Add visualdl callback function

paddle/scripts/paddle_build.sh

@@ -1348,7 +1348,7 @@ EOF
     # run paddle version to install python packages first
     RUN apt-get update && ${NCCL_DEPS}
     RUN apt-get install -y wget python3 python3-pip libgtk2.0-dev dmidecode python3-tk && \
-        pip3 install opencv-python py-cpuinfo==5.0.0 && wget ${ref_web}/${ref_paddle35} && ${ref_paddle35_mv1} pip3 install ${ref_paddle35_whl} ${ref_paddle35_mv2}; apt-get install -f -y && \
+        pip3 install opencv-python visualdl py-cpuinfo==5.0.0 && wget ${ref_web}/${ref_paddle35} && ${ref_paddle35_mv1} pip3 install ${ref_paddle35_whl} ${ref_paddle35_mv2}; apt-get install -f -y && \
         apt-get clean -y && \
         rm -f ${ref_paddle35} && \
         ldconfig
@@ -1370,7 +1370,7 @@ EOF
         CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
         make -j8 > /dev/null && make altinstall > /dev/null && cd ../ && rm Python-3.6.0.tgz
     RUN apt-get install -y libgtk2.0-dev dmidecode python3-tk && ldconfig && \
-        pip3.6 install opencv-python && wget ${ref_web}/${ref_paddle36} && ${ref_paddle36_mv1} pip3.6 install ${ref_paddle36_whl} ${ref_paddle36_mv2}; apt-get install -f -y && \
+        pip3.6 install opencv-python visualdl && wget ${ref_web}/${ref_paddle36} && ${ref_paddle36_mv1} pip3.6 install ${ref_paddle36_whl} ${ref_paddle36_mv2}; apt-get install -f -y && \
         apt-get clean -y && \
         rm -f ${ref_paddle36} && \
         ldconfig
@@ -1386,7 +1386,7 @@ EOF
         CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
         make -j8 > /dev/null && make altinstall > /dev/null && cd ../ && rm Python-3.7.0.tgz
     RUN apt-get install -y libgtk2.0-dev dmidecode python3-tk && ldconfig && \
-        pip3.7 install opencv-python && wget ${ref_web}/${ref_paddle37} && pip3.7 install ${ref_paddle37_whl}; apt-get install -f -y && \
+        pip3.7 install opencv-python visualdl && wget ${ref_web}/${ref_paddle37} && pip3.7 install ${ref_paddle37_whl}; apt-get install -f -y && \
         apt-get clean -y && \
         rm -f ${ref_paddle37} && \
         ldconfig
@@ -1402,7 +1402,7 @@ EOF
         CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
         make -j8 > /dev/null && make altinstall > /dev/null && cd ../ && rm Python-3.8.0.tgz
     RUN apt-get install -y libgtk2.0-dev dmidecode python3-tk && ldconfig && \
-        pip3.8 install opencv-python && wget ${ref_web}/${ref_paddle38} && pip3.8 install ${ref_paddle38_whl}; apt-get install -f -y && \
+        pip3.8 install opencv-python visualdl && wget ${ref_web}/${ref_paddle38} && pip3.8 install ${ref_paddle38_whl}; apt-get install -f -y && \
         apt-get clean -y && \
         rm -f ${ref_paddle38} && \
         ldconfig

python/paddle/hapi/callbacks.py

@@ -15,10 +15,11 @@
 import os
 
 from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.utils import try_import
 
 from .progressbar import ProgressBar
 
-__all__ = ['Callback', 'ProgBarLogger', 'ModelCheckpoint']
+__all__ = ['Callback', 'ProgBarLogger', 'ModelCheckpoint', 'VisualDL']
 
