| auto_scale_lr = dict(base_batch_size=16) | |
| backend_args = None | |
| batch_augments = [ | |
| dict(size=( | |
| 1024, | |
| 1024, | |
| ), type='BatchFixedSizePad'), | |
| ] | |
| classes = 'license_plate' | |
| custom_imports = dict( | |
| allow_failed_imports=False, imports=[ | |
| 'projects.CO-DETR.codetr', | |
| ]) | |
| data_root = '/home/worawit.tepsan/Project_AI/Detection/data' | |
| dataset_type = 'CocoDataset' | |
| default_hooks = dict( | |
| checkpoint=dict( | |
| _scope_='mmdet', | |
| by_epoch=True, | |
| interval=1, | |
| max_keep_ckpts=3, | |
| type='CheckpointHook'), | |
| logger=dict(_scope_='mmdet', interval=50, type='LoggerHook'), | |
| param_scheduler=dict(_scope_='mmdet', type='ParamSchedulerHook'), | |
| sampler_seed=dict(_scope_='mmdet', type='DistSamplerSeedHook'), | |
| timer=dict(_scope_='mmdet', type='IterTimerHook'), | |
| visualization=dict( | |
| _scope_='mmdet', | |
| draw=True, | |
| test_out_dir= | |
| '/home/worawit.tepsan/Project_AI/Detection/data_testing_LPR', | |
| type='DetVisualizationHook')) | |
| default_scope = 'mmdet' | |
| env_cfg = dict( | |
| cudnn_benchmark=False, | |
| dist_cfg=dict(backend='nccl'), | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) | |
| image_size = ( | |
| 1024, | |
| 1024, | |
| ) | |
| launcher = 'slurm' | |
| load_from = '/home/worawit.tepsan/Project_AI/Detection/object_detection/workdir/epoch_13.pth' | |
| load_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| keep_ratio=True, | |
| ratio_range=( | |
| 0.1, | |
| 2.0, | |
| ), | |
| scale=( | |
| 1024, | |
| 1024, | |
| ), | |
| type='RandomResize'), | |
| dict( | |
| allow_negative_crop=True, | |
| crop_size=( | |
| 1024, | |
| 1024, | |
| ), | |
| crop_type='absolute_range', | |
| recompute_bbox=True, | |
| type='RandomCrop'), | |
| dict(min_gt_bbox_wh=( | |
| 0.01, | |
| 0.01, | |
| ), type='FilterAnnotations'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| )), size=( | |
| 1024, | |
| 1024, | |
| ), type='Pad'), | |
| ] | |
| log_level = 'INFO' | |
| log_processor = dict( | |
| _scope_='mmdet', by_epoch=True, type='LogProcessor', window_size=50) | |
| loss_lambda = 2.0 | |
| max_epochs = 32 | |
| max_iters = 270000 | |
| metainfo = dict(classes='license_plate') | |
| model = dict( | |
| backbone=dict( | |
| attn_drop_rate=0.0, | |
| convert_weights=True, | |
| depths=[ | |
| 2, | |
| 2, | |
| 18, | |
| 2, | |
| ], | |
| drop_path_rate=0.3, | |
| drop_rate=0.0, | |
| embed_dims=192, | |
| init_cfg=dict( | |
| checkpoint= | |
| '/home/worawit.tepsan/Project_AI/Detection/pretrained_models/swin_large_patch4_window12_384_22k.pth', | |
| type='Pretrained'), | |
| mlp_ratio=4, | |
| num_heads=[ | |
| 6, | |
| 12, | |
| 24, | |
| 48, | |
| ], | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| patch_norm=True, | |
| pretrain_img_size=384, | |
| qk_scale=None, | |
| qkv_bias=True, | |
| type='SwinTransformer', | |
| window_size=12, | |
| with_cp=False), | |
| bbox_head=[ | |
| dict( | |
| anchor_generator=dict( | |
| octave_base_scale=8, | |
| ratios=[ | |
| 1.0, | |
| ], | |
| scales_per_octave=1, | |
| strides=[ | |
| 4, | |
| 8, | |
| 16, | |
| 32, | |
| 64, | |
| 128, | |
| ], | |
| type='AnchorGenerator'), | |
| bbox_coder=dict( | |
| target_means=[ | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| ], | |
| target_stds=[ | |
| 0.1, | |
| 0.1, | |
| 0.2, | |
| 0.2, | |
| ], | |
| type='DeltaXYWHBBoxCoder'), | |
| feat_channels=256, | |
| in_channels=256, | |
| loss_bbox=dict(loss_weight=24.0, type='GIoULoss'), | |
| loss_centerness=dict( | |
| loss_weight=12.0, type='CrossEntropyLoss', use_sigmoid=True), | |
| loss_cls=dict( | |
| alpha=0.25, | |
| gamma=2.0, | |
