+    - **on_eval_epoch_begin** (Function) - 推理的epoch开始时被调用。
+    - **on_eval_epoch_end** (Function) - 推理的epoch结束时被调用。
+    - **on_eval_step_begin** (Function) - 推理的每个step开始时被调用。
+    - **on_eval_step_end** (Function) - 推理的每个step结束时被调用。
+    - **on_eval_begin** (Function) - 模型推理开始时被调用。
+    - **on_eval_end** (Function) - 模型推理结束时被调用。

docs/api/api_python/mindspore/mindspore.LossMonitor.rst

@@ -3,7 +3,7 @@ mindspore.LossMonitor
 
 .. py:class:: mindspore.LossMonitor(per_print_times=1)
 
-    监控训练的loss。
+    训练场景下，监控训练的loss；边训练边推理场景下，监控训练的loss和推理的metrics。
 
     如果loss是NAN或INF，则终止训练。
 
@@ -25,3 +25,11 @@ mindspore.LossMonitor
         **参数：**
 
         - **run_context** (RunContext) - 包含模型的相关信息。详情请参考 :class:`mindspore.RunContext`。
+
+    .. py:method:: on_train_epoch_end(run_context)
+
+        LossMoniter用于 `model.fit`，即边训练边推理场景时，打印训练的loss和当前epoch推理的metrics。
+
+        **参数：**
+
+        - **run_context** (RunContext) - 包含模型的相关信息。详情请参考 :class:`mindspore.RunContext`。

docs/api/api_python/mindspore/mindspore.Model.rst

@@ -162,6 +162,24 @@
         - **sink_size** (int) – 控制每次数据下沉的数据量。`dataset_sink_mode` 为False时 `sink_size` 无效。如果sink_size=-1，则每一次epoch下沉完整数据集。如果sink_size>0，则每一次epoch下沉数据量为sink_size的数据集。默认值：-1。
         - **initial_epoch** (int) - 从哪个epoch开始训练，一般用于中断恢复训练场景。
 
+    .. py:method:: fit(epoch, train_dataset, valid_dataset=None, valid_frequency=1, callbacks=None, dataset_sink_mode=True, valid_dataset_sink_mode=True, sink_size=-1, initial_epoch=0)
+
+        模型边训练边推理接口。
+
+        如果 `valid_dataset` 不为None，在训练过程中同时执行推理。更多详细信息请参考 `mindspore.Model.model.train`。
+
+        **参数：**
+
+        - **epoch** (int) – 训练执行轮次。通常每个epoch都会使用全量数据集进行训练。当 `dataset_sink_mode` 设置为True且 `sink_size` 大于零时，则每个epoch训练次数为 `sink_size` 而不是数据集的总步数。如果 `epoch` 与 `initial_epoch` 一起使用，它表示训练的最后一个 `epoch` 是多少。
+        - **train_dataset** (Dataset) – 训练数据集迭代器。如果定义了 `loss_fn` ，则数据和标签会被分别传给 `network` 和 `loss_fn` ，此时数据集需要返回一个元组（data, label）。如果数据集中有多个数据或者标签，可以设置 `loss_fn` 为None，并在 `network` 中实现损失函数计算，此时数据集返回的所有数据组成的元组（data1, data2, data3, ...）会传给 `network` 。
+        - **valid_dataset** (Dataset) – 评估模型的数据集迭代器。默认值：None。
+        - **valid_frequency** (Dataset) – 此参数只有在valid_dataset不为None时生效。如果为int类型，表示执行推理的频率，例如 `valid_frequency=2`，则每2个训练epoch执行一次推理；如果为list类型，指明在哪几个epoch时执行推理，例如 `valid_frequency=[1, 5]`，则在第1个和第5个epoch执行推理。默认值：1。
+        - **callbacks** (Optional[list[Callback], Callback]) – 训练过程中需要执行的回调对象或者回调对象列表。默认值：None。
+        - **dataset_sink_mode** (bool) – 训练数据是否直接下沉至处理器进行处理。使用PYNATIVE_MODE模式或CPU处理器时，模型训练流程将以非下沉模式执行。默认值：True。
+        - **valid_dataset_sink_mode** (bool) - 推理数据是否直接下沉至处理器进行处理。默认值：True。
+        - **sink_size** (int) – 控制每次数据下沉的数据量。`dataset_sink_mode` 为False时 `sink_size` 无效。如果sink_size=-1，则每一次epoch下沉完整数据集。如果sink_size>0，则每一次epoch下沉数据量为sink_size的数据集。默认值：-1。
+        - **initial_epoch** (int) - 从哪个epoch开始训练，一般用于中断恢复训练场景。
+
     .. py:method:: train_network
         :property:
 
@@ -169,4 +187,4 @@
 
         **返回：**
 
-        预测网络实例。
+        预测网络实例。

docs/api/api_python/mindspore/mindspore.TimeMonitor.rst

@@ -3,7 +3,7 @@ mindspore.TimeMonitor
 
 .. py:class:: mindspore.TimeMonitor(data_size=None)
 
-    监控训练时间。
+    监控训练或推理的时间。
 
     **参数：**
 

mindspore/python/mindspore/train/callback/_callback.py

@@ -82,7 +82,8 @@ class Callback:
     Each method of Callback class corresponds to a stage in training or eval process, and those methods
     have the same input `run_context`, which hold context information of the model in
     training or eval process. When defining a Callback subclass or creating a custom Callback,
-    override these methods.
+    Note that you should override methods with names prefixed with "on_train" or "on_eval",
+    otherwise ValueError will be raised if the custimized Callbacks used in `model.fit`.
 
     When creating a custom Callback, model context information can be obtained in Callback
     methods by calling `RunContext.original_args()`, which is a dictionary varivable
@@ -122,6 +123,7 @@ class Callback:
     def begin(self, run_context):
         """
         Called once before the network executing.
+        A backwards compatibility alias for `on_train_begin` and `on_eval_begin`.
 
         Args:
             run_context (RunContext): Include some information of the model.
@@ -130,6 +132,7 @@ class Callback:
     def epoch_begin(self, run_context):
         """
         Called before each epoch beginning.
+        A backwards compatibility alias for `on_train_epoch_begin` and `on_eval_epoch_begin`.
 
         Args:
             run_context (RunContext): Include some information of the model.
@@ -138,6 +141,7 @@ class Callback:
     def epoch_end(self, run_context):
         """
         Called after each epoch finished.
+        A backwards compatibility alias for `on_train_epoch_end` and `on_eval_epoch_end`.
 
         Args:
             run_context (RunContext): Include some information of the model.
@@ -146,6 +150,7 @@ class Callback:
     def step_begin(self, run_context):
         """
         Called before each step beginning.
+        A backwards compatibility alias for `on_train_step_begin` and `on_eval_step_begin`.
 
         Args:
             run_context (RunContext): Include some information of the model.
@@ -154,6 +159,7 @@ class Callback:
     def step_end(self, run_context):
         """
         Called after each step finished.
+        A backwards compatibility alias for `on_train_step_end` and `on_eval_step_end`.
 
