Upload checkpoints/floor_boundary_seg_segformer.py with huggingface_hub

checkpoints/floor_boundary_seg_segformer.py

@@ -0,0 +1,337 @@
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.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=0,
+    size=(
+        1024,
+        1024,
+    ),
+    std=[
+        58.395,
+        57.12,
+        57.375,
+    ],
+    type='SegDataPreProcessor')
+data_root = '/root/floor_boundary_segm/'
+dataset_type = 'BoundaryDataset'
+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(draw=True, interval=1, 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,
+]
+load_from = '/opt/sdb/disk-sdb/layout_parser/checkpoints/iter_20000.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_b4_20220624-d588d980.pth',
+            type='Pretrained'),
+        mlp_ratio=4,
+        num_heads=[
+            1,
+            2,
+            5,
+            8,
+        ],
+        num_layers=[
+            3,
+            8,
+            27,
+            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=0,
+        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=5,
+        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 = True
+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='/root/floor_boundary_segm/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                1024,
+                1024,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='BoundaryDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ], type='IoUMetric')
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        interpolation='bicubic',
+        keep_ratio=True,
+        scale=(
+            1024,
+            1024,
+        ),
+        type='Resize'),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs'),
+]
+train_cfg = dict(max_iters=20000, type='IterBasedTrainLoop', val_interval=2000)
+train_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='img_dir/train', seg_map_path='ann_dir/train'),
+        data_root='/root/floor_boundary_segm/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations'),
+            dict(
+                keep_ratio=True,
+                ratio_range=(
+                    0.5,
+                    2.0,
+                ),
+                scale=(
+                    1024,
+                    1024,
+                ),
+                type='RandomResize'),
+            dict(
+                cat_max_ratio=0.75,
+                crop_size=(
+                    1024,
+                    1024,
+                ),
+                type='RandomCrop'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PhotoMetricDistortion'),
+            dict(type='PackSegInputs'),
+        ],
+        type='BoundaryDataset'),
+    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=(
+            1024,
+            1024,
+        ),
+        type='RandomResize'),
+    dict(cat_max_ratio=0.75, crop_size=(
+        1024,
+        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='img_dir/val', seg_map_path='ann_dir/val'),
+        data_root='/root/floor_boundary_segm/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                1024,
+                1024,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='BoundaryDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ], type='IoUMetric')
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1024,
+        1024,
+    ), type='Resize'),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs'),
+]
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='SegLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+        dict(
+            init_kwargs=dict(
+                group='segformer',
+                name='1024x1024',
+                project='floorplan parser'),
+            type='WandbVisBackend'),
+    ])