Add distillation demo and doc (#3411)

* add distillation demo and doc

PaddleSlim/classification/distillation/README.md

+>运行该示例前请安装Paddle1.6或更高版本
+
+# 分类模型知识蒸馏示例
+
+## 概述
+
+该示例使用PaddleSlim提供的[蒸馏策略]([https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#3-%E8%92%B8%E9%A6%8F](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#3-蒸馏))对分类模型进行知识蒸馏。
+在阅读该示例前，建议您先了解以下内容：
+
+- [分类模型的常规训练方法](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification)
+- [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
+
+
+## 配置文件说明
+
+关于配置文件如何编写您可以参考：
+
+- [PaddleSlim配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#122-%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E7%9A%84%E4%BD%BF%E7%94%A8)
+- [蒸馏策略配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#23-%E8%92%B8%E9%A6%8F)
+
+这里以MobileNetV2模型为例，MobileNetV2的主要结构为Inverted residuals, 如图1所示：
+
+
+<p align="center">
+<img src="images/mobilenetv2.jpg" height=300 width=600 hspace='10'/> <br />
+<strong>图1</strong>
+</p>
+
+
+首先，为了对`student model`和`teacher model`有个总体的认识，从而进一步确认蒸馏的对象，我们通过以下命令分别观察两个网络变量（Variable）的名称和形状：
+
+```python
+# 观察student model的Variable
+for v in fluid.default_main_program().list_vars():
+    print v.name, v.shape
+```
+```python
+# 观察teacher model的Variable
+for v in teacher_program.list_vars():
+    print v.name, v.shape
+```
+
+经过对比可以发现，`student model`和`teacher model`预测的输出分别为：
+
+```bash
+# student model
+fc_0.tmp_0 (-1, 1000)
+# teacher model
+res50_fc_0.tmp_0 (-1, 1000)
+```
+
+所以，我们用`l2_distiller`对这两个特征图做蒸馏。在配置文件中进行如下配置：
+
+```yaml
+distillers:
+    l2_distiller:
+        class: 'L2Distiller'
+        teacher_feature_map: 'res50_fc_0.tmp_1'
+        student_feature_map: 'fc_0.tmp_1'
+        distillation_loss_weight: 1
+strategies:
+    distillation_strategy:
+        class: 'DistillationStrategy'
+        distillers: ['l2_distiller']
+        start_epoch: 0
+        end_epoch: 130
+```
+
+我们也可以根据上述操作为蒸馏策略选择其他loss，PaddleSlim支持的有`FSP_loss`, `L2_loss`和`softmax_with_cross_entropy_loss` 。
+
+
+## 训练
+
+根据[PaddleCV/image_classification/train.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/train.py)编写压缩脚本compress.py。
+在该脚本中定义了Compressor对象，用于执行压缩任务。
+
+可以通过命令`python compress.py`用默认参数执行压缩任务，通过`python compress.py --help`查看可配置参数，简述如下：
+
+- use_gpu: 是否使用gpu。如果选择使用GPU，请确保当前环境和Paddle版本支持GPU。默认为True。
+- batch_size: 蒸馏训练用的batch size。
+- total_images：使用数据集的训练集总图片数
+- class_dim：使用数据集的类别数。
+- image_shape：使用数据集的图片尺寸。
+- model: 要压缩的目标模型，该示例支持'MobileNetV1', 'MobileNetV2'和'ResNet34'。
