add se_resnext for dygraph (#2596)

* fix typo

* fix reademe

* fix init_path for test

* add se_resnext for dygraph

dygraph/se_resnext/README.md

+SE_ResNeXt
+===========
+
+简介
+--------
+SE (Sequeeze-and-Excitation) block 并不是一个完整的网络结构，而是一个子结构，可以嵌到其他分类或检测模型中。SENet block 和 ResNeXt 的结合在 ILSVRC 2017 的分类项目中取得 了第一名的成绩。在 ImageNet 数据集上将 top-5 错误率从原先的最好成绩 2.991% 降低到 2.251%。
+
+运行本目录下的程序示例需要使用PaddlePaddle develop最新版本。如果您的PaddlePaddle安装版本低于此要求，请按照[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)中的说明更新PaddlePaddle安装版本。
+
+
+## 代码结构
+```
+└── train.py     # 训练脚本。
+```
+
+## 使用的数据
+
+教程中使用`paddle.dataset.flowers`数据集作为训练数据，该数据集通过`paddle.dataset`模块自动下载到本地。
+
+## 训练测试Residual Network
+
+在GPU单卡上训练 Network:
+
+```
+env CUDA_VISIBLE_DEVICES=0 python train.py
+```
+
+这里`CUDA_VISIBLE_DEVICES=0`表示是执行在0号设备卡上，请根据自身情况修改这个参数。
+
+
+
+## 输出
+执行训练开始后，将得到类似如下的输出。每一轮`batch`训练将会打印当前epoch、step以及loss值。当前默认执行`epoch=10`, `batch_size=64`。您可以调整参数以得到更好的训练效果，同时也意味着消耗更多的内存（显存）以及需要花费更长的时间。
+
+```text
+epoch 0 | batch step 0, loss 4.594 acc1 0.000 acc5 0.078 lr 0.01250
+epoch 0 | batch step 10, loss 4.499 acc1 0.067 acc5 0.153 lr 0.01250
+epoch 0 | batch step 20, loss 4.536 acc1 0.051 acc5 0.139 lr 0.01250
+epoch 0 | batch step 30, loss 4.532 acc1 0.048 acc5 0.141 lr 0.01250
+```

dygraph/se_resnext/train.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
+import sys
+import math
+
+batch_size = 64
+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": "cosine_decay",
+        "batch_size": batch_size,
+        "epochs": [40, 80, 100],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    },
+    "batch_size": batch_size,
+    "lr": 0.0125,
+    "total_images": 6149,
+    "num_epochs":200
+}
+
+momentum_rate = 0.9
+l2_decay = 1.2e-4
+
+def optimizer_setting(params):
+    ls = params["learning_strategy"]
+    if "total_images" not in params:
+        total_images = 6149
+    else:
+        total_images = params["total_images"]
+    
+    batch_size = ls["batch_size"]
+    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"]
+    print("lr:",lr)
+    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))
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 name_scope,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__(name_scope)
+
+        self._conv = Conv2D(
+            "conv2d",
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False,
+	    param_attr=fluid.ParamAttr(name="weights"))
+
+        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)
+        stdv = 1.0/math.sqrt(num_channels*1.0)
+        self._squeeze = FC(
+            self.full_name(),
+            size=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 = FC(
+            self.full_name(),
+            size=num_channels,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv,stdv)),
+            act='sigmoid')
+    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_filters=num_filters,
+            filter_size=1,
+            act="relu")
+        self.conv1 = ConvBNLayer(
+            self.full_name(),
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            groups=cardinality,
+            act="relu")
+        self.conv2 = ConvBNLayer(
+            self.full_name(),
+            num_filters=num_filters * 2,
+            filter_size=1,
+            act=None)
+
+        self.scale = SqueezeExcitation(
+            self.full_name(),
+            num_channels=num_filters * 2,
+            reduction_ratio=reduction_ratio)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                self.full_name(),
+                num_filters=num_filters * 2,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 2
+
+    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, 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_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_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 == 152:
+            cardinality = 64
+            reduction_ratio = 16
+            depth = [3, 8, 36, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                self.full_name(),
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                act='relu')
+            self.conv1 = ConvBNLayer(
+                self.full_name(),
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            self.conv2 = ConvBNLayer(
+                self.full_name(),
+                num_filters=128,
+                filter_size=3,
+                stride=1,
+                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)
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = FC(self.full_name(),
+                      size=class_dim,
+                      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=0.5,seed=100)
+        y = self.out(y)
+        return y
+
+
+def eval(model, data):
+
+    model.eval()
+    batch_size=32
+    total_loss = 0.0
+    total_acc1 = 0.0
+    total_acc5 = 0.0
+    total_sample = 0
+    for batch_id, data in enumerate(data()):
+        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(
+            batch_size, 1)
+
+        img = to_variable(dy_x_data)
+        label = to_variable(y_data)
+        label._stop_gradient = True
+        out = model(img)
+        cost,pred = fluid.layers.softmax_with_cross_entropy(out,label,return_softmax=True)
+        avg_loss = fluid.layers.mean(x=cost)
+
+        acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
+
+        dy_out = avg_loss.numpy()
+
+        total_loss += dy_out
+        total_acc1 += acc_top1.numpy()
+        total_acc5 += acc_top5.numpy()
+        total_sample += 1
+        if batch_id % 10 == 0:
+            print("test | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  ( batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+	    
+    print("final eval loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+          (total_loss / total_sample, \
+           total_acc1 / total_sample, total_acc5 / total_sample))
+
+def train():
+    seed = 90
+    epoch_num = train_parameters["num_epochs"]
+
+    batch_size = train_parameters["batch_size"]
+
+    with fluid.dygraph.guard():
+        fluid.default_startup_program().random_seed = 90
+        fluid.default_main_program().random_seed = 90
+        
+        se_resnext = SeResNeXt("se_resnext")
+        optimizer = optimizer_setting(train_parameters)
+        train_reader = paddle.batch(
+            paddle.dataset.flowers.train(use_xmap=False),
+            batch_size=batch_size,
+            drop_last=True
+            )
+        
+        test_reader = paddle.batch(
+            paddle.dataset.flowers.test(use_xmap=False), batch_size=32)       
+
+        total_loss = 0.0
+        total_acc1 = 0.0
+        total_acc5 = 0.0
+        total_sample = 0
+        for epoch_id in range(epoch_num):
+            for batch_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
+
+                out = se_resnext(img)
+                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)
+
+                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 batch_id % 10 == 0:
+                    print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f lr %0.5f" % \
+                           ( epoch_id, batch_id, total_loss / total_sample, \
+                             total_acc1 / total_sample, total_acc5 / total_sample, lr))
+
+            print("epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  (epoch_id, batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+            se_resnext.eval()
+            eval(se_resnext, test_reader)
+            se_resnext.train()
+
+if __name__ == '__main__':
+    train()