First, the machine environment
Ubuntu16.04
Cuda9.0 + cudnn7.5
Python3.6
GCC 7.2
Anaconda3
Second, the environment configuration
Official Configuration Tutorial (Tutorial recommending such conduct)
1. Create a virtual environment using conda
conda create -n mmdetection python=3.6
source activate mmdetectionAnaconda mirror source ready to use
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
# 设置搜索时显示通道地址
conda config --set show_channel_urls yes
安装pytorch需要添加pytorch源
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
2. Install pytorch
pytorch installation directly to the official website of the command, but should otherwise not be used to remove the -c python pytorch Tsinghua source
For example, my cuda is 9.0, the command I used to
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0
#其中cudatoolkit只是一个cuda的工具包3.clone warehouse
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection4. Run the command
python setup.py developIs recommended before running the following packaged (Although this command is automatically download the following package, but very slow)
mmdetection step is now complete. Demo.py can create files, test wave. (Models are stored in the Amazon cloud servers, downloading will be slower)
from mmdet.apis import init_detector, inference_detector, show_result
import mmcv
config_file = 'configs/faster_rcnn_x101_32x4d_fpn_1x.py'
checkpoint_file = 'checkpoints/faster_rcnn_x101_32x4d_fpn_2x_20181218-0ed58946.pth'
# build the model from a config file and a checkpoint file
model = init_detector(config_file, checkpoint_file, device='cuda:0')
# test a single image and show the results
img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once
result = inference_detector(model, img)
# visualize the results in a new window
show_result(img, result, model.CLASSES)
# or save the visualization results to image files
#show_result(img, result, model.CLASSES, out_file='result.jpg')
# test a video and show the results
'''
video = mmcv.VideoReader('video.mp4')
for frame in video:
result = inference_detector(model, frame)
show_result(frame, result, model.CLASSES, wait_time=1)
Three -VOC training and test data sets format
1. Data set storage location (if the server already has this data set, we recommend using soft links)
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── VOCdevkit
│ │ ├── VOC2007
│ │ │ ├── Annotations
│ │ │ ├── JPEGImages
│ │ │ ├── ImageSets
│ │ │ │ ├── Main
│ │ │ │ │ ├── test.txt
│ │ │ │ │ ├── train.txt
|—— trainval.txt
2 code modifications
①mmdet / dataset / voc.py, the category into which category your own data set.
②mmdet / core / evaluation / class_name.py, the inside of the voc_classes () to change the category of your own data set.
③ modify the config file, I use the following sample RetinaNet
# model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs=True,
num_outs=5),
bbox_head=dict(
type='RetinaHead',
num_classes=21, #修改为自己数据集的类别数+1
in_channels=256,
stacked_convs=4,
feat_channels=256,
octave_base_scale=4,
scales_per_octave=3,
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[8, 16, 32, 64, 128],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False)
test_cfg = dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_thr=0.5),
max_per_img=100)
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'VOC2007/ImageSets/Main/train.txt',
img_prefix=data_root + 'VOC2007/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'VOC2007/ImageSets/Main/trainval.txt',
img_prefix=data_root + 'VOC2007/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
img_prefix=data_root + 'VOC2007/',
pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 32
device_ids = range(8)
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/retinanet_x101_32x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
Four training
python tools/train.py configs/RetinaNet.py --gpus 1 --gpus gpu use the number, the default is from the first card 0
Five test
Since only the file test.py coco data sets the eval, so the first calculation map generated by test.py pkl file, then eval_voc.py
python tools/test.py configs/RetinaNet.py work_dirs/latest.pth --out=eval/result.pklUse pkl computer file for each class ap
python tools/voc_eval.py eval/result.pkl configs/RetinaNet.pyMany use the command, refer to tutorial on GitHub
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