# 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. 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 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform import math __all__ = [ "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264" ] class BNACConvLayer(nn.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, pad=0, groups=1, act="relu", name=None): super(BNACConvLayer, self).__init__() self._batch_norm = BatchNorm( num_channels, act=act, param_attr=ParamAttr(name=name + '_bn_scale'), bias_attr=ParamAttr(name + '_bn_offset'), moving_mean_name=name + '_bn_mean', moving_variance_name=name + '_bn_variance') self._conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=pad, groups=groups, weight_attr=ParamAttr(name=name + "_weights"), bias_attr=False) def forward(self, input): y = self._batch_norm(input) y = self._conv(y) return y class DenseLayer(nn.Layer): def __init__(self, num_channels, growth_rate, bn_size, dropout, name=None): super(DenseLayer, self).__init__() self.dropout = dropout self.bn_ac_func1 = BNACConvLayer( num_channels=num_channels, num_filters=bn_size * growth_rate, filter_size=1, pad=0, stride=1, name=name + "_x1") self.bn_ac_func2 = BNACConvLayer( num_channels=bn_size * growth_rate, num_filters=growth_rate, filter_size=3, pad=1, stride=1, name=name + "_x2") if dropout: self.dropout_func = Dropout(p=dropout, mode="downscale_in_infer") def forward(self, input): conv = self.bn_ac_func1(input) conv = self.bn_ac_func2(conv) if self.dropout: conv = self.dropout_func(conv) conv = paddle.concat([input, conv], axis=1) return conv class DenseBlock(nn.Layer): def __init__(self, num_channels, num_layers, bn_size, growth_rate, dropout, name=None): super(DenseBlock, self).__init__() self.dropout = dropout self.dense_layer_func = [] pre_channel = num_channels for layer in range(num_layers): self.dense_layer_func.append( self.add_sublayer( "{}_{}".format(name, layer + 1), DenseLayer( num_channels=pre_channel, growth_rate=growth_rate, bn_size=bn_size, dropout=dropout, name=name + '_' + str(layer + 1)))) pre_channel = pre_channel + growth_rate def forward(self, input): conv = input for func in self.dense_layer_func: conv = func(conv) return conv class TransitionLayer(nn.Layer): def __init__(self, num_channels, num_output_features, name=None): super(TransitionLayer, self).__init__() self.conv_ac_func = BNACConvLayer( num_channels=num_channels, num_filters=num_output_features, filter_size=1, pad=0, stride=1, name=name) self.pool2d_avg = AvgPool2D(kernel_size=2, stride=2, padding=0) def forward(self, input): y = self.conv_ac_func(input) y = self.pool2d_avg(y) return y class ConvBNLayer(nn.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, pad=0, groups=1, act="relu", name=None): super(ConvBNLayer, self).__init__() self._conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=pad, groups=groups, weight_attr=ParamAttr(name=name + "_weights"), bias_attr=False) self._batch_norm = BatchNorm( num_filters, act=act, param_attr=ParamAttr(name=name + '_bn_scale'), bias_attr=ParamAttr(name + '_bn_offset'), moving_mean_name=name + '_bn_mean', moving_variance_name=name + '_bn_variance') def forward(self, input): y = self._conv(input) y = self._batch_norm(y) return y class DenseNet(nn.Layer): def __init__(self, layers=60, bn_size=4, dropout=0, class_dim=1000): super(DenseNet, self).__init__() supported_layers = [121, 161, 169, 201, 264] assert layers in supported_layers, \ "supported layers are {} but input layer is {}".format( supported_layers, layers) densenet_spec = { 121: (64, 32, [6, 12, 24, 16]), 161: (96, 48, [6, 12, 36, 24]), 169: (64, 32, [6, 12, 32, 32]), 201: (64, 32, [6, 12, 48, 32]), 264: (64, 32, [6, 12, 64, 48]) } num_init_features, growth_rate, block_config = densenet_spec[layers] self.conv1_func = ConvBNLayer( num_channels=3, num_filters=num_init_features, filter_size=7, stride=2, pad=3, act='relu', name="conv1") self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1) self.block_config = block_config self.dense_block_func_list = [] self.transition_func_list = [] pre_num_channels = num_init_features num_features = num_init_features for i, num_layers in enumerate(block_config): self.dense_block_func_list.append( self.add_sublayer( "db_conv_{}".format(i + 2), DenseBlock( num_channels=pre_num_channels, num_layers=num_layers, bn_size=bn_size, growth_rate=growth_rate, dropout=dropout, name='conv' + str(i + 2)))) num_features = num_features + num_layers * growth_rate pre_num_channels = num_features if i != len(block_config) - 1: self.transition_func_list.append( self.add_sublayer( "tr_conv{}_blk".format(i + 2), TransitionLayer( num_channels=pre_num_channels, num_output_features=num_features // 2, name='conv' + str(i + 2) + "_blk"))) pre_num_channels = num_features // 2 num_features = num_features // 2 self.batch_norm = BatchNorm( num_features, act="relu", param_attr=ParamAttr(name='conv5_blk_bn_scale'), bias_attr=ParamAttr(name='conv5_blk_bn_offset'), moving_mean_name='conv5_blk_bn_mean', moving_variance_name='conv5_blk_bn_variance') self.pool2d_avg = AdaptiveAvgPool2D(1) stdv = 1.0 / math.sqrt(num_features * 1.0) self.out = Linear( num_features, class_dim, weight_attr=ParamAttr( initializer=Uniform(-stdv, stdv), name="fc_weights"), bias_attr=ParamAttr(name="fc_offset")) def forward(self, input): conv = self.conv1_func(input) conv = self.pool2d_max(conv) for i, num_layers in enumerate(self.block_config): conv = self.dense_block_func_list[i](conv) if i != len(self.block_config) - 1: conv = self.transition_func_list[i](conv) conv = self.batch_norm(conv) y = self.pool2d_avg(conv) y = paddle.flatten(y, start_axis=1, stop_axis=-1) y = self.out(y) return y def DenseNet121(**args): model = DenseNet(layers=121, **args) return model def DenseNet161(**args): model = DenseNet(layers=161, **args) return model def DenseNet169(**args): model = DenseNet(layers=169, **args) return model def DenseNet201(**args): model = DenseNet(layers=201, **args) return model def DenseNet264(**args): model = DenseNet(layers=264, **args) return model