charlie880624
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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
148 lines
4.4 KiB
Python
148 lines
4.4 KiB
Python
norm_cfg = dict(type='BN', requires_grad=True)
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model = dict(
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type='EncoderDecoder',
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pretrained=None,
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backbone=dict(
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type='STDCContextPathNet',
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backbone_cfg=dict(
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type='STDCNet',
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stdc_type='STDCNet1',
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in_channels=3,
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channels=(32, 64, 256, 512, 1024),
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bottleneck_type='cat',
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num_convs=4,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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with_final_conv=False),
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last_in_channels=(1024, 512),
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out_channels=128,
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ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4)),
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decode_head=dict(
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type='FCNHead',
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in_channels=256,
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channels=256,
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num_convs=1,
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num_classes=8, # ✅ 8 類
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in_index=3,
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concat_input=False,
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dropout_ratio=0.1,
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norm_cfg=norm_cfg,
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align_corners=True,
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sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
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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=[
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dict(
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type='FCNHead',
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in_channels=128,
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channels=64,
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num_convs=1,
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num_classes=8, # ✅ 8 類
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in_index=2,
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norm_cfg=norm_cfg,
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concat_input=False,
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align_corners=False,
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sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
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dict(
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type='FCNHead',
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in_channels=128,
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channels=64,
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num_convs=1,
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num_classes=8, # ✅ 8 類
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in_index=1,
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norm_cfg=norm_cfg,
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concat_input=False,
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align_corners=False,
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sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
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],
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train_cfg=dict(),
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test_cfg=dict(mode='whole')
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)
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dataset_type = 'Golf8Dataset'
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data_root = 'data/cityscapes/'
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img_norm_cfg = dict(mean=[128., 128., 128.], std=[256., 256., 256.], to_rgb=True)
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crop_size = (360, 720)
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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=(724, 362), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, 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=(724, 362),
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flip=False,
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transforms=[
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dict(type='Resize', img_scale=(724, 362), keep_ratio=False),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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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=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/train',
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ann_dir='gtFine/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/val',
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ann_dir='gtFine/val',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/test',
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ann_dir='gtFine/test',
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pipeline=test_pipeline)
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)
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log_config = dict(
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interval=50,
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hooks=[dict(type='TextLoggerHook', by_epoch=False)]
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)
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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# ✅ Fine-tune 用設定
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load_from = 'C:/Users/rd_de/kneronstdc/work_dirs/meconfig8/0619/latest.pth'
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resume_from = None
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workflow = [('train', 1)]
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cudnn_benchmark = True
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# ✅ Fine-tune 推薦學習率
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optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
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optimizer_config = dict()
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lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
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runner = dict(type='IterBasedRunner', max_iters=160000)
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checkpoint_config = dict(by_epoch=False, interval=16000)
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evaluation = dict(interval=16000, metric='mIoU', pre_eval=True)
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