add acnet based on resnet (#4224)

PaddleCV/image_classification/README.md

@@ -751,6 +751,18 @@ python -m paddle.distributed.launch train.py \
 |[HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W48_C_pretrained.tar) | 78.95% | 94.42% | 30.064 | 19.963 |
 |[HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W64_C_pretrained.tar) | 79.30% | 94.61% | 38.921 | 24.742 |
 
+
+### ResNet_ACNet Series
+|Model | Top-1 | Top-5 | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
+|- |:-: |:-: |:-: |:-: |
+|[ResNet50_ACNet](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_pretrained.tar)<sub>1</sub> | 76.71% | 93.24% | 13.205 | 8.804 |
+|[ResNet50_ACNet](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_deploy_pretrained.tar)<sub>2</sub> | 76.71% | 93.24% | 7.418 | 5.950 |
+
+* 注:
+    * `1`. 不对训练模型结果进行参数转换，进行评估。
+    * `2`. 使用`sh ./utils/acnet/convert_model.sh`命令对训练模型结果进行参数转换，并设置`deploy mode=True`，进行评估。
+    * `./utils/acnet/convert_model.sh`包含4个参数，分别是模型名称、输入的模型地址、输出的模型地址以及类别数量。
+
 ## FAQ
 
 **Q:** 加载预训练模型报错，Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:1000 != labels_dims[1]:6.
@@ -793,6 +805,7 @@ python -m paddle.distributed.launch train.py \
 - Res2Net: [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169), Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr
 - HRNet: [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/abs/1908.07919), Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao
 - DARTS: [DARTS: Differentiable Architecture Search](https://arxiv.org/pdf/1806.09055.pdf), Hanxiao Liu, Karen Simonyan, Yiming Yang
+- ACNet: [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://arxiv.org/abs/1908.03930), Xiaohan Ding, Yuchen Guo, Guiguang Ding, Jungong Han
 
 ## 版本更新
 - 2018/12/03 **Stage1**: 更新AlexNet，ResNet50，ResNet101，MobileNetV1

PaddleCV/image_classification/README_en.md

@@ -627,6 +627,18 @@ Pretrained models can be downloaded by clicking related model names.
 |[HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W48_C_pretrained.tar) | 78.95% | 94.42% | 30.064 | 19.963 |
 |[HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W64_C_pretrained.tar) | 79.30% | 94.61% | 38.921 | 24.742 |
 
+### ResNet_ACNet Series
+|Model | Top-1 | Top-5 | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
+|- |:-: |:-: |:-: |:-: |
+|[ResNet50_ACNet](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_pretrained.tar)<sub>1</sub> | 76.71% | 93.24% | 13.205 | 8.804 |
+|[ResNet50_ACNet](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_deploy_pretrained.tar)<sub>2</sub> | 76.71% | 93.24% | 7.418 | 5.950 |
+
+* Note:
+    * `1`. deploy mode is set as False to eval.
+    * `2`. Use `sh ./utils/acnet/convert_model.sh` to convert to trained model, and set deploy mode as True to eval.
+    * `./utils/acnet/convert_model.sh` contains 4 parmeters, which are model name, input model directory, output model directory and class number.
+
+
 ## FAQ
 
 **Q:** How to solve this problem when I try to train a 6-classes dataset with indicating pretrained_model parameter ?
@@ -661,9 +673,12 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100
 - EfficientNet: [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946), Mingxing Tan, Quoc V. Le
 - Res2Net: [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169), Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr
 - HRNet: [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/abs/1908.07919), Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao
+- DARTS: [DARTS: Differentiable Architecture Search](https://arxiv.org/pdf/1806.09055.pdf), Hanxiao Liu, Karen Simonyan, Yiming Yang
+- ACNet: [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://arxiv.org/abs/1908.03930), Xiaohan Ding, Yuchen Guo, Guiguang Ding, Jungong Han
 
-## Update
 
+
+## Update
 - 2018/12/03 **Stage1**: Update AlexNet, ResNet50, ResNet101, MobileNetV1
 - 2018/12/23 **Stage2**: Update VGG Series, SeResNeXt50_32x4d, SeResNeXt101_32x4d, ResNet152
 - 2019/01/31 Update MobileNetV2_x1_0

