Delete model_configs/CVRP_mask2former.py

model_configs/CVRP_mask2former.py

@@ -1,572 +0,0 @@
-auto_scale_lr = dict(base_batch_size=16, enable=False)
-backbone_embed_multi = dict(decay_mult=0.0, lr_mult=0.1)
-backbone_norm_multi = dict(decay_mult=0.0, lr_mult=0.1)
-crop_size = (
-    512,
-    512,
-)
-custom_keys = dict({
-    'absolute_pos_embed':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone':
-    dict(decay_mult=1.0, lr_mult=0.1),
-    'backbone.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.patch_embed.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.0.blocks.0.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.0.blocks.1.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.0.downsample.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.1.blocks.0.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.1.blocks.1.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.1.downsample.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.0.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.1.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.10.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.11.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.12.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.13.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.14.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.15.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.16.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.17.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.2.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.3.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.4.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.5.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.6.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.7.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.8.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.blocks.9.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.2.downsample.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.3.blocks.0.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'backbone.stages.3.blocks.1.norm':
-    dict(decay_mult=0.0, lr_mult=0.1),
-    'level_embed':
-    dict(decay_mult=0.0, lr_mult=1.0),
-    'query_embed':
-    dict(decay_mult=0.0, lr_mult=1.0),
-    'query_feat':
-    dict(decay_mult=0.0, lr_mult=1.0),
-    'relative_position_bias_table':
-    dict(decay_mult=0.0, lr_mult=0.1)
-})
-data_preprocessor = dict(
-    bgr_to_rgb=True,
-    mean=[
-        123.675,
-        116.28,
-        103.53,
-    ],
-    pad_val=0,
-    seg_pad_val=255,
-    size=(
-        640,
-        640,
-    ),
-    std=[
-        58.395,
-        57.12,
-        57.375,
-    ],
-    type='SegDataPreProcessor')
-data_root = 'CVRPDataset/'
-dataset_type = 'CVRPDataset'
-default_hooks = dict(
-    checkpoint=dict(
-        by_epoch=False,
-        interval=2500,
-        max_keep_ckpts=1,
-        save_best='mIoU',
-        type='CheckpointHook'),
-    logger=dict(interval=100, log_metric_by_epoch=False, type='LoggerHook'),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    timer=dict(type='IterTimerHook'),
-    visualization=dict(type='SegVisualizationHook'))
-default_scope = 'mmseg'
-depths = [
-    2,
-    2,
-    18,
-    2,
-]
-embed_multi = dict(decay_mult=0.0, lr_mult=1.0)
-env_cfg = dict(
-    cudnn_benchmark=True,
-    dist_cfg=dict(backend='nccl'),
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
-img_ratios = [
-    0.5,
-    0.75,
-    1.0,
-    1.25,
-    1.5,
-    1.75,
-]
-load_from = None
-log_level = 'INFO'
-log_processor = dict(by_epoch=False)
-model = dict(
-    backbone=dict(
-        attn_drop_rate=0.0,
-        depths=[
-            2,
-            2,
-            18,
-            2,
-        ],
-        drop_path_rate=0.3,
-        drop_rate=0.0,
-        embed_dims=192,
-        frozen_stages=-1,
-        init_cfg=dict(
-            checkpoint=
-            'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.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),
-    data_preprocessor=dict(
-        bgr_to_rgb=True,
-        mean=[
-            123.675,
-            116.28,
-            103.53,
-        ],
-        pad_val=0,
-        seg_pad_val=255,
-        size=(
-            512,
-            512,
-        ),
-        std=[
-            58.395,
-            57.12,
-            57.375,
-        ],
-        type='SegDataPreProcessor'),
-    decode_head=dict(
-        align_corners=False,
-        enforce_decoder_input_project=False,
-        feat_channels=256,
-        in_channels=[
-            192,
-            384,
-            768,
-            1536,
-        ],
-        loss_cls=dict(
-            class_weight=[
-                1.0,
-                1.0,
-                0.1,
-            ],
-            loss_weight=2.0,
-            reduction='mean',
-            type='mmdet.CrossEntropyLoss',
-            use_sigmoid=False),
-        loss_dice=dict(
-            activate=True,
-            eps=1.0,
-            loss_weight=5.0,
-            naive_dice=True,
-            reduction='mean',
-            type='mmdet.DiceLoss',
-            use_sigmoid=True),
-        loss_mask=dict(
-            loss_weight=5.0,
-            reduction='mean',
-            type='mmdet.CrossEntropyLoss',
-            use_sigmoid=True),
