Delete model_configs/CVRP_deeplabv3plus.py

model_configs/CVRP_deeplabv3plus.py

@@ -1,303 +0,0 @@
-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 = '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'
-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=1024,
-        in_index=2,
-        loss_decode=dict(
-            loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
-        norm_cfg=dict(requires_grad=True, type='BN'),
-        num_classes=2,
-        num_convs=1,
-        type='FCNHead'),
-    backbone=dict(
-        contract_dilation=True,
-        depth=101,
-        dilations=(
-            1,
-            1,
-            2,
-            4,
-        ),
-        norm_cfg=dict(requires_grad=True, type='BN'),
-        norm_eval=False,
-        num_stages=4,
-        out_indices=(
-            0,
-            1,
-            2,
-            3,
-        ),
-        strides=(
-            1,
-            2,
-            1,
-            1,
-        ),
-        style='pytorch',
-        type='ResNetV1c'),
-    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,
-        c1_channels=48,
-        c1_in_channels=256,
-        channels=512,
-        dilations=(
-            1,
-            12,
-            24,
-            36,
-        ),
-        dropout_ratio=0.1,
-        in_channels=2048,
-        in_index=3,
-        loss_decode=dict(
-            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
-        norm_cfg=dict(requires_grad=True, type='BN'),
-        num_classes=2,
-        type='DepthwiseSeparableASPPHead'),
-    pretrained='open-mmlab://resnet101_v1c',
-    test_cfg=dict(mode='whole'),
-    train_cfg=dict(),
-    type='EncoderDecoder')
-norm_cfg = dict(requires_grad=True, type='BN')
-optim_wrapper = dict(
-    clip_grad=None,
-    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005),
-    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=160000,
-        eta_min=0.0001,
-        power=0.9,
-        type='PolyLR'),
-]
-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=4,
-    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_DeepLabV3plus'