chore: import upstream snapshot with attribution

test/legacy_test/test_callback_visualdl.py

@@ -0,0 +1,62 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import shutil
+import tempfile
+import unittest
+
+import paddle
+import paddle.vision.transforms as T
+from paddle.static import InputSpec
+from paddle.vision.datasets import MNIST
+
+
+class MnistDataset(MNIST):
+    def __len__(self):
+        return 512
+
+
+class TestCallbacks(unittest.TestCase):
+    def setUp(self):
+        self.save_dir = tempfile.mkdtemp()
+
+    def tearDown(self):
+        shutil.rmtree(self.save_dir)
+
+    def test_visualdl_callback(self):
+        inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+        labels = [InputSpec([None, 1], 'int64', 'label')]
+
+        transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
+        train_dataset = MnistDataset(mode='train', transform=transform)
+        eval_dataset = MnistDataset(mode='test', transform=transform)
+
+        net = paddle.vision.models.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()