# coding: utf8 # copyright (c) 2019 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 contextlib import paddle import paddle.fluid as fluid from utils.config import cfg from models.libs.model_libs import scope, name_scope from models.libs.model_libs import bn, bn_relu, relu from models.libs.model_libs import conv from models.libs.model_libs import separate_conv from models.backbone.mobilenet_v2 import MobileNetV2 as mobilenet_backbone from models.backbone.xception import Xception as xception_backbone def encoder(input): # 编码器配置，采用ASPP架构，pooling + 1x1_conv + 三个不同尺度的空洞卷积并行, concat后1x1conv # ASPP_WITH_SEP_CONV：默认为真，使用depthwise可分离卷积，否则使用普通卷积 # OUTPUT_STRIDE: 下采样倍数，8或16，决定aspp_ratios大小 # aspp_ratios：ASPP模块空洞卷积的采样率 if cfg.MODEL.DEEPLAB.OUTPUT_STRIDE == 16: aspp_ratios = [6, 12, 18] elif cfg.MODEL.DEEPLAB.OUTPUT_STRIDE == 8: aspp_ratios = [12, 24, 36] else: raise Exception("deeplab only support stride 8 or 16") param_attr = fluid.ParamAttr( name=name_scope + 'weights', regularizer=None, initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.06)) with scope('encoder'): channel = 256 with scope("image_pool"): image_avg = fluid.layers.reduce_mean( input, [2, 3], keep_dim=True) image_avg = bn_relu( conv( image_avg, channel, 1, 1, groups=1, padding=0, param_attr=param_attr)) image_avg = fluid.layers.resize_bilinear(image_avg, input.shape[2:]) with scope("aspp0"): aspp0 = bn_relu( conv( input, channel, 1, 1, groups=1, padding=0, param_attr=param_attr)) with scope("aspp1"): if cfg.MODEL.DEEPLAB.ASPP_WITH_SEP_CONV: aspp1 = separate_conv( input, channel, 1, 3, dilation=aspp_ratios[0], act=relu) else: aspp1 = bn_relu( conv( input, channel, stride=1, filter_size=3, dilation=aspp_ratios[0], padding=aspp_ratios[0], param_attr=param_attr)) with scope("aspp2"): if cfg.MODEL.DEEPLAB.ASPP_WITH_SEP_CONV: aspp2 = separate_conv( input, channel, 1, 3, dilation=aspp_ratios[1], act=relu) else: aspp2 = bn_relu( conv( input, channel, stride=1, filter_size=3, dilation=aspp_ratios[1], padding=aspp_ratios[1], param_attr=param_attr)) with scope("aspp3"): if cfg.MODEL.DEEPLAB.ASPP_WITH_SEP_CONV: aspp3 = separate_conv( input, channel, 1, 3, dilation=aspp_ratios[2], act=relu) else: aspp3 = bn_relu( conv( input, channel, stride=1, filter_size=3, dilation=aspp_ratios[2], padding=aspp_ratios[2], param_attr=param_attr)) with scope("concat"): data = fluid.layers.concat([image_avg, aspp0, aspp1, aspp2, aspp3], axis=1) data = bn_relu( conv( data, channel, 1, 1, groups=1, padding=0, param_attr=param_attr)) data = fluid.layers.dropout(data, 0.9) return data def decoder(encode_data, decode_shortcut): # 解码器配置 # encode_data：编码器输出 # decode_shortcut: 从backbone引出的分支, resize后与encode_data concat # DECODER_USE_SEP_CONV: 默认为真，则concat后连接两个可分离卷积，否则为普通卷积 param_attr = fluid.ParamAttr( name=name_scope + 'weights', regularizer=None, initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.06)) with scope('decoder'): with scope('concat'): decode_shortcut = bn_relu( conv( decode_shortcut, 48, 1, 1, groups=1, padding=0, param_attr=param_attr)) encode_data = fluid.layers.resize_bilinear( encode_data, decode_shortcut.shape[2:]) encode_data = fluid.layers.concat([encode_data, decode_shortcut], axis=1) if cfg.MODEL.DEEPLAB.DECODER_USE_SEP_CONV: with scope("separable_conv1"): encode_data = separate_conv( encode_data, 256, 1, 3, dilation=1, act=relu) with scope("separable_conv2"): encode_data = separate_conv( encode_data, 256, 1, 3, dilation=1, act=relu) else: with scope("decoder_conv1"): encode_data = bn_relu( conv( encode_data, 256, stride=1, filter_size=3, dilation=1, padding=1, param_attr=param_attr)) with scope("decoder_conv2"): encode_data = bn_relu( conv( encode_data, 256, stride=1, filter_size=3, dilation=1, padding=1, param_attr=param_attr)) return encode_data def nas_backbone(input, arch): # scale = cfg.MODEL.DEEPLAB.DEPTH_MULTIPLIER # output_stride = cfg.MODEL.DEEPLAB.OUTPUT_STRIDE # model = mobilenet_backbone(scale=scale, output_stride=output_stride) end_points = 8 decode_point = 3 data, decode_shortcuts = arch( input, end_points=end_points, return_block=decode_point, output_stride=16) decode_shortcut = decode_shortcuts[decode_point] return data, decode_shortcut def deeplabv3p_nas(img, num_classes, arch=None): data, decode_shortcut = nas_backbone(img, arch) # 编码器解码器设置 cfg.MODEL.DEFAULT_EPSILON = 1e-5 if cfg.MODEL.DEEPLAB.ENCODER_WITH_ASPP: data = encoder(data) if cfg.MODEL.DEEPLAB.ENABLE_DECODER: data = decoder(data, decode_shortcut) # 根据类别数设置最后一个卷积层输出，并resize到图片原始尺寸 param_attr = fluid.ParamAttr( name=name_scope + 'weights', regularizer=fluid.regularizer.L2DecayRegularizer( regularization_coeff=0.0), initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01)) with scope('logit'): logit = conv( data, num_classes, 1, stride=1, padding=0, bias_attr=True, param_attr=param_attr) logit = fluid.layers.resize_bilinear(logit, img.shape[2:]) return logit