Delete model_configs/CVRP_knet.py

model_configs/CVRP_knet.py

@@ -1,404 +0,0 @@
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220308-d5bdebaf.pth'
-conv_kernel_size = 1
-crop_size = (
-    512,
-    512,
-)
-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')
-data_root = 'PanicleDataset/'
-dataset_type = 'TzyDataset'
-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'
-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(
-    auxiliary_head=dict(
-        align_corners=False,
-        channels=256,
-        concat_input=False,
-        dropout_ratio=0.1,
-        in_channels=768,
-        in_index=2,
-        loss_decode=dict(
-            loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
-        norm_cfg=dict(requires_grad=True, type='SyncBN'),
-        num_classes=2,
-        num_convs=1,
-        type='FCNHead'),
-    backbone=dict(
-        attn_drop_rate=0.0,
-        depths=[
-            2,
-            2,
-            18,
-            2,
-        ],
-        drop_path_rate=0.3,
-        drop_rate=0.0,
-        embed_dims=192,
-        mlp_ratio=4,
-        num_heads=[
-            6,
-            12,
-            24,
-            48,
-        ],
-        out_indices=(
-            0,
-            1,
-            2,
-            3,
-        ),
-        patch_norm=True,
-        qk_scale=None,
-        qkv_bias=True,
-        type='SwinTransformer',
-        use_abs_pos_embed=False,
-        window_size=7),
-    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(
-        kernel_generate_head=dict(
-            align_corners=False,
-            channels=512,
-            dropout_ratio=0.1,
-            in_channels=[
-                192,
-                384,
-                768,
-                1536,
-            ],
-            in_index=[
-                0,
-                1,
-                2,
-                3,
-            ],
-            loss_decode=dict(
-                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
-            norm_cfg=dict(requires_grad=True, type='SyncBN'),
-            num_classes=2,
-            pool_scales=(
-                1,
-                2,
-                3,
-                6,
-            ),
-            type='UPerHead'),
-        kernel_update_head=[
-            dict(
-                conv_kernel_size=1,
-                dropout=0.0,
-                feat_transform_cfg=dict(
-                    act_cfg=None, conv_cfg=dict(type='Conv2d')),
-                feedforward_channels=2048,
-                ffn_act_cfg=dict(inplace=True, type='ReLU'),
-                in_channels=512,
-                kernel_updator_cfg=dict(
-                    act_cfg=dict(inplace=True, type='ReLU'),
-                    feat_channels=256,
-                    in_channels=256,
-                    norm_cfg=dict(type='LN'),
-                    out_channels=256,
-                    type='KernelUpdator'),
-                num_classes=150,
-                num_ffn_fcs=2,
-                num_heads=8,
-                num_mask_fcs=1,
-                out_channels=512,
-                type='KernelUpdateHead',
-                with_ffn=True),
-            dict(
-                conv_kernel_size=1,
-                dropout=0.0,
-                feat_transform_cfg=dict(
-                    act_cfg=None, conv_cfg=dict(type='Conv2d')),
-                feedforward_channels=2048,
-                ffn_act_cfg=dict(inplace=True, type='ReLU'),
-                in_channels=512,
-                kernel_updator_cfg=dict(
-                    act_cfg=dict(inplace=True, type='ReLU'),
-                    feat_channels=256,
-                    in_channels=256,
-                    norm_cfg=dict(type='LN'),
-                    out_channels=256,
-                    type='KernelUpdator'),
-                num_classes=150,
-                num_ffn_fcs=2,
-                num_heads=8,
-                num_mask_fcs=1,
-                out_channels=512,
-                type='KernelUpdateHead',
-                with_ffn=True),
-            dict(
-                conv_kernel_size=1,
-                dropout=0.0,
-                feat_transform_cfg=dict(
-                    act_cfg=None, conv_cfg=dict(type='Conv2d')),
-                feedforward_channels=2048,
-                ffn_act_cfg=dict(inplace=True, type='ReLU'),
-                in_channels=512,
-                kernel_updator_cfg=dict(
-                    act_cfg=dict(inplace=True, type='ReLU'),
-                    feat_channels=256,
-                    in_channels=256,
-                    norm_cfg=dict(type='LN'),
-                    out_channels=256,
-                    type='KernelUpdator'),
-                num_classes=150,
-                num_ffn_fcs=2,
-                num_heads=8,
-                num_mask_fcs=1,
-                out_channels=512,
-                type='KernelUpdateHead',
-                with_ffn=True),
-        ],
-        num_stages=3,
-        type='IterativeDecodeHead'),
-    pretrained=
-    'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220308-d5bdebaf.pth',
-    test_cfg=dict(mode='whole'),
-    train_cfg=dict(),
-    type='EncoderDecoder')
-norm_cfg = dict(requires_grad=True, type='BN')
-num_stages = 3
-optim_wrapper = dict(
-    clip_grad=dict(max_norm=1, norm_type=2),
-    optimizer=dict(
-        betas=(
-            0.9,
-            0.999,
-        ), lr=6e-05, type='AdamW', weight_decay=0.0005),
-    paramwise_cfg=dict(
-        custom_keys=dict(
-            absolute_pos_embed=dict(decay_mult=0.0),
-            norm=dict(decay_mult=0.0),
-            relative_position_bias_table=dict(decay_mult=0.0))),
-    type='OptimWrapper')
-optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
-param_scheduler = [
-    dict(
-        begin=0, by_epoch=False, end=1000, start_factor=0.001,
-        type='LinearLR'),
-    dict(
-        begin=1000,
-        by_epoch=False,
-        end=80000,
-        milestones=[
-            60000,
-            72000,
-        ],
-        type='MultiStepLR'),
-]
-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='PanicleDataset/',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(keep_ratio=True, scale=(
-                2048,
-                1024,
-            ), type='Resize'),
-            dict(type='LoadAnnotations'),
-            dict(type='PackSegInputs'),
-        ],
-        type='TzyDataset'),
-    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='PanicleDataset/',
-        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='TzyDataset'),
-    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='PanicleDataset/',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(keep_ratio=True, scale=(
-                2048,
-                1024,
-            ), type='Resize'),
-            dict(type='LoadAnnotations'),
-            dict(type='PackSegInputs'),
-        ],
-        type='TzyDataset'),
-    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/TzyDataset-KNet-0721'