PaddleCV/image_classification/eval.py

@@ -52,6 +52,7 @@ add_arg('use_se',           bool, True,                 "Whether to use Squeeze-
 add_arg('save_json_path',   str,  None,                 "Whether to save output in json file.")
 add_arg('same_feed',        int,  0,                    "Whether to feed same images")
 add_arg('print_step',       int,  1,                    "the batch step to print info")
+add_arg('deploy',                bool,    False,                      "deploy mode, currently used in ACNet")
 # yapf: enable
 
 logging.basicConfig(level=logging.INFO)
@@ -84,6 +85,8 @@ def eval(args):
         model = models.__dict__[args.model](is_test=True,
                                             padding_type=args.padding_type,
                                             use_se=args.use_se)
+    elif "ACNet" in args.model:
+        model = models.__dict__[args.model](deploy=args.deploy)
     else:
         model = models.__dict__[args.model]()
 

PaddleCV/image_classification/models/__init__.py

@@ -42,3 +42,4 @@ from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2N
 from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s
 from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C
 from .autodl import DARTS_6M, DARTS_4M
+from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet

PaddleCV/image_classification/models/resnet_acnet.py

+#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+
+__all__ = [
+    "ResNet_ACNet", "ResNet18_ACNet", "ResNet34_ACNet", "ResNet50_ACNet",
+    "ResNet101_ACNet", "ResNet152_ACNet"
+]
+
+
+class ResNetACNet(object):
+    """ ACNet """
+
+    def __init__(self, layers=50, deploy=False):
+        """init"""
+        self.layers = layers
+        self.deploy = deploy
+
+    def net(self, input, class_dim=1000):
+        """model"""
+        layers = self.layers
+        supported_layers = [18, 34, 50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_filters = [64, 128, 256, 512]
+
+        conv = self.conv_bn_layer(
+            input=input,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu',
+            name="conv1")
+        conv = fluid.layers.pool2d(
+            input=conv,
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
+        if layers >= 50:
+            for block in range(len(depth)):
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    conv = self.bottleneck_block(
+                        input=conv,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        name=conv_name)
+        else:
+            for block in range(len(depth)):
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    conv = self.basic_block(
+                        input=conv,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        is_first=block == i == 0,
+                        name=conv_name)
+
+        pool = fluid.layers.pool2d(
+            input=conv, pool_size=7, pool_type='avg', global_pooling=True)
+
+        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
+        out = fluid.layers.fc(
+            input=pool,
+            size=class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)))
+        return out
+
+    def conv_bn_layer(self, **kwargs):
+        """
+        conv_bn_layer
+        """
+        if kwargs['filter_size'] == 1:
+            return self.conv_bn_layer_ori(**kwargs)
+        else:
+            return self.conv_bn_layer_ac(**kwargs)
+
+    # conv bn+relu
+    def conv_bn_layer_ori(self,
+                          input,
+                          num_filters,
+                          filter_size,
+                          stride=1,
+                          groups=1,
+                          act=None,
+                          name=None):
+        """
+        standard convbn
+        used for 1x1 convbn in acnet
+        """
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False,
+            name=name + '.conv2d.output.1')
+
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            name=bn_name + '.output.1',
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance', )
+
+    # conv bn+relu
+    def conv_bn_layer_ac(self,
+                         input,
+                         num_filters,
+                         filter_size,
+                         stride=1,
+                         groups=1,
+                         act=None,
+                         name=None):
+        """ ACNet conv bn """
+        padding = (filter_size - 1) // 2
+
+        square_conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            act=act if self.deploy else None,
+            param_attr=ParamAttr(name=name + "_acsquare_weights"),
+            bias_attr=ParamAttr(name=name + "_acsquare_bias")
+            if self.deploy else None,
+            name=name + '.acsquare.conv2d.output.1')
+
+        if self.deploy:
+            return square_conv
+        else:
+            ver_conv = fluid.layers.conv2d(
