add SeResNeXt unittest (#16503)

* add seresnet unittest test=develop

* add dropout layer test=develop

* fix ci test=develop

* fix comment test=develop

* fix comment test=develop

* fix ci test=develop

* fix ci test=develop

* fix ci

* fix module name test=develop

* run imperative serenext unit test serially test=develop

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -78,6 +78,7 @@ list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
 list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op)
 list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
 list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
+list(REMOVE_ITEM TEST_OPS test_imperative_se_resnext)
 list(REMOVE_ITEM TEST_OPS test_imperative_mnist)
 list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
 foreach(TEST_OP ${TEST_OPS})
@@ -91,6 +92,9 @@ py_test_modules(test_imperative_resnet MODULES test_imperative_resnet ENVS
   FLAGS_cudnn_deterministic=1)
 py_test_modules(test_imperative_mnist MODULES test_imperative_mnist ENVS
   FLAGS_cudnn_deterministic=1)
+py_test_modules(test_imperative_se_resnext MODULES test_imperative_se_resnext ENVS
+  FLAGS_cudnn_deterministic=1 SERIAL)
+
 if(WITH_DISTRIBUTE)
     py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
     set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)

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

+# Copyright (c) 2018 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 contextlib
+import unittest
+import numpy as np
+import six
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
+from paddle.fluid.dygraph.base import to_variable
+from test_imperative_base import new_program_scope
+
+batch_size = 8
+train_parameters = {
+    "input_size": [3, 224, 224],
+    "input_mean": [0.485, 0.456, 0.406],
+    "input_std": [0.229, 0.224, 0.225],
+    "learning_strategy": {
+        "name": "piecewise_decay",
+        "batch_size": batch_size,
+        "epochs": [30, 60, 90],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    },
+    "batch_size": batch_size,
+    "lr": 0.1,
+    "total_images": 6149,
+}
+
+
+def optimizer_setting(params):
+    ls = params["learning_strategy"]
+    if ls["name"] == "piecewise_decay":
+        if "total_images" not in params:
+            total_images = 6149
+        else:
+            total_images = params["total_images"]
+        # TODO(Yancey1989): using lr decay if it is ready.
+        #batch_size = ls["batch_size"]
+        #step = int(total_images / batch_size + 1)
+
+        #bd = [step * e for e in ls["epochs"]]
+        #base_lr = params["lr"]
+        #lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
+        optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 name_scope,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__(name_scope)
+
+        self._conv = Conv2D(
+            self.full_name(),
+            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=None)
+
+        self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class SqueezeExcitation(fluid.dygraph.Layer):
+    def __init__(self, name_scope, num_channels, reduction_ratio):
+
+        super(SqueezeExcitation, self).__init__(name_scope)
+        self._pool = Pool2D(
+            self.full_name(), pool_size=0, pool_type='avg', global_pooling=True)
+        self._squeeze = FC(
+            self.full_name(),
+            size=num_channels // reduction_ratio,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=0.05)),
+            act='relu')
+        self._excitation = FC(
+            self.full_name(),
+            size=num_channels,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=0.05)),
+            act='relu')
+
+    def forward(self, input):
+        y = self._pool(input)
+        y = self._squeeze(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,
+                 name_scope,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 cardinality,
+                 reduction_ratio,
+                 shortcut=True):
+        super(BottleneckBlock, self).__init__(name_scope)
+
+        self.conv0 = ConvBNLayer(
+            self.full_name(),
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1)
+        self.conv1 = ConvBNLayer(
+            self.full_name(),
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            groups=cardinality)
+        self.conv2 = ConvBNLayer(
+            self.full_name(),
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act='relu')
+
+        self.scale = SqueezeExcitation(
+            self.full_name(),
+            num_channels=num_filters * 4,
+            reduction_ratio=reduction_ratio)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                self.full_name(),
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    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)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        y = layer_helper.append_activation(y)
+        return y
+
+
+class SeResNeXt(fluid.dygraph.Layer):
+    def __init__(self, name_scope, layers=50, class_dim=102):
+        super(SeResNeXt, self).__init__(name_scope)
+
+        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(
+                self.full_name(),
+                num_channels=3,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                self.full_name(),
+                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(
+                self.full_name(),
+                num_channels=3,
+                num_filters=3,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                self.full_name(),
+                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(
+                self.full_name(),
+                num_channels=3,
+                num_filters=3,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.conv1 = ConvBNLayer(
+                self.full_name(),
+                num_channels=64,
+                num_filters=3,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.conv2 = ConvBNLayer(
+                self.full_name(),
+                num_channels=64,
+                num_filters=3,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                self.full_name(),
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+
+        self.bottleneck_block_list = []
+        num_channels = 64
+        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(
+                        self.full_name(),
+                        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(
+            self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
