# 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. # This code is refer from: https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/arch/backbone/legendary_models/pp_lcnet.py from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import KaimingNormal import math import numpy as np import paddle from paddle import ParamAttr, reshape, transpose, concat, split import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import KaimingNormal import math from paddle.nn.functional import hardswish, hardsigmoid from paddle.regularizer import L2Decay class ConvBNLayer(nn.Layer): def __init__(self, num_channels, filter_size, num_filters, stride, padding, channels=None, num_groups=1, act='hard_swish'): super(ConvBNLayer, self).__init__() self._conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=padding, groups=num_groups, weight_attr=ParamAttr(initializer=KaimingNormal()), bias_attr=False) self._batch_norm = BatchNorm( num_filters, act=act, param_attr=ParamAttr(regularizer=L2Decay(0.0)), bias_attr=ParamAttr(regularizer=L2Decay(0.0))) def forward(self, inputs): y = self._conv(inputs) y = self._batch_norm(y) return y class DepthwiseSeparable(nn.Layer): def __init__(self, num_channels, num_filters1, num_filters2, num_groups, stride, scale, dw_size=3, padding=1, use_se=False): super(DepthwiseSeparable, self).__init__() self.use_se = use_se self._depthwise_conv = ConvBNLayer( num_channels=num_channels, num_filters=int(num_filters1 * scale), filter_size=dw_size, stride=stride, padding=padding, num_groups=int(num_groups * scale)) if use_se: self._se = SEModule(int(num_filters1 * scale)) self._pointwise_conv = ConvBNLayer( num_channels=int(num_filters1 * scale), filter_size=1, num_filters=int(num_filters2 * scale), stride=1, padding=0) def forward(self, inputs): y = self._depthwise_conv(inputs) if self.use_se: y = self._se(y) y = self._pointwise_conv(y) return y class MobileNetV1Enhance(nn.Layer): def __init__(self, in_channels=3, scale=0.5, **kwargs): super().__init__() self.scale = scale self.block_list = [] self.conv1 = ConvBNLayer( num_channels=3, filter_size=3, channels=3, num_filters=int(32 * scale), stride=2, padding=1) conv2_1 = DepthwiseSeparable( num_channels=int(32 * scale), num_filters1=32, num_filters2=64, num_groups=32, stride=1, scale=scale) self.block_list.append(conv2_1) conv2_2 = DepthwiseSeparable( num_channels=int(64 * scale), num_filters1=64, num_filters2=128, num_groups=64, stride=1, scale=scale) self.block_list.append(conv2_2) conv3_1 = DepthwiseSeparable( num_channels=int(128 * scale), num_filters1=128, num_filters2=128, num_groups=128, stride=1, scale=scale) self.block_list.append(conv3_1) conv3_2 = DepthwiseSeparable( num_channels=int(128 * scale), num_filters1=128, num_filters2=256, num_groups=128, stride=(2, 1), scale=scale) self.block_list.append(conv3_2) conv4_1 = DepthwiseSeparable( num_channels=int(256 * scale), num_filters1=256, num_filters2=256, num_groups=256, stride=1, scale=scale) self.block_list.append(conv4_1) conv4_2 = DepthwiseSeparable( num_channels=int(256 * scale), num_filters1=256, num_filters2=512, num_groups=256, stride=(2, 1), scale=scale) self.block_list.append(conv4_2) for _ in range(5): conv5 = DepthwiseSeparable( num_channels=int(512 * scale), num_filters1=512, num_filters2=512, num_groups=512, stride=1, dw_size=5, padding=2, scale=scale, use_se=False) self.block_list.append(conv5) conv5_6 = DepthwiseSeparable( num_channels=int(512 * scale), num_filters1=512, num_filters2=1024, num_groups=512, stride=(2, 1), dw_size=5, padding=2, scale=scale, use_se=True) self.block_list.append(conv5_6) conv6 = DepthwiseSeparable( num_channels=int(1024 * scale), num_filters1=1024, num_filters2=1024, num_groups=1024, stride=1, dw_size=5, padding=2, use_se=True, scale=scale) self.block_list.append(conv6) self.block_list = nn.Sequential(*self.block_list) self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) self.out_channels = int(1024 * scale) def forward(self, inputs): y = self.conv1(inputs) y = self.block_list(y) y = self.pool(y) return y class SEModule(nn.Layer): def __init__(self, channel, reduction=4): super(SEModule, self).__init__() self.avg_pool = AdaptiveAvgPool2D(1) self.conv1 = Conv2D( in_channels=channel, out_channels=channel // reduction, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr(), bias_attr=ParamAttr()) self.conv2 = Conv2D( in_channels=channel // reduction, out_channels=channel, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr(), bias_attr=ParamAttr()) def forward(self, inputs): outputs = self.avg_pool(inputs) outputs = self.conv1(outputs) outputs = F.relu(outputs) outputs = self.conv2(outputs) outputs = hardsigmoid(outputs) return paddle.multiply(x=inputs, y=outputs)