test ResNet decorated by dygraph_to_static_output in static mode. (#22779)

* test ResNet decorated by dygraph_to_static_output in static mode. test=develop

* Follow comments and change dygraph_to_static_output to dygraph_to_static_graph.test=develop

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

+# Copyright (c) 2019 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.
+
+# Copyright (c) 2019 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 paddle
+import paddle.fluid as fluid
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.jit import dygraph_to_static_graph
+
+import unittest
+import time
+import math
+import numpy as np
+
+IMAGENET1000 = 1281167
+base_lr = 0.1
+momentum_rate = 0.9
+l2_decay = 1e-4
+batch_size = 8
+epoch_num = 1
+place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() \
+    else fluid.CPUPlace()
+
+
+def optimizer_setting(parameter_list=None):
+    total_images = IMAGENET1000
+    step = int(math.ceil(float(total_images) / batch_size))
+    epochs = [30, 60, 90]
+    bd = [step * e for e in epochs]
+
+    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
+    if fluid.in_dygraph_mode():
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=fluid.layers.piecewise_decay(
+                boundaries=bd, values=lr),
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay),
+            parameter_list=parameter_list)
+    else:
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=fluid.layers.piecewise_decay(
+                boundaries=bd, values=lr),
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay))
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    @dygraph_to_static_graph
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self, num_channels, num_filters, stride, shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    @dygraph_to_static_graph
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = fluid.layer_helper.LayerHelper(
+            self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.bottleneck_block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block]
+                        if i == 0 else num_filters[block] * 4,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        shortcut=shortcut))
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1
+
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_output,
+            class_dim,
+            act='softmax',
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    @dygraph_to_static_graph
+    def forward(self, inputs, label):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        pred = self.out(y)
+
+        loss = fluid.layers.cross_entropy(input=pred, label=label)
+        avg_loss_ = fluid.layers.mean(x=loss)
+        acc_top1_ = fluid.layers.accuracy(input=pred, label=label, k=1)
+        acc_top5_ = fluid.layers.accuracy(input=pred, label=label, k=5)
+
+        return pred, avg_loss_, acc_top1_, acc_top5_
+
+
+def train_resnet_in_static_mode():
+    """
+    Tests model decorated by `dygraph_to_static_output` in static mode. For users, the model is defined in dygraph mode and trained in static mode.
+    """
+
+    exe = fluid.Executor(place)
+    startup_prog = fluid.Program()
+    main_prog = fluid.Program()
+
+    with fluid.program_guard(main_prog, startup_prog):
+
+        img = fluid.data(name="img", shape=[None, 3, 224, 224], dtype="float32")
+        label = fluid.data(name="label", shape=[None, 1], dtype="int64")
+        label.stop_gradient = True
+        resnet = ResNet()
+        pred, avg_loss_, acc_top1_, acc_top5_ = resnet(img, label)
+        optimizer = optimizer_setting(parameter_list=resnet.parameters())
+        optimizer.minimize(avg_loss_)
+
+    exe.run(startup_prog)
+
+    train_reader = paddle.batch(
+        paddle.dataset.flowers.train(use_xmap=False), batch_size=batch_size)
+
+    for epoch in range(epoch_num):
+        total_loss = 0.0
+        total_acc1 = 0.0
+        total_acc5 = 0.0
+        total_sample = 0
+
+        for batch_id, data in enumerate(train_reader()):
+            start_time = time.time()
+            dy_x_data = np.array(
+                [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
+            if len(np.array([x[1]
+                             for x in data]).astype('int64')) != batch_size:
+                continue
+            y_data = np.array([x[1] for x in data]).astype('int64').reshape(-1,
+                                                                            1)
+
+            avg_loss, acc_top1, acc_top5 = exe.run(
+                main_prog,
+                feed={"img": dy_x_data,
+                      "label": y_data},
+                fetch_list=[avg_loss_, acc_top1_, acc_top5_])
+
+            total_loss += avg_loss
+            total_acc1 += acc_top1
+            total_acc5 += acc_top5
+            total_sample += 1
+
+            end_time = time.time()
+            if batch_id % 2 == 0:
+                print( "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" % \
+                       ( epoch, batch_id, total_loss / total_sample, \
+                         total_acc1 / total_sample, total_acc5 / total_sample, end_time-start_time))
+            if batch_id == 10:
+                break
+
+
+class TestResnet(unittest.TestCase):
+    def test_in_static_mode(self):
+        train_resnet_in_static_mode()
+
+
+if __name__ == '__main__':
+    unittest.main()