Add unittest with mnist model to test dygraph_to_static (#22777)

* add mnist to test dygraph_to_static  test=develop

python/paddle/fluid/tests/unittests/dygraph_to_static/test_mnist.py

+#   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
+from time import time
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import AdamOptimizer
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
+
+from paddle.fluid.dygraph.jit import dygraph_to_static_output
+
+import unittest
+
+
+class SimpleImgConvPool(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 pool_size,
+                 pool_stride,
+                 pool_padding=0,
+                 pool_type='max',
+                 global_pooling=False,
+                 conv_stride=1,
+                 conv_padding=0,
+                 conv_dilation=1,
+                 conv_groups=1,
+                 act=None,
+                 use_cudnn=False,
+                 param_attr=None,
+                 bias_attr=None):
+        super(SimpleImgConvPool, self).__init__()
+
+        self._conv2d = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=conv_stride,
+            padding=conv_padding,
+            dilation=conv_dilation,
+            groups=conv_groups,
+            param_attr=None,
+            bias_attr=None,
+            act=act,
+            use_cudnn=use_cudnn)
+
+        self._pool2d = Pool2D(
+            pool_size=pool_size,
+            pool_type=pool_type,
+            pool_stride=pool_stride,
+            pool_padding=pool_padding,
+            global_pooling=global_pooling,
+            use_cudnn=use_cudnn)
+
+    @dygraph_to_static_output
+    def forward(self, inputs):
+        x = self._conv2d(inputs)
+        x = self._pool2d(x)
+        return x
+
+
+class MNIST(fluid.dygraph.Layer):
+    def __init__(self):
+        super(MNIST, self).__init__()
+
+        self._simple_img_conv_pool_1 = SimpleImgConvPool(
+            1, 20, 5, 2, 2, act="relu")
+
+        self._simple_img_conv_pool_2 = SimpleImgConvPool(
+            20, 50, 5, 2, 2, act="relu")
+
+        self.pool_2_shape = 50 * 4 * 4
+        SIZE = 10
+        scale = (2.0 / (self.pool_2_shape**2 * SIZE))**0.5
+        self._fc = Linear(
+            self.pool_2_shape,
+            10,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.NormalInitializer(
+                    loc=0.0, scale=scale)),
+            act="softmax")
+
+    @dygraph_to_static_output
+    def forward(self, inputs, label=None):
+        x = self.inference(inputs)
+        if label is not None:
+            acc = fluid.layers.accuracy(input=x, label=label)
+            loss = fluid.layers.cross_entropy(x, label)
+            avg_loss = fluid.layers.mean(loss)
+            return x, acc, avg_loss
+        else:
+            return x
+
+    @dygraph_to_static_output
+    def inference(self, inputs):
+        x = self._simple_img_conv_pool_1(inputs)
+        x = self._simple_img_conv_pool_2(x)
+        x = fluid.layers.reshape(x, shape=[-1, self.pool_2_shape])
+        x = self._fc(x)
+        return x
+
+
+class TestMNIST(unittest.TestCase):
+    def setUp(self):
+        self.epoch_num = 1
+        self.batch_size = 64
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        self.train_reader = paddle.batch(
+            paddle.dataset.mnist.train(),
+            batch_size=self.batch_size,
+            drop_last=True)
+
+
+class TestMNISTWithStaticMode(TestMNIST):
+    """
+    Tests model when using `dygraph_to_static_output` to convert dygraph into static
+    model. It allows user to add customized code to train static model, such as `with`
+    and `Executor` statement.
+    """
+
+    def test_train(self):
+
+        main_prog = fluid.Program()
+        with fluid.program_guard(main_prog):
+            mnist = MNIST()
+            adam = AdamOptimizer(
+                learning_rate=0.001, parameter_list=mnist.parameters())
+
+            exe = fluid.Executor(self.place)
+            start = time()
+
+            img = fluid.data(
+                name='img', shape=[None, 1, 28, 28], dtype='float32')
+            label = fluid.data(name='label', shape=[None, 1], dtype='int64')
+            label.stop_gradient = True
+
+            prediction, acc, avg_loss = mnist(img, label)
+            adam.minimize(avg_loss)
+        exe.run(fluid.default_startup_program())
+
+        for epoch in range(self.epoch_num):
+            for batch_id, data in enumerate(self.train_reader()):
+                dy_x_data = np.array([x[0].reshape(1, 28, 28)
+                                      for x in data]).astype('float32')
+                y_data = np.array(
+                    [x[1] for x in data]).astype('int64').reshape(-1, 1)
+
+                out = exe.run(main_prog,
+                              fetch_list=[avg_loss, acc],
+                              feed={'img': dy_x_data,
+                                    'label': y_data})
+                if batch_id % 100 == 0:
+                    print(
+                        "Loss at epoch {} step {}: loss: {:}, acc: {}, cost: {}"
+                        .format(epoch, batch_id,
+                                np.array(out[0]),
+                                np.array(out[1]), time() - start))
+                    if batch_id == 300:
+                        # The accuracy of mnist should converge over 0.9 after 300 batch.
+                        accuracy = np.array(out[1])
+                        self.assertGreater(
+                            accuracy,
+                            0.9,
+                            msg="The accuracy {} of mnist should converge over 0.9 after 300 batch."
+                            .format(accuracy))
+                        break
+
+
+# TODO: TestCase with cached program is required when building program in `for` loop.
+
+if __name__ == "__main__":
+    unittest.main()