+- pretrained_model: student预训练模型的路径，可以从[这里](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E5%B7%B2%E5%8F%91%E5%B8%83%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E6%80%A7%E8%83%BD)下载。
+- teacher_model: teacher模型，该示例支持'ResNet50'。
+- teacher_pretrained_model: teacher预训练模型的路径，可以从[这里](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E5%B7%B2%E5%8F%91%E5%B8%83%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E6%80%A7%E8%83%BD)下载。
+- config_file: 压缩策略的配置文件。
+
+您可以通过运行脚本`run.sh`运行改示例，请确保已正确下载[pretrained model](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E5%B7%B2%E5%8F%91%E5%B8%83%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E6%80%A7%E8%83%BD)。
+
+
+### 保存断点（checkpoint）
+
+如果在配置文件中设置了`checkpoint_path`, 则在压缩任务执行过程中会自动保存断点，当任务异常中断时，
+重启任务会自动从`checkpoint_path`路径下按数字顺序加载最新的checkpoint文件。如果不想让重启的任务从断点恢复，
+需要修改配置文件中的`checkpoint_path`，或者将`checkpoint_path`路径下文件清空。
+
+>注意：配置文件中的信息不会保存在断点中，重启前对配置文件的修改将会生效。
+
+
+## 评估
+
+如果在配置文件中设置了`checkpoint_path`，则每个epoch会保存一个压缩后的用于评估的模型，
+该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__`和`__params__`两个文件。
+其中，`__model__`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
+
+如果不需要保存评估模型，可以在定义Compressor对象时，将`save_eval_model`选项设置为False（默认为True）。
+
+脚本<a href="../eval.py">PaddleSlim/classification/eval.py</a>中为使用该模型在评估数据集上做评估的示例。
+
+## 预测
+
+如果在配置文件中设置了`checkpoint_path`，并且在定义Compressor对象时指定了`prune_infer_model`选项，则每个epoch都会
+保存一个`inference model`。该模型是通过删除eval_program中多余的operators而得到的。
+
+该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__.infer`和`__params__`两个文件。
+其中，`__model__.infer`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
+
+更多关于`prune_infer_model`选项的介绍，请参考：[Compressor介绍](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#121-%E5%A6%82%E4%BD%95%E6%94%B9%E5%86%99%E6%99%AE%E9%80%9A%E8%AE%AD%E7%BB%83%E8%84%9A%E6%9C%AC)
+
+### python预测
+
+在脚本<a href="../infer.py">PaddleSlim/classification/infer.py</a>中展示了如何使用fluid python API加载使用预测模型进行预测。
+
+### PaddleLite
+
+该示例中产出的预测（inference）模型可以直接用PaddleLite进行加载使用。
+关于PaddleLite如何使用，请参考：[PaddleLite使用文档](https://github.com/PaddlePaddle/Paddle-Lite/wiki#%E4%BD%BF%E7%94%A8)
+
+## 示例结果
+
+### MobileNetV1
+
+| FLOPS    | top1_acc/top5_acc |
+| -------- | ----------------- |
+| baseline | 70.99%/89.68%     |
+| 蒸馏后     | -                 |
+
+>训练超参：
+
+### MobileNetV2
+
+| FLOPS    | top1_acc/top5_acc |
+| -------- | ----------------- |
+| baseline | 72.15%/90.65%     |
+| 蒸馏后     | -                 |
+
+>训练超参：
+
+### ResNet34
+
+| FLOPS    | top1_acc/top5_acc |
+| -------- | ----------------- |
+| baseline | 74.57%/92.14%     |
+| 蒸馏后     | -                 |
+
+>训练超参：
+
+## FAQ