         Args:
             run_context (RunContext): Include some information of the model.
@@ -162,11 +168,120 @@ class Callback:
     def end(self, run_context):
         """
         Called once after network training.
+        A backwards compatibility alias for `on_train_end` and `on_eval_end`.
 
         Args:
             run_context (RunContext): Include some information of the model.
         """
 
+    def on_train_begin(self, run_context):
+        """
+        Called once before the network training.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.begin(run_context)
+
+    def on_train_epoch_begin(self, run_context):
+        """
+        Called before each training epoch begin.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.epoch_begin(run_context)
+
+    def on_train_epoch_end(self, run_context):
+        """
+        Called after each training epoch end.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.epoch_end(run_context)
+
+    def on_train_step_begin(self, run_context):
+        """
+        Called before each training step begin.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.step_begin(run_context)
+
+    def on_train_step_end(self, run_context):
+        """
+        Called after each training step end.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.step_end(run_context)
+
+    def on_train_end(self, run_context):
+        """
+        Called after training end.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.end(run_context)
+
+    def on_eval_begin(self, run_context):
+        """
+        Called before eval begin.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.begin(run_context)
+
+    def on_eval_epoch_begin(self, run_context):
+        """
+        Called before eval epoch begin.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.epoch_begin(run_context)
+
+    def on_eval_epoch_end(self, run_context):
+        """
+        Called after eval epoch end.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.epoch_end(run_context)
+
+    def on_eval_step_begin(self, run_context):
+        """
+        Called before each eval step begin.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.step_begin(run_context)
+
+    def on_eval_step_end(self, run_context):
+        """
+        Called after each eval step end.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.step_end(run_context)
+
+    def on_eval_end(self, run_context):
+        """
+        Called after eval end.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        self.end(run_context)
+
 
 class CallbackManager(Callback):
     """
@@ -208,7 +323,7 @@ class CallbackManager(Callback):
         return self._stack.__exit__(*err)
 
     def begin(self, run_context):
-        """Called once before network training."""
+        """Called once before network train or eval."""
         for cb in self._callbacks:
             cb.begin(run_context)
 
@@ -223,7 +338,7 @@ class CallbackManager(Callback):
             cb.epoch_end(run_context)
 
     def step_begin(self, run_context):
-        """Called before each epoch begin."""
+        """Called before each step begin."""
         for cb in self._callbacks:
             cb.step_begin(run_context)
 
@@ -233,10 +348,70 @@ class CallbackManager(Callback):
             cb.step_end(run_context)
 
     def end(self, run_context):
-        """Called once after network training."""
+        """Called once after network train or eval."""
         for cb in self._callbacks:
             cb.end(run_context)
 
+    def on_train_begin(self, run_context):
+        """Called before network train."""
+        for cb in self._callbacks:
+            cb.on_train_begin(run_context)
+
+    def on_train_epoch_begin(self, run_context):
+        """Called before each train epoch begin."""
+        for cb in self._callbacks:
+            cb.on_train_epoch_begin(run_context)
+
+    def on_train_epoch_end(self, run_context):
+        """Called after each train epoch finished."""
+        for cb in self._callbacks:
+            cb.on_train_epoch_end(run_context)
+
+    def on_train_step_begin(self, run_context):
+        """Called before each train step begin."""
+        for cb in self._callbacks:
+            cb.on_train_step_begin(run_context)
+
+    def on_train_step_end(self, run_context):
+        """Called after each train step finished."""
+        for cb in self._callbacks:
+            cb.step_end(run_context)
+
+    def on_train_end(self, run_context):
+        """Called after network train end."""
+        for cb in self._callbacks:
+            cb.on_train_end(run_context)
+
+    def on_eval_begin(self, run_context):
+        """Called before network eval."""
+        for cb in self._callbacks:
+            cb.on_eval_begin(run_context)
+
+    def on_eval_epoch_begin(self, run_context):
+        """Called before eval epoch begin."""
+        for cb in self._callbacks:
+            cb.on_eval_epoch_begin(run_context)
+
+    def on_eval_epoch_end(self, run_context):
+        """Called after eval epoch finished."""
+        for cb in self._callbacks:
+            cb.on_eval_epoch_end(run_context)
+
+    def on_eval_step_begin(self, run_context):
+        """Called before each eval step begin."""
+        for cb in self._callbacks:
+            cb.on_eval_step_begin(run_context)
+
+    def on_eval_step_end(self, run_context):
+        """Called after each eval step finished."""
+        for cb in self._callbacks:
+            cb.on_eval_step_end(run_context)
+
+    def on_eval_end(self, run_context):
+        """Called after network eval end."""
+        for cb in self._callbacks:
+            cb.on_eval_end(run_context)
+
 
 class InternalCallbackParam(dict):
     """Internal callback object's parameters."""

mindspore/python/mindspore/train/callback/_history.py

@@ -21,7 +21,7 @@ from ._callback import Callback
 
 class History(Callback):
     """
-    Records the network outputs information into a `History` object.
+    Records the network outputs and metrics information into a `History` object.
 
     The network outputs information will be the loss value if not custimizing the train network or eval network;
     if the custimized network returns a `Tensor` or `numpy.ndarray`, the mean value of network output
@@ -29,7 +29,7 @@ class History(Callback):
     outputs will be recorded.
 
     Note:
-        Normally used in `mindspore.Model.train`.
+        Normally used in `mindspore.Model.train` or `mindspore.Model.fit`.
 
     Examples:
         >>> import numpy as np
@@ -65,7 +65,7 @@ class History(Callback):
 
     def epoch_end(self, run_context):
         """
-        Records the first element of network outputs at the end of epoch.
+        Records the first element of network outputs and metrics information at the end of epoch.
 
         Args:
             run_context (RunContext): Context of the `mindspore.Model.{train | eval}`.  For more details,

mindspore/python/mindspore/train/callback/_lambda_callback.py

@@ -22,19 +22,25 @@ class LambdaCallback(Callback):
     Callback for creating simple, custom callbacks.
 
     This callback is constructed with anonymous functions that will be called
-    at the appropriate time (during `mindspore.Model.{train | eval}`). Note that
+    at the appropriate time (during `mindspore.Model.{train | eval | fit}`). Note that
     each stage of callbacks expects one positional arguments: `run_context`.
 
     Note:
         This is an experimental interface that is subject to change or deletion.
 
     Args:
-        epoch_begin (Function): called at the beginning of every epoch.
-        epoch_end (Function): called at the end of every epoch.
-        step_begin (Function): called at the beginning of every batch.
-        step_end (Function): called at the end of every batch.
-        begin (Function): called at the beginning of model train/eval.
-        end (Function): called at the end of model train/eval.
+        on_train_epoch_begin (Function): called at each train epoch begin.
+        on_train_epoch_end (Function): called at each train epoch end.
+        on_train_step_begin (Function):  called at each train step begin.
+        on_train_step_end (Function): called at each train step end.
+        on_train_begin (Function): called at the beginning of model train.
+        on_train_end (Function): called at the end of model train.
+        on_eval_epoch_begin (Function): called at eval epoch begin.
+        on_eval_epoch_end (Function): called at eval epoch end.
+        on_eval_step_begin (Function): called at each eval step begin.
+        on_eval_step_end (Function): called at each eval step end.
+        on_eval_begin (Function): called at the beginning of model eval.
+        on_eval_end (Function): called at the end of model eval.
 
