Delete ViTDet/projects/ViTDet/configs/001a_vitdet_mask-rcnn_vit-b-mae_lsj-100e_ReUtah-112px_HexGrid2_Imgs:small-only_Anno:small-only.py

ViTDet/projects/ViTDet/configs/001a_vitdet_mask-rcnn_vit-b-mae_lsj-100e_ReUtah-112px_HexGrid2_Imgs:small-only_Anno:small-only.py

@@ -1,435 +0,0 @@
-_base_ = [
-    '../../../configs/_base_/default_runtime.py',
-    '../../../configs/_base_/models/mask-rcnn_r50_fpn.py',
-]
-
-custom_imports = dict(imports=['projects.ViTDet.vitdet'])
-
-
-## TRAIN DATASET
-data_root_train = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/train/'
-
-
-## VALIDATION DATASET
-data_root_val  = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/validation_subset025.0_seed0/'
-
-# TEST DATASET
-## LINZ
-data_root_test  = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/test/'
-
-## Utah
-# data_root_test  = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-Utah_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/test/'
-
-
-train_batch_size_per_gpu = 24
-val_batch_size_per_gpu = 12
-test_batch_size_per_gpu = 60
-
-num_workers = 8
-
-max_epochs = 100
-
-
-# img_scale = (1024, 1024)
-# img_scale = (384, 384)
-img_scale = (128, 128)
-
-affine_scale = 0.9
-
-class_name = ('small',)
-num_classes = len(class_name)
-metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
-
-
-
-load_from = 'https://download.openmmlab.com/mmdetection/v3.0/vitdet/vitdet_mask-rcnn_vit-b-mae_lsj-100e/vitdet_mask-rcnn_vit-b-mae_lsj-100e_20230328_153519-e15fe294.pth'
-
-
-# MODEL SETTINGS
-backbone_norm_cfg = dict(type='LN', requires_grad=True)
-norm_cfg = dict(type='LN2d', requires_grad=True)
-
-batch_augments = [
-    dict(type='BatchFixedSizePad', size=img_scale, pad_mask=True)
-]
-
-model = dict(
-    data_preprocessor=dict(pad_size_divisor=32, batch_augments=batch_augments),
-    backbone=dict(
-        _delete_=True,
-        type='ViT',
-        # img_size=1024,
-        # img_size=384,
-        img_size=img_scale[0],
-        patch_size=16,
-        embed_dim=768,
-        depth=12,
-        num_heads=12,
-        drop_path_rate=0.1,
-        window_size=14,
-        mlp_ratio=4,
-        qkv_bias=True,
-        norm_cfg=backbone_norm_cfg,
-        window_block_indexes=[
-            0,
-            1,
-            3,
-            4,
-            6,
-            7,
-            9,
-            10,
-        ],
-        use_rel_pos=True,
-        init_cfg=dict(
-            type='Pretrained', 
-            # checkpoint='mae_pretrain_vit_base.pth'
-            # checkpoint='detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_base.pth'
-            checkpoint='vitdet_mask-rcnn_vit-b-mae_lsj-100e_20230328_153519-e15fe294.pth'
-        )
-    ),
-    neck=dict(
-        _delete_=True,
-        type='SimpleFPN',
-        backbone_channel=768,
-        in_channels=[192, 384, 768, 768],
-        out_channels=256,
-        num_outs=5,
-        norm_cfg=norm_cfg),
-    rpn_head=dict(num_convs=2),
-    roi_head=dict(
-        bbox_head=dict(
-            type='Shared4Conv1FCBBoxHead',
-            conv_out_channels=256,
-            norm_cfg=norm_cfg,
-            num_classes=num_classes
-        ),
-        # mask_head=dict(         # No masks as used
-        #     norm_cfg=norm_cfg,
-        #     num_classes=1,
-        #     loss_mask=dict(
-        #         use_mask=False
-        #     ),
-        # )
-        mask_head=None
-    )
-)
-
-custom_hooks = [dict(type='Fp16CompresssionHook')]
-
-
-
-
-
-
-
-
-## 
-dataset_type = 'CocoDataset'
-backend_args = None
-
-# Original
-# train_pipeline = [
-#     dict(type='LoadImageFromFile', backend_args=backend_args),
-#     dict(
