mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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269 lines
8.7 KiB
Python
Executable File
269 lines
8.7 KiB
Python
Executable File
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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__all__ = ['MobileNetV3']
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def make_divisible(v, divisor=8, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class MobileNetV3(nn.Layer):
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def __init__(self,
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in_channels=3,
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model_name='large',
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scale=0.5,
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disable_se=False,
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**kwargs):
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"""
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the MobilenetV3 backbone network for detection module.
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Args:
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params(dict): the super parameters for build network
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"""
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super(MobileNetV3, self).__init__()
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self.disable_se = disable_se
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if model_name == "large":
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cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, False, 'relu', 1],
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[3, 64, 24, False, 'relu', 2],
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[3, 72, 24, False, 'relu', 1],
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[5, 72, 40, True, 'relu', 2],
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[5, 120, 40, True, 'relu', 1],
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[5, 120, 40, True, 'relu', 1],
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[3, 240, 80, False, 'hardswish', 2],
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[3, 200, 80, False, 'hardswish', 1],
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[3, 184, 80, False, 'hardswish', 1],
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[3, 184, 80, False, 'hardswish', 1],
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[3, 480, 112, True, 'hardswish', 1],
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[3, 672, 112, True, 'hardswish', 1],
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[5, 672, 160, True, 'hardswish', 2],
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[5, 960, 160, True, 'hardswish', 1],
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[5, 960, 160, True, 'hardswish', 1],
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]
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cls_ch_squeeze = 960
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elif model_name == "small":
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cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, True, 'relu', 2],
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[3, 72, 24, False, 'relu', 2],
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[3, 88, 24, False, 'relu', 1],
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[5, 96, 40, True, 'hardswish', 2],
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[5, 240, 40, True, 'hardswish', 1],
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[5, 240, 40, True, 'hardswish', 1],
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[5, 120, 48, True, 'hardswish', 1],
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[5, 144, 48, True, 'hardswish', 1],
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[5, 288, 96, True, 'hardswish', 2],
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[5, 576, 96, True, 'hardswish', 1],
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[5, 576, 96, True, 'hardswish', 1],
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]
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cls_ch_squeeze = 576
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else:
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raise NotImplementedError("mode[" + model_name +
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"_model] is not implemented!")
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supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
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assert scale in supported_scale, \
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"supported scale are {} but input scale is {}".format(supported_scale, scale)
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inplanes = 16
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# conv1
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self.conv = ConvBNLayer(
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in_channels=in_channels,
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out_channels=make_divisible(inplanes * scale),
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kernel_size=3,
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stride=2,
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padding=1,
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groups=1,
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if_act=True,
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act='hardswish')
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self.stages = []
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self.out_channels = []
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block_list = []
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i = 0
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inplanes = make_divisible(inplanes * scale)
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for (k, exp, c, se, nl, s) in cfg:
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se = se and not self.disable_se
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start_idx = 2 if model_name == 'large' else 0
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if s == 2 and i > start_idx:
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self.out_channels.append(inplanes)
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self.stages.append(nn.Sequential(*block_list))
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block_list = []
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block_list.append(
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ResidualUnit(
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in_channels=inplanes,
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mid_channels=make_divisible(scale * exp),
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out_channels=make_divisible(scale * c),
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kernel_size=k,
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stride=s,
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use_se=se,
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act=nl))
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inplanes = make_divisible(scale * c)
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i += 1
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block_list.append(
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ConvBNLayer(
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in_channels=inplanes,
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out_channels=make_divisible(scale * cls_ch_squeeze),
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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if_act=True,
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act='hardswish'))
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self.stages.append(nn.Sequential(*block_list))
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self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
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for i, stage in enumerate(self.stages):
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self.add_sublayer(sublayer=stage, name="stage{}".format(i))
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def forward(self, x):
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x = self.conv(x)
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out_list = []
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for stage in self.stages:
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x = stage(x)
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out_list.append(x)
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return out_list
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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groups=1,
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if_act=True,
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act=None):
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super(ConvBNLayer, self).__init__()
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self.if_act = if_act
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self.act = act
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self.conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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bias_attr=False)
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self.bn = nn.BatchNorm(num_channels=out_channels, act=None)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.if_act:
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if self.act == "relu":
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x = F.relu(x)
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elif self.act == "hardswish":
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x = F.hardswish(x)
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else:
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print("The activation function({}) is selected incorrectly.".
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format(self.act))
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exit()
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return x
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class ResidualUnit(nn.Layer):
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def __init__(self,
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in_channels,
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mid_channels,
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out_channels,
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kernel_size,
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stride,
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use_se,
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act=None):
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super(ResidualUnit, self).__init__()
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self.if_shortcut = stride == 1 and in_channels == out_channels
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self.if_se = use_se
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self.expand_conv = ConvBNLayer(
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in_channels=in_channels,
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out_channels=mid_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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if_act=True,
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act=act)
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self.bottleneck_conv = ConvBNLayer(
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in_channels=mid_channels,
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out_channels=mid_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=int((kernel_size - 1) // 2),
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groups=mid_channels,
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if_act=True,
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act=act)
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if self.if_se:
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self.mid_se = SEModule(mid_channels)
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self.linear_conv = ConvBNLayer(
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in_channels=mid_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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if_act=False,
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act=None)
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def forward(self, inputs):
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x = self.expand_conv(inputs)
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x = self.bottleneck_conv(x)
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if self.if_se:
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x = self.mid_se(x)
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x = self.linear_conv(x)
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if self.if_shortcut:
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x = paddle.add(inputs, x)
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return x
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class SEModule(nn.Layer):
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def __init__(self, in_channels, reduction=4):
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super(SEModule, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2D(1)
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self.conv1 = nn.Conv2D(
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in_channels=in_channels,
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out_channels=in_channels // reduction,
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kernel_size=1,
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stride=1,
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padding=0)
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self.conv2 = nn.Conv2D(
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in_channels=in_channels // reduction,
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out_channels=in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, inputs):
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outputs = self.avg_pool(inputs)
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outputs = self.conv1(outputs)
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outputs = F.relu(outputs)
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outputs = self.conv2(outputs)
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outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
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return inputs * outputs
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