person_monitor/dependence/PaddleDetection/ppdet/modeling/backbones/lcnet.py

# copyright (c) 2021 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.nn as nn
from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, Conv2D
from paddle.regularizer import L2Decay
from paddle.nn.initializer import KaimingNormal

from ppdet.core.workspace import register, serializable
from numbers import Integral
from ..shape_spec import ShapeSpec

__all__ = ['LCNet']

NET_CONFIG = {
    "blocks2":
    #k, in_c, out_c, s, use_se
    [[3, 16, 32, 1, False], ],
    "blocks3": [
        [3, 32, 64, 2, False],
        [3, 64, 64, 1, False],
    ],
    "blocks4": [
        [3, 64, 128, 2, False],
        [3, 128, 128, 1, False],
    ],
    "blocks5": [
        [3, 128, 256, 2, False],
        [5, 256, 256, 1, False],
        [5, 256, 256, 1, False],
        [5, 256, 256, 1, False],
        [5, 256, 256, 1, False],
        [5, 256, 256, 1, False],
    ],
    "blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True]]
}


def make_divisible(v, divisor=8, min_value=None):
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 filter_size,
                 num_filters,
                 stride,
                 num_groups=1):
        super().__init__()

        self.conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=num_groups,
            weight_attr=ParamAttr(initializer=KaimingNormal()),
            bias_attr=False)

        self.bn = nn.BatchNorm2D(
            num_filters,
            weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
            bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
        self.hardswish = nn.Hardswish()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.hardswish(x)
        return x


class DepthwiseSeparable(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 dw_size=3,
                 use_se=False):
        super().__init__()
        self.use_se = use_se
        self.dw_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_channels,
            filter_size=dw_size,
            stride=stride,
            num_groups=num_channels)
        if use_se:
            self.se = SEModule(num_channels)
        self.pw_conv = ConvBNLayer(
            num_channels=num_channels,
            filter_size=1,
            num_filters=num_filters,
            stride=1)

    def forward(self, x):
        x = self.dw_conv(x)
        if self.use_se:
            x = self.se(x)
        x = self.pw_conv(x)
        return x


class SEModule(nn.Layer):
    def __init__(self, channel, reduction=4):
        super().__init__()
        self.avg_pool = AdaptiveAvgPool2D(1)
        self.conv1 = Conv2D(
            in_channels=channel,
            out_channels=channel // reduction,
            kernel_size=1,
            stride=1,
            padding=0)
        self.relu = nn.ReLU()
        self.conv2 = Conv2D(
            in_channels=channel // reduction,
            out_channels=channel,
            kernel_size=1,
            stride=1,
            padding=0)
        self.hardsigmoid = nn.Hardsigmoid()

    def forward(self, x):
        identity = x
        x = self.avg_pool(x)
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.hardsigmoid(x)
        x = paddle.multiply(x=identity, y=x)
        return x


@register
@serializable
class LCNet(nn.Layer):
    def __init__(self, scale=1.0, feature_maps=[3, 4, 5]):
        super().__init__()
        self.scale = scale
        self.feature_maps = feature_maps

        out_channels = []

        self.conv1 = ConvBNLayer(
            num_channels=3,
            filter_size=3,
            num_filters=make_divisible(16 * scale),
            stride=2)

        self.blocks2 = nn.Sequential(* [
            DepthwiseSeparable(
                num_channels=make_divisible(in_c * scale),
                num_filters=make_divisible(out_c * scale),
                dw_size=k,
                stride=s,
                use_se=se)
            for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"])
        ])

        self.blocks3 = nn.Sequential(* [
            DepthwiseSeparable(
                num_channels=make_divisible(in_c * scale),
                num_filters=make_divisible(out_c * scale),
                dw_size=k,
                stride=s,
                use_se=se)
            for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"])
        ])

        out_channels.append(
            make_divisible(NET_CONFIG["blocks3"][-1][2] * scale))

        self.blocks4 = nn.Sequential(* [
            DepthwiseSeparable(
                num_channels=make_divisible(in_c * scale),
                num_filters=make_divisible(out_c * scale),
                dw_size=k,
                stride=s,
                use_se=se)
            for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"])
        ])

        out_channels.append(
            make_divisible(NET_CONFIG["blocks4"][-1][2] * scale))

        self.blocks5 = nn.Sequential(* [
            DepthwiseSeparable(
                num_channels=make_divisible(in_c * scale),
                num_filters=make_divisible(out_c * scale),
                dw_size=k,
                stride=s,
                use_se=se)
            for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"])
        ])

        out_channels.append(
            make_divisible(NET_CONFIG["blocks5"][-1][2] * scale))

        self.blocks6 = nn.Sequential(* [
            DepthwiseSeparable(
                num_channels=make_divisible(in_c * scale),
                num_filters=make_divisible(out_c * scale),
                dw_size=k,
                stride=s,
                use_se=se)
            for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"])
        ])

        out_channels.append(
            make_divisible(NET_CONFIG["blocks6"][-1][2] * scale))
        self._out_channels = [
            ch for idx, ch in enumerate(out_channels) if idx + 2 in feature_maps
        ]

    def forward(self, inputs):
        x = inputs['image']
        outs = []

        x = self.conv1(x)
        x = self.blocks2(x)
        x = self.blocks3(x)
        outs.append(x)
        x = self.blocks4(x)
        outs.append(x)
        x = self.blocks5(x)
        outs.append(x)
        x = self.blocks6(x)
        outs.append(x)
        outs = [o for i, o in enumerate(outs) if i + 2 in self.feature_maps]
        return outs

    @property
    def out_shape(self):
        return [ShapeSpec(channels=c) for c in self._out_channels]