# -*- coding: utf-8 -*- # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # 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 print_function from __future__ import unicode_literals from .collect import SegConfig import numpy as np cfg = SegConfig() ########################## 基本配置 ########################################### # 均值，图像预处理减去的均值 #cfg.MEAN = [0.5, 0.5, 0.5] cfg.MEAN = [0.485, 0.456, 0.406] # 标准差，图像预处理除以标准差· cfg.STD = [0.229, 0.224, 0.225] # 批处理大小 cfg.TRAIN_BATCH_SIZE_PER_GPU = 2 cfg.TRAIN_BATCH_SIZE= 8 cfg.EVAL_BATCH_SIZE= 8 # 多进程训练总进程数 cfg.NUM_TRAINERS = 1 # 多进程训练进程ID cfg.TRAINER_ID = 0 ########################## 数据载入配置 ####################################### # 数据载入时的并发数, 建议值8 cfg.DATALOADER.NUM_WORKERS = 8 # 数据载入时缓存队列大小, 建议值256 cfg.DATALOADER.BUF_SIZE = 256 ########################## 数据集配置 ######################################### cfg.DATASET.DATASET_NAME = 'cityscapes' # 数据主目录目录 cfg.DATASET.DATA_DIR = './data_local/cityscapes/' # 训练集列表 cfg.DATASET.TRAIN_FILE_LIST = './data_local/cityscapes/train.list' # 训练集数量 cfg.DATASET.TRAIN_TOTAL_IMAGES = 5 # 验证集列表 cfg.DATASET.VAL_FILE_LIST = './data_local/cityscapes/val.list' # 验证数据数量 cfg.DATASET.VAL_TOTAL_IMAGES = 50 # 测试数据列表 cfg.DATASET.TEST_FILE_LIST = './data_local/cityscapes/test.list' # 测试数据数量 cfg.DATASET.TEST_TOTAL_IMAGES = 1525 # Tensorboard 可视化的数据集 cfg.DATASET.VIS_FILE_LIST = None # 类别数(需包括背景类) cfg.DATASET.NUM_CLASSES = 19 # 输入图像类型, 支持三通道'rgb',四通道'rgba',单通道灰度图'gray' cfg.DATASET.IMAGE_TYPE = 'rgb' # 输入图片的通道数 cfg.DATASET.DATA_DIM = 3 # 数据列表分割符, 默认为空格 cfg.DATASET.SEPARATOR = '\t' # 忽略的像素标签值, 默认为255，一般无需改动 cfg.DATASET.IGNORE_INDEX = 255 # 数据增强是图像的padding值 cfg.DATASET.PADDING_VALUE = [127.5, 127.5, 127.5] ########################### 数据增强配置 ###################################### cfg.DATAAUG.EXTRA = True cfg.DATAAUG.BASE_SIZE = 1024 cfg.DATAAUG.CROP_SIZE = 769 cfg.DATAAUG.RAND_SCALE_MIN = 0.75 cfg.DATAAUG.RAND_SCALE_MAX = 2.0 ########################### 训练配置 ########################################## # 模型保存路径 cfg.TRAIN.MODEL_SAVE_DIR = '' # 预训练模型路径 cfg.TRAIN.PRETRAINED_MODEL_DIR = '' # 是否resume，继续训练 cfg.TRAIN.RESUME_MODEL_DIR = '' # 是否使用多卡间同步BatchNorm均值和方差 cfg.TRAIN.SYNC_BATCH_NORM = True # 模型参数保存的epoch间隔数，可用来继续训练中断的模型 cfg.TRAIN.SNAPSHOT_EPOCH = 10 ########################### 模型优化相关配置 ################################## # 初始学习率 cfg.SOLVER.LR = 0.001 # 学习率下降方法, 支持poly piecewise cosine 三种 cfg.SOLVER.LR_POLICY = "poly" # 优化算法, 支持SGD和Adam两种算法 cfg.SOLVER.OPTIMIZER = "sgd" # 动量参数 cfg.SOLVER.MOMENTUM = 0.9 # 二阶矩估计的指数衰减率 cfg.SOLVER.MOMENTUM2 = 0.999 # 学习率Poly下降指数 cfg.SOLVER.POWER = 0.9 # step下降指数 cfg.SOLVER.GAMMA = 0.1 # step下降间隔 cfg.SOLVER.DECAY_EPOCH = [10, 20] # 学习率权重衰减，0-1 #cfg.SOLVER.WEIGHT_DECAY = 0.0001 cfg.SOLVER.WEIGHT_DECAY = 0.00004 # 训练开始epoch数，默认为1 cfg.SOLVER.BEGIN_EPOCH = 1 # 训练epoch数，正整数 cfg.SOLVER.NUM_EPOCHS = 30 # loss的选择，支持softmax_loss, bce_loss, dice_loss cfg.SOLVER.LOSS = ["softmax_loss"] # 是否开启warmup学习策略 cfg.SOLVER.LR_WARMUP = False # warmup的迭代次数 cfg.SOLVER.LR_WARMUP_STEPS = 2000 ########################## 测试配置 ########################################### # 测试模型路径 cfg.TEST.TEST_MODEL = '' cfg.TEST.BASE_SIZE = 2048 cfg.TEST.CROP_SIZE = 769 cfg.TEST.SLIDE_WINDOW = True ########################## 模型通用配置 ####################################### # 模型名称, 支持pspnet, deeplabv3, glore, ginet cfg.MODEL.MODEL_NAME = '' # BatchNorm类型: bn、gn(group_norm) cfg.MODEL.DEFAULT_NORM_TYPE = 'bn' # 多路损失加权值 cfg.MODEL.MULTI_LOSS_WEIGHT = [1.0, 0.4] # DEFAULT_NORM_TYPE为gn时group数 cfg.MODEL.DEFAULT_GROUP_NUMBER = 32 # 极小值, 防止分母除0溢出，一般无需改动 cfg.MODEL.DEFAULT_EPSILON = 1e-5 # BatchNorm动量, 一般无需改动 cfg.MODEL.BN_MOMENTUM = 0.99 # 是否使用FP16训练 cfg.MODEL.FP16 = False # 混合精度训练需对LOSS进行scale, 默认为动态scale，静态scale可以设置为512.0 cfg.MODEL.SCALE_LOSS = "DYNAMIC" # backbone network, (resnet, hrnet, xception_65, mobilenetv2) cfg.MODEL.BACKBONE= "resnet" # backbone_layer: 101 and 50 for resnet cfg.MODEL.BACKBONE_LAYERS=101 # strides= input.size / feature_maps.size cfg.MODEL.BACKBONE_OUTPUT_STRIDE=8 cfg.MODEL.BACKBONE_MULTI_GRID = False ########################## PSPNET模型配置 ###################################### # RESNET backbone scale 设置 cfg.MODEL.PSPNET.DEPTH_MULTIPLIER = 1 # Aux loss cfg.MODEL.PSPNET.AuxHead= True ########################## GloRe模型配置 ###################################### # RESNET backbone scale 设置 cfg.MODEL.GLORE.DEPTH_MULTIPLIER = 1 # Aux loss cfg.MODEL.GLORE.AuxHead= True ########################## DeepLabv3模型配置 #################################### # MobileNet v2 backbone scale 设置 cfg.MODEL.DEEPLABv3.DEPTH_MULTIPLIER = 1.0 # ASPP是否使用可分离卷积 cfg.MODEL.DEEPLABv3.ASPP_WITH_SEP_CONV = True cfg.MODEL.DEEPLABv3.AuxHead= True ########################## HRNET模型配置 ###################################### # HRNET STAGE2 设置 cfg.MODEL.HRNET.STAGE2.NUM_MODULES = 1 cfg.MODEL.HRNET.STAGE2.NUM_CHANNELS = [40, 80] # HRNET STAGE3 设置 cfg.MODEL.HRNET.STAGE3.NUM_MODULES = 4 cfg.MODEL.HRNET.STAGE3.NUM_CHANNELS = [40, 80, 160] # HRNET STAGE4 设置 cfg.MODEL.HRNET.STAGE4.NUM_MODULES = 3 cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS = [40, 80, 160, 320]