Add unittest for se_resnet in dygraph_to_static. test=develop (#23566)

python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.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.
+
+import logging
+import math
+import numpy as np
+import time
+import unittest
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+from paddle.fluid.dygraph.jit import dygraph_to_static_func
+
+SEED = 2020
+np.random.seed(SEED)
+
+BATCH_SIZE = 8
+EPOCH_NUM = 1
+PRINT_STEP = 2
+STEP_NUM = 10
+
+place = fluid.CPUPlace()
+# TODO(liym27): Diff exists between dygraph and static graph on CUDA place.
+# place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
+
+train_parameters = {
+    "learning_strategy": {
+        "name": "cosine_decay",
+        "batch_size": BATCH_SIZE,
+        "epochs": [40, 80, 100],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    },
+    "lr": 0.0125,
+    "total_images": 6149,
+    "momentum_rate": 0.9,
+    "l2_decay": 1.2e-4,
+    "num_epochs": 1,
+}
+
+
+def optimizer_setting(params, parameter_list):
+    ls = params["learning_strategy"]
+    if "total_images" not in params:
+        total_images = 6149
+    else:
+        total_images = params["total_images"]
+
+    batch_size = ls["batch_size"]
+    l2_decay = params["l2_decay"]
+    momentum_rate = params["momentum_rate"]
+
+    step = int(math.ceil(float(total_images) / batch_size))
+    bd = [step * e for e in ls["epochs"]]
+    lr = params["lr"]
+    num_epochs = params["num_epochs"]
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=fluid.layers.cosine_decay(
+            learning_rate=lr, step_each_epoch=step, epochs=num_epochs),
+        momentum=momentum_rate,
+        regularization=fluid.regularizer.L2Decay(l2_decay),
+        parameter_list=parameter_list)
+
+    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_func
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class SqueezeExcitation(fluid.dygraph.Layer):
+    def __init__(self, num_channels, reduction_ratio):
+
+        super(SqueezeExcitation, self).__init__()
+        self._num_channels = num_channels
+        self._pool = Pool2D(pool_size=0, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(num_channels * 1.0)
+        self._fc = Linear(
+            num_channels,
+            num_channels // reduction_ratio,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)),
+            act='relu')
+        stdv = 1.0 / math.sqrt(num_channels / 16.0 * 1.0)
+        self._excitation = Linear(
+            num_channels // reduction_ratio,
+            num_channels,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)),
+            act='sigmoid')
+
+    @dygraph_to_static_func
+    def forward(self, input):
+        y = self._pool(input)
+        y = fluid.layers.reshape(y, shape=[-1, self._num_channels])
+        y = self._fc(y)
+        y = self._excitation(y)
+        y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 cardinality,
+                 reduction_ratio,
+                 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,
+            groups=cardinality,
+            act="relu")
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 2,
+            filter_size=1,
+            act=None)
+
+        self.scale = SqueezeExcitation(
+            num_channels=num_filters * 2, reduction_ratio=reduction_ratio)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 2,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 2
+
+    @dygraph_to_static_func
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+        scale = self.scale(conv2)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=scale, act='relu')
+        return y
+
+
+class SeResNeXt(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(SeResNeXt, 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:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 6, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+        elif layers == 101:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 23, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+        elif layers == 152:
+            cardinality = 64
+            reduction_ratio = 16
+            depth = [3, 8, 36, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                act='relu')
+            self.conv1 = ConvBNLayer(
+                num_channels=64,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            self.conv2 = ConvBNLayer(
+                num_channels=64,
+                num_filters=128,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.bottleneck_block_list = []
+        num_channels = 64
+        if layers == 152:
+            num_channels = 128
+        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,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        cardinality=cardinality,
+                        reduction_ratio=reduction_ratio,
+                        shortcut=shortcut))
+                num_channels = bottleneck_block._num_channels_out
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 2 * 1 * 1
+
+        self.out = Linear(
+            self.pool2d_avg_output,
+            class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    @dygraph_to_static_func
+    def forward(self, inputs, label):
+        if self.layers == 50 or self.layers == 101:
+            y = self.conv0(inputs)
+            y = self.pool(y)
+        elif self.layers == 152:
+            y = self.conv0(inputs)
+            y = self.conv1(y)
+            y = self.conv2(y)
+            y = self.pool(y)
+
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.dropout(y, dropout_prob=0.5, seed=100)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        out = self.out(y)
+
+        softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+        loss = fluid.layers.cross_entropy(input=softmax_out, label=label)
+        avg_loss = fluid.layers.mean(x=loss)
+
+        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
+        return out, avg_loss, acc_top1, acc_top5
+
+
+def train_dygraph(train_reader):
+    np.random.seed(SEED)
+
+    with fluid.dygraph.guard(place):
+        fluid.default_startup_program().random_seed = SEED
