#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 math import paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr __all__ = [ "SE_ResNeXt", "SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d", "SE_ResNeXt152_32x4d" ] class SE_ResNeXt(): def __init__(self, layers=50): self.layers = layers def net(self, input, class_dim=1000): layers = self.layers supported_layers = [50, 101, 152] assert layers in supported_layers, \ "supported layers are {} but input layer is {}".format(supported_layers, layers) if layers == 50: cardinality = 32 reduction_ratio = 16 depth = [3, 4, 6, 3] num_filters = [128, 256, 512, 1024] conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=7, stride=2, act='relu', name='conv1', ) conv = fluid.layers.pool2d( input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max', use_cudnn=False) elif layers == 101: cardinality = 32 reduction_ratio = 16 depth = [3, 4, 23, 3] num_filters = [128, 256, 512, 1024] conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=7, stride=2, act='relu', name="conv1", ) conv = fluid.layers.pool2d( input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max', use_cudnn=False) elif layers == 152: cardinality = 64 reduction_ratio = 16 depth = [3, 8, 36, 3] num_filters = [128, 256, 512, 1024] conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=3, stride=2, act='relu', name='conv1') conv = self.conv_bn_layer( input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv2') conv = self.conv_bn_layer( input=conv, num_filters=128, filter_size=3, stride=1, act='relu', name='conv3') conv = fluid.layers.pool2d( input=conv, pool_size=3, pool_stride=2, pool_padding=1, \ pool_type='max', use_cudnn=False) n = 1 if layers == 50 or layers == 101 else 3 for block in range(len(depth)): n += 1 for i in range(depth[block]): conv = self.bottleneck_block( input=conv, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, cardinality=cardinality, reduction_ratio=reduction_ratio, name=str(n) + '_' + str(i + 1)) pool = fluid.layers.pool2d( input=conv, pool_type='avg', global_pooling=True, use_cudnn=False) drop = fluid.layers.dropout(x=pool, dropout_prob=0.5) stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0) out = fluid.layers.fc( input=drop, size=class_dim, param_attr=ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), name='fc6_weights'), bias_attr=ParamAttr(name='fc6_offset')) return out def shortcut(self, input, ch_out, stride, name): ch_in = input.shape[1] if ch_in != ch_out or stride != 1: filter_size = 1 return self.conv_bn_layer( input, ch_out, filter_size, stride, name='conv' + name + '_prj') else: return input def bottleneck_block(self, input, num_filters, stride, cardinality, reduction_ratio, name=None): conv0 = self.conv_bn_layer( input=input, num_filters=num_filters, filter_size=1, act='relu', name='conv' + name + '_x1') conv1 = self.conv_bn_layer( input=conv0, num_filters=num_filters, filter_size=3, stride=stride, groups=cardinality, act='relu', name='conv' + name + '_x2') conv2 = self.conv_bn_layer( input=conv1, num_filters=num_filters * 2, filter_size=1, act=None, name='conv' + name + '_x3') scale = self.squeeze_excitation( input=conv2, num_channels=num_filters * 2, reduction_ratio=reduction_ratio, name='fc' + name) short = self.shortcut(input, num_filters * 2, stride, name=name) return fluid.layers.elementwise_add(x=short, y=scale, act='relu') def conv_bn_layer(self, input, num_filters, filter_size, stride=1, groups=1, act=None, name=None): conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, bias_attr=False, param_attr=ParamAttr(name=name + '_weights'), ) bn_name = name + "_bn" return fluid.layers.batch_norm( input=conv, act=act, param_attr=ParamAttr(name=bn_name + '_scale'), bias_attr=ParamAttr(bn_name + '_offset'), moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance') def squeeze_excitation(self, input, num_channels, reduction_ratio, name=None): pool = fluid.layers.pool2d( input=input, pool_type='avg', global_pooling=True, use_cudnn=False) stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) squeeze = fluid.layers.fc( input=pool, size=num_channels // reduction_ratio, act='relu', param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), name=name + '_sqz_weights'), bias_attr=ParamAttr(name=name + '_sqz_offset')) stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0) excitation = fluid.layers.fc( input=squeeze, size=num_channels, act='sigmoid', param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), name=name + '_exc_weights'), bias_attr=ParamAttr(name=name + '_exc_offset')) scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0) return scale def SE_ResNeXt50_32x4d(): model = SE_ResNeXt(layers=50) return model def SE_ResNeXt101_32x4d(): model = SE_ResNeXt(layers=101) return model def SE_ResNeXt152_32x4d(): model = SE_ResNeXt(layers=152) return model