标签: 模型参数配置
以faster_rcnn_r50_fpn_1x_coco.yml
为例,这个模型由五个子配置文件组成:
- 数据配置文件
coco_detection.yml
# 数据评估类型
metric: COCO
# 数据集的类别数
num_classes: 80
# TrainDataset
TrainDataset:
!COCODataSet
# 图像数据路径,相对 dataset_dir 路径,os.path.join(dataset_dir, image_dir)
image_dir: train2017
# 标注文件路径,相对 dataset_dir 路径,os.path.join(dataset_dir, anno_path)
anno_path: annotations/instances_train2017.json
# 数据文件夹
dataset_dir: dataset/coco
# data_fields
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
EvalDataset:
!COCODataSet
# 图像数据路径,相对 dataset_dir 路径,os.path.join(dataset_dir, image_dir)
image_dir: val2017
# 标注文件路径,相对 dataset_dir 路径,os.path.join(dataset_dir, anno_path)
anno_path: annotations/instances_val2017.json
# 数据文件夹
dataset_dir: dataset/coco
TestDataset:
!ImageFolder
# 标注文件路径,相对 dataset_dir 路径,os.path.join(dataset_dir, anno_path)
anno_path: annotations/instances_val2017.json
- 优化器配置文件
optimizer_1x.yml
# 总训练轮数
epoch: 12
# 学习率设置
LearningRate:
# 默认为8卡训学习率
base_lr: 0.01
# 学习率调整策略
schedulers:
- !PiecewiseDecay
gamma: 0.1
# 学习率变化位置(轮数)
milestones: [8, 11]
- !LinearWarmup
start_factor: 0.1
steps: 1000
# 优化器
OptimizerBuilder:
# 优化器
optimizer:
momentum: 0.9
type: Momentum
# 正则化
regularizer:
factor: 0.0001
type: L2
- 数据读取配置文件
faster_fpn_reader.yml
# 每张GPU reader进程个数
worker_num: 2
# 训练数据
TrainReader:
# 训练数据transforms
sample_transforms:
- Decode: {}
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
- RandomFlip: {prob: 0.5}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
# 由于模型存在FPN结构,输入图片需要padding为32的倍数
- PadBatch: {pad_to_stride: 32}
# 训练时batch_size
batch_size: 1
# 读取数据是否乱序
shuffle: true
# 是否丢弃最后不能完整组成batch的数据
drop_last: true
# 表示reader是否对gt进行组batch的操作,在rcnn系列算法中设置为false,得到的gt格式为list[Tensor]
collate_batch: false
# 评估数据
EvalReader:
# 评估数据transforms
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
# 由于模型存在FPN结构,输入图片需要padding为32的倍数
- PadBatch: {pad_to_stride: 32}
# 评估时batch_size
batch_size: 1
# 读取数据是否乱序
shuffle: false
# 是否丢弃最后不能完整组成batch的数据
drop_last: false
# 是否丢弃没有标注的数据
drop_empty: false
# 测试数据
TestReader:
# 测试数据transforms
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
# 由于模型存在FPN结构,输入图片需要padding为32的倍数
- PadBatch: {pad_to_stride: 32}
# 测试时batch_size
batch_size: 1
# 读取数据是否乱序
shuffle: false
# 是否丢弃最后不能完整组成batch的数据
drop_last: false
- 模型配置文件
faster_rcnn_r50_fpn.yml
# 模型结构类型
architecture: FasterRCNN
# 预训练模型地址
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
# FasterRCNN
FasterRCNN:
# backbone
backbone: ResNet
# neck
neck: FPN
# rpn_head
rpn_head: RPNHead
# bbox_head
bbox_head: BBoxHead
# post process
bbox_post_process: BBoxPostProcess
# backbone
ResNet:
# index 0 stands for res2
depth: 50
# norm_type,可设置参数:bn 或 sync_bn
norm_type: bn
# freeze_at index, 0 represent res2
freeze_at: 0
# return_idx
return_idx: [0,1,2,3]
# num_stages
num_stages: 4
# FPN
FPN:
# channel of FPN
out_channel: 256
# RPNHead
RPNHead:
# anchor generator
anchor_generator:
aspect_ratios: [0.5, 1.0, 2.0]
anchor_sizes: [[32], [64], [128], [256], [512]]
strides: [4, 8, 16, 32, 64]
# rpn_target_assign
rpn_target_assign:
batch_size_per_im: 256
fg_fraction: 0.5
negative_overlap: 0.3
positive_overlap: 0.7
use_random: True
# 训练时生成proposal的参数
train_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 2000
post_nms_top_n: 1000
topk_after_collect: True
# 评估时生成proposal的参数
test_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 1000
post_nms_top_n: 1000
# BBoxHead
BBoxHead:
# TwoFCHead as BBoxHead
head: TwoFCHead
# roi align
roi_extractor:
resolution: 7
sampling_ratio: 0
aligned: True
# bbox_assigner
bbox_assigner: BBoxAssigner
# BBoxAssigner
BBoxAssigner:
# batch_size_per_im
batch_size_per_im: 512
# 背景阈值
bg_thresh: 0.5
# 前景阈值
fg_thresh: 0.5
# 前景比例
fg_fraction: 0.25
# 是否随机采样
use_random: True
# TwoFCHead
TwoFCHead:
# TwoFCHead特征维度
out_channel: 1024
# BBoxPostProcess
BBoxPostProcess:
# 解码
decode: RCNNBox
# nms
nms:
# 使用MultiClassNMS
name: MultiClassNMS
keep_top_k: 100
score_threshold: 0.05
nms_threshold: 0.5
- 运行时置文件
runtime.yml
# 是否使用gpu
use_gpu: true
# 日志打印间隔
log_iter: 20
# save_dir
save_dir: output
# 模型保存间隔时间
snapshot_epoch: 1