【Object Detection】【maskrcnn_benckmark】配置文件
文章目录
- 1. 配置文件的原理
- 2. 配置文件的内容
- 3. cfg python 对象与 yaml 配置文件的相互转换
- 4. 关于 yacs 和 yaml
- 4.1 PyYAML 的简单介绍
1. 配置文件的原理
maskrcnn_benchmark 使用配置文件设置模型、数据、优化器等选项。.../maskrcnn_benchmark/maskrcnn_benchmark/config/ 下的 defaults.py 定义并设置了默认选项。
在 defaults.py 中，使用 yacs 中的 CfgNode 类的一个实例对象 _C 作为保存配置信息的容器.
# .../yacs/config.py
class CfgNode(dict):
"""
CfgNode represents an internal node in the configuration tree. It's a simple
dict-like container that allows for attribute-based access to keys.
"""
...
# .../maskrcnn_benchmark/maskrcnn_benchmark/config/defaults.py
# 第4行
from yacs.config import CfgNode as CN
...
# 第21行
# CfgNode 的实例 _C 保存了所有配置信息。
_C = CN()
在 ../maskrcnn_benchmark/maskrcnn_benchmark/config/__init__.py 中， 将保存配置信息的变量 _C 封装为 cfg ，cfg 本质上是 CfgNode 类的一个实例对象.
# .../maskrcnn_benchmark/maskrcnn_benchmark/config/__init__.py
from .defaults import _C as cfg
在训练或测试脚本中，导入 cfg，作为默认配置信息，使用 CfgNode 类的实例方法 merge_from_file() 从配置文件获取配置信息，merge_from_list() 从命令行接收配置信息.
# .../maskrcnn_benchmark/tools/train_net.py
# 14行
from maskrcnn_benchmark.config import cfg
...
# 146行
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
args.config_file 从命令行接收配置文件的路径，args.opts 从命令行接收一个保存配置文件信息的列表格式.
一般修改配置文件就行了，args.opts 较少使用 .
配置文件是一个 yaml 格式文件。
在 ../maskrcnn_benchmark/configs/ 保存了所有的论文实现模型的配置文件，使用对应的配置文件配合训练脚本，即可复现论文中的模型.
也可以自定义配置文件，在自己的数据集上训练对应的模型.
2. 配置文件的内容
我将 .../maskrcnn_benchmark/maskrcnn_benchmark/config/defaults.py 中的配置信息的内容转化为 defaults.yaml，并按 defaults.py 顺序重新排版如下:
MODEL:
RPN_ONLY: false # True for training RPN only, False for training whole model
MASK_ON: false # True for instance segmentation task enabled, False for not
RETINANET_ON: false # True for using Retinanet, False for not
KEYPOINT_ON: false # True for key point task enabled, False for not
DEVICE: cuda # cuda or cpu
META_ARCHITECTURE: GeneralizedRCNN
CLS_AGNOSTIC_BBOX_REG: false
WEIGHT: ''
BACKBONE:
CONV_BODY: R-50-C4
FREEZE_CONV_BODY_AT: 2
FPN:
USE_GN: false
USE_RELU: false
GROUP_NORM:
DIM_PER_GP: -1
NUM_GROUPS: 32
EPSILON: 1.0e-05
RPN:
USE_FPN: false
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (16,)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
STRADDLE_THRESH: 0
FG_IOU_THRESHOLD: 0.7
BG_IOU_THRESHOLD: 0.3
BATCH_SIZE_PER_IMAGE: 256
POSITIVE_FRACTION: 0.5
PRE_NMS_TOP_N_TRAIN: 12000
PRE_NMS_TOP_N_TEST: 6000
POST_NMS_TOP_N_TRAIN: 2000
POST_NMS_TOP_N_TEST: 1000
NMS_THRESH: 0.7
MIN_SIZE: 0
FPN_POST_NMS_TOP_N_TRAIN: 2000
FPN_POST_NMS_TOP_N_TEST: 2000
FPN_POST_NMS_PER_BATCH: true
RPN_HEAD: SingleConvRPNHead
ROI_HEADS:
USE_FPN: false
FG_IOU_THRESHOLD: 0.5
BG_IOU_THRESHOLD: 0.5
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
BATCH_SIZE_PER_IMAGE: 512
POSITIVE_FRACTION: 0.25
SCORE_THRESH: 0.05
NMS: 0.5
DETECTIONS_PER_IMG: 100
ROI_BOX_HEAD:
FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
PREDICTOR: FastRCNNPredictor
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (1.0 / 16,)
NUM_CLASSES: 81
MLP_HEAD_DIM: 1024
USE_GN: false
DILATION: 1
CONV_HEAD_DIM: 256
NUM_STACKED_CONVS: 4
ROI_MASK_HEAD:
FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
PREDICTOR: MaskRCNNC4Predictor
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (1.0 / 16,)
MLP_HEAD_DIM: 1024
CONV_LAYERS: (256, 256, 256, 256)
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: true
POSTPROCESS_MASKS: false
POSTPROCESS_MASKS_THRESHOLD: 0.5
DILATION: 1
USE_GN: false
ROI_KEYPOINT_HEAD:
FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
PREDICTOR: KeypointRCNNPredictor
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (1.0 / 16,)
MLP_HEAD_DIM: 1024
CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
RESOLUTION: 14
