# Copyright (c) 2022 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 paddlers.models.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]