272 lines
7.9 KiB
Python
272 lines
7.9 KiB
Python
# copyright (c) 2021 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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import paddle.nn as nn
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from paddle import ParamAttr
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from paddle.nn import AdaptiveAvgPool2D, Conv2D
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from paddle.regularizer import L2Decay
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from paddle.nn.initializer import KaimingNormal
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from ppdet.core.workspace import register, serializable
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from numbers import Integral
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from ..shape_spec import ShapeSpec
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__all__ = ['LCNet']
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NET_CONFIG = {
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"blocks2":
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#k, in_c, out_c, s, use_se
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[[3, 16, 32, 1, False], ],
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"blocks3": [
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[3, 32, 64, 2, False],
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[3, 64, 64, 1, False],
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],
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"blocks4": [
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[3, 64, 128, 2, False],
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[3, 128, 128, 1, False],
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],
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"blocks5": [
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[3, 128, 256, 2, False],
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[5, 256, 256, 1, False],
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[5, 256, 256, 1, False],
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[5, 256, 256, 1, False],
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[5, 256, 256, 1, False],
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[5, 256, 256, 1, False],
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],
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"blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True]]
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}
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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 ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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filter_size,
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num_filters,
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stride,
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num_groups=1,
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act='hard_swish'):
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super().__init__()
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self.conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=num_groups,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False)
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self.bn = nn.BatchNorm2D(
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num_filters,
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weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
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bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
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if act == 'hard_swish':
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self.act = nn.Hardswish()
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elif act == 'relu6':
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self.act = nn.ReLU6()
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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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x = self.act(x)
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return x
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class DepthwiseSeparable(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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dw_size=3,
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use_se=False,
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act='hard_swish'):
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super().__init__()
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self.use_se = use_se
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self.dw_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_channels,
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filter_size=dw_size,
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stride=stride,
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num_groups=num_channels,
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act=act)
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if use_se:
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self.se = SEModule(num_channels)
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self.pw_conv = ConvBNLayer(
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num_channels=num_channels,
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filter_size=1,
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num_filters=num_filters,
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stride=1,
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act=act)
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def forward(self, x):
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x = self.dw_conv(x)
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if self.use_se:
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x = self.se(x)
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x = self.pw_conv(x)
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return x
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class SEModule(nn.Layer):
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def __init__(self, channel, reduction=4):
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super().__init__()
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self.avg_pool = AdaptiveAvgPool2D(1)
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self.conv1 = Conv2D(
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in_channels=channel,
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out_channels=channel // 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.relu = nn.ReLU()
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self.conv2 = Conv2D(
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in_channels=channel // reduction,
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out_channels=channel,
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kernel_size=1,
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stride=1,
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padding=0)
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self.hardsigmoid = nn.Hardsigmoid()
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def forward(self, x):
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identity = x
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x = self.avg_pool(x)
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x = self.conv1(x)
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x = self.relu(x)
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x = self.conv2(x)
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x = self.hardsigmoid(x)
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x = paddle.multiply(x=identity, y=x)
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return x
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@register
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@serializable
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class LCNet(nn.Layer):
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def __init__(self, scale=1.0, feature_maps=[3, 4, 5], act='hard_swish'):
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super().__init__()
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self.scale = scale
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self.feature_maps = feature_maps
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out_channels = []
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self.conv1 = ConvBNLayer(
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num_channels=3,
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filter_size=3,
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num_filters=make_divisible(16 * scale),
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stride=2,
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act=act)
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self.blocks2 = nn.Sequential(* [
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DepthwiseSeparable(
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num_channels=make_divisible(in_c * scale),
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num_filters=make_divisible(out_c * scale),
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dw_size=k,
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stride=s,
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use_se=se,
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act=act)
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"])
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])
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self.blocks3 = nn.Sequential(* [
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DepthwiseSeparable(
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num_channels=make_divisible(in_c * scale),
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num_filters=make_divisible(out_c * scale),
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dw_size=k,
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stride=s,
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use_se=se,
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act=act)
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"])
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])
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out_channels.append(
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make_divisible(NET_CONFIG["blocks3"][-1][2] * scale))
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self.blocks4 = nn.Sequential(* [
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DepthwiseSeparable(
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num_channels=make_divisible(in_c * scale),
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num_filters=make_divisible(out_c * scale),
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dw_size=k,
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stride=s,
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use_se=se,
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act=act)
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"])
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])
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out_channels.append(
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make_divisible(NET_CONFIG["blocks4"][-1][2] * scale))
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self.blocks5 = nn.Sequential(* [
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DepthwiseSeparable(
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num_channels=make_divisible(in_c * scale),
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num_filters=make_divisible(out_c * scale),
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dw_size=k,
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stride=s,
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use_se=se,
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act=act)
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"])
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])
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out_channels.append(
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make_divisible(NET_CONFIG["blocks5"][-1][2] * scale))
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self.blocks6 = nn.Sequential(* [
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DepthwiseSeparable(
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num_channels=make_divisible(in_c * scale),
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num_filters=make_divisible(out_c * scale),
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dw_size=k,
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stride=s,
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use_se=se,
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act=act)
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"])
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])
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out_channels.append(
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make_divisible(NET_CONFIG["blocks6"][-1][2] * scale))
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self._out_channels = [
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ch for idx, ch in enumerate(out_channels) if idx + 2 in feature_maps
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]
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def forward(self, inputs):
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x = inputs['image']
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outs = []
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x = self.conv1(x)
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x = self.blocks2(x)
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x = self.blocks3(x)
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outs.append(x)
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x = self.blocks4(x)
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outs.append(x)
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x = self.blocks5(x)
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outs.append(x)
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x = self.blocks6(x)
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outs.append(x)
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outs = [o for i, o in enumerate(outs) if i + 2 in self.feature_maps]
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return outs
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@property
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def out_shape(self):
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return [ShapeSpec(channels=c) for c in self._out_channels]
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