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co_dino_5scale_swin_l_16xb1_1x_coco.py

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+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'

epoch_12.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87be24bdab40769b9811906b18e59a7d0272708352fe578822e761a5e5984949
+size 2883789421