 
 def config_callbacks(callbacks=None,
@@ -469,3 +470,112 @@ def on_train_end(self, logs=None):
             path = '{}/final'.format(self.save_dir)
             print('save checkpoint at {}'.format(os.path.abspath(path)))
             self.model.save(path)
+
+
+class VisualDL(Callback):
+    """VisualDL callback function
+    Args:
+        log_dir (str): The directory to save visualdl log file.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            from paddle.static import InputSpec
+
+            inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+            labels = [InputSpec([None, 1], 'int64', 'label')]
+
+            train_dataset = paddle.vision.datasets.MNIST(mode='train')
+            eval_dataset = paddle.vision.datasets.MNIST(mode='test')
+
+            net = paddle.vision.LeNet()
+            model = paddle.Model(net, inputs, labels)
+
+            optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
+            model.prepare(optimizer=optim,
+                        loss=paddle.nn.CrossEntropyLoss(),
+                        metrics=paddle.metric.Accuracy())
+
+            # uncomment following lines to fit model with visualdl callback function
+            # callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')
+            # model.fit(train_dataset, eval_dataset, batch_size=64, callbacks=callback)
+
+    """
+
+    def __init__(self, log_dir):
+        self.log_dir = log_dir
+        self.epochs = None
+        self.steps = None
+
+    def _is_write(self):
+        return ParallelEnv().local_rank == 0
+
+    def on_train_begin(self, logs=None):
+        self.epochs = self.params['epochs']
+        assert self.epochs
+        self.train_metrics = self.params['metrics']
+        assert self.train_metrics
+        self._is_fit = True
+
+    def on_epoch_begin(self, epoch=None, logs=None):
+        visualdl = try_import('visualdl')
+        self.steps = self.params['steps']
+        self.epoch = epoch
+        self.train_step = 0
+        self.train_writer = visualdl.LogWriter(self.log_dir)
+
+    def _updates(self, logs, mode):
+        metrics = getattr(self, '%s_metrics' % (mode))
+        writer = getattr(self, '%s_writer' % (mode))
+        current_step = getattr(self, '%s_step' % (mode))
+        if mode == 'train':
+            total_step = self.epoch * self.steps + current_step
+        else:
+            total_step = self.epoch
+
+        for k in metrics:
+            if k in logs:
+                temp_tag = mode + '/' + k
+
+                if isinstance(logs[k], (list, tuple)):
+                    temp_value = logs[k][0]
+                elif isinstance(logs[k], numbers.Number):
+                    temp_value = logs[k]
+                else:
+                    continue
+                writer.add_scalar(
+                    tag=temp_tag, step=total_step, value=temp_value)
+
+    def on_train_batch_end(self, step, logs=None):
+        logs = logs or {}
+        self.train_step += 1
+
+        if self._is_write():
+            if self.steps is None or self.train_step < self.steps:
+                self._updates(logs, 'train')
+
+    def on_epoch_end(self, epoch, logs=None):
+        logs = logs or {}
+        if self._is_write() and (self.steps is not None):
+            self._updates(logs, 'train')
+
+    def on_eval_begin(self, logs=None):
+        visualdl = try_import('visualdl')
+        self.eval_steps = logs.get('steps', None)
+        self.eval_metrics = logs.get('metrics', [])
+        self.eval_step = 0
+        self.evaled_samples = 0
+        self.eval_writer = visualdl.LogWriter(self.log_dir)
+
+    def on_train_end(self, logs=None):
+        if hasattr(self, 'train_writer'):
+            self.train_writer.close()
+        if hasattr(self, 'eval_writer'):
+            self.eval_writer.close()
+
+    def on_eval_end(self, logs=None):
+        self._updates(logs, 'eval')
+
+        if (not hasattr(self, '_is_fit')) and hasattr(self, 'eval_writer'):
+            self.eval_writer.close()

python/paddle/tests/test_callbacks.py

@@ -17,6 +17,7 @@
 import random
 import tempfile
 import shutil
+import paddle
 
 from paddle import Model
 from paddle.static import InputSpec
@@ -102,6 +103,33 @@ def test_callback_verbose_2(self):
         self.verbose = 2
         self.run_callback()
 
+    def test_visualdl_callback(self):
+        try:
+            import visualdl
+        except:
+            return
+
+        inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+        labels = [InputSpec([None, 1], 'int64', 'label')]
+
+        train_dataset = paddle.vision.datasets.MNIST(mode='train')
+        eval_dataset = paddle.vision.datasets.MNIST(mode='test')
+
+        net = paddle.vision.LeNet()
+        model = paddle.Model(net, inputs, labels)
+
+        optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
+        model.prepare(
+            optimizer=optim,
+            loss=paddle.nn.CrossEntropyLoss(),
+            metrics=paddle.metric.Accuracy())
+
+        callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')
+        model.fit(train_dataset,
+                  eval_dataset,
+                  batch_size=64,
+                  callbacks=callback)
+
 
 if __name__ == '__main__':
     unittest.main()