     Examples:
         >>> import numpy as np
@@ -46,19 +52,28 @@ class LambdaCallback(Callback):
         >>> net = nn.Dense(10, 5)
         >>> crit = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
         >>> opt = nn.Momentum(net.trainable_params(), 0.01, 0.9)
-        >>> lambda_callback = LambdaCallback(epoch_end=
+        >>> lambda_callback = LambdaCallback(on_train_epoch_end=
         ... lambda run_context: print("loss: ", run_context.original_args().net_outputs))
         >>> model = Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"})
         >>> model.train(2, train_dataset, callbacks=[lambda_callback])
         loss: 1.6127687
         loss: 1.6106578
     """
-    def __init__(self, epoch_begin=None, epoch_end=None, step_begin=None,
-                 step_end=None, begin=None, end=None):
+    def __init__(self, on_train_epoch_begin=None, on_train_epoch_end=None, on_train_step_begin=None,
+                 on_train_step_end=None, on_train_begin=None, on_train_end=None,
+                 on_eval_epoch_begin=None, on_eval_epoch_end=None, on_eval_step_begin=None,
+                 on_eval_step_end=None, on_eval_begin=None, on_eval_end=None):
         super(LambdaCallback, self).__init__()
-        self.epoch_begin = epoch_begin if epoch_begin else lambda run_context: None
-        self.epoch_end = epoch_end if epoch_end else lambda run_context: None
-        self.step_begin = step_begin if step_begin else lambda run_context: None
-        self.step_end = step_end if step_end else lambda run_context: None
-        self.begin = begin if begin else lambda run_context: None
-        self.end = end if end else lambda run_context: None
+        self.on_train_epoch_begin = on_train_epoch_begin if on_train_epoch_begin else lambda run_context: None
+        self.on_train_epoch_end = on_train_epoch_end if on_train_epoch_end else lambda run_context: None
+        self.on_train_step_begin = on_train_step_begin if on_train_step_begin else lambda run_context: None
+        self.on_train_step_end = on_train_step_end if on_train_step_end else lambda run_context: None
+        self.on_train_begin = on_train_begin if on_train_begin else lambda run_context: None
+        self.on_train_end = on_train_end if on_train_end else lambda run_context: None
+
+        self.on_eval_epoch_begin = on_eval_epoch_begin if on_eval_epoch_begin else lambda run_context: None
+        self.on_eval_epoch_end = on_eval_epoch_end if on_eval_epoch_end else lambda run_context: None
+        self.on_eval_step_begin = on_eval_step_begin if on_eval_step_begin else lambda run_context: None
+        self.on_eval_step_end = on_eval_step_end if on_eval_step_end else lambda run_context: None
+        self.on_eval_begin = on_eval_begin if on_eval_begin else lambda run_context: None
+        self.on_eval_end = on_eval_end if on_eval_end else lambda run_context: None

mindspore/python/mindspore/train/callback/_loss_monitor.py

@@ -23,7 +23,7 @@ from ._callback import Callback
 
 class LossMonitor(Callback):
     """
-    Monitor the loss in training.
+    Monitor the loss in train or monitor the loss and eval metrics in fit.
 
     If the loss is NAN or INF, it will terminate training.
 
@@ -90,3 +90,17 @@ class LossMonitor(Callback):
         if self._per_print_times != 0 and (cb_params.cur_step_num - self._last_print_time) >= self._per_print_times:
             self._last_print_time = cb_params.cur_step_num
             print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss), flush=True)
+
+    def on_train_epoch_end(self, run_context):
+        """
+        When LossMoniter used in `model.fit`, print eval metrics at the end of epoch if current epoch
+        should do evaluation.
+
+        Args:
+            run_context (RunContext): Include some information of the model. For more details,
+                    please refer to :class:`mindspore.RunContext`.
+        """
+        cb_params = run_context.original_args()
+        metrics = cb_params.get("metrics")
+        if metrics:
+            print("Eval result: epoch %d, metrics: %s" % (cb_params.cur_epoch_num, metrics))

mindspore/python/mindspore/train/callback/_time_monitor.py

@@ -22,7 +22,7 @@ from ._callback import Callback
 
 class TimeMonitor(Callback):
     """
-    Monitor the time in training.
+    Monitor the time in train or eval process.
 
     Args:
         data_size (int): How many steps are the intervals between print information each time.
@@ -71,6 +71,7 @@ class TimeMonitor(Callback):
         epoch_seconds = (time.time() - self.epoch_time) * 1000
         step_size = self.data_size
         cb_params = run_context.original_args()
+        mode = cb_params.get("mode", "")
         if hasattr(cb_params, "batch_num"):
             batch_num = cb_params.batch_num
             if isinstance(batch_num, int) and batch_num > 0:
@@ -78,4 +79,5 @@ class TimeMonitor(Callback):
         Validator.check_positive_int(step_size)
 
         step_seconds = epoch_seconds / step_size
-        print("epoch time: {:5.3f} ms, per step time: {:5.3f} ms".format(epoch_seconds, step_seconds), flush=True)
+        print("{} epoch time: {:5.3f} ms, per step time: {:5.3f} ms".format
+              (mode.title(), epoch_seconds, step_seconds), flush=True)

mindspore/python/mindspore/train/model.py

@@ -27,7 +27,8 @@ from .callback._checkpoint import _chg_ckpt_file_name_if_same_exist
 from ..common.tensor import Tensor
 from ..nn.metrics import get_metrics
 from .._checkparam import check_input_data, check_output_data, Validator
-from .callback import _InternalCallbackParam, RunContext, _CallbackManager, Callback
+from .callback import _InternalCallbackParam, RunContext, _CallbackManager, Callback, TimeMonitor
+from .callback import __all__ as internal_cb_names
 from .. import context
 from ..parallel._utils import _get_parallel_mode, _get_device_num, _get_global_rank, \
     _get_parameter_broadcast, _device_number_check, _parameter_broadcast_check, _parallel_predict_check, \
@@ -503,7 +504,8 @@ class Model:
         return [callbacks]
 
     @_save_final_ckpt
-    def _train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1, initial_epoch=0):
+    def _train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1, initial_epoch=0,
+               valid_dataset=None, valid_frequency=1, valid_dataset_sink_mode=True):
         """
         Training.
 
