Official Sea Ice model upload

.gitattributes

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+*.pth filter=lfs diff=lfs merge=lfs -text

model/iter_160000.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9782394b4c725ad13283db293d2fc232633a34ea1312c00ab8c1868379bb24d7
+size 1005871349

model/segformer_mit-b5_8xb1-160k_pre-cityscapes_seaicergb0-1024x1024.py

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+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'
+crop_size = (
+    1024,
+    1024,
+)
+data_preprocessor = dict(
+    bgr_to_rgb=True,
+    mean=[
+        123.675,
+        116.28,
+        103.53,
+    ],
+    pad_val=0,
+    seg_pad_val=255,
+    size=(
+        1024,
+        1024,
+    ),
+    std=[
+        58.395,
+        57.12,
+        57.375,
+    ],
+    type='SegDataPreProcessor')
+data_root = 'data/rgb0/yolo-seg/'
+dataset_type = 'SeaIceRGB0'
+default_hooks = dict(
+    checkpoint=dict(by_epoch=False, interval=40000, type='CheckpointHook'),
+    logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(draw=True, 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,
+]
+launcher = 'none'
+load_from = '/m/work/t410/T41011/work/gorada2/ENHANCE/SeaIce/mmseg/work_dir/seaicergb0/segformer_mit-b5_8xb1-160k_pre-cityscapes_seaicergb0-1024x1024/iter_80000.pth'
+log_level = 'INFO'
+log_processor = dict(by_epoch=False)
+model = dict(
+    backbone=dict(
+        attn_drop_rate=0.0,
+        drop_path_rate=0.1,
+        drop_rate=0.0,
+        embed_dims=64,
+        in_channels=3,
+        init_cfg=dict(
+            checkpoint=
+            'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth',
+            type='Pretrained'),
+        mlp_ratio=4,
+        num_heads=[
+            1,
+            2,
+            5,
+            8,
+        ],
+        num_layers=[
+            3,
+            6,
+            40,
+            3,
+        ],
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        patch_sizes=[
+            7,
+            3,
+            3,
+            3,
+        ],
+        qkv_bias=True,
+        sr_ratios=[
+            8,
+            4,
+            2,
+            1,
+        ],
+        type='MixVisionTransformer'),
+    data_preprocessor=dict(
+        bgr_to_rgb=True,
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        pad_val=0,
+        seg_pad_val=255,
+        size=(
+            1024,
+            1024,
+        ),
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        type='SegDataPreProcessor'),
+    decode_head=dict(
+        align_corners=False,
+        channels=256,
+        dropout_ratio=0.1,
+        in_channels=[
+            64,
+            128,
+            320,
+            512,
+        ],
+        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=19,
+        type='SegformerHead'),
+    pretrained=None,
+    test_cfg=dict(crop_size=(
+        1024,
+        1024,
+    ), mode='slide', stride=(
+        768,
+        768,
+    )),
+    train_cfg=dict(),
+    type='EncoderDecoder')
+norm_cfg = dict(requires_grad=True, type='SyncBN')
+optim_wrapper = dict(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ), lr=6e-05, type='AdamW', weight_decay=0.01),
+    paramwise_cfg=dict(
+        custom_keys=dict(
+            head=dict(lr_mult=10.0),
+            norm=dict(decay_mult=0.0),
+            pos_block=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=1500, start_factor=1e-06,
+        type='LinearLR'),
+    dict(
+        begin=1500,
+        by_epoch=False,
+        end=160000,
+        eta_min=0.0,
+        power=1.0,
+        type='PolyLR'),
+]
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(img_path='images/test', seg_map_path='labels/test'),
+        data_root='data/rgb0/yolo-seg/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                2048,
+                1024,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='SeaIceRGB0'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ],
+    keep_results=True,
+    output_dir=
+    '/m/work/t410/T41011/work/gorada2/ENHANCE/SeaIce/mmseg/work_dir/seaicergb0/segformer_mit-b5_8xb1-160k_pre-cityscapes_seaicergb0-1024x1024/pred_result.pkl',
+    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=160000, type='IterBasedTrainLoop', val_interval=16000)
+train_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(img_path='images/train', seg_map_path='labels/train'),
+        data_root='data/rgb0/yolo-seg/',
+        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,
+                    1024,
+                ), type='RandomCrop'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PhotoMetricDistortion'),
+            dict(type='PackSegInputs'),
+        ],
+        type='SeaIceRGB0'),
+    num_workers=4,
+    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,
+        1024,
+    ), type='RandomCrop'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs'),
+]
+tta_model = dict(type='SegTTAModel')
+tta_pipeline = [
+    dict(backend_args=None, 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='images/test', seg_map_path='labels/test'),
+        data_root='data/rgb0/yolo-seg/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                2048,
+                1024,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='SeaIceRGB0'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ], type='IoUMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    save_dir=
+    '/work/t410/T41011/work/gorada2/ENHANCE/SeaIce/mmseg/work_dir/seaicergb0/segformer_mit-b5_8xb1-160k_pre-cityscapes_seaicergb0-1024x1024/show',
+    type='SegLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/work/t410/T41011/work/gorada2/ENHANCE/SeaIce/mmseg/work_dir/seaicergb0/segformer_mit-b5_8xb1-160k_pre-cityscapes_seaicergb0-1024x1024'