PaddleSlim/classification/distillation/compress.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import time
+import sys
+import logging
+import paddle
+import argparse
+import functools
+import paddle.fluid as fluid
+sys.path.append("..")
+import imagenet_reader as reader
+import models
+sys.path.append("../../")
+from utility import add_arguments, print_arguments
+
+from paddle.fluid.contrib.slim import Compressor
+
+logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
+_logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('batch_size',       int,  64*4,                 "Minibatch size.")
+add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
+add_arg('total_images',     int,  1281167,              "Training image number.")
+add_arg('class_dim',        int,  1000,                "Class number.")
+add_arg('image_shape',      str,  "3,224,224",         "Input image size")
+add_arg('model',            str,  "MobileNet",          "Set the network to use.")
+add_arg('pretrained_model', str,  None,                "Whether to use pretrained model.")
+add_arg('teacher_model',    str,  None,          "Set the teacher network to use.")
+add_arg('teacher_pretrained_model', str,  None,                "Whether to use pretrained model.")
+add_arg('compress_config',  str,  None,                 "The config file for compression with yaml format.")
+add_arg('quant_only',       bool, False,                "Only do quantization-aware training.")
+# yapf: enable
+
+model_list = [m for m in dir(models) if "__" not in m]
+
+
+def compress(args):
+    image_shape = [int(m) for m in args.image_shape.split(",")]
+
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
+    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    # model definition
+    model = models.__dict__[args.model]()
+
+    if args.model is "GoogleNet":
+        out0, out1, out2 = model.net(input=image, class_dim=args.class_dim)
+        cost0 = fluid.layers.cross_entropy(input=out0, label=label)
+        cost1 = fluid.layers.cross_entropy(input=out1, label=label)
+        cost2 = fluid.layers.cross_entropy(input=out2, label=label)
+        avg_cost0 = fluid.layers.mean(x=cost0)
+        avg_cost1 = fluid.layers.mean(x=cost1)
+        avg_cost2 = fluid.layers.mean(x=cost2)
+        avg_cost = avg_cost0 + 0.3 * avg_cost1 + 0.3 * avg_cost2
+        acc_top1 = fluid.layers.accuracy(input=out0, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
+    else:
+        if args.model == 'ResNet34':
+            model.prefix_name = 'res34'
+            out = model.net(input=image,
+                            class_dim=args.class_dim,
+                            fc_name='fc_0')
+        else:
+            out = model.net(input=image, class_dim=args.class_dim)
+        cost = fluid.layers.cross_entropy(input=out, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+        acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+    #print("="*50+"student_model_params"+"="*50)
+    #for v in fluid.default_main_program().list_vars():
+    #    print(v.name, v.shape)
+    val_program = fluid.default_main_program().clone()
+    if args.quant_only:
+        boundaries = [
+            args.total_images / args.batch_size * 10,
+            args.total_images / args.batch_size * 16
+        ]
+        values = [1e-4, 1e-5, 1e-6]
+    else:
+        boundaries = [
+            args.total_images / args.batch_size * 30, args.total_images /
+            args.batch_size * 60, args.total_images / args.batch_size * 90
+        ]
+        values = [0.1, 0.01, 0.001, 0.0001]
+    opt = fluid.optimizer.Momentum(
+        momentum=0.9,
+        learning_rate=fluid.layers.piecewise_decay(
+            boundaries=boundaries, values=values),
+        regularization=fluid.regularizer.L2Decay(4e-5))
+
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    if args.pretrained_model:
+
+        def if_exist(var):
+            return os.path.exists(os.path.join(args.pretrained_model, var.name))
+
+        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
+
+    val_reader = paddle.batch(
+        reader.val(data_dir='../data/ILSVRC2012'), batch_size=args.batch_size)
+    val_feed_list = [('image', image.name), ('label', label.name)]
+    val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5', acc_top5.name)]
+
+    train_reader = paddle.batch(
+        reader.train(data_dir='../data/ILSVRC2012'),
+        batch_size=args.batch_size,
+        drop_last=True)
+    train_feed_list = [('image', image.name), ('label', label.name)]
+    train_fetch_list = [('loss', avg_cost.name)]
+
+    teacher_programs = []
+    distiller_optimizer = None
+    if args.teacher_model:
+        teacher_model = models.__dict__[args.teacher_model](prefix_name='res50')
+        # define teacher program
+        teacher_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.program_guard(teacher_program, startup_program):
+            img = teacher_program.global_block()._clone_variable(
+                image, force_persistable=False)
+            predict = teacher_model.net(img,
+                                        class_dim=args.class_dim,
+                                        fc_name='fc_0')
+        #print("="*50+"teacher_model_params"+"="*50)
+        #for v in teacher_program.list_vars():
+        #    print(v.name, v.shape)
+        exe.run(startup_program)
+        assert args.teacher_pretrained_model and os.path.exists(
+            args.teacher_pretrained_model
+        ), "teacher_pretrained_model should be set when teacher_model is not None."
+
+        def if_exist(var):
+            return os.path.exists(
+                os.path.join(args.teacher_pretrained_model, var.name))
+
+        fluid.io.load_vars(
+            exe,
+            args.teacher_pretrained_model,
+            main_program=teacher_program,
+            predicate=if_exist)
+
+        distiller_optimizer = opt
+        teacher_programs.append(teacher_program.clone(for_test=True))
+
+    com_pass = Compressor(
+        place,
+        fluid.global_scope(),
+        fluid.default_main_program(),
+        train_reader=train_reader,
+        train_feed_list=train_feed_list,
+        train_fetch_list=train_fetch_list,
+        eval_program=val_program,
+        eval_reader=val_reader,
+        eval_feed_list=val_feed_list,
+        eval_fetch_list=val_fetch_list,
+        teacher_programs=teacher_programs,
+        save_eval_model=True,
+        prune_infer_model=[[image.name], [out.name]],
+        train_optimizer=opt,
+        distiller_optimizer=distiller_optimizer)
+    com_pass.config(args.compress_config)
+    com_pass.run()
+
+
+def main():
+    args = parser.parse_args()
+    print_arguments(args)
+    compress(args)
+
+
+if __name__ == '__main__':
+    main()