@@ -541,6 +543,7 @@ class Model:
         cb_params.device_number = self._device_number
         cb_params.train_dataset = train_dataset
         cb_params.list_callback = self._transform_callbacks(callbacks)
+        valid_infos = (valid_dataset, valid_frequency, valid_dataset_sink_mode)
         if context.get_context("mode") == context.PYNATIVE_MODE:
             cb_params.list_callback.insert(0, _StepSync())
             callbacks = cb_params.list_callback
@@ -555,17 +558,21 @@ class Model:
         with _CallbackManager(callbacks) as list_callback:
             self._check_reuse_dataset(train_dataset)
             if not dataset_sink_mode:
-                self._train_process(epoch, train_dataset, list_callback, cb_params, initial_epoch)
+                self._train_process(epoch, train_dataset, list_callback, cb_params, initial_epoch, valid_infos)
             elif context.get_context("device_target") == "CPU":
                 logger.info("The CPU cannot support dataset sink mode currently."
                             "So the training process will be performed with dataset not sink.")
-                self._train_process(epoch, train_dataset, list_callback, cb_params, initial_epoch)
+                self._train_process(epoch, train_dataset, list_callback, cb_params, initial_epoch, valid_infos)
             else:
                 self._train_dataset_sink_process(epoch, train_dataset, list_callback,
-                                                 cb_params, sink_size, initial_epoch)
+                                                 cb_params, sink_size, initial_epoch, valid_infos)
+
+    @staticmethod
+    def _should_eval(epoch, validation_freq):
+        return epoch % validation_freq == 0 if isinstance(validation_freq, int) else epoch in validation_freq
 
     def _train_dataset_sink_process(self, epoch, train_dataset, list_callback=None, cb_params=None,
-                                    sink_size=-1, initial_epoch=0):
+                                    sink_size=-1, initial_epoch=0, valid_infos=None):
         """
         Training process. The data would be passed to network through dataset channel.
 
@@ -593,7 +600,7 @@ class Model:
         cb_params.dataset_sink_mode = True
 
         run_context = RunContext(cb_params)
-        list_callback.begin(run_context)
+        list_callback.on_train_begin(run_context)
         # used to stop training for early stop, such as stopAtTIme or stopATStep
         dataset_helper = None
         if hasattr(train_dataset, '_dataset_helper'):
@@ -609,7 +616,7 @@ class Model:
             cb_params.cur_epoch_num = self.epoch_iter + 1 + initial_epoch
             self._current_epoch_num = cb_params.cur_epoch_num
             self._current_step_num = 0
-            list_callback.epoch_begin(run_context)
+            list_callback.on_train_epoch_begin(run_context)
             dataset_helper, train_network = self._exec_preprocess(is_train=True,
                                                                   dataset=train_dataset,
                                                                   dataset_sink_mode=True,
@@ -632,22 +639,51 @@ class Model:
                     cb_params.cur_step_num += 1
                 self._current_step_num = int((cb_params.cur_step_num - 1) % cb_params.batch_num + 1)
                 cb_params.train_dataset_element = inputs
-                list_callback.step_begin(run_context)
+                list_callback.on_train_step_begin(run_context)
                 outputs = train_network(*inputs)
                 cb_params.net_outputs = outputs
                 # In disaster recovery scenarios, need not to execute callbacks if this step executes failed.
                 need_exec_callback_step_end = not (self.enable_recovery and _get_recovery_context("need_reset"))
                 if need_exec_callback_step_end:
-                    list_callback.step_end(run_context)
+                    list_callback.on_train_step_end(run_context)
 
                 if (_is_role_pserver() and not _enable_distributed_mindrt()) or _is_role_sched():
                     os._exit(0)
 
             dataset_helper.continue_send()
+
+            valid_dataset, valid_frequency, valid_dataset_sink_mode = valid_infos
+            if valid_dataset and self._should_eval(cb_params.cur_epoch_num, valid_frequency):
+
+                train_cur_step_num = cb_params.cur_step_num
+                train_batch_num = cb_params.batch_num
+                train_dataset_sink_mode = cb_params.dataset_sink_mode
+                train_net_outputs = cb_params.net_outputs
+
+                eval_callback = []
+                for cb in list_callback._callbacks:
+                    if cb.__class__.__name__ in internal_cb_names:
+                        if isinstance(cb, TimeMonitor):
+                            eval_callback.append(cb)
+                    else:
+                        eval_callback.append(cb)
+
+                self._eval_in_fit(valid_dataset,
+                                  callbacks=eval_callback,
+                                  dataset_sink_mode=valid_dataset_sink_mode,
+                                  cb_params=cb_params)
+                cb_params.mode = "train"
+                cb_params.cur_step_num = train_cur_step_num
+                cb_params.batch_num = train_batch_num
+                cb_params.dataset_sink_mode = train_dataset_sink_mode
+                cb_params.net_outputs = train_net_outputs
+
             # In disaster recovery scenarios, need not to execute callbacks if this epoch executes failed.
             need_exec_callback_epoch_end = not (self.enable_recovery and _get_recovery_context("need_reset"))
             if need_exec_callback_epoch_end:
-                list_callback.epoch_end(run_context)
+                list_callback.on_train_epoch_end(run_context)
+            if "metrics" in cb_params:
+                cb_params.pop("metrics")
 
             should_stop = run_context.get_stop_requested()
             if should_stop:
@@ -663,7 +699,7 @@ class Model:
         dataset_helper.stop_send()
         dataset_helper.release()
 
-        list_callback.end(run_context)
+        list_callback.on_train_end(run_context)
 
     def _check_enable_recovery(self):
         """
@@ -753,7 +789,8 @@ class Model:
 
             _set_recovery_context(need_reset=False)
 
-    def _train_process(self, epoch, train_dataset, list_callback=None, cb_params=None, initial_epoch=0):
+    def _train_process(self, epoch, train_dataset, list_callback=None, cb_params=None, initial_epoch=0,
+                       valid_infos=None):
         """
         Training process. The data would be passed to network directly.
 
@@ -776,13 +813,13 @@ class Model:
         cb_params.cur_step_num = 0
         cb_params.dataset_sink_mode = False
         run_context = RunContext(cb_params)
-        list_callback.begin(run_context)
+        list_callback.on_train_begin(run_context)
         for i in range(initial_epoch, epoch):
             cb_params.cur_epoch_num = i + 1
             self._current_epoch_num = cb_params.cur_epoch_num
             self._current_step_num = 0
 
-            list_callback.epoch_begin(run_context)
+            list_callback.on_train_epoch_begin(run_context)
 
             for next_element in dataset_helper:
                 len_element = len(next_element)
@@ -795,7 +832,7 @@ class Model:
                 self._current_step_num = int((cb_params.cur_step_num - 1) % cb_params.batch_num + 1)
 
                 cb_params.train_dataset_element = next_element
-                list_callback.step_begin(run_context)
+                list_callback.on_train_step_begin(run_context)
                 outputs = self._train_network(*next_element)
                 cb_params.net_outputs = outputs
                 if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update():
@@ -803,24 +840,52 @@ class Model:
                     overflow = np.all(overflow.asnumpy())
                     self._loss_scale_manager.update_loss_scale(overflow)
 