| loss_weight=12.0, | |
| type='FocalLoss', | |
| use_sigmoid=True), | |
| num_classes=1, | |
| stacked_convs=1, | |
| type='CoATSSHead'), | |
| ], | |
| data_preprocessor=dict( | |
| batch_augments=None, | |
| bgr_to_rgb=True, | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| pad_mask=False, | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| type='DetDataPreprocessor'), | |
| eval_module='detr', | |
| neck=dict( | |
| act_cfg=None, | |
| in_channels=[ | |
| 192, | |
| 384, | |
| 768, | |
| 1536, | |
| ], | |
| kernel_size=1, | |
| norm_cfg=dict(num_groups=32, type='GN'), | |
| num_outs=5, | |
| out_channels=256, | |
| type='ChannelMapper'), | |
| query_head=dict( | |
| as_two_stage=True, | |
| dn_cfg=dict( | |
| box_noise_scale=1.0, | |
| group_cfg=dict(dynamic=True, num_dn_queries=100, num_groups=None), | |
| label_noise_scale=0.5), | |
| in_channels=2048, | |
| loss_bbox=dict(loss_weight=5.0, type='L1Loss'), | |
| loss_cls=dict( | |
| beta=2.0, | |
| loss_weight=1.0, | |
| type='QualityFocalLoss', | |
| use_sigmoid=True), | |
| loss_iou=dict(loss_weight=2.0, type='GIoULoss'), | |
| num_classes=1, | |
| num_query=900, | |
| positional_encoding=dict( | |
| normalize=True, | |
| num_feats=128, | |
| temperature=20, | |
| type='SinePositionalEncoding'), | |
| transformer=dict( | |
| decoder=dict( | |
| num_layers=6, | |
| return_intermediate=True, | |
| transformerlayers=dict( | |
| attn_cfgs=[ | |
| dict( | |
| dropout=0.0, | |
| embed_dims=256, | |
| num_heads=8, | |
| type='MultiheadAttention'), | |
| dict( | |
| dropout=0.0, | |
| embed_dims=256, | |
| num_levels=5, | |
| type='MultiScaleDeformableAttention'), | |
| ], | |
| feedforward_channels=2048, | |
| ffn_dropout=0.0, | |
| operation_order=( | |
| 'self_attn', | |
| 'norm', | |
| 'cross_attn', | |
| 'norm', | |
| 'ffn', | |
| 'norm', | |
| ), | |
| type='DetrTransformerDecoderLayer'), | |
| type='DinoTransformerDecoder'), | |
| encoder=dict( | |
| num_layers=6, | |
| transformerlayers=dict( | |
| attn_cfgs=dict( | |
| dropout=0.0, | |
| embed_dims=256, | |
| num_levels=5, | |
| type='MultiScaleDeformableAttention'), | |
| feedforward_channels=2048, | |
| ffn_dropout=0.0, | |
| operation_order=( | |
| 'self_attn', | |
| 'norm', | |
| 'ffn', | |
| 'norm', | |
| ), | |
| type='BaseTransformerLayer'), | |
| type='DetrTransformerEncoder', | |
| with_cp=6), | |
| num_co_heads=2, | |
| num_feature_levels=5, | |
| type='CoDinoTransformer', | |
| with_coord_feat=False), | |
| type='CoDINOHead'), | |
| roi_head=[ | |
| dict( | |
| bbox_head=dict( | |
| bbox_coder=dict( | |
| target_means=[ | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| ], | |
| target_stds=[ | |
| 0.1, | |
| 0.1, | |
| 0.2, | |
| 0.2, | |
| ], | |
| type='DeltaXYWHBBoxCoder'), | |
| fc_out_channels=1024, | |
| in_channels=256, | |
| loss_bbox=dict(loss_weight=120.0, type='GIoULoss'), | |
| loss_cls=dict( | |
| loss_weight=12.0, | |
| type='CrossEntropyLoss', | |
| use_sigmoid=False), | |
| num_classes=1, | |
| reg_class_agnostic=False, | |
| reg_decoded_bbox=True, | |
| roi_feat_size=7, | |
| type='Shared2FCBBoxHead'), | |
| bbox_roi_extractor=dict( | |
| featmap_strides=[ | |
| 4, | |
| 8, | |
| 16, | |
| 32, | |
| 64, | |
| ], | |
| finest_scale=56, | |
| out_channels=256, | |
| roi_layer=dict( | |
| output_size=7, sampling_ratio=0, type='RoIAlign'), | |
| type='SingleRoIExtractor'), | |
| type='CoStandardRoIHead'), | |
| ], | |
| rpn_head=dict( | |
| anchor_generator=dict( | |
| octave_base_scale=4, | |
| ratios=[ | |
| 0.5, | |
| 1.0, | |
| 2.0, | |
| ], | |
| scales_per_octave=3, | |
| strides=[ | |
| 4, | |
| 8, | |
| 16, | |
| 32, | |
| 64, | |
| 128, | |
| ], | |