-        num_classes=2,
-        num_queries=100,
-        num_transformer_feat_level=3,
-        out_channels=256,
-        pixel_decoder=dict(
-            act_cfg=dict(type='ReLU'),
-            encoder=dict(
-                init_cfg=None,
-                layer_cfg=dict(
-                    ffn_cfg=dict(
-                        act_cfg=dict(inplace=True, type='ReLU'),
-                        embed_dims=256,
-                        feedforward_channels=1024,
-                        ffn_drop=0.0,
-                        num_fcs=2),
-                    self_attn_cfg=dict(
-                        batch_first=True,
-                        dropout=0.0,
-                        embed_dims=256,
-                        im2col_step=64,
-                        init_cfg=None,
-                        norm_cfg=None,
-                        num_heads=8,
-                        num_levels=3,
-                        num_points=4)),
-                num_layers=6),
-            init_cfg=None,
-            norm_cfg=dict(num_groups=32, type='GN'),
-            num_outs=3,
-            positional_encoding=dict(normalize=True, num_feats=128),
-            type='mmdet.MSDeformAttnPixelDecoder'),
-        positional_encoding=dict(normalize=True, num_feats=128),
-        strides=[
-            4,
-            8,
-            16,
-            32,
-        ],
-        train_cfg=dict(
-            assigner=dict(
-                match_costs=[
-                    dict(type='mmdet.ClassificationCost', weight=2.0),
-                    dict(
-                        type='mmdet.CrossEntropyLossCost',
-                        use_sigmoid=True,
-                        weight=5.0),
-                    dict(
-                        eps=1.0,
-                        pred_act=True,
-                        type='mmdet.DiceCost',
-                        weight=5.0),
-                ],
-                type='mmdet.HungarianAssigner'),
-            importance_sample_ratio=0.75,
-            num_points=12544,
-            oversample_ratio=3.0,
-            sampler=dict(type='mmdet.MaskPseudoSampler')),
-        transformer_decoder=dict(
-            init_cfg=None,
-            layer_cfg=dict(
-                cross_attn_cfg=dict(
-                    attn_drop=0.0,
-                    batch_first=True,
-                    dropout_layer=None,
-                    embed_dims=256,
-                    num_heads=8,
-                    proj_drop=0.0),
-                ffn_cfg=dict(
-                    act_cfg=dict(inplace=True, type='ReLU'),
-                    add_identity=True,
-                    dropout_layer=None,
-                    embed_dims=256,
-                    feedforward_channels=2048,
-                    ffn_drop=0.0,
-                    num_fcs=2),
-                self_attn_cfg=dict(
-                    attn_drop=0.0,
-                    batch_first=True,
-                    dropout_layer=None,
-                    embed_dims=256,
-                    num_heads=8,
-                    proj_drop=0.0)),
-            num_layers=9,
-            return_intermediate=True),
-        type='Mask2FormerHead'),
-    test_cfg=dict(mode='whole'),
-    train_cfg=dict(),
-    type='EncoderDecoder')
-norm_cfg = dict(requires_grad=True, type='BN')
-num_classes = 150
-optim_wrapper = dict(
-    clip_grad=dict(max_norm=0.01, norm_type=2),
-    optimizer=dict(
-        betas=(
-            0.9,
-            0.999,
-        ),
-        eps=1e-08,
-        lr=0.0001,
-        type='AdamW',
-        weight_decay=0.05),
-    paramwise_cfg=dict(
-        custom_keys=dict({
-            'absolute_pos_embed':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone':
-            dict(decay_mult=1.0, lr_mult=0.1),
-            'backbone.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.patch_embed.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.0.blocks.0.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.0.blocks.1.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.0.downsample.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.1.blocks.0.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.1.blocks.1.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.1.downsample.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.0.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.1.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.10.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.11.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.12.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.13.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.14.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.15.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.16.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.17.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.2.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.3.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.4.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.5.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.6.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.7.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.8.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.blocks.9.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.2.downsample.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.3.blocks.0.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'backbone.stages.3.blocks.1.norm':