-                list_callback.step_end(run_context)
+                list_callback.on_train_step_end(run_context)
                 if (_is_role_pserver() and not _enable_distributed_mindrt()) or _is_role_sched():
                     os._exit(0)
                 should_stop = run_context.get_stop_requested()
                 if should_stop:
                     break
 
+            valid_dataset, valid_frequency, valid_dataset_sink_mode = valid_infos
+            if valid_dataset and self._should_eval(cb_params.cur_epoch_num, valid_frequency):
+                train_cur_step_num = cb_params.cur_step_num
+                train_batch_num = cb_params.batch_num
+                train_dataset_sink_mode = cb_params.dataset_sink_mode
+                train_net_outputs = cb_params.net_outputs
+
+                eval_callback = []
+                for cb in list_callback._callbacks:
+                    if cb.__class__.__name__ in internal_cb_names:
+                        if isinstance(cb, TimeMonitor):
+                            eval_callback.append(cb)
+                    else:
+                        eval_callback.append(cb)
+
+                self._eval_in_fit(valid_dataset,
+                                  callbacks=eval_callback,
+                                  dataset_sink_mode=valid_dataset_sink_mode,
+                                  cb_params=cb_params)
+
+                cb_params.mode = "train"
+                cb_params.cur_step_num = train_cur_step_num
+                cb_params.batch_num = train_batch_num
+                cb_params.dataset_sink_mode = train_dataset_sink_mode
+                cb_params.net_outputs = train_net_outputs
+
             train_dataset.reset()
 
             # if param is cache enable, flush data from cache to host before epoch end
             self._flush_from_cache(cb_params)
 
-            list_callback.epoch_end(run_context)
+            list_callback.on_train_epoch_end(run_context)
+            if "metrics" in cb_params:
+                cb_params.pop("metrics")
             should_stop = run_context.get_stop_requested()
             if should_stop:
                 break
 
-        list_callback.end(run_context)
+        list_callback.on_train_end(run_context)
 
     def train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1, initial_epoch=0):
         """
@@ -912,6 +977,9 @@ class Model:
 
         _device_number_check(self._parallel_mode, self._device_number)
 
+        if callbacks:
+            self._check_methods_for_custom_callbacks(callbacks, "train")
+
         self._train(epoch,
                     train_dataset,
                     callbacks=callbacks,
@@ -924,6 +992,140 @@ class Model:
         if _is_ps_mode() and _enable_distributed_mindrt():
             _reset_op_id_with_offset()
 
+    @staticmethod
+    def _check_methods_for_custom_callbacks(callbacks, current_mode):
+        """
+        Check whether methods of custimized callbacks are valid.
+
+        Args:
+            callbacks (Optional[list[Callback], Callback]): List of callback objects or callback object.
+            current_mode (str): 'fit', 'train' or 'eval'.
+        """
+        old_version_methods_names = {'begin', 'end', 'epoch_begin', 'epoch_end', 'step_begin', 'step_end'}
+        if not isinstance(callbacks, list):
+            callbacks = [callbacks]
+        for cb in callbacks:
+            cb_name = cb.__class__.__name__
+            if  cb_name not in internal_cb_names:
+                cb_methods_names = set(cb.__class__.__dict__.keys())
+                invalid_methods_names = cb_methods_names & old_version_methods_names
+                if invalid_methods_names:
+                    if current_mode in ["train", "eval"]:
+                        logger.warning("For %s callback, %s methods may not be supported in later version, "
+                                       "Use methods prefixed with 'on_train' or 'on_eval' instead "
+                                       "when using customized callbacks." % (cb_name, invalid_methods_names))
+                    else:
+                        raise ValueError("For %s callback, %s methods may not be supported in later version, "
+                                         "Use methods prefixed with 'on_train' or 'on_eval' instead when"
+                                         "using customized callbacks." % (cb_name, invalid_methods_names))
+
+    def fit(self, epoch, train_dataset, valid_dataset=None, valid_frequency=1, callbacks=None,
+            dataset_sink_mode=True, valid_dataset_sink_mode=True, sink_size=-1, initial_epoch=0):
+        """
+        Fit API.
+
+        Evaluation process will be performed during training process if `valid_dataset` is provided.
+
+        More details please refer to `mindspore.Model.train` and `mindspore.Model.eval`.
+
+        Args:
+            epoch (int): Total training epochs. Generally, train network will be trained on complete dataset per epoch.
+                         If `dataset_sink_mode` is set to True and `sink_size` is greater than 0, each epoch will
+                         train `sink_size` steps instead of total steps of dataset.
+                         If `epoch` used with `initial_epoch`, it is to be understood as "final epoch".
+            train_dataset (Dataset): A training dataset iterator. If `loss_fn` is defined, the data and label will be
+                                     passed to the `network` and the `loss_fn` respectively, so a tuple (data, label)
+                                     should be returned from dataset. If there is multiple data or labels, set `loss_fn`
+                                     to None and implement calculation of loss in `network`,
+                                     then a tuple (data1, data2, data3, ...) with all data returned from dataset
+                                     will be passed to the `network`.
+            valid_dataset (Dataset): Dataset to evaluate the model. If `valid_dataset` is provided, evaluation process
+                                     will be performed on the end of training process. Default: None.
+            valid_frequency (int, list): Only relevant if `valid_dataset` is provided.  If an integer, specifies
+                         how many training epochs to run before a new validation run is performed,
+                         e.g. `valid_frequency=2` runs validation every 2 epochs.
+                         If a list, specifies the epochs on which to run validation,
+                         e.g. `valid_frequency=[1, 5]` runs validation at the end of the 1st, 5th epochs.
+                         Default: 1
+            callbacks (Optional[list[Callback], Callback]): List of callback objects or callback object,
+                                                            which should be executed while training.
+                                                            Default: None.
+            dataset_sink_mode (bool): Determines whether to pass the train data through dataset channel.
+                                      Configure pynative mode or CPU, the training process will be performed with
+                                      dataset not sink. Default: True.
+            valid_dataset_sink_mode (bool): Determines whether to pass the validation data through dataset channel.
+                                      Default: True.
+            sink_size (int): Control the amount of data in each sink. `sink_size` is invalid if `dataset_sink_mode`
+                             is False.
+                             If sink_size = -1, sink the complete dataset for each epoch.
+                             If sink_size > 0, sink sink_size data for each epoch.
+                             Default: -1.
+            initial_epoch (int): Epoch at which to start train, it useful for resuming a previous training run.
+                                 Default: 0.
+
+        Examples:
+            >>> from mindspore import Model, nn, FixedLossScaleManager
+            >>>
+            >>> # For details about how to build the dataset, please refer to the tutorial
+            >>> # document on the official website.
+            >>> train_dataset = create_custom_dataset()
+            >>> valid_dataset = create_custom_dataset()
+            >>> net = Net()
+            >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+            >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+            >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics="accuracy")
+            >>> model.fit(2, train_dataset, valid_dataset)
+        """
+
+        dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
+        valid_dataset_sink_mode = Validator.check_bool(valid_dataset_sink_mode)
+
+        if isinstance(self._train_network, nn.GraphCell) and dataset_sink_mode:
+            raise ValueError("Dataset sink mode is currently not supported when training with a GraphCell.")
+
+        if hasattr(train_dataset, '_warmup_epoch') and train_dataset._warmup_epoch != epoch:
+            raise ValueError("when use Model.build to initialize model, the value of parameter `epoch` in Model.build "
+                             "should be equal to value in Model.fit, but got {} and {} separately."
+                             .format(train_dataset._warmup_epoch, epoch))
+
+        if dataset_sink_mode and _is_ps_mode() and not _cache_enable():
+            raise ValueError("Parameter server mode does not support 'data_sink_mode=True'.")
+
+        Validator.check_is_int(sink_size)
+        Validator.check_non_negative_int(initial_epoch)
+        if initial_epoch >= epoch:
+            raise ValueError(f"For 'Model.train', the parameter 'epoch' must bigger than parameter 'initial_epoch',"
+                             f" but got the parameter 'epoch' is {epoch}, 'initial_epoch' is {initial_epoch}.")
+        dataset_size = train_dataset.get_dataset_size()
+        if dataset_size == 0:
+            raise ValueError("There is no valid data in dataset, please check dataset file firstly.")
+        if sink_size == -1:
+            sink_size = dataset_size
+        if sink_size < -1 or sink_size == 0:
+            raise ValueError("For 'Model.fit', The parameter 'sink_size' must be -1 or positive, "
+                             "but got {}.".format(sink_size))
+
+        _device_number_check(self._parallel_mode, self._device_number)
+
+        if not isinstance(valid_frequency, (int, list)):
+            raise ValueError(f"For 'Model.fit', the type of 'valid_frequency' must be a list or a integer, but got"
+                             "type {type(validation_freq)}.")
+
+        if valid_dataset and not self._metric_fns:
+            raise ValueError("For 'Model.fit', if valid_dataset is not None, the model argument 'metrics' can not be"
+                             "None or empty, you should set the argument 'metrics' for model.")
+        if callbacks:
+            self._check_methods_for_custom_callbacks(callbacks, "fit")
+        self._train(epoch,
+                    train_dataset,
+                    callbacks=callbacks,
+                    dataset_sink_mode=dataset_sink_mode,
+                    sink_size=sink_size,
+                    initial_epoch=initial_epoch,
+                    valid_dataset=valid_dataset,
+                    valid_frequency=valid_frequency,
+                    valid_dataset_sink_mode=valid_dataset_sink_mode)
+
     def build(self, train_dataset=None, valid_dataset=None, sink_size=-1, epoch=1, jit_config=None):
         """
         Build computational graphs and data graphs with the sink mode.
@@ -971,6 +1173,40 @@ class Model:
             _cell_graph_executor.set_jit_config(jit_config)
         self._init(train_dataset, valid_dataset, sink_size, epoch)
 