+        fluid.default_main_program().random_seed = SEED
+        se_resnext = SeResNeXt()
+        optimizer = optimizer_setting(train_parameters, se_resnext.parameters())
+
+        for epoch_id in range(EPOCH_NUM):
+            total_loss = 0.0
+            total_acc1 = 0.0
+            total_acc5 = 0.0
+            total_sample = 0
+            step_idx = 0
+            speed_list = []
+            for step_id, data in enumerate(train_reader()):
+                dy_x_data = np.array([x[0].reshape(3, 224, 224)
+                                      for x in data]).astype('float32')
+                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+                    BATCH_SIZE, 1)
+
+                img = to_variable(dy_x_data)
+                label = to_variable(y_data)
+                label.stop_gradient = True
+
+                pred, avg_loss, acc_top1, acc_top5 = se_resnext(img, label)
+
+                dy_out = avg_loss.numpy()
+                avg_loss.backward()
+
+                optimizer.minimize(avg_loss)
+                se_resnext.clear_gradients()
+
+                lr = optimizer._global_learning_rate().numpy()
+                total_loss += dy_out
+                total_acc1 += acc_top1.numpy()
+                total_acc5 += acc_top5.numpy()
+                total_sample += 1
+                if step_id % PRINT_STEP == 0:
+                    if step_id == 0:
+                        logging.info( "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f" % \
+                                      ( epoch_id, step_id, total_loss / total_sample, \
+                                        total_acc1 / total_sample, total_acc5 / total_sample))
+                        avg_batch_time = time.time()
+                    else:
+                        speed = PRINT_STEP / (time.time() - avg_batch_time)
+                        speed_list.append(speed)
+                        logging.info( "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, speed %.3f steps/s" % \
+                                      ( epoch_id, step_id, total_loss / total_sample, \
+                                        total_acc1 / total_sample, total_acc5 / total_sample, speed))
+                        avg_batch_time = time.time()
+
+                step_idx += 1
+                if step_idx == STEP_NUM:
+                    break
+        return pred.numpy(), avg_loss.numpy(), acc_top1.numpy(), acc_top5.numpy(
+        )
+
+
+def train_static(train_reader):
+    np.random.seed(SEED)
+    exe = fluid.Executor(place)
+
+    main_prog = fluid.Program()
+    startup_prog = fluid.Program()
+    with fluid.program_guard(main_prog, startup_prog):
+        with fluid.unique_name.guard():
+            main_prog.random_seed = SEED
+            startup_prog.random_seed = SEED
+            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
+
+            se_resnext = SeResNeXt()
+            pred, avg_loss_, acc_top1_, acc_top5_ = se_resnext(img, label)
+
+            optimizer = optimizer_setting(train_parameters,
+                                          se_resnext.parameters())
+            optimizer.minimize(avg_loss_)
+
+    exe.run(startup_prog)
+
+    for epoch_id in range(EPOCH_NUM):
+        total_loss = 0.0
+        total_acc1 = 0.0
+        total_acc5 = 0.0
+        total_sample = 0
+        step_idx = 0
+        speed_list = []
+        for step_id, data in enumerate(train_reader()):
+            dy_x_data = np.array([x[0].reshape(3, 224, 224)
+                                  for x in data]).astype('float32')
+            y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+                BATCH_SIZE, 1)
+
+            pred_, avg_loss, acc_top1, acc_top5 = exe.run(
+                main_prog,
+                feed={"img": dy_x_data,
+                      "label": y_data},
+                fetch_list=[pred, avg_loss_, acc_top1_, acc_top5_])
+
+            total_loss += avg_loss
+            total_acc1 += acc_top1
+            total_acc5 += acc_top5
+            total_sample += 1
+
+            if step_id % PRINT_STEP == 0:
+                if step_id == 0:
+                    logging.info( "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f" % \
+                            ( epoch_id, step_id, total_loss / total_sample, \
+                              total_acc1 / total_sample, total_acc5 / total_sample))
+                    avg_batch_time = time.time()
+                else:
+                    speed = PRINT_STEP / (time.time() - avg_batch_time)
+                    speed_list.append(speed)
+                    logging.info( "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, speed %.3f steps/s" % \
+                           ( epoch_id, step_id, total_loss / total_sample, \
+                             total_acc1 / total_sample, total_acc5 / total_sample, speed))
+                    avg_batch_time = time.time()
+
+            step_idx += 1
+            if step_idx == STEP_NUM:
+                break
+
+        return pred_, avg_loss, acc_top1, acc_top5
+
+
+class TestSeResnet(unittest.TestCase):
+    def setUp(self):
+        self.train_reader = paddle.batch(
+            paddle.dataset.flowers.train(
+                use_xmap=False, cycle=True),
+            batch_size=BATCH_SIZE,
+            drop_last=True)
+
+    def test_check_result(self):
+        pred_1, loss_1, acc1_1, acc5_1 = train_static(self.train_reader)
+        pred_2, loss_2, acc1_2, acc5_2 = train_dygraph(self.train_reader)
+
+        self.assertTrue(
+            np.allclose(pred_1, pred_2),
+            msg="static pred: {} \ndygraph pred: {}".format(pred_1, pred_2))
+        self.assertTrue(
+            np.allclose(loss_1, loss_2),
+            msg="static loss: {} \ndygraph loss: {}".format(loss_1, loss_2))
+        self.assertTrue(
+            np.allclose(acc1_1, acc1_2),
+            msg="static acc1: {} \ndygraph acc1: {}".format(acc1_1, acc1_2))
+        self.assertTrue(
+            np.allclose(acc5_1, acc5_2),
+            msg="static acc5: {} \ndygraph acc5: {}".format(acc5_1, acc5_2))
+
+
+if __name__ == '__main__':
+    unittest.main()