PaddleSlim/classification/distillation/configs/mobilenetv1_resnet50_distillation.yaml

+version: 1.0
+distillers:
+    fsp_distiller:
+        class: 'FSPDistiller'
+        teacher_pairs: [['res50_res2a_branch2a.conv2d.output.1.tmp_0', 'res50_res3a_branch2a.conv2d.output.1.tmp_0']]
+        student_pairs: [['depthwise_conv2d_1.tmp_0', 'conv2d_3.tmp_0']]
+        distillation_loss_weight: 1
+    l2_distiller:
+        class: 'L2Distiller'
+        teacher_feature_map: 'res50_fc_0.tmp_0'
+        student_feature_map: 'fc_0.tmp_0'
+        distillation_loss_weight: 1
+strategies:
+    distillation_strategy:
+        class: 'DistillationStrategy'
+        distillers: ['fsp_distiller', 'l2_distiller']
+        start_epoch: 0
+        end_epoch: 130
+compressor:
+    epoch: 130
+    checkpoint_path: './checkpoints/'
+    strategies:
+        - distillation_strategy

PaddleSlim/classification/distillation/configs/mobilenetv2_resnet50_distillation.yaml

+version: 1.0
+distillers:
+    l2_distiller:
+        class: 'L2Distiller'
+        teacher_feature_map: 'res50_fc_0.tmp_1'
+        student_feature_map: 'fc_0.tmp_1'
+        distillation_loss_weight: 1
+strategies:
+    distillation_strategy:
+        class: 'DistillationStrategy'
+        distillers: ['l2_distiller']
+        start_epoch: 0
+        end_epoch: 130
+compressor:
+    epoch: 130
+    checkpoint_path: './checkpoints/'
+    strategies:
+        - distillation_strategy

PaddleSlim/classification/distillation/configs/resnet34_resnet50_distillation.yaml

+version: 1.0
+distillers:
+    fsp_distiller:
+        class: 'FSPDistiller'
+        teacher_pairs: [['res50_res2a_branch2a.conv2d.output.1.tmp_0', 'res50_res2a_branch2c.conv2d.output.1.tmp_0'], ['res50_res3b_branch2a.conv2d.output.1.tmp_0', 'res50_res3b_branch2c.conv2d.output.1.tmp_0']]
+        student_pairs: [['res34_res2a_branch2a.conv2d.output.1.tmp_0', 'res34_res2a_branch2c.conv2d.output.1.tmp_0'], ['res34_res3b_branch2a.conv2d.output.1.tmp_0', 'res34_res3b_branch2c.conv2d.output.1.tmp_0']]
+        distillation_loss_weight: 1
+    l2_distiller:
+        class: 'L2Distiller'
+        teacher_feature_map: 'res50_fc_0.tmp_0'
+        student_feature_map: 'res34_fc_0.tmp_0'
+        distillation_loss_weight: 1
+strategies:
+    distillation_strategy:
+        class: 'DistillationStrategy'
+        distillers: ['fsp_distiller', 'l2_distiller']
+        start_epoch: 0
+        end_epoch: 130
+compressor:
+    epoch: 130
+    checkpoint_path: './checkpoints/'
+    strategies:
+        - distillation_strategy

