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未验证 提交 3238bdba 编写于 作者: C cuicheng01 提交者： GitHub
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Add InceptionV3 architecture (#4937) 

* Create inception_v3.py

* Update __init__.py

* Create InceptionV3.sh
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PaddleCV/image_classification/models/__init__.py
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......@@ -23,6 +23,7 @@ from .resnet_vc import ResNet50_vc, ResNet101_vc, ResNet152_vc
from .resnet_vd import ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet101_vd, ResNet152_vd, ResNet200_vd
from .resnext import ResNeXt50_64x4d, ResNeXt101_64x4d, ResNeXt152_64x4d, ResNeXt50_32x4d, ResNeXt101_32x4d, ResNeXt152_32x4d
from .resnext_vd import ResNeXt50_vd_64x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_64x4d, ResNeXt50_vd_32x4d, ResNeXt101_vd_32x4d, ResNeXt152_vd_32x4d
from .inception_v3 import InceptionV3
from .inception_v4 import InceptionV4
from .se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_ResNet101_vd, SE_ResNet152_vd, SE_ResNet200_vd
from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_32x4d
......
PaddleCV/image_classification/models/inception_v3.py 0 → 100644
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#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 paddle
import paddle.fluid as fluid
import math
from paddle.fluid.param_attr import ParamAttr
__all__ = ["InceptionV3"]
class InceptionV3():
def __init__(self):
self.inception_a_list = [32, 64, 64]
self.inception_c_list = [128, 160, 160, 192]
def net(self, input, class_dim=1000):
x = self.inception_stem(input)
for i, pool_features in enumerate(self.inception_a_list):
x = self.inceptionA(x, pool_features, name=str(i+1))
x = self.inceptionB(x, name="1")
for i, channels_7x7 in enumerate(self.inception_c_list):
x = self.inceptionC(x, channels_7x7, name=str(i+1))
x = self.inceptionD(x, name="1")
x = self.inceptionE(x, name="1")
x = self.inceptionE(x, name="2")
pool = fluid.layers.pool2d(input=x, pool_type="avg", global_pooling=True)
drop = fluid.layers.dropout(x=pool, dropout_prob=0.2)
stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
out = fluid.layers.fc(
input=drop,
size=class_dim,
param_attr=ParamAttr(initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_offset"))
return out
def conv_bn_layer(self,
data,
num_filters,
filter_size,
stride=1,
padding=0,
groups=1,
act="relu",
name=None):
conv = fluid.layers.conv2d(
input=data,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
act=None,
param_attr=ParamAttr(name=name+"_weights"),
bias_attr=False,
name=name)
return fluid.layers.batch_norm(input=conv,
act=act,
param_attr = ParamAttr(name=name+"_bn_scale"),
bias_attr=ParamAttr(name=name+"_bn_offset"),
moving_mean_name=name+"_bn_mean",
moving_variance_name=name+"_bn_variance")
def inception_stem(self, x):
x = self.conv_bn_layer(x,
num_filters=32,
filter_size=3,
stride=2,
act="relu",
name="conv_1a_3x3")
x = self.conv_bn_layer(x,
num_filters=32,
filter_size=3,
stride=1,
act="relu",
name="conv_2a_3x3")
x = self.conv_bn_layer(x,
num_filters=64,
filter_size=3,
padding=1,
act="relu",
name="conv_2b_3x3")
x = fluid.layers.pool2d(input=x, pool_size=3, pool_stride=2, pool_type="max")
x = self.conv_bn_layer(x,
num_filters=80,
filter_size=1,
act="relu",
name="conv_3b_1x1")
x = self.conv_bn_layer(x,
num_filters=192,
filter_size=3,
