remove wrong files

pretrain/selfsup_mask-rcnn_swin-b_lsj-3x-coco_simmim-pretrain/final_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7aca88dfee95a9cb04041b5b93a19169aaa3bb14ff12c237042bc981205d85ab
-size 422177783

pretrain/selfsup_mask-rcnn_swin-b_lsj-3x-coco_simmim-pretrain/selfsup_mask-rcnn_swin-b_simmim.py

@@ -1,447 +0,0 @@
-model = dict(
-    type='SelfSupDetector',
-    backbone=dict(
-        type='SelfSupMaskRCNN',
-        backbone=dict(
-            type='SwinTransformer',
-            embed_dims=128,
-            depths=[2, 2, 18, 2],
-            num_heads=[4, 8, 16, 32],
-            window_size=7,
-            mlp_ratio=4,
-            qkv_bias=True,
-            qk_scale=None,
-            drop_rate=0.0,
-            attn_drop_rate=0.0,
-            drop_path_rate=0.2,
-            patch_norm=True,
-            out_indices=(0, 1, 2, 3),
-            with_cp=False,
-            frozen_stages=4,
-            convert_weights=True,
-            init_cfg=dict(
-                type='Pretrained',
-                checkpoint=
-                'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth'
-            )),
-        neck=dict(
-            type='FPN',
-            in_channels=[128, 256, 512, 1024],
-            out_channels=256,
-            num_outs=5),
-        rpn_head=dict(
-            type='RPNHead',
-            in_channels=256,
-            feat_channels=256,
-            anchor_generator=dict(
-                type='AnchorGenerator',
-                scales=[8],
-                ratios=[0.5, 1.0, 2.0],
-                strides=[4, 8, 16, 32, 64]),
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0.0, 0.0, 0.0, 0.0],
-                target_stds=[1.0, 1.0, 1.0, 1.0]),
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-        roi_head=dict(
-            type='SelfSupStandardRoIHead',
-            bbox_roi_extractor=dict(
-                type='SingleRoIExtractor',
-                roi_layer=dict(
-                    type='RoIAlign', output_size=7, sampling_ratio=0),
-                out_channels=256,
-                featmap_strides=[4, 8, 16, 32]),
-            bbox_head=dict(
-                type='SelfSupShared4Conv1FCBBoxHead',
-                in_channels=256,
-                num_classes=256,
-                roi_feat_size=7,
-                reg_class_agnostic=False,
-                loss_bbox=dict(type='L1Loss', loss_weight=1.0),
-                loss_cls=dict(
-                    type='ContrastiveLoss', loss_weight=1.0, temperature=0.5)),
-            mask_roi_extractor=None,
-            mask_head=None),
-        train_cfg=dict(
-            rpn=dict(
-                assigner=dict(
-                    type='MaxIoUAssigner',
-                    pos_iou_thr=0.7,
-                    neg_iou_thr=0.3,
-                    min_pos_iou=0.3,
-                    match_low_quality=True,
-                    ignore_iof_thr=-1),
-                sampler=dict(
-                    type='RandomSampler',
-                    num=4096,
-                    pos_fraction=1.0,
-                    neg_pos_ub=-1,
-                    add_gt_as_proposals=False),
-                allowed_border=-1,
-                pos_weight=-1,
-                debug=False),
-            rpn_proposal=dict(
-                nms_pre=2000,
-                max_per_img=1000,
-                nms=dict(type='nms', iou_threshold=0.7),
-                min_bbox_size=0),
-            rcnn=dict(
-                assigner=dict(
-                    type='MaxIoUAssigner',
-                    pos_iou_thr=0.5,
-                    neg_iou_thr=0.5,
-                    min_pos_iou=0.5,
-                    match_low_quality=True,
-                    ignore_iof_thr=-1,
-                    gt_max_assign_all=False),
-                sampler=dict(
-                    type='RandomSampler',
-                    num=4096,
-                    pos_fraction=1,
-                    neg_pos_ub=0,
-                    add_gt_as_proposals=True),
-                mask_size=28,
-                pos_weight=-1,
-                debug=False)),
-        test_cfg=dict(
-            rpn=dict(
-                nms_pre=1000,
-                max_per_img=1000,
-                nms=dict(type='nms', iou_threshold=0.7),
-                min_bbox_size=0),
-            rcnn=dict(
-                score_thr=0.05,
-                nms=dict(type='nms', iou_threshold=0.5),
-                max_per_img=100,
-                mask_thr_binary=0.5)),
-        init_cfg=dict(
-            type='Pretrained',
-            checkpoint='pretrain/simmim_swin-b_mmselfsup-pretrain.pth')))
-train_dataset_type = 'MultiViewCocoDataset'
-test_dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-classes = ['selective_search']
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-load_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=False)
-]
-train_pipeline1 = [