+    def _eval_in_fit(self, valid_dataset, callbacks=None, dataset_sink_mode=True, cb_params=None):
+        """
+        Evaluation process in `mindspore.Model.fit`.
+
+        Args:
+            valid_dataset (Dataset): Dataset to evaluate the model. If `valid_dataset` is provided, evaluation process
+                                     will be performed on the end of training process. Default: None.
+            callbacks (Optional[list[Callback], Callback]): List of callback objects or callback object, which should be
+                                     executed while evaluation. Default: None.
+            valid_dataset_sink_mode (bool): Determines whether to pass the validation data through dataset channel.
+                                     Default: True.
+            cb_params (_InternalCallbackParam): Callback parameters. Default: None.
+        """
+        if isinstance(self._eval_network, nn.GraphCell) and dataset_sink_mode:
+            raise ValueError("Sink mode is currently not supported when evaluating with a GraphCell.")
+
+        cb_params.eval_network = self._eval_network
+        cb_params.valid_dataset = valid_dataset
+        cb_params.batch_num = valid_dataset.get_dataset_size()
+        cb_params.mode = "eval"
+        cb_params.cur_step_num = 0
+
+        self._clear_metrics()
+
+        if context.get_context("device_target") == "CPU" and dataset_sink_mode:
+            dataset_sink_mode = False
+            logger.info("CPU cannot support dataset sink mode currently."
+                        "So the evaluating process will be performed with dataset non-sink mode.")
+
+        with _CallbackManager(callbacks) as list_callback:
+            if dataset_sink_mode:
+                return self._eval_dataset_sink_process(valid_dataset, list_callback, cb_params)
+            return self._eval_process(valid_dataset, list_callback, cb_params)
+
     def _eval_dataset_sink_process(self, valid_dataset, list_callback=None, cb_params=None):
         """
         Evaluation. The data would be passed to network through dataset channel.
@@ -990,20 +1226,20 @@ class Model:
                                                              dataset_sink_mode=True)
         cb_params.eval_network = eval_network
         cb_params.dataset_sink_mode = True
-        list_callback.begin(run_context)
-        list_callback.epoch_begin(run_context)
+        list_callback.on_eval_begin(run_context)
+        list_callback.on_eval_epoch_begin(run_context)
         for inputs in dataset_helper:
             cb_params.cur_step_num += 1
-            list_callback.step_begin(run_context)
+            list_callback.on_eval_step_begin(run_context)
             outputs = eval_network(*inputs)
             cb_params.net_outputs = outputs
-            list_callback.step_end(run_context)
+            list_callback.on_eval_step_end(run_context)
             self._update_metrics(outputs)
 
-        list_callback.epoch_end(run_context)
+        list_callback.on_eval_epoch_end(run_context)
         metrics = self._get_metrics()
         cb_params.metrics = metrics
-        list_callback.end(run_context)
+        list_callback.on_eval_end(run_context)
 
         return metrics
 
@@ -1021,25 +1257,25 @@ class Model:
         """
         run_context = RunContext(cb_params)
         cb_params.dataset_sink_mode = False
-        list_callback.begin(run_context)
+        list_callback.on_eval_begin(run_context)
         dataset_helper, _ = self._exec_preprocess(is_train=False,
                                                   dataset=valid_dataset,
                                                   dataset_sink_mode=False)
-        list_callback.epoch_begin(run_context)
+        list_callback.on_eval_epoch_begin(run_context)
         for next_element in dataset_helper:
             cb_params.cur_step_num += 1
-            list_callback.step_begin(run_context)
+            list_callback.on_eval_step_begin(run_context)
             next_element = _transfer_tensor_to_tuple(next_element)
             outputs = self._eval_network(*next_element)
             cb_params.net_outputs = outputs
-            list_callback.step_end(run_context)
+            list_callback.on_eval_step_end(run_context)
             self._update_metrics(outputs)
 
