Add TranslatedLayer.program method to get program (#26961)

* add TranslatedLayer.program method

* add unittests & update example code

* polish example code

* update program api example code

python/paddle/fluid/tests/unittests/test_translated_layer.py

@@ -0,0 +1,157 @@
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.nn as nn
+import paddle.optimizer as opt
+
+BATCH_SIZE = 16
+BATCH_NUM = 4
+EPOCH_NUM = 4
+SEED = 10
+
+IMAGE_SIZE = 784
+CLASS_NUM = 10
+
+
+# define a random dataset
+class RandomDataset(paddle.io.Dataset):
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+
+    def __getitem__(self, idx):
+        np.random.seed(SEED)
+        image = np.random.random([IMAGE_SIZE]).astype('float32')
+        label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
+        return image, label
+
+    def __len__(self):
+        return self.num_samples
+
+
+class LinearNet(nn.Layer):
+    def __init__(self):
+        super(LinearNet, self).__init__()
+        self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
+
+    @paddle.jit.to_static
+    def forward(self, x):
+        return self._linear(x)
+
+
+def train(layer, loader, loss_fn, opt):
+    for epoch_id in range(EPOCH_NUM):
+        for batch_id, (image, label) in enumerate(loader()):
+            out = layer(image)
+            loss = loss_fn(out, label)
+            loss.backward()
+            opt.step()
+            opt.clear_grad()
+            print("Epoch {} batch {}: loss = {}".format(epoch_id, batch_id,
+                                                        np.mean(loss.numpy())))
+    return loss
+
+
+class TestTranslatedLayer(unittest.TestCase):
+    def setUp(self):
+        # enable dygraph mode
+        place = paddle.CPUPlace()
+        paddle.disable_static(place)
+
+        # config seed
+        paddle.manual_seed(SEED)
+        paddle.framework.random._manual_program_seed(SEED)
+
+        # create network
+        self.layer = LinearNet()
+        self.loss_fn = nn.CrossEntropyLoss()
+        self.sgd = opt.SGD(learning_rate=0.001,
+                           parameters=self.layer.parameters())
+
+        # create data loader
+        dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
+        self.loader = paddle.io.DataLoader(
+            dataset,
+            places=place,
+            batch_size=BATCH_SIZE,
+            shuffle=True,
+            drop_last=True,
+            num_workers=2)
+
+        # train
+        train(self.layer, self.loader, self.loss_fn, self.sgd)
+
+        # save
+        self.model_path = "linear.example.model"
+        paddle.jit.save(self.layer, self.model_path)
+
+    def test_inference_and_fine_tuning(self):
+        self.load_and_inference()
+        self.load_and_fine_tuning()
+
+    def load_and_inference(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        # inference
+        x = paddle.randn([1, IMAGE_SIZE], 'float32')
+
+        self.layer.eval()
+        orig_pred = self.layer(x)
+
+        translated_layer.eval()
+        pred = translated_layer(x)
+
+        self.assertTrue(np.array_equal(orig_pred.numpy(), pred.numpy()))
+
+    def load_and_fine_tuning(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        # train original layer continue
+        self.layer.train()
+        orig_loss = train(self.layer, self.loader, self.loss_fn, self.sgd)
+
+        # fine-tuning
+        translated_layer.train()
+        sgd = opt.SGD(learning_rate=0.001,
+                      parameters=translated_layer.parameters())
+        loss = train(translated_layer, self.loader, self.loss_fn, sgd)
+
+        self.assertTrue(
+            np.array_equal(orig_loss.numpy(), loss.numpy()),
+            msg="original loss:\n{}\nnew loss:\n{}\n".format(orig_loss.numpy(),
+                                                             loss.numpy()))
+
+    def test_get_program(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        program = translated_layer.program()
+        self.assertTrue(isinstance(program, paddle.static.Program))
+
+    def test_get_program_method_not_exists(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        with self.assertRaises(ValueError):
+            program = translated_layer.program('not_exists')
+
+
+if __name__ == '__main__':
+    unittest.main()