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
-    dict(type='Pad', size_divisor=32),
-    dict(type='RandFlip', flip_ratio=0.5),
-    dict(
-        type='OneOf',
-        transforms=[
-            dict(type='Identity'),
-            dict(type='AutoContrast'),
-            dict(type='RandEqualize'),
-            dict(type='RandSolarize'),
-            dict(type='RandColor'),
-            dict(type='RandContrast'),
-            dict(type='RandBrightness'),
-            dict(type='RandSharpness'),
-            dict(type='RandPosterize')
-        ]),
-    dict(
-        type='Normalize',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        to_rgb=True),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-train_pipeline2 = [
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
-    dict(type='Pad', size_divisor=32),
-    dict(type='RandFlip', flip_ratio=0.5),
-    dict(
-        type='OneOf',
-        transforms=[
-            dict(type='Identity'),
-            dict(type='AutoContrast'),
-            dict(type='RandEqualize'),
-            dict(type='RandSolarize'),
-            dict(type='RandColor'),
-            dict(type='RandContrast'),
-            dict(type='RandBrightness'),
-            dict(type='RandSharpness'),
-            dict(type='RandPosterize')
-        ]),
-    dict(
-        type='Normalize',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        to_rgb=True),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(
-                type='Normalize',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ])
-]
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=2,
-    train=dict(
-        type='MultiViewCocoDataset',
-        dataset=dict(
-            type='CocoDataset',
-            classes=['selective_search'],
-            ann_file=
-            'data/coco/filtered_proposals/train2017_ratio3size0008@0.5.json',
-            img_prefix='data/coco/train2017/',
-            pipeline=[
-                dict(type='LoadImageFromFile'),
-                dict(type='LoadAnnotations', with_bbox=True, with_mask=False)
-            ]),
-        num_views=2,
-        pipelines=[[{
-            'type':
-            'Resize',
-            'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                          (1333, 768), (1333, 800)],
-            'multiscale_mode':
-            'value',
-            'keep_ratio':
-            True
-        }, {
-            'type': 'FilterAnnotations',
-            'min_gt_bbox_wh': (0.01, 0.01)
-        }, {
-            'type': 'Pad',
-            'size_divisor': 32
-        }, {
-            'type': 'RandFlip',
-            'flip_ratio': 0.5
-        }, {
-            'type':
-            'OneOf',
-            'transforms': [{
-                'type': 'Identity'
-            }, {
-                'type': 'AutoContrast'
-            }, {
-                'type': 'RandEqualize'
-            }, {
-                'type': 'RandSolarize'
-            }, {
-                'type': 'RandColor'
-            }, {
-                'type': 'RandContrast'
-            }, {
-                'type': 'RandBrightness'
-            }, {
-                'type': 'RandSharpness'
-            }, {
-                'type': 'RandPosterize'
-            }]
-        }, {
-            'type': 'Normalize',
-            'mean': [123.675, 116.28, 103.53],
-            'std': [58.395, 57.12, 57.375],
-            'to_rgb': True
-        }, {
-            'type': 'DefaultFormatBundle'
-        }, {
-            'type': 'Collect',
-            'keys': ['img', 'gt_bboxes', 'gt_labels']
-        }],
-                   [{
-                       'type':
-                       'Resize',
-                       'img_scale': [(1333, 640), (1333, 672), (1333, 704),
-                                     (1333, 736), (1333, 768), (1333, 800)],
-                       'multiscale_mode':
-                       'value',
-                       'keep_ratio':
-                       True
-                   }, {
-                       'type': 'FilterAnnotations',
-                       'min_gt_bbox_wh': (0.01, 0.01)
-                   }, {
-                       'type': 'Pad',
-                       'size_divisor': 32
-                   }, {
-                       'type': 'RandFlip',
-                       'flip_ratio': 0.5
-                   }, {
-                       'type':
-                       'OneOf',
-                       'transforms': [{
-                           'type': 'Identity'
-                       }, {
-                           'type': 'AutoContrast'
-                       }, {
-                           'type': 'RandEqualize'
-                       }, {
-                           'type': 'RandSolarize'
-                       }, {
-                           'type': 'RandColor'
-                       }, {
-                           'type': 'RandContrast'
-                       }, {