act="relu",
name="conv_4a_3x3")
x = fluid.layers.pool2d(input=x, pool_size=3, pool_stride=2, pool_type="max")
return x
def inceptionA(self, x, pool_features, name=None):
branch1x1 = self.conv_bn_layer(x,
num_filters=64,
filter_size=1,
act="relu",
name="inception_a_branch1x1_"+name)
branch5x5 = self.conv_bn_layer(x,
num_filters=48,
filter_size=1,
act="relu",
name="inception_a_branch5x5_1_"+name)
branch5x5 = self.conv_bn_layer(branch5x5,
num_filters=64,
filter_size=5,
padding=2,
act="relu",
name="inception_a_branch5x5_2_"+name)
branch3x3dbl = self.conv_bn_layer(x,
num_filters=64,
filter_size=1,
act="relu",
name="inception_a_branch3x3dbl_1_"+name)
branch3x3dbl = self.conv_bn_layer(branch3x3dbl,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_a_branch3x3dbl_2_"+name)
branch3x3dbl = self.conv_bn_layer(branch3x3dbl,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_a_branch3x3dbl_3_"+name)
branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_padding=1, pool_type="avg")
branch_pool = self.conv_bn_layer(branch_pool,
num_filters=pool_features,
filter_size=1,
act="relu",
name="inception_a_branch_pool_"+name)
concat = fluid.layers.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
return concat
def inceptionB(self, x, name=None):
branch3x3 = self.conv_bn_layer(x,
num_filters=384,
filter_size=3,
stride=2,
act="relu",
name="inception_b_branch3x3_"+name)
branch3x3dbl = self.conv_bn_layer(x,
num_filters=64,
filter_size=1,
act="relu",
name="inception_b_branch3x3dbl_1_"+name)
branch3x3dbl = self.conv_bn_layer(branch3x3dbl,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_b_branch3x3dbl_2_"+name)
branch3x3dbl = self.conv_bn_layer(branch3x3dbl,
num_filters=96,
filter_size=3,
stride=2,
act="relu",
name="inception_b_branch3x3dbl_3_"+name)
branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=2, pool_type="max")
concat = fluid.layers.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
return concat
def inceptionC(self, x, channels_7x7, name=None):
branch1x1 = self.conv_bn_layer(x,
num_filters=192,
filter_size=1,
act="relu",
name="inception_c_branch1x1_"+name)
branch7x7 = self.conv_bn_layer(x,
num_filters=channels_7x7,
filter_size=1,
stride=1,
act="relu",
name="inception_c_branch7x7_1_"+name)
branch7x7 = self.conv_bn_layer(branch7x7,
num_filters=channels_7x7,
filter_size=(1, 7),
stride=1,
padding=(0, 3),
act="relu",
name="inception_c_branch7x7_2_"+name)
branch7x7 = self.conv_bn_layer(branch7x7,
num_filters=192,
filter_size=(7, 1),
stride=1,
padding=(3, 0),
act="relu",
name="inception_c_branch7x7_3_"+name)
branch7x7dbl = self.conv_bn_layer(x,
num_filters=channels_7x7,
filter_size=1,
act="relu",
name="inception_c_branch7x7dbl_1_"+name)
branch7x7dbl = self.conv_bn_layer(branch7x7dbl,
num_filters=channels_7x7,
filter_size=(7, 1),
padding = (3, 0),
act="relu",
name="inception_c_branch7x7dbl_2_"+name)
branch7x7dbl = self.conv_bn_layer(branch7x7dbl,
num_filters=channels_7x7,
filter_size=(1, 7),
padding = (0, 3),
act="relu",
name="inception_c_branch7x7dbl_3_"+name)
branch7x7dbl = self.conv_bn_layer(branch7x7dbl,
num_filters=channels_7x7,
filter_size=(7, 1),
padding = (3, 0),
act="relu",
name="inception_c_branch7x7dbl_4_"+name)
branch7x7dbl = self.conv_bn_layer(branch7x7dbl,
num_filters=192,
filter_size=(1, 7),
padding = (0, 3),
act="relu",
name="inception_c_branch7x7dbl_5_"+name)
branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, pool_type="avg")