-            dict(decay_mult=0.0, lr_mult=0.1),
-            'level_embed':
-            dict(decay_mult=0.0, lr_mult=1.0),
-            'query_embed':
-            dict(decay_mult=0.0, lr_mult=1.0),
-            'query_feat':
-            dict(decay_mult=0.0, lr_mult=1.0),
-            'relative_position_bias_table':
-            dict(decay_mult=0.0, lr_mult=0.1)
-        }),
-        norm_decay_mult=0.0),
-    type='OptimWrapper')
-optimizer = dict(
-    betas=(
-        0.9,
-        0.999,
-    ),
-    eps=1e-08,
-    lr=0.0001,
-    type='AdamW',
-    weight_decay=0.05)
-param_scheduler = [
-    dict(
-        begin=0,
-        by_epoch=False,
-        end=160000,
-        eta_min=0,
-        power=0.9,
-        type='PolyLR'),
-]
-pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.pth'
-randomness = dict(seed=0)
-resume = False
-test_cfg = dict(type='TestLoop')
-test_dataloader = dict(
-    batch_size=1,
-    dataset=dict(
-        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
-        data_root='CVRPDataset/',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(keep_ratio=True, scale=(
-                2048,
-                1024,
-            ), type='Resize'),
-            dict(type='LoadAnnotations'),
-            dict(type='PackSegInputs'),
-        ],
-        type='CVRPDataset'),
-    num_workers=4,
-    persistent_workers=True,
-    sampler=dict(shuffle=False, type='DefaultSampler'))
-test_evaluator = dict(
-    iou_metrics=[
-        'mIoU',
-        'mDice',
-        'mFscore',
-    ], type='IoUMetric')
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(keep_ratio=True, scale=(
-        2048,
-        1024,
-    ), type='Resize'),
-    dict(type='LoadAnnotations'),
-    dict(type='PackSegInputs'),
-]
-train_cfg = dict(max_iters=20000, type='IterBasedTrainLoop', val_interval=500)
-train_dataloader = dict(
-    batch_size=2,
-    dataset=dict(
-        data_prefix=dict(
-            img_path='img_dir/train', seg_map_path='ann_dir/train'),
-        data_root='CVRPDataset/',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='LoadAnnotations'),
-            dict(
-                keep_ratio=True,
-                ratio_range=(
-                    0.5,
-                    2.0,
-                ),
-                scale=(
-                    2048,
-                    1024,
-                ),
-                type='RandomResize'),
-            dict(
-                cat_max_ratio=0.75, crop_size=(
-                    512,
-                    512,
-                ), type='RandomCrop'),
-            dict(prob=0.5, type='RandomFlip'),
-            dict(type='PhotoMetricDistortion'),
-            dict(type='PackSegInputs'),
-        ],
-        type='CVRPDataset'),
-    num_workers=2,
-    persistent_workers=True,
-    sampler=dict(shuffle=True, type='InfiniteSampler'))
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(
-        keep_ratio=True,
-        ratio_range=(
-            0.5,
-            2.0,
-        ),
-        scale=(
-            2048,
-            1024,
-        ),
-        type='RandomResize'),
-    dict(cat_max_ratio=0.75, crop_size=(
-        512,
-        512,
-    ), type='RandomCrop'),
-    dict(prob=0.5, type='RandomFlip'),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='PackSegInputs'),
-]
-tta_model = dict(type='SegTTAModel')
-tta_pipeline = [
-    dict(file_client_args=dict(backend='disk'), type='LoadImageFromFile'),
-    dict(
-        transforms=[
-            [
-                dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
-                dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
-                dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
-                dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
-                dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
-                dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
-            ],
-            [
-                dict(direction='horizontal', prob=0.0, type='RandomFlip'),
-                dict(direction='horizontal', prob=1.0, type='RandomFlip'),
-            ],
-            [
-                dict(type='LoadAnnotations'),
-            ],
-            [
-                dict(type='PackSegInputs'),
-            ],
-        ],
-        type='TestTimeAug'),
-]
-val_cfg = dict(type='ValLoop')
-val_dataloader = dict(
-    batch_size=1,
-    dataset=dict(
-        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
-        data_root='CVRPDataset/',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(keep_ratio=True, scale=(
-                2048,
-                1024,
-            ), type='Resize'),
-            dict(type='LoadAnnotations'),
-            dict(type='PackSegInputs'),
-        ],
-        type='CVRPDataset'),
-    num_workers=4,
-    persistent_workers=True,
-    sampler=dict(shuffle=False, type='DefaultSampler'))
-val_evaluator = dict(
-    iou_metrics=[
-        'mIoU',
-        'mDice',
-        'mFscore',
-    ], type='IoUMetric')
-vis_backends = [
-    dict(type='LocalVisBackend'),
-]
-visualizer = dict(
-    name='visualizer',
-    type='SegLocalVisualizer',
-    vis_backends=[
-        dict(type='LocalVisBackend'),
-    ])
-work_dir = './work_dirs/CVRPDataset_mask2former'