+                input=input,
+                num_filters=num_filters,
+                filter_size=(filter_size, 1),
+                stride=stride,
+                padding=(padding, 0),
+                groups=groups,
+                act=None,
+                param_attr=ParamAttr(name=name + "_acver_weights"),
+                bias_attr=False,
+                name=name + '.acver.conv2d.output.1')
+
+            hor_conv = fluid.layers.conv2d(
+                input=input,
+                num_filters=num_filters,
+                filter_size=(1, filter_size),
+                stride=stride,
+                padding=(0, padding),
+                groups=groups,
+                act=None,
+                param_attr=ParamAttr(name=name + "_achor_weights"),
+                bias_attr=False,
+                name=name + '.achor.conv2d.output.1')
+
+            if name == "conv1":
+                bn_name = "bn_" + name
+            else:
+                bn_name = "bn" + name[3:]
+
+            square_bn = fluid.layers.batch_norm(
+                input=square_conv,
+                act=None,
+                name=bn_name + '.acsquare.output.1',
+                param_attr=ParamAttr(name=bn_name + '_acsquare_scale'),
+                bias_attr=ParamAttr(bn_name + '_acsquare_offset'),
+                moving_mean_name=bn_name + '_acsquare_mean',
+                moving_variance_name=bn_name + '_acsquare_variance', )
+
+            ver_bn = fluid.layers.batch_norm(
+                input=ver_conv,
+                act=None,
+                name=bn_name + '.acver.output.1',
+                param_attr=ParamAttr(name=bn_name + '_acver_scale'),
+                bias_attr=ParamAttr(bn_name + '_acver_offset'),
+                moving_mean_name=bn_name + '_acver_mean',
+                moving_variance_name=bn_name + '_acver_variance', )
+
+            hor_bn = fluid.layers.batch_norm(
+                input=hor_conv,
+                act=None,
+                name=bn_name + '.achor.output.1',
+                param_attr=ParamAttr(name=bn_name + '_achor_scale'),
+                bias_attr=ParamAttr(bn_name + '_achor_offset'),
+                moving_mean_name=bn_name + '_achor_mean',
+                moving_variance_name=bn_name + '_achor_variance', )
+
+            return fluid.layers.elementwise_add(
+                x=square_bn, y=ver_bn + hor_bn, act=act)
+
+    def shortcut(self, input, ch_out, stride, is_first, name):
+        """ shortcut """
+        ch_in = input.shape[1]
+        if ch_in != ch_out or stride != 1 or is_first == True:
+            return self.conv_bn_layer(
+                input=input,
+                num_filters=ch_out,
+                filter_size=1,
+                stride=stride,
+                name=name)
+        else:
+            return input
+
+    def bottleneck_block(self, input, num_filters, stride, name):
+        """" bottleneck_block """
+        conv0 = self.conv_bn_layer(
+            input=input,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        conv1 = self.conv_bn_layer(
+            input=conv0,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+        conv2 = self.conv_bn_layer(
+            input=conv1,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        short = self.shortcut(
+            input,
+            num_filters * 4,
+            stride,
+            is_first=False,
+            name=name + "_branch1")
+
+        return fluid.layers.elementwise_add(
+            x=short, y=conv2, act='relu', name=name + ".add.output.5")
+
+    def basic_block(self, input, num_filters, stride, is_first, name):
+        """ basic_block """
+        conv0 = self.conv_bn_layer(
+            input=input,
+            num_filters=num_filters,
+            filter_size=3,
+            act='relu',
+            stride=stride,
+            name=name + "_branch2a")
+        conv1 = self.conv_bn_layer(
+            input=conv0,
+            num_filters=num_filters,
+            filter_size=3,
+            act=None,
+            name=name + "_branch2b")
+        short = self.shortcut(
+            input, num_filters, stride, is_first, name=name + "_branch1")
+        return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
+
+
+def ResNet18_ACNet(deploy=False):
+    """ResNet18 + ACNet"""
+    model = ResNet_ACNet(layers=18, deploy=deploy)
+    return model
+
+
+def ResNet34_ACNet(deploy=False):
+    """ResNet34 + ACNet"""
+    model = ResNetACNet(layers=34, deploy=deploy)
+    return model
+
+
+def ResNet50_ACNet(deploy=False):
+    """ResNet50 + ACNet"""
+    model = ResNetACNet(layers=50, deploy=deploy)
+    return model
+
+
+def ResNet101_ACNet(deploy=False):
+    """ResNet101 + ACNet"""
+    model = ResNetACNet(layers=101, deploy=deploy)
+    return model
+
+
+def ResNet152_ACNet(deploy=False):
+    """ResNet152 + ACNet"""
+    model = ResNetACNet(layers=152, deploy=deploy)
+    return model

