jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
195 lines
6.3 KiB
Python
195 lines
6.3 KiB
Python
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
|
model = dict(
|
|
type='EncoderDecoder',
|
|
pretrained = None,
|
|
backbone=dict(
|
|
type='ResNetV1c',
|
|
depth=101,
|
|
num_stages=4,
|
|
out_indices=(0, 1, 2, 3),
|
|
dilations=(1, 1, 2, 4),
|
|
strides=(1, 2, 1, 1),
|
|
norm_cfg=dict(type='SyncBN', requires_grad=True),
|
|
norm_eval=False,
|
|
style='pytorch',
|
|
contract_dilation=True),
|
|
decode_head=dict(
|
|
type='DAHead',
|
|
in_channels=2048,
|
|
in_index=3,
|
|
channels=512,
|
|
pam_channels=64,
|
|
dropout_ratio=0.1,
|
|
num_classes=5,
|
|
norm_cfg=dict(type='SyncBN', requires_grad=True),
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
|
auxiliary_head=dict(
|
|
type='FCNHead',
|
|
in_channels=1024,
|
|
in_index=2,
|
|
channels=256,
|
|
num_convs=1,
|
|
concat_input=False,
|
|
dropout_ratio=0.1,
|
|
num_classes=5,
|
|
norm_cfg=dict(type='SyncBN', requires_grad=True),
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
|
|
train_cfg=dict(),
|
|
test_cfg=dict(mode='whole'))
|
|
dataset_type = 'CustomDataset'
|
|
data_root = 'data/my_dataset_v7'
|
|
img_norm_cfg = dict(
|
|
mean=[132.01150988, 117.50650988, 102.74611112],
|
|
std=[48.42106271, 49.25131565, 52.27428472],
|
|
to_rgb=True)
|
|
crop_size = (512, 512)
|
|
train_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(type='LoadAnnotations'),
|
|
dict(type='Resize', img_scale=(512, 512)),
|
|
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
|
|
dict(type='RandomFlip', flip_ratio=0.5),
|
|
dict(type='PhotoMetricDistortion'),
|
|
dict(
|
|
type='Normalize',
|
|
mean=[132.01150988, 117.50650988, 102.74611112],
|
|
std=[48.42106271, 49.25131565, 52.27428472],
|
|
to_rgb=True),
|
|
dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
|
|
dict(type='DefaultFormatBundle'),
|
|
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
|
|
]
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='MultiScaleFlipAug',
|
|
img_scale=(512, 512),
|
|
flip=False,
|
|
transforms=[
|
|
dict(type='Resize', keep_ratio=True),
|
|
dict(type='RandomFlip'),
|
|
dict(
|
|
type='Normalize',
|
|
mean=[132.01150988, 117.50650988, 102.74611112],
|
|
std=[48.42106271, 49.25131565, 52.27428472],
|
|
to_rgb=True),
|
|
dict(type='ImageToTensor', keys=['img']),
|
|
dict(type='Collect', keys=['img'])
|
|
])
|
|
]
|
|
data = dict(
|
|
samples_per_gpu=2,
|
|
workers_per_gpu=2,
|
|
train=dict(
|
|
type='CustomDataset',
|
|
data_root='data/my_dataset_v7',
|
|
img_dir='img_dir/train',
|
|
ann_dir='ann_dir/train',
|
|
pipeline=[
|
|
dict(type='LoadImageFromFile'),
|
|
dict(type='LoadAnnotations'),
|
|
dict(type='Resize', img_scale=(512, 512)),
|
|
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
|
|
dict(type='RandomFlip', flip_ratio=0.5),
|
|
dict(type='PhotoMetricDistortion'),
|
|
dict(
|
|
type='Normalize',
|
|
mean=[132.01150988, 117.50650988, 102.74611112],
|
|
std=[48.42106271, 49.25131565, 52.27428472],
|
|
to_rgb=True),
|
|
dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
|
|
dict(type='DefaultFormatBundle'),
|
|
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
|
|
]),
|
|
val=dict(
|
|
type='CustomDataset',
|
|
data_root='data/my_dataset_v7',
|
|
img_dir='img_dir/val',
|
|
ann_dir='ann_dir/val',
|
|
pipeline=[
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='MultiScaleFlipAug',
|
|
img_scale=(512, 512),
|
|
flip=False,
|
|
transforms=[
|
|
dict(type='Resize', keep_ratio=True),
|
|
dict(type='RandomFlip'),
|
|
dict(
|
|
type='Normalize',
|
|
mean=[132.01150988, 117.50650988, 102.74611112],
|
|
std=[48.42106271, 49.25131565, 52.27428472],
|
|
to_rgb=True),
|
|
dict(type='ImageToTensor', keys=['img']),
|
|
dict(type='Collect', keys=['img'])
|
|
])
|
|
]),
|
|
test=dict(
|
|
type='CustomDataset',
|
|
data_root='data/my_dataset_v7',
|
|
img_dir='img_dir/test',
|
|
ann_dir='ann_dir/test',
|
|
pipeline=[
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='MultiScaleFlipAug',
|
|
img_scale=(512, 512),
|
|
flip=False,
|
|
transforms=[
|
|
dict(type='Resize', keep_ratio=True),
|
|
dict(type='RandomFlip'),
|
|
dict(
|
|
type='Normalize',
|
|
mean=[132.01150988, 117.50650988, 102.74611112],
|
|
std=[48.42106271, 49.25131565, 52.27428472],
|
|
to_rgb=True),
|
|
dict(type='ImageToTensor', keys=['img']),
|
|
dict(type='Collect', keys=['img'])
|
|
])
|
|
]))
|
|
dist_params = dict(backend='nccl')
|
|
log_level = 'INFO'
|
|
load_from = None
|
|
resume_from = None
|
|
workflow = [('train', 1), ('val', 1)]
|
|
cudnn_benchmark = True
|
|
optimizer = dict(
|
|
type='AdamW',
|
|
lr=3e-05,
|
|
betas=(0.9, 0.999),
|
|
weight_decay=0.01,
|
|
paramwise_cfg=dict(
|
|
custom_keys=dict(
|
|
pos_block=dict(decay_mult=0.0),
|
|
norm=dict(decay_mult=0.0),
|
|
head=dict(lr_mult=10.0))))
|
|
optimizer_config = dict()
|
|
lr_config = dict(
|
|
policy='poly',
|
|
warmup='linear',
|
|
warmup_iters=1500,
|
|
warmup_ratio=1e-06,
|
|
power=1.0,
|
|
min_lr=0.0,
|
|
by_epoch=False)
|
|
runner = dict(type='EpochBasedRunner', max_epochs=200)
|
|
checkpoint_config = dict(by_epoch=True, interval=1)
|
|
evaluation = dict(by_epoch=True, interval=1, metric='mIoU')
|
|
log_config = dict(
|
|
interval=1000,
|
|
hooks=[
|
|
dict(type='TextLoggerHook'),
|
|
dict(
|
|
type='WandbLoggerHook',
|
|
init_kwargs=dict(
|
|
project='Oil_Spill',
|
|
name='V7_REV8'))
|
|
])
|
|
auto_resume = False
|
|
work_dir = 'work_dirs/V7_REV8'
|
|
gpu_ids = [0]
|