-                           'type': 'RandBrightness'
-                       }, {
-                           'type': 'RandSharpness'
-                       }, {
-                           'type': 'RandPosterize'
-                       }]
-                   }, {
-                       'type': 'Normalize',
-                       'mean': [123.675, 116.28, 103.53],
-                       'std': [58.395, 57.12, 57.375],
-                       'to_rgb': True
-                   }, {
-                       'type': 'DefaultFormatBundle'
-                   }, {
-                       'type': 'Collect',
-                       'keys': ['img', 'gt_bboxes', 'gt_labels']
-                   }]]),
-    val=dict(
-        type='CocoDataset',
-        classes=['selective_search'],
-        ann_file='data/coco/annotations/instances_val2017.json',
-        img_prefix='data/coco/val2017/',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(
-                type='MultiScaleFlipAug',
-                img_scale=(1333, 800),
-                flip=False,
-                transforms=[
-                    dict(type='Resize', keep_ratio=True),
-                    dict(type='RandomFlip'),
-                    dict(
-                        type='Normalize',
-                        mean=[123.675, 116.28, 103.53],
-                        std=[58.395, 57.12, 57.375],
-                        to_rgb=True),
-                    dict(type='Pad', size_divisor=32),
-                    dict(type='ImageToTensor', keys=['img']),
-                    dict(type='Collect', keys=['img'])
-                ])
-        ]),
-    test=dict(
-        type='CocoDataset',
-        classes=['selective_search'],
-        ann_file='data/coco/annotations/instances_val2017.json',
-        img_prefix='data/coco/val2017/',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(
-                type='MultiScaleFlipAug',
-                img_scale=(1333, 800),
-                flip=False,
-                transforms=[
-                    dict(type='Resize', keep_ratio=True),
-                    dict(type='RandomFlip'),
-                    dict(
-                        type='Normalize',
-                        mean=[123.675, 116.28, 103.53],
-                        std=[58.395, 57.12, 57.375],
-                        to_rgb=True),
-                    dict(type='Pad', size_divisor=32),
-                    dict(type='ImageToTensor', keys=['img']),
-                    dict(type='Collect', keys=['img'])
-                ])
-        ]))
-evaluation = dict(interval=65535, gpu_collect=True, metric='bbox')
-optimizer = dict(
-    type='AdamW',
-    lr=6e-05,
-    betas=(0.9, 0.999),
-    weight_decay=0.05,
-    paramwise_cfg=dict(
-        custom_keys=dict(
-            absolute_pos_embed=dict(decay_mult=0.0),
-            relative_position_bias_table=dict(decay_mult=0.0),
-            norm=dict(decay_mult=0.0))))
-optimizer_config = dict(grad_clip=None)
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=0.001,
-    step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-custom_hooks = [
-    dict(type='MomentumUpdateHook'),
-    dict(
-        type='MMDetWandbHook',
-        init_kwargs=dict(project='I2B', group='pretrain'),
-        interval=50,
-        num_eval_images=0,
-        log_checkpoint=False)
-]
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-opencv_num_threads = 0
-mp_start_method = 'fork'
-auto_scale_lr = dict(enable=True, base_batch_size=32)
-custom_imports = dict(
-    imports=[
-        'mmselfsup.datasets.pipelines',
-        'selfsup.core.hook.momentum_update_hook',
-        'selfsup.datasets.pipelines.selfsup_pipelines',
-        'selfsup.datasets.pipelines.rand_aug',
-        'selfsup.datasets.single_view_coco',
-        'selfsup.datasets.multi_view_coco',
-        'selfsup.models.losses.contrastive_loss',
-        'selfsup.models.dense_heads.fcos_head',
-        'selfsup.models.dense_heads.retina_head',
-        'selfsup.models.dense_heads.detr_head',
-        'selfsup.models.dense_heads.deformable_detr_head',
-        'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head',
-        'selfsup.models.roi_heads.standard_roi_head',
-        'selfsup.models.detectors.selfsup_detector',
-        'selfsup.models.detectors.selfsup_fcos',
-        'selfsup.models.detectors.selfsup_detr',
-        'selfsup.models.detectors.selfsup_deformable_detr',
-        'selfsup.models.detectors.selfsup_retinanet',
-        'selfsup.models.detectors.selfsup_mask_rcnn',
-        'selfsup.core.bbox.assigners.hungarian_assigner',
-        'selfsup.core.bbox.assigners.pseudo_hungarian_assigner',
-        'selfsup.core.bbox.match_costs.match_cost'
-    ],
-    allow_failed_imports=False)
-pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth'
-find_unused_parameters = True
-work_dir = 'work_dirs/selfsup_mask-rcnn_swin-b_lsj-3x-coco_simmim-pretrain'
-auto_resume = False
-gpu_ids = range(0, 8)