Upload 7 files

laseg_best/20241010_140609/iter_25000.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87721cc6e55acd291f39888c6b9e7ddc0654b950528403d7378a462acf01d011
+size 562100232

laseg_best/20241010_140609/last_checkpoint

@@ -0,0 +1 @@
+/home/zhangxingyu/mmlab/mmsegmentation/work_dirs/fcn_psvt_256x256_25k_laseg/iter_25000.pth

laseg_best/20241010_140609/vis_data/config.py

@@ -0,0 +1,308 @@
+checkpoint_file = './work_dirs/fcn_psvt_256x256_80k_cardiac/20240916_112410/iter_80000.pth'
+crop_size = (
+    256,
+    256,
+)
+data_preprocessor = dict(
+    bgr_to_rgb=True,
+    mean=[
+        123.675,
+        116.28,
+        103.53,
+    ],
+    pad_val=0,
+    seg_pad_val=255,
+    size=(
+        256,
+        256,
+    ),
+    std=[
+        58.395,
+        57.12,
+        57.375,
+    ],
+    type='SegDataPreProcessor')
+data_root = 'data/LAseg2013'
+dataset_type = 'MyLAseg2013Dataset'
+default_hooks = dict(
+    checkpoint=dict(by_epoch=False, interval=2000, 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(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))
+find_unused_parameters = True
+img_norm_cfg = dict(
+    mean=[
+        123.675,
+        116.28,
+        103.53,
+    ],
+    std=[
+        58.395,
+        57.12,
+        57.375,
+    ],
+    to_rgb=True)
+img_scale = (
+    300,
+    300,
+)
+launcher = 'pytorch'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=False)
+model = dict(
+    backbone=dict(
+        ape=False,
+        depths=[
+            2,
+            2,
+            6,
+            2,
+        ],
+        drop_path_rate=0.2,
+        img_size=256,
+        in_channels=3,
+        type='PSVT',
+        window_size=[
+            8,
+            8,
+            8,
+            8,
+        ]),
+    data_preprocessor=dict(
+        bgr_to_rgb=True,
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        pad_val=0,
+        seg_pad_val=255,
+        size=(
+            256,
+            256,
+        ),
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        type='SegDataPreProcessor'),
+    decode_head=dict(
+        channels=96,
+        in_channels=96,
+        in_index=-1,
+        loss_decode=[
+            dict(
+                loss_name='loss_ce', loss_weight=1.0, type='CrossEntropyLoss'),
+            dict(loss_name='loss_dice', loss_weight=2.0, type='DiceLoss'),
+        ],
+        norm_cfg=dict(requires_grad=True, type='SyncBN'),
+        num_classes=2,
+        type='FCNHead'),
+    test_cfg=dict(crop_size=(
+        512,
+        512,
+    ), mode='whole', stride=(
+        85,
+        85,
+    )),
+    train_cfg=dict(),
+    type='EncoderDecoder')
+norm_cfg = dict(requires_grad=True, type='SyncBN')
+optim_wrapper = dict(
+    clip_grad=None,
+    optimizer=dict(lr=0.01, type='AdamW', weight_decay=0.1),
+    type='OptimWrapper')
+optimizer = dict(lr=0.01, type='AdamW', weight_decay=0.1)
+param_scheduler = [
+    dict(
+        begin=0, by_epoch=False, end=12000, start_factor=0.03,
+        type='LinearLR'),
+    dict(
+        begin=12000,
+        by_epoch=False,
+        end=24000,
+        eta_min_ratio=0.03,
+        power=0.9,
+        type='PolyLRRatio'),
+    dict(begin=24000, by_epoch=False, end=25000, factor=1, type='ConstantLR'),
+]
+resume = False
+step = 's1'
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=16,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='mri_test_2d/image', seg_map_path='mri_test_2d/label'),
+        data_root='data/LAseg2013',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=False, scale=(
+                256,
+                256,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='MyLAseg2013Dataset'),
+    drop_last=False,
+    num_workers=16,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ], type='IoUMetric')
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        flip=False,
+        img_scale=(
+            256,
+            256,
+        ),
+        transforms=[
+            dict(keep_ratio=False, type='Resize'),
+            dict(type='RandomFlip'),
+            dict(
+                mean=[
+                    123.675,
+                    116.28,
+                    103.53,
+                ],
+                std=[
+                    58.395,
+                    57.12,
+                    57.375,
+                ],
+                to_rgb=True,
+                type='Normalize'),
+            dict(keys=[
+                'img',
+            ], type='ImageToTensor'),
+            dict(keys=[
+                'img',
+            ], type='Collect'),
+        ],
+        type='MultiScaleFlipAug'),
+]
+train_cfg = dict(max_iters=25000, type='IterBasedTrainLoop', val_interval=1000)
+train_dataloader = dict(
+    batch_size=16,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='mri_train_2d/image', seg_map_path='mri_train_2d/label'),
+        data_root='data/LAseg2013',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations'),
+            dict(
+                keep_ratio=True,
+                ratio_range=(
+                    0.5,
+                    2.0,
+                ),
+                scale=(
+                    300,
+                    300,
+                ),
+                type='RandomResize'),
+            dict(
+                cat_max_ratio=0.75, crop_size=(
+                    256,
+                    256,
+                ), type='RandomCrop'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PhotoMetricDistortion'),
+            dict(type='PackSegInputs'),
+        ],
+        type='MyLAseg2013Dataset'),
+    num_workers=16,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='InfiniteSampler'))
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(img_scale=(
+        300,
+        300,
+    ), ratio_range=(
+        0.5,
+        2.0,
+    ), type='Resize'),
+    dict(cat_max_ratio=0.75, crop_size=(
+        256,
+        256,
+    ), type='RandomCrop'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PhotoMetricDistortion'),
+    dict(
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        to_rgb=True,
+        type='Normalize'),
+    dict(pad_val=0, seg_pad_val=255, size=(
+        256,
+        256,
+    ), type='Pad'),
+    dict(type='DefaultFormatBundle'),
+    dict(keys=[
+        'img',
+        'gt_semantic_seg',
+    ], type='Collect'),
+]
+tta_model = dict(type='SegTTAModel')
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=16,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='mri_test_2d/image', seg_map_path='mri_test_2d/label'),
+        data_root='data/LAseg2013',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=False, scale=(
+                256,
+                256,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='MyLAseg2013Dataset'),
+    drop_last=False,
+    num_workers=16,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ], type='IoUMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+    dict(type='TensorboardVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='SegLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+        dict(type='TensorboardVisBackend'),
+    ])
+work_dir = './work_dirs/fcn_psvt_256x256_25k_laseg'

laseg_best/20241010_140609/vis_data/events.out.tfevents.1728540372.localhost.localdomain.134804.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13ba645d16720d5d54989dacafb2ed66c8d05ce7ad20928f79790bcc4b618f59
+size 227053