PaddleCV/image_classification/scripts/train/ResNet50_ACNet.sh

+##Training details
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+export FLAGS_fast_eager_deletion_mode=1
+export FLAGS_eager_delete_tensor_gb=0.0
+export FLAGS_fraction_of_gpu_memory_to_use=0.98
+
+#ResNet50:
+python train.py \
+       --model=ResNet50_ACNet.sh \
+       --batch_size=256 \
+       --model_save_dir=output/ \
+       --lr_strategy=piecewise_decay \
+       --num_epochs=120 \
+       --lr=0.1 \
+       --l2_decay=1e-4

PaddleCV/image_classification/utils/acnet/convert_model.sh

+python utils/acnet/weights_aggregator.py \
+    ResNet50ACNet \
+    ./ResNet50ACNet_pretrained \
+    ./ResNet50ACNet_pretrained_after_fuse \
+    1000

PaddleCV/image_classification/utils/acnet/weights_aggregator.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#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 sys
+import os
+import shutil
+import logging
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+
+import models
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def get_ac_tensor(name):
+    gamma = fluid.global_scope().find_var(name + '_scale').get_tensor()
+    beta = fluid.global_scope().find_var(name + '_offset').get_tensor()
+    mean = fluid.global_scope().find_var(name + '_mean').get_tensor()
+    var = fluid.global_scope().find_var(name + '_variance').get_tensor()
+    return gamma, beta, mean, var
+
+
+def get_kernel_bn_tensors(name):
+    if "conv1" in name:
+        bn_name = "bn_" + name
+    else:
+        bn_name = "bn" + name[3:]
+
+    ac_square = fluid.global_scope().find_var(name +
+                                              "_acsquare_weights").get_tensor()
+    ac_ver = fluid.global_scope().find_var(name + "_acver_weights").get_tensor()
+    ac_hor = fluid.global_scope().find_var(name + "_achor_weights").get_tensor()
+
+    ac_square_bn_gamma, ac_square_bn_beta, ac_square_bn_mean, ac_square_bn_var = \
+            get_ac_tensor(bn_name + '_acsquare')
+    ac_ver_bn_gamma, ac_ver_bn_beta, ac_ver_bn_mean, ac_ver_bn_var = \
+            get_ac_tensor(bn_name + '_acver')
+    ac_hor_bn_gamma, ac_hor_bn_beta, ac_hor_bn_mean, ac_hor_bn_var = \
+            get_ac_tensor(bn_name + '_achor')
+
+    kernels = [np.array(ac_square), np.array(ac_ver), np.array(ac_hor)]
+    gammas = [
+        np.array(ac_square_bn_gamma), np.array(ac_ver_bn_gamma),
+        np.array(ac_hor_bn_gamma)
+    ]
+    betas = [
+        np.array(ac_square_bn_beta), np.array(ac_ver_bn_beta),
+        np.array(ac_hor_bn_beta)
+    ]
+    means = [
+        np.array(ac_square_bn_mean), np.array(ac_ver_bn_mean),
+        np.array(ac_hor_bn_mean)
+    ]
+    var = [
+        np.array(ac_square_bn_var), np.array(ac_ver_bn_var),
+        np.array(ac_hor_bn_var)
+    ]
+
+    return {"kernels": kernels, "bn": (gammas, betas, means, var)}
+
+
+def kernel_fusion(kernels, gammas, betas, means, var):
+    """fuse conv + BN"""
+    kernel_size_h, kernel_size_w = kernels[0].shape[2:]
+
+    square = (gammas[0] / (var[0] + 1e-5)
+              **0.5).reshape(-1, 1, 1, 1) * kernels[0]
+    ver = (gammas[1] / (var[1] + 1e-5)**0.5).reshape(-1, 1, 1, 1) * kernels[1]
+    hor = (gammas[2] / (var[2] + 1e-5)**0.5).reshape(-1, 1, 1, 1) * kernels[2]
+
+    b = 0
+    for i in range(3):
+        b += -((means[i] * gammas[i]) / (var[i] + 1e-5)**0.5) + betas[i]  # eq.7
+
+    square[:, :, :, kernel_size_w // 2:kernel_size_w // 2 + 1] += ver
+    square[:, :, kernel_size_h // 2:kernel_size_h // 2 + 1, :] += hor
+
+    return square, b
+
+
+def convert_main(model_name, input_path, output_path, class_num=1000):
+    model = models.__dict__[model_name]()
+
