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
6.5 KiB
Python
195 lines
6.5 KiB
Python
norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='EncoderDecoder',
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pretrained='open-mmlab://resnet101_v1c',
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backbone=dict(
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type='ResNetV1c',
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depth=101,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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dilations=(1, 1, 2, 4),
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strides=(1, 2, 1, 1),
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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norm_eval=False,
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style='pytorch',
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contract_dilation=True),
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decode_head=dict(
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type='DAHead',
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in_channels=2048,
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in_index=3,
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channels=512,
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pam_channels=64,
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dropout_ratio=0.1,
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num_classes=5,
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
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auxiliary_head=dict(
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type='FCNHead',
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in_channels=1024,
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in_index=2,
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channels=256,
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num_convs=1,
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concat_input=False,
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dropout_ratio=0.1,
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num_classes=5,
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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dataset_type = 'CustomDataset'
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data_root = 'data/my_dataset_v7'
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img_norm_cfg = dict(
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mean=[132.01150988, 117.50650988, 102.74611112],
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std=[48.42106271, 49.25131565, 52.27428472],
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to_rgb=True)
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crop_size = (512, 512)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(512, 512)),
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dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(
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type='Normalize',
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mean=[132.01150988, 117.50650988, 102.74611112],
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std=[48.42106271, 49.25131565, 52.27428472],
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to_rgb=True),
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dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(512, 512),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[132.01150988, 117.50650988, 102.74611112],
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std=[48.42106271, 49.25131565, 52.27428472],
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to_rgb=True),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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type='CustomDataset',
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data_root='data/my_dataset_v7',
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img_dir='img_dir/train',
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ann_dir='ann_dir/train',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(512, 512)),
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dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(
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type='Normalize',
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mean=[132.01150988, 117.50650988, 102.74611112],
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std=[48.42106271, 49.25131565, 52.27428472],
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to_rgb=True),
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dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg'])
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]),
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val=dict(
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type='CustomDataset',
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data_root='data/my_dataset_v7',
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img_dir='img_dir/val',
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ann_dir='ann_dir/val',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(512, 512),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[132.01150988, 117.50650988, 102.74611112],
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std=[48.42106271, 49.25131565, 52.27428472],
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to_rgb=True),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]),
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test=dict(
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type='CustomDataset',
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data_root='data/my_dataset_v7',
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img_dir='img_dir/test',
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ann_dir='ann_dir/test',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(512, 512),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[132.01150988, 117.50650988, 102.74611112],
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std=[48.42106271, 49.25131565, 52.27428472],
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to_rgb=True),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]))
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = 'work_dirs/danet_r101-d8_512x512_40k_voc12aug_Black_overlap_v6/epoch_45.pth'
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resume_from = None
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workflow = [('train', 1), ('val', 1)]
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cudnn_benchmark = True
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optimizer = dict(
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type='AdamW',
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lr=3e-05,
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betas=(0.9, 0.999),
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weight_decay=0.01,
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paramwise_cfg=dict(
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custom_keys=dict(
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pos_block=dict(decay_mult=0.0),
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norm=dict(decay_mult=0.0),
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head=dict(lr_mult=10.0))))
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optimizer_config = dict()
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lr_config = dict(
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policy='poly',
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warmup='linear',
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warmup_iters=1500,
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warmup_ratio=1e-06,
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power=1.0,
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min_lr=0.0,
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by_epoch=False)
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runner = dict(type='EpochBasedRunner', max_epochs=200)
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checkpoint_config = dict(by_epoch=True, interval=1)
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evaluation = dict(by_epoch=True, interval=1, metric='mIoU')
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log_config = dict(
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interval=1000,
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hooks=[
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dict(type='TextLoggerHook'),
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dict(
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type='WandbLoggerHook',
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init_kwargs=dict(
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project='Oil_Spill',
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name='V7_REV2_BL_RB_BR_SV_cropoverlap_r101_200epochs_from_black_pretrained'))
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])
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auto_resume = False
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work_dir = 'work_dirs/V7_REV2'
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gpu_ids = [0]
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