| type='AnchorGenerator'), | |
| bbox_coder=dict( | |
| target_means=[ | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| ], | |
| target_stds=[ | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| ], | |
| type='DeltaXYWHBBoxCoder'), | |
| feat_channels=256, | |
| in_channels=256, | |
| loss_bbox=dict(loss_weight=12.0, type='L1Loss'), | |
| loss_cls=dict( | |
| loss_weight=12.0, type='CrossEntropyLoss', use_sigmoid=True), | |
| type='RPNHead'), | |
| test_cfg=[ | |
| dict(max_per_img=300, nms=dict(iou_threshold=0.8, type='soft_nms')), | |
| dict( | |
| rcnn=dict( | |
| max_per_img=100, | |
| nms=dict(iou_threshold=0.5, type='nms'), | |
| score_thr=0.0), | |
| rpn=dict( | |
| max_per_img=1000, | |
| min_bbox_size=0, | |
| nms=dict(iou_threshold=0.7, type='nms'), | |
| nms_pre=1000)), | |
| dict( | |
| max_per_img=100, | |
| min_bbox_size=0, | |
| nms=dict(iou_threshold=0.6, type='nms'), | |
| nms_pre=1000, | |
| score_thr=0.0), | |
| ], | |
| train_cfg=[ | |
| dict( | |
| assigner=dict( | |
| match_costs=[ | |
| dict(type='FocalLossCost', weight=2.0), | |
| dict(box_format='xywh', type='BBoxL1Cost', weight=5.0), | |
| dict(iou_mode='giou', type='IoUCost', weight=2.0), | |
| ], | |
| type='HungarianAssigner')), | |
| dict( | |
| rcnn=dict( | |
| assigner=dict( | |
| ignore_iof_thr=-1, | |
| match_low_quality=False, | |
| min_pos_iou=0.5, | |
| neg_iou_thr=0.5, | |
| pos_iou_thr=0.5, | |
| type='MaxIoUAssigner'), | |
| debug=False, | |
| pos_weight=-1, | |
| sampler=dict( | |
| add_gt_as_proposals=True, | |
| neg_pos_ub=-1, | |
| num=512, | |
| pos_fraction=0.25, | |
| type='RandomSampler')), | |
| rpn=dict( | |
| allowed_border=-1, | |
| assigner=dict( | |
| ignore_iof_thr=-1, | |
| match_low_quality=True, | |
| min_pos_iou=0.3, | |
| neg_iou_thr=0.3, | |
| pos_iou_thr=0.7, | |
| type='MaxIoUAssigner'), | |
| debug=False, | |
| pos_weight=-1, | |
| sampler=dict( | |
| add_gt_as_proposals=False, | |
| neg_pos_ub=-1, | |
| num=256, | |
| pos_fraction=0.5, | |
| type='RandomSampler')), | |
| rpn_proposal=dict( | |
| max_per_img=1000, | |
| min_bbox_size=0, | |
| nms=dict(iou_threshold=0.7, type='nms'), | |
| nms_pre=4000)), | |
| dict( | |
| allowed_border=-1, | |
| assigner=dict(topk=9, type='ATSSAssigner'), | |
| debug=False, | |
| pos_weight=-1), | |
| ], | |
| type='CoDETR', | |
| use_lsj=False) | |
| num_classes = 1 | |
| num_dec_layer = 6 | |
| optim_wrapper = dict( | |
| clip_grad=dict(max_norm=0.1, norm_type=2), | |
| optimizer=dict(lr=0.0002, type='AdamW', weight_decay=0.0001), | |
| paramwise_cfg=dict(custom_keys=dict(backbone=dict(lr_mult=0.1))), | |
| type='OptimWrapper') | |
| param_scheduler = [ | |
| dict( | |
| begin=0, | |
| by_epoch=True, | |
| end=12, | |
| gamma=0.1, | |
| milestones=[ | |
| 11, | |
| ], | |
| type='MultiStepLR'), | |
| ] | |
| pretrained = '/home/worawit.tepsan/Project_AI/Detection/pretrained_models/swin_large_patch4_window12_384_22k.pth' | |
| resume = False | |
| test_cfg = dict(_scope_='mmdet', type='TestLoop') | |
| test_dataloader = dict( | |
| batch_size=2, | |
| dataset=dict( | |
| _scope_='mmdet', | |
| ann_file='annotations/instances_test.json', | |
| data_prefix=dict(img='test/'), | |
| data_root='/home/worawit.tepsan/Project_AI/Detection/data', | |
| metainfo=dict(classes='license_plate'), | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1333, | |
| 800, | |
| ), type='Resize'), | |
| dict( | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| ), | |
| type='PackDetInputs'), | |
| ], | |
| test_mode=True, | |
| type='CocoDataset'), | |
| drop_last=False, | |
| num_workers=8, | |
| persistent_workers=True, | |
| sampler=dict(_scope_='mmdet', shuffle=False, type='DefaultSampler')) | |