+    main_prog = fluid.Program()
+    acnet_prog = fluid.Program()
+    startup_prog = fluid.Program()
+
+    with fluid.program_guard(acnet_prog, startup_prog):
+        with fluid.unique_name.guard():
+            image = fluid.data(
+                name="image",
+                shape=[-1, 3, 224, 224],
+                dtype="float32",
+                lod_level=0)
+            model_train = models.__dict__[model_name](deploy=False)
+            model_train.net(image, class_dim=1000)
+
+    with fluid.program_guard(main_prog, startup_prog):
+        with fluid.unique_name.guard():
+            image = fluid.data(
+                name="image",
+                shape=[-1, 3, 224, 224],
+                dtype="float32",
+                lod_level=0)
+            model_infer = models.__dict__[model_name](deploy=True)
+            model_infer.net(image, class_dim=1000)
+
+    acnet_prog = acnet_prog.clone(for_test=True)
+    main_prog = main_prog.clone(for_test=True)
+
+    place = fluid.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    exe.run(startup_prog)
+
+    assert os.path.exists(
+        input_path), "Pretrained model path {} not exist!".format(input_path)
+    fluid.io.load_vars(exe, input_path,
+                        main_program=acnet_prog,
+                        predicate=lambda var: os.path.exists(os.path.join(input_path, var.name)))
+
+    mapping = {}
+
+    for param in main_prog.blocks[0].all_parameters():
+        if "acsquare" in param.name:
+            name_root = "_".join(param.name.split("_")[:-2])
+            if name_root in mapping.keys():
+                mapping[name_root].append(param.name)
+            else:
+                mapping[name_root] = [param.name]
+        else:
+            assert param.name not in mapping.keys()
+            mapping[param.name] = [param.name]
+
+    for name_root, names in mapping.items():
+        if len(names) == 1:
+            pass
+        else:
+            if "bias" in names[0]:
+                bias_id = 0
+                kernel_id = 1
+            else:
+                bias_id = 1
+                kernel_id = 0
+
+            tensor_bias = fluid.global_scope().find_var(names[
+                bias_id]).get_tensor()
+            tensor_kernel = fluid.global_scope().find_var(names[
+                kernel_id]).get_tensor()
+
+            ret = get_kernel_bn_tensors(name_root)
+            kernels = ret['kernels']
+            gammas, betas, means, var = ret['bn']
+
+            kernel, bias = kernel_fusion(kernels, gammas, betas, means, var)
+
+            logger.info("Before {}: {}".format(names[
+                kernel_id], np.array(tensor_kernel).ravel()[:5]))
+
+            tensor_bias.set(bias, place)
+            tensor_kernel.set(kernel, place)
+
+            logger.info("After {}: {}\n".format(names[
+                kernel_id], np.array(tensor_kernel).ravel()[:5]))
+
+    if os.path.isdir(output_path):
+        shutil.rmtree(output_path)
+    os.makedirs(output_path)
+    fluid.io.save_persistables(exe, output_path, main_program=main_prog)
+
+
+if __name__ == "__main__":
+    assert len(
+        sys.argv
+    ) == 5, "input format: python weights_aggregator.py $model_name $input_path $output_path $class_num"
+    model_name = sys.argv[1]
+    input_path = sys.argv[2]
+    output_path = sys.argv[3]
+    class_num = int(sys.argv[4])
+    logger.info("model_name: {}".format(model_name))
+    logger.info("input_path: {}".format(input_path))
+    logger.info("output_path: {}".format(output_path))
+    logger.info("class_num: {}".format(class_num))
+    convert_main(model_name, input_path, output_path, class_num)