-        list_callback.epoch_end(run_context)
+        list_callback.on_eval_epoch_end(run_context)
         valid_dataset.reset()
         metrics = self._get_metrics()
         cb_params.metrics = metrics
-        list_callback.end(run_context)
+        list_callback.on_eval_end(run_context)
         return metrics
 
     def eval(self, valid_dataset, callbacks=None, dataset_sink_mode=True):
@@ -1087,7 +1323,8 @@ class Model:
                              "you should set the argument 'metrics' for model.")
         if isinstance(self._eval_network, nn.GraphCell) and dataset_sink_mode:
             raise ValueError("Sink mode is currently not supported when evaluating with a GraphCell.")
-
+        if callbacks:
+            self._check_methods_for_custom_callbacks(callbacks, "eval")
         cb_params = _InternalCallbackParam()
         cb_params.eval_network = self._eval_network
         cb_params.valid_dataset = valid_dataset

mindspore/python/mindspore/train/train_thor/model_thor.py

@@ -150,7 +150,7 @@ class ModelThor(Model):
             self.switch_branch_one = not self.switch_branch_one
             outputs = self._train_network(*inputs)
             cb_params.net_outputs = outputs
-            list_callback.step_end(run_context)
+            list_callback.on_train_step_end(run_context)
         else:
             cb_params.cur_step_num += 1
             if self.train_network_init_flag:
@@ -163,7 +163,7 @@ class ModelThor(Model):
             if self.index_first_order == iter_first_order:
                 self.index_first_order = 0
                 self.switch_branch_one = not self.switch_branch_one
-                list_callback.step_end(run_context)
+                list_callback.on_train_step_end(run_context)
 
     def _train_ascend_sink_step(self, cb_params, train_dataset, iter_first_order, inputs, list_callback, run_context):
         """train ascend sink step"""
@@ -184,10 +184,10 @@ class ModelThor(Model):
         self.switch_branch_one = not self.switch_branch_one
         outputs = self._train_network(*inputs)
         cb_params.net_outputs = outputs
-        list_callback.step_end(run_context)
+        list_callback.on_train_step_end(run_context)
 
     def _train_dataset_sink_process(self, epoch, train_dataset, list_callback=None, cb_params=None,
-                                    sink_size=-1, initial_epoch=0):
+                                    sink_size=-1, initial_epoch=0, valid_infos=None):
         """
         Training process. The data would be passed to network through dataset channel.
 
@@ -204,6 +204,9 @@ class ModelThor(Model):
             initial_epoch (int): Epoch at which to start train, it useful for resuming a previous training run.
                                  Default: 0.
         """
+        valid_dataset, _, _ = valid_infos
+        if valid_dataset:
+            raise ValueError("Evaluation in training is currently not supported in the second-order scenario of thor.")
         if sink_size == -1:
             epoch_num = epoch - initial_epoch
         else:
@@ -226,28 +229,28 @@ class ModelThor(Model):
         cb_params.cur_step_num = 0
 
         run_context = RunContext(cb_params)
-        list_callback.begin(run_context)
+        list_callback.on_train_begin(run_context)
 
         for i in range(initial_epoch, epoch):
             cb_params.cur_epoch_num = i + 1
-            list_callback.epoch_begin(run_context)
+            list_callback.on_train_epoch_begin(run_context)
             # for data sink dataset_helper only iter once, other wise iter epoch_size times.
             for inputs in dataset_helper:
                 if _need_to_full() and context.get_context("device_target") == "GPU":
                     inputs = _to_full_tensor(inputs, self._device_number, self._global_rank)
-                list_callback.step_begin(run_context)
+                list_callback.on_train_step_begin(run_context)
                 if context.get_context("device_target") == "GPU":
                     self._train_gpu_sink_step(cb_params, inputs, list_callback, iter_first_order, run_context)
                 else:
                     self._train_ascend_sink_step(cb_params, train_dataset, iter_first_order, inputs, list_callback,
                                                  run_context)
-            list_callback.epoch_end(run_context)
+            list_callback.on_train_epoch_end(run_context)
             should_stop = False or run_context.get_stop_requested()
             if should_stop:
                 break
         dataset_helper.stop_send()
 
-        list_callback.end(run_context)
+        list_callback.on_train_end(run_context)
 
