diff --git a/PaddleCV/image_classification/README.md b/PaddleCV/image_classification/README.md
index 507932b40d184ee352a602598244cf1523393234..96468b456042d46bbca2011fcd92d13b739ab2d6 100644
--- a/PaddleCV/image_classification/README.md
+++ b/PaddleCV/image_classification/README.md
@@ -377,7 +377,7 @@ Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv
节点数*卡数|吞吐|加速比|test\_acc1|test\_acc5
---|---|---|---|---
-1*1|1035 ins/s|1|0.75333|0.92702
+1*1|1035 ins/s|1|0.75333|0.92702
1*8|7840 ins/s|7.57|0.75603|0.92771
2*8|14277 ins/s|13.79|0.75872|0.92793
4*8|28594 ins/s|27.63|0.75253|0.92713
@@ -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)1 | 76.71% | 93.24% | 13.205 | 8.804 |
+|[ResNet50_ACNet](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_deploy_pretrained.tar)2 | 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
diff --git a/PaddleCV/image_classification/README_en.md b/PaddleCV/image_classification/README_en.md
index 437cd1ba01352650437e0cdbda2dcdb406568708..1baa86d963ccf8f3df39095085ba2fdd2eb796fb 100644
--- a/PaddleCV/image_classification/README_en.md
+++ b/PaddleCV/image_classification/README_en.md
@@ -265,7 +265,7 @@ Performing on Tesla V100 single machine with 8 cards, two machines with 16 cards
nodes*crads|throughput|speedup|test\_acc1|test\_acc5
---|---|---|---|---
-1*1|1035 ins/s|1|0.75333|0.92702
+1*1|1035 ins/s|1|0.75333|0.92702
1*8|7840 ins/s|7.57|0.75603|0.92771
2*8|14277 ins/s|13.79|0.75872|0.92793
4*8|28594 ins/s|27.63|0.75253|0.92713
@@ -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)1 | 76.71% | 93.24% | 13.205 | 8.804 |
+|[ResNet50_ACNet](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_deploy_pretrained.tar)2 | 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
diff --git a/PaddleCV/image_classification/eval.py b/PaddleCV/image_classification/eval.py
index 7de1f09abc849f55c1476aa79dfdcb91d333a828..8d592006fda2d9af01daee3f0a6e32e1865a4cf3 100644
--- a/PaddleCV/image_classification/eval.py
+++ b/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]()
diff --git a/PaddleCV/image_classification/models/__init__.py b/PaddleCV/image_classification/models/__init__.py
index bb12a824d203f5f5698c62f7aa0ba381ca644405..63e6220688306b55d6dfe9369b4176c232314282 100644
--- a/PaddleCV/image_classification/models/__init__.py
+++ b/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
diff --git a/PaddleCV/image_classification/models/resnet_acnet.py b/PaddleCV/image_classification/models/resnet_acnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..575603382a2f8676d43d51bbbbe70c499c442b46
--- /dev/null
+++ b/PaddleCV/image_classification/models/resnet_acnet.py
@@ -0,0 +1,332 @@
+#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
diff --git a/PaddleCV/image_classification/scripts/train/ResNet50_ACNet.sh b/PaddleCV/image_classification/scripts/train/ResNet50_ACNet.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4c83be5f773ea817d94339bcb04960458a94e727
--- /dev/null
+++ b/PaddleCV/image_classification/scripts/train/ResNet50_ACNet.sh
@@ -0,0 +1,15 @@
+##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
diff --git a/PaddleCV/image_classification/utils/acnet/convert_model.sh b/PaddleCV/image_classification/utils/acnet/convert_model.sh
new file mode 100644
index 0000000000000000000000000000000000000000..20f43b16d5a8a0d3bd16b07c6ba26499f275ca30
--- /dev/null
+++ b/PaddleCV/image_classification/utils/acnet/convert_model.sh
@@ -0,0 +1,5 @@
+python utils/acnet/weights_aggregator.py \
+ ResNet50ACNet \
+ ./ResNet50ACNet_pretrained \
+ ./ResNet50ACNet_pretrained_after_fuse \
+ 1000
diff --git a/PaddleCV/image_classification/utils/acnet/weights_aggregator.py b/PaddleCV/image_classification/utils/acnet/weights_aggregator.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb2db23f3337b745ddd4d0e18987782a2294ed0c
--- /dev/null
+++ b/PaddleCV/image_classification/utils/acnet/weights_aggregator.py
@@ -0,0 +1,198 @@
+#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)