branch_pool = self.conv_bn_layer(branch_pool,
num_filters=192,
filter_size=1,
act="relu",
name="inception_c_branch_pool_"+name)
concat = fluid.layers.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
return concat
def inceptionD(self, x, name=None):
branch3x3 = self.conv_bn_layer(x,
num_filters=192,
filter_size=1,
act="relu",
name="inception_d_branch3x3_1_"+name)
branch3x3 = self.conv_bn_layer(branch3x3,
num_filters=320,
filter_size=3,
stride=2,
act="relu",
name="inception_d_branch3x3_2_"+name)
branch7x7x3 = self.conv_bn_layer(x,
num_filters=192,
filter_size=1,
act="relu",
name="inception_d_branch7x7x3_1_"+name)
branch7x7x3 = self.conv_bn_layer(branch7x7x3,
num_filters=192,
filter_size=(1, 7),
padding=(0, 3),
act="relu",
name="inception_d_branch7x7x3_2_"+name)
branch7x7x3 = self.conv_bn_layer(branch7x7x3,
num_filters=192,
filter_size=(7, 1),
padding=(3, 0),
act="relu",
name="inception_d_branch7x7x3_3_"+name)
branch7x7x3 = self.conv_bn_layer(branch7x7x3,
num_filters=192,
filter_size=3,
stride=2,
act="relu",
name="inception_d_branch7x7x3_4_"+name)
branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=2, pool_type="max")
concat = fluid.layers.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
return concat
def inceptionE(self, x, name=None):
branch1x1 = self.conv_bn_layer(x,
num_filters=320,
filter_size=1,
act="relu",
name="inception_e_branch1x1_"+name)
branch3x3 = self.conv_bn_layer(x,
num_filters=384,
filter_size=1,
act="relu",
name="inception_e_branch3x3_1_"+name)
branch3x3_2a = self.conv_bn_layer(branch3x3,
num_filters=384,
filter_size=(1, 3),
padding=(0, 1),
act="relu",
name="inception_e_branch3x3_2a_"+name)
branch3x3_2b = self.conv_bn_layer(branch3x3,
num_filters=384,
filter_size=(3, 1),
padding=(1, 0),
act="relu",
name="inception_e_branch3x3_2b_"+name)
branch3x3 = fluid.layers.concat([branch3x3_2a, branch3x3_2b], axis=1)
branch3x3dbl = self.conv_bn_layer(x,
num_filters=448,
filter_size=1,
act="relu",
name="inception_e_branch3x3dbl_1_"+name)
branch3x3dbl = self.conv_bn_layer(branch3x3dbl,
num_filters=384,
filter_size=3,
padding=1,
act="relu",
name="inception_e_branch3x3dbl_2_"+name)
branch3x3dbl_3a = self.conv_bn_layer(branch3x3dbl,
num_filters=384,
filter_size=(1, 3),
padding=(0, 1),
act="relu",
name="inception_e_branch3x3dbl_3a_"+name)
branch3x3dbl_3b = self.conv_bn_layer(branch3x3dbl,
num_filters=384,
filter_size=(3, 1),
padding=(1, 0),
act="relu",
name="inception_e_branch3x3dbl_3b_"+name)
branch3x3dbl = fluid.layers.concat([branch3x3dbl_3a, branch3x3dbl_3b], axis=1)
branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, pool_type="avg")
branch_pool = self.conv_bn_layer(branch_pool,
num_filters=192,
filter_size=1,
act="relu",
name="inception_e_branch_pool_"+name)
concat = fluid.layers.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
return concat
PaddleCV/image_classification/scripts/train/InceptionV3.sh 0 → 100644
浏览文件 @ 3238bdba
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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
#InceptionV3
python train.py \
--model=InceptionV3 \
--batch_size=256 \
--image_shape 3 299 299 \
--lr_strategy=cosine_decay \
--lr=0.045 \
--num_epochs=200 \
--model_save_dir=output/ \
--l2_decay=1e-4 \
--use_mixup=True \
--resize_short_size=320 \
--use_label_smoothing=True \
--label_smoothing_epsilon=0.1 \
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