PaddleSlim/classification/distillation/run.sh

+#!/usr/bin/env bash
+
+# download pretrain model
+root_url="http://paddle-imagenet-models-name.bj.bcebos.com"
+MobileNetV1="MobileNetV1_pretrained.tar"
+MobileNetV2="MobileNetV2_pretrained.tar"
+ResNet34="ResNet34_pretrained.tar"
+ResNet50="ResNet50_pretrained.tar"
+pretrain_dir='../pretrain'
+
+if [ ! -d ${pretrain_dir} ]; then
+  mkdir ${pretrain_dir}
+fi
+
+cd ${pretrain_dir}
+
+if [ ! -f ${MobileNetV2} ]; then
+    wget ${root_url}/${MobileNetV2}
+    tar xf ${MobileNetV2}
+fi
+
+if [ ! -f ${ResNet34} ]; then
+    wget ${root_url}/${ResNet34}
+    tar xf ${ResNet34}
+fi
+if [ ! -f ${ResNet50} ]; then
+    wget ${root_url}/${ResNet50}
+    tar xf ${ResNet50}
+fi
+
+cd -
+
+# enable GC strategy
+export FLAGS_fast_eager_deletion_mode=1
+export FLAGS_eager_delete_tensor_gb=0.0
+
+# for distillation
+#-----------------
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+# for mobilenet_v1 distillation
+cd ${pretrain_dir}/ResNet50_pretrained
+for files in $(ls res50_*)
+    do mv $files ${files#*_}
+done
+for files in $(ls *)
+    do mv $files "res50_"$files
+done
+cd -
+
+python -u compress.py \
+--model "MobileNet" \
+--teacher_model "ResNet50" \
+--teacher_pretrained_model ../pretrain/ResNet50_pretrained \
+--compress_config ./configs/mobilenetv1_resnet50_distillation.yaml \
+> mobilenet_v1.log 2>&1 &
+tailf mobilenet_v1.log
+
+cd ${pretrain_dir}/ResNet50_pretrained
+for files in $(ls res50_*)
+    do mv $files ${files#*_}
+done
+cd -
+
+# for mobilenet_v2 distillation
+#cd ${pretrain_dir}/ResNet50_pretrained
+#for files in $(ls res50_*)
+#    do mv $files ${files#*_}
+#done
+#for files in $(ls *)
+#    do mv $files "res50_"$files
+#done
+#cd -
+#
+#python -u compress.py \
+#--model "MobileNetV2" \
+#--teacher_model "ResNet50" \
+#--teacher_pretrained_model ../pretrain/ResNet50_pretrained \
+#--compress_config ./configs/mobilenetv2_resnet50_distillation.yaml\
+#> mobilenet_v2.log 2>&1 &
+#tailf mobilenet_v2.log
+#
+#cd ${pretrain_dir}/ResNet50_pretrained
+#for files in $(ls res50_*)
+#    do mv $files ${files#*_}
+#done
+#cd -
+
+# for resnet34 distillation
+#cd ${pretrain_dir}/ResNet50_pretrained
+#for files in $(ls res50_*)
+#    do mv $files ${files#*_}
+#done
+#for files in $(ls *)
+#    do mv $files "res50_"$files
+#done
+#cd -
+#
+#cd ${pretrain_dir}/ResNet34_pretrained
+#for files in $(ls res34_*)
+#    do mv $files ${files#*_}
+#done
+#for files in $(ls *)
+#    do mv $files "res34_"$files
+#done
+#cd -
+#
+#python compress.py \
+#--model "ResNet34" \
+#--teacher_model "ResNet50" \
+#--teacher_pretrained_model ../pretrain/ResNet50_pretrained \
+#--compress_config ./configs/resnet34_resnet50_distillation.yaml \
+#> resnet34.log 2>&1 &
+#tailf resnet34.log
+#
+#cd ${pretrain_dir}/ResNet50_pretrained
+#for files in $(ls res50_*)
+#    do mv $files ${files#*_}
+#done
+#cd -
+#
+#cd ${pretrain_dir}/ResNet34_pretrained
+#for files in $(ls res34_*)
+#    do mv $files ${files#*_}
+#done
+#cd -
+