 
 __all__ = ["ModelThor"]

tests/st/train/test_fit.py

@@ -0,0 +1,202 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+""" test_fit """
+
+import pytest
+import numpy as np
+from mindspore import Model, nn, Tensor
+from mindspore.common.initializer import Normal
+from mindspore.train.callback import Callback, TimeMonitor, LossMonitor
+from mindspore import dataset as ds
+
+
+def get_data(num, w=2.0, b=3.0):
+    for _ in range(num):
+        x = np.random.uniform(-10.0, 10.0)
+        noise = np.random.normal(0, 1)
+        y = x * w + b + noise
+        yield np.array([x]).astype(np.float32), np.array([y]).astype(np.float32)
+
+
+def create_dataset(num_data, batch_size=16, repeat_size=1):
+    input_data = ds.GeneratorDataset(list(get_data(num_data)), column_names=['data', 'label'])
+    input_data = input_data.batch(batch_size, drop_remainder=True)
+    input_data = input_data.repeat(repeat_size)
+    return input_data
+
+
+def define_model():
+    net = nn.Dense(1, 1, Normal(0.02), Normal(0.02))
+    net_loss = nn.MSELoss()
+    net_opt = nn.Momentum(net.trainable_params(), 0.01, 0.9)
+    return Model(net, loss_fn=net_loss, optimizer=net_opt, metrics={'mse', 'mae'})
+
+
+class MyCallbackOldMethod(Callback):
+    """ Raise warning in `mindspore.Model.train` and  `mindspore.Model.eval`; raise error  in `mindspore.Model.fit`"""
+    def begin(self, run_context):
+        print("custom callback: print on begin, just for test.")
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        outputs = cb_params.get("net_outputs")
+        result = outputs if isinstance(outputs, Tensor) else outputs[0]
+        print("custom train callback: step end, loss is %s" % (result))
+
+    def on_train_epoch_end(self, run_context):
+        cb_params = run_context.original_args()
+        print("custom train callback: epoch end, loss is %s" % (cb_params.get("net_outputs")))
+
+
+class MyCallbackNewMethod(Callback):
+    """ Custom callback running in `mindspore.Model.train`, `mindspore.Model.eval`, `mindspore.Model.fit`"""
+    def on_train_epoch_end(self, run_context):
+        cb_params = run_context.original_args()
+        print("custom callback: train epoch end, loss is %s" % (cb_params.get("net_outputs")))
+
+    def on_eval_epoch_end(self, run_context):
+        cb_params = run_context.original_args()
+        print("custom callback: eval epoch end, metric is %s" % (cb_params.get("net_outputs")[0]))
+
+
+def test_fit_train_dataset_non_sink_mode():
+    """
+    Feature: `mindspore.Model.fit` with train dataset in non-sink mode.
+    Description: test fit with train dataset in non-sink mode.
+    Expectation: run in non-sink mode.
+    """
+    model = define_model()
+    ds_train = create_dataset(4096, 1024)
+    ds_eval = create_dataset(1024, 512)
+    callbacks = [LossMonitor()]
+    model.fit(3, ds_train, ds_eval, callbacks=callbacks, dataset_sink_mode=False)
+
+
+def test_fit_train_dataset_sink_mode():
+    """
+    Feature: `mindspore.Model.fit` with train dataset in sink mode.
+    Description: test fit with train dataset in sink mode.
+    Expectation: run in sink mode.
+    """
+    model = define_model()
+    ds_train = create_dataset(4096, 1024)
+    ds_eval = create_dataset(1024, 512)
+    callbacks = [LossMonitor()]
+    model.fit(3, ds_train, ds_eval, callbacks=callbacks, dataset_sink_mode=True, sink_size=256)
+
+
+def test_fit_valid_dataset_non_sink_mode():
+    """
+    Feature: `mindspore.Model.fit` with valid dataset in non-sink mode.
+    Description: test fit with valid dataset in non-sink mode.
+    Expectation: run in non-sink mode.
+    """
+    model = define_model()
+    ds_train = create_dataset(4096, 1024)
+    ds_eval = create_dataset(1024, 512)
+    callbacks = [LossMonitor()]
+    model.fit(3, ds_train, ds_eval, callbacks=callbacks, valid_dataset_sink_mode=False)
+
+
+def test_fit_valid_dataset_sink_mode():
+    """
+    Feature: `mindspore.Model.fit` with valid dataset in sink mode.
+    Description: test fit with valid dataset in sink mode.
+    Expectation: run in sink mode.
+    """
+    model = define_model()
+    ds_train = create_dataset(4096, 1024)
+    ds_eval = create_dataset(1024, 512)
+    callbacks = [LossMonitor()]
+    model.fit(3, ds_train, ds_eval, callbacks=callbacks, valid_dataset_sink_mode=True)
+
+
+def test_fit_without_valid_dataset():
+    """
+    Feature: `mindspore.Model.fit` without `valid_dataset` input .
+    Description: test fit when `valid_dataset` is None and `valid_dataset_sink_mode` is True or False.
+    Expectation: network train without eval process, `valid_dataset_sink_mode` does not take effect.
+    """
+    model = define_model()
+    ds_train = create_dataset(4096, 1024)
+    callbacks = [LossMonitor()]
+    model.fit(3, ds_train, None, callbacks=callbacks, valid_dataset_sink_mode=False)
+    model.fit(3, ds_train, None, callbacks=callbacks)
+
+
+def test_fit_valid_frequency():
+    """
+    Feature: check `valid_frequency` input  in `mindspore.Model.fit`.
+    Description: when `valid_frequency` is integer, list or other types.
+    Expectation: raise ValueError when the type of valid_frequency is not int or list.
+    """
+    model = define_model()
+    callbacks = [LossMonitor()]
+    ds_train = create_dataset(4096, 1024)
+    ds_eval = create_dataset(1024, 512)
+    model.fit(3, ds_train, ds_eval, valid_frequency=1, callbacks=callbacks)
+    model.fit(5, ds_train, ds_eval, valid_frequency=2, callbacks=callbacks)
+    model.fit(5, ds_train, ds_eval, valid_frequency=[0, 1, 4], callbacks=callbacks)
+    with pytest.raises(ValueError):
+        model.fit(5, ds_train, ds_eval, valid_frequency=(0, 2), callbacks=callbacks)
+
+
+def test_fit_callbacks():
+    """
+    Feature: check `callbacks` input in `mindspore.Model.fit`.
+    Description: test internal or custom callbacks in fit.
+    Expectation: raise ValueError when methods of custom callbacks are not prefixed with 'on_train' or  'on_eval'.
+    """
+    model = define_model()
+    ds_train = create_dataset(4096, 1024)
+    ds_eval = create_dataset(1024, 512)
+    model.fit(3, ds_train, ds_eval, callbacks=None)
+    model.fit(3, ds_train, ds_eval, callbacks=[TimeMonitor()])
+    model.fit(3, ds_train, ds_eval, callbacks=[TimeMonitor(), LossMonitor()])
+    model.fit(3, ds_train, ds_eval, callbacks=[MyCallbackNewMethod()])
+    model.fit(3, ds_train, ds_eval, callbacks=[TimeMonitor(), MyCallbackNewMethod()])
+    with pytest.raises(ValueError):
+        model.fit(3, ds_train, ds_eval, callbacks=[MyCallbackOldMethod()])
+    with pytest.raises(ValueError):
+        model.fit(3, ds_train, ds_eval, callbacks=[TimeMonitor(), MyCallbackOldMethod()])
+    with pytest.raises(ValueError):
+        model.fit(3, ds_train, valid_dataset=None, callbacks=[TimeMonitor(), MyCallbackOldMethod()])
+
+
+def test_train_eval_callbacks():
+    """
+    Feature: check `callbacks` input in `mindspore.Model.train` or `mindspore.Model.eval`.
+    Description: test internal or custom callbacks in train or eval.
+    Expectation: raise warning when methods of custom callbacks are not prefixed with 'on_train' or  'on_eval'.
+    """
+    model = define_model()
+    ds_train = create_dataset(4096, 1024)
+    ds_eval = create_dataset(1024, 512)
+
+    model.train(3, ds_train, callbacks=None)
+    model.train(3, ds_train, callbacks=[TimeMonitor()])
+    model.train(3, ds_train, callbacks=[LossMonitor()])
+    model.train(3, ds_train, callbacks=[MyCallbackNewMethod()])
+    model.train(3, ds_train, callbacks=[MyCallbackOldMethod()])
+
+    metric_results = model.eval(ds_eval, callbacks=None)
+    print("{}".format(metric_results))
+    metric_results = model.eval(ds_eval, callbacks=[TimeMonitor()])
+    print("{}".format(metric_results))
+    metric_results = model.eval(ds_eval, callbacks=[MyCallbackNewMethod()])
+    print("{}".format(metric_results))
+    metric_results = model.eval(ds_eval, callbacks=[MyCallbackOldMethod()])
+    print("{}".format(metric_results))

tests/ut/python/utils/test_callback.py

@@ -513,13 +513,13 @@ def test_lambda():
 
     run_context = RunContext(cb_params)
     lambda_cb = LambdaCallback(
-        epoch_end=lambda run_context: print("loss result: ", run_context.original_args().net_outputs))
+        on_train_epoch_end=lambda run_context: print("loss result: ", run_context.original_args().net_outputs))
 
     callbacks = [lambda_cb]
     with _CallbackManager(callbacks) as callbacklist:
-        callbacklist.begin(run_context)
-        callbacklist.epoch_begin(run_context)
-        callbacklist.step_begin(run_context)
-        callbacklist.step_end(run_context)
-        callbacklist.epoch_end(run_context)
-        callbacklist.end(run_context)
+        callbacklist.on_train_begin(run_context)
+        callbacklist.on_train_epoch_begin(run_context)
+        callbacklist.on_train_step_begin(run_context)
+        callbacklist.on_train_step_end(run_context)
+        callbacklist.on_train_epoch_end(run_context)
+        callbacklist.on_train_end(run_context)