+        import math
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = FC(self.full_name(),
+                      size=class_dim,
+                      act='softmax',
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        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(inputs)
+            y = self.conv2(inputs)
+            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=0.2)
+        y = self.out(y)
+        return y
+
+
+class TestImperativeResneXt(unittest.TestCase):
+    def test_se_resnext_float32(self):
+        seed = 90
+
+        batch_size = train_parameters["batch_size"]
+        batch_num = 2
+        with fluid.dygraph.guard():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            se_resnext = SeResNeXt("se_resnext")
+            optimizer = optimizer_setting(train_parameters)
+            np.random.seed(seed)
+            import random
+            random.seed = seed
+            train_reader = paddle.batch(
+                paddle.dataset.flowers.train(use_xmap=False),
+                batch_size=batch_size)
+
+            dy_param_init_value = {}
+            for param in se_resnext.parameters():
+                dy_param_init_value[param.name] = param._numpy()
+
+            for batch_id, data in enumerate(train_reader()):
+                if batch_id >= batch_num:
+                    break
+
+                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
+
+                out = se_resnext(img)
+                loss = fluid.layers.cross_entropy(input=out, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+
+                dy_out = avg_loss._numpy()
+
+                if batch_id == 0:
+                    for param in se_resnext.parameters():
+                        if param.name not in dy_param_init_value:
+                            dy_param_init_value[param.name] = param._numpy()
+
+                avg_loss._backward()
+
+                dy_grad_value = {}
+                for param in se_resnext.parameters():
+                    if param.trainable:
+                        np_array = np.array(param._ivar._grad_ivar().value()
+                                            .get_tensor())
+                        dy_grad_value[param.name + core.grad_var_suffix(
+                        )] = np_array
+
+                optimizer.minimize(avg_loss)
+                se_resnext.clear_gradients()
+
+                dy_param_value = {}
+                for param in se_resnext.parameters():
+                    dy_param_value[param.name] = param._numpy()
+
+        with new_program_scope():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+
+            se_resnext = SeResNeXt("se_resnext")
+            optimizer = optimizer_setting(train_parameters)
+
+            np.random.seed(seed)
+            import random
+            random.seed = seed
+            train_reader = paddle.batch(
+                paddle.dataset.flowers.train(use_xmap=False),
+                batch_size=batch_size)
+
+            img = fluid.layers.data(
+                name='pixel', shape=[3, 224, 224], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            out = se_resnext(img)
+            loss = fluid.layers.cross_entropy(input=out, label=label)
+            avg_loss = fluid.layers.mean(x=loss)
+            optimizer.minimize(avg_loss)
+
+            # initialize params and fetch them
+            static_param_init_value = {}
+            static_param_name_list = []
+            static_grad_name_list = []
+            for param in se_resnext.parameters():
+                static_param_name_list.append(param.name)
+            for param in se_resnext.parameters():
+                if param.trainable:
+                    static_grad_name_list.append(param.name +
+                                                 core.grad_var_suffix())
+
+            out = exe.run(fluid.default_startup_program(),
+                          fetch_list=static_param_name_list)
+
+            for i in range(len(static_param_name_list)):
+                static_param_init_value[static_param_name_list[i]] = out[i]
+
+            for batch_id, data in enumerate(train_reader()):
+                if batch_id >= batch_num:
+                    break
+
+                static_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])
+
+                fetch_list = [avg_loss.name]
+                fetch_list.extend(static_param_name_list)
+                fetch_list.extend(static_grad_name_list)
+                out = exe.run(fluid.default_main_program(),
+                              feed={"pixel": static_x_data,
+                                    "label": y_data},
+                              fetch_list=fetch_list)
+
+                static_param_value = {}
+                static_grad_value = {}
+                static_out = out[0]
+                param_start_pos = 1
+                grad_start_pos = len(static_param_name_list) + param_start_pos
+                for i in range(param_start_pos,
+                               len(static_param_name_list) + param_start_pos):
+                    static_param_value[static_param_name_list[
+                        i - param_start_pos]] = out[i]
+                for i in range(grad_start_pos,
+                               len(static_grad_name_list) + grad_start_pos):
+                    static_grad_value[static_grad_name_list[
+                        i - grad_start_pos]] = out[i]
+        self.assertTrue(np.allclose(static_out, dy_out))
+
+        self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
+
+        for key, value in six.iteritems(static_param_init_value):
+            self.assertTrue(np.allclose(value, dy_param_init_value[key]))
+            self.assertTrue(np.isfinite(value.all()))
+            self.assertFalse(np.isnan(value.any()))
+
+        self.assertEqual(len(dy_grad_value), len(static_grad_value))
+        for key, value in six.iteritems(static_grad_value):
+            self.assertTrue(np.allclose(value, dy_grad_value[key]))
+            self.assertTrue(np.isfinite(value.all()))
+            self.assertFalse(np.isnan(value.any()))
+
+        self.assertEqual(len(dy_param_value), len(static_param_value))
+        for key, value in six.iteritems(static_param_value):
+            self.assertTrue(np.allclose(value, dy_param_value[key]))
+            self.assertTrue(np.isfinite(value.all()))
+            self.assertFalse(np.isnan(value.any()))
+
+
+if __name__ == '__main__':
+    unittest.main()