NUM_CLASSES: 17
SHARE_BOX_FEATURE_EXTRACTOR: true
RESNETS:
NUM_GROUPS: 1
WIDTH_PER_GROUP: 64
STRIDE_IN_1X1: true
TRANS_FUNC: BottleneckWithFixedBatchNorm
STEM_FUNC: StemWithFixedBatchNorm
RES5_DILATION: 1
BACKBONE_OUT_CHANNELS: 1024
RES2_OUT_CHANNELS: 256
STEM_OUT_CHANNELS: 64
STAGE_WITH_DCN: (false, false, false, false)
WITH_MODULATED_DCN: false
DEFORMABLE_GROUPS: 1
RETINANET:
NUM_CLASSES: 81
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
ANCHOR_STRIDES: (8, 16, 32, 64, 128)
STRADDLE_THRESH: 0
OCTAVE: 2.0
SCALES_PER_OCTAVE: 3
USE_C5: true
NUM_CONVS: 4
BBOX_REG_WEIGHT: 4.0
BBOX_REG_BETA: 0.11
PRE_NMS_TOP_N: 1000
FG_IOU_THRESHOLD: 0.5
BG_IOU_THRESHOLD: 0.4
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
PRIOR_PROB: 0.01
INFERENCE_TH: 0.05
NMS_TH: 0.4
FBNET:
ARCH: default
ARCH_DEF: ''
BN_TYPE: bn
SCALE_FACTOR: 1.0
WIDTH_DIVISOR: 1
DW_CONV_SKIP_BN: true
DW_CONV_SKIP_RELU: true
DET_HEAD_LAST_SCALE: 1.0
DET_HEAD_BLOCKS: []
DET_HEAD_STRIDE: 0
KPTS_HEAD_LAST_SCALE: 0.0
KPTS_HEAD_BLOCKS: []
KPTS_HEAD_STRIDE: 0
MASK_HEAD_LAST_SCALE: 0.0
MASK_HEAD_BLOCKS: []
MASK_HEAD_STRIDE: 0
RPN_HEAD_BLOCKS: 0
RPN_BN_TYPE: ''
INPUT:
MIN_SIZE_TRAIN: (800,)
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
PIXEL_MEAN: (102.9801, 115.9465, 122.7717)
PIXEL_STD: (1.0, 1.0, 1.0)
TO_BGR255: true
BRIGHTNESS: 0.0
CONTRAST: 0.0
SATURATION: 0.0
HUE: 0.0
HORIZONTAL_FLIP_PROB_TRAIN: 0.5
VERTICAL_FLIP_PROB_TRAIN: 0.0
DATASETS:
TRAIN: []
TEST: []
DATALOADER:
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 0
ASPECT_RATIO_GROUPING: true
SOLVER:
MAX_ITER: 40000
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
MOMENTUM: 0.9
WEIGHT_DECAY: 0.0005
WEIGHT_DECAY_BIAS: 0
GAMMA: 0.1
STEPS: (30000,)
WARMUP_FACTOR: 1.0 / 3
WARMUP_ITERS: 500
WARMUP_METHOD: linear
CHECKPOINT_PERIOD: 2500
TEST_PERIOD: 0
IMS_PER_BATCH: 16
TEST:
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 8
DETECTIONS_PER_IMG: 100
BBOX_AUG:
ENABLED: false
H_FLIP: false
SCALES: []
MAX_SIZE: 4000
SCALE_H_FLIP: false
OUTPUT_DIR: .
PATHS_CATALOG: /home/lucifer/Projects/05_maskrcnn-benchmark/maskrcnn-benchmark/maskrcnn_benchmark/config/paths_catalog.py
DTYPE: float32
AMP_VERBOSE: false
3. cfg python 对象与 yaml 配置文件的相互转换
（1）cfg 到 yaml
from maskrcnn_benchmark.config import cfg
yaml_file_path = 'defaults.yaml'
with open(yaml_file_path, 'w') as yaml_file:
cfg.dump(stream=yaml_file)
（2）yaml 到 cfg
from yacs.config import CfgNode as CN
# or
from maskrcnn_benchmark.config import cfg
yaml_file_path = 'e2e_faster_rcnn_self.yaml`'
with open(yaml_file_path, 'r') as yaml_file:
cfg = CN.load_cfg(yaml_file)
# or
cfg = cfg.load_cfg(yaml_file)
load_cfg() 是类方法，不会改动 cfg.
4. 关于 yacs 和 yaml
使用配置文件，可以使训练模型更为便利和灵活。maskrcnn_benchmark 的配置文件基于 yacs 实现。 yacs 是一个轻量化的配置文件管理库，它基于 PyYAML 库实现。PyYaml 可以轻松的在 Python 与 YAML 之间交互信息.
4.1 PyYAML 的简单介绍
（1）yaml.safe_dump()
import yaml
data = {
'name': 'Lucy',
'age': 18,
'birth': [2000, 12, 26],
'mother': {
'name': 'Kitty',
'age': 30,
'birth': [1988, 1, 26]
}
}
yaml_file_path = 'data.yaml'
with open(yaml_file_path, 'w') as yaml_file:
yaml.safe_dump(data, yaml_file)
content = yaml.safe_dump(data)
print(content)
输出
age: 18
birth:
- 2000
- 12
- 26
mother:
age: 30
birth:
- 1988
- 1
- 26
name: Kitty
name: Lucy
得到 data.yaml
age: 18
birth:
- 2000
- 12
- 26
mother:
age: 30
birth:
- 1988
- 1
- 26
name: Kitty
name: Lucy
（2）yaml.save_load()
设置 data.yaml 为
age: 18
birth:
- 2000
- 12
- 26
mother:
age: 30
birth:
- 1988
- 1
- 26
name: Kitty
name: Lucy
father:
age: 34
birth: [1984, 12, 26]
代码
import yaml
yaml_file_path = 'data.yaml'
with open(yaml_file_path, 'r') as yaml_file:
content = yaml.safe_load(yaml_file)
print(content)
输出
<class 'dict'>