| test_evaluator = dict( | |
| _scope_='mmdet', | |
| ann_file='annotations/instances_test.json', | |
| format_only=False, | |
| metric='bbox', | |
| outfile_prefix= | |
| '/home/worawit.tepsan/Project_AI/Detection/object_detection/workdir/coco_detection/test', | |
| type='CocoMetric') | |
| test_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1333, | |
| 800, | |
| ), type='Resize'), | |
| dict( | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| ), | |
| type='PackDetInputs'), | |
| ] | |
| train_cfg = dict(max_epochs=32, type='EpochBasedTrainLoop', val_interval=1) | |
| train_dataloader = dict( | |
| batch_size=2, | |
| dataset=dict( | |
| ann_file='annotations/instances_train.json', | |
| backend_args=None, | |
| data_prefix=dict(img='train/'), | |
| data_root='/home/worawit.tepsan/Project_AI/Detection/data', | |
| filter_cfg=dict(filter_empty_gt=False, min_size=32), | |
| metainfo=dict(classes='license_plate'), | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict( | |
| transforms=[ | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 400, | |
| 4200, | |
| ), | |
| ( | |
| 500, | |
| 4200, | |
| ), | |
| ( | |
| 600, | |
| 4200, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| dict( | |
| allow_negative_crop=True, | |
| crop_size=( | |
| 384, | |
| 600, | |
| ), | |
| crop_type='absolute_range', | |
| type='RandomCrop'), | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| ], | |
| type='RandomChoice'), | |
| dict(type='PackDetInputs'), | |
| ], | |
| type='CocoDataset'), | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(_scope_='mmdet', shuffle=True, type='DefaultSampler')) | |
| train_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict( | |
| transforms=[ | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 400, | |
| 4200, | |
| ), | |
| ( | |
| 500, | |
| 4200, | |
| ), | |
| ( | |
| 600, | |
| 4200, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| dict( | |
| allow_negative_crop=True, | |
| crop_size=( | |
| 384, | |
| 600, | |
| ), | |
| crop_type='absolute_range', | |
| type='RandomCrop'), | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| ], | |
| type='RandomChoice'), | |
| dict(type='PackDetInputs'), | |
| ] | |
| val_cfg = dict(_scope_='mmdet', type='ValLoop') | |
| val_dataloader = dict( | |
| batch_size=2, | |
| dataset=dict( | |
| _scope_='mmdet', | |
| ann_file='annotations/instances_val.json', | |
| backend_args=None, | |
| data_prefix=dict(img='val/'), | |
| data_root='/home/worawit.tepsan/Project_AI/Detection/data', | |
| metainfo=dict(classes='license_plate'), | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1333, | |
| 800, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| ), | |
| type='PackDetInputs'), | |
| ], | |
| test_mode=True, | |
| type='CocoDataset'), | |
| drop_last=False, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(_scope_='mmdet', shuffle=False, type='DefaultSampler')) | |
| val_evaluator = dict( | |
| _scope_='mmdet', | |
| ann_file= | |
| '/home/worawit.tepsan/Project_AI/Detection/data/annotations/instances_val.json', | |
| backend_args=None, | |
| format_only=False, | |
| metric='bbox', | |
| type='CocoMetric') | |
| val_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1333, | |
| 800, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| ), | |
| type='PackDetInputs'), | |
| ] | |
| vis_backends = [ | |
| dict(_scope_='mmdet', type='LocalVisBackend'), | |
| ] | |
| visualizer = dict( | |
| _scope_='mmdet', | |
| name='visualizer', | |
| type='DetLocalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| ]) | |
| work_dir = '/home/worawit.tepsan/Project_AI/Detection/object_detection/workdir' | |