PaddleSlim/classification/models/__init__.py

 from .mobilenet import MobileNet
-from .resnet import ResNet50
+from .resnet import ResNet34, ResNet50
 from .mobilenet_v2 import MobileNetV2
 
-__all__=['MobileNet', 'ResNet50', 'MobileNetV2']
+__all__ = ['MobileNet', 'ResNet34', 'ResNet50', 'MobileNetV2']

PaddleSlim/classification/models/resnet.py

@@ -6,7 +6,7 @@ import paddle.fluid as fluid
 import math
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ["ResNet", "ResNet50", "ResNet101", "ResNet152"]
+__all__ = ["ResNet", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
 
 train_parameters = {
     "input_size": [3, 224, 224],
@@ -22,17 +22,19 @@ train_parameters = {
 
 
 class ResNet():
-    def __init__(self, layers=50):
+    def __init__(self, layers=50, prefix_name=''):
         self.params = train_parameters
         self.layers = layers
+        self.prefix_name = prefix_name
 
     def net(self, input, class_dim=1000, conv1_name='conv1', fc_name=None):
         layers = self.layers
-        supported_layers = [50, 101, 152]
+        prefix_name = self.prefix_name + '_'
+        supported_layers = [34, 50, 101, 152]
         assert layers in supported_layers, \
             "supported layers are {} but input layer is {}".format(supported_layers, layers)
 
-        if layers == 50:
+        if layers == 34 or layers == 50:
             depth = [3, 4, 6, 3]
         elif layers == 101:
             depth = [3, 4, 23, 3]
@@ -48,7 +50,7 @@ class ResNet():
             filter_size=7,
             stride=2,
             act='relu',
-            name=conv1_name)
+            name=prefix_name + conv1_name)
         conv = fluid.layers.pool2d(
             input=conv,
             pool_size=3,
@@ -65,6 +67,7 @@ class ResNet():
                         conv_name = "res" + str(block + 2) + "b" + str(i)
                 else:
                     conv_name = "res" + str(block + 2) + chr(97 + i)
+                conv_name = prefix_name + conv_name
                 conv = self.bottleneck_block(
                     input=conv,
                     num_filters=num_filters[block],
@@ -77,7 +80,7 @@ class ResNet():
         out = fluid.layers.fc(input=pool,
                               size=class_dim,
                               act='softmax',
-                              name=fc_name,
+                              name=prefix_name + fc_name,
                               param_attr=fluid.param_attr.ParamAttr(
                                   initializer=fluid.initializer.Uniform(-stdv,
                                                                         stdv)))
@@ -102,10 +105,17 @@ class ResNet():
             param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False,
             name=name + '.conv2d.output.1')
-        if name == "conv1":
-            bn_name = "bn_" + name
+        if self.prefix_name == '':
+            if name == "conv1":
+                bn_name = "bn_" + name
+            else:
+                bn_name = "bn" + name[3:]
         else:
-            bn_name = "bn" + name[3:]
+            if name.split("_")[1] == "conv1":
+                bn_name = name.split("_", 1)[0] + "_bn_" + name.split("_", 1)[1]
+            else:
+                bn_name = name.split("_", 1)[0] + "_bn" + name.split("_",
+                                                                     1)[1][3:]
         return fluid.layers.batch_norm(
             input=conv,
             act=act,
@@ -150,8 +160,13 @@ class ResNet():
             x=short, y=conv2, act='relu', name=name + ".add.output.5")
 
 
-def ResNet50():
-    model = ResNet(layers=50)
+def ResNet34(prefix_name=''):
+    model = ResNet(layers=34, prefix_name=prefix_name)
+    return model
+
+
+def ResNet50(prefix_name=''):
+    model = ResNet(layers=50, prefix_name=prefix_name)
     return model