-#         type='LoadAnnotations', 
-#         with_bbox=True, 
-#         # with_mask=True
-#         with_mask=False
-#     ),
-#     dict(type='RandomFlip', prob=0.5),
-#     dict(
-#         type='RandomResize',
-#         scale=img_scale,
-#         ratio_range=(0.1, 2.0),
-#         keep_ratio=True),
-#     dict(
-#         type='RandomCrop',
-#         crop_type='absolute_range',
-#         crop_size=img_scale,
-#         recompute_bbox=True,
-#         allow_negative_crop=True),
-#     dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
-#     dict(type='Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
-#     dict(type='PackDetInputs')
-# ]
-
-pre_transform = [
-    dict(type='LoadImageFromFile', backend_args=backend_args),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=False)
-]
-
-albu_train_transforms = [
-    dict(type='Blur', p=0.01),
-    dict(type='MedianBlur', p=0.01),
-    dict(type='ToGray', p=0.01),
-    dict(type='CLAHE', p=0.01)
-]
-
-last_transform = [
-    dict(
-        type='Albu',
-        transforms=albu_train_transforms,
-        bbox_params=dict(
-            type='BboxParams',
-            format='pascal_voc',
-            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
-        keymap={
-            'img': 'image',
-            'gt_bboxes': 'bboxes'
-        }),
-    dict(type='YOLOXHSVRandomAug'), # ???
-    dict(type='RandomFlip', prob=0.5),
-    dict(
-        type='PackDetInputs',
-        meta_keys=(
-            'img_id',
-            'img_path',
-            'ori_shape',
-            'img_shape',
-            'flip',
-            'flip_direction'
-        )
-    )
-]
-
-mosaic_affine_transform = [
-    dict(
-        type='Mosaic',
-        img_scale=img_scale,
-        pad_val=114.0,
-    ),
-    dict(
-        type='RandomAffine',
-        max_rotate_degree=0.0,
-        max_shear_degree=0.0,
-        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
-        # img_scale is (width, height)
-        border=(-img_scale[0] // 2, -img_scale[1] // 2),
-        border_val=(114, 114, 114))
-]
-
-train_pipeline = [
-    *pre_transform,
-    *mosaic_affine_transform,
-    dict(
-        type='MixUp',
-        img_scale=img_scale,
-    ),
-    *last_transform
-]
-
-
-
-# Original
-# train_dataloader = dict(
-#     batch_size=train_batch_size_per_gpu,
-#     num_workers=num_workers,
-#     persistent_workers=True,
-#     sampler=dict(type='DefaultSampler', shuffle=True),
-#     dataset=dict(
-#         type=dataset_type,
-#         data_root=data_root_train,
-#         ann_file=data_root_train + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-#         data_prefix=dict(img='images/'),
-#         # filter_cfg=dict(filter_empty_gt=True, min_size=32),
-#         filter_cfg=dict(filter_empty_gt=False),
-#         pipeline=train_pipeline,
-#         metainfo=metainfo,
-#     )
-# )
-
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu,
-    num_workers=num_workers,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    batch_sampler=dict(type='AspectRatioBatchSampler'),
-    dataset=dict(
-        # _delete_=True,
-        type='MultiImageMixDataset',
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root_train,
-            ann_file=data_root_train + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-            data_prefix=dict(img='images/'),
-            filter_cfg=dict(filter_empty_gt=False, min_size=32),
-            metainfo=metainfo,
-            backend_args=backend_args,
-            pipeline=pre_transform
-        ),
-        pipeline=train_pipeline,
-    )
-)
-
-
-test_pipeline = [
-    dict(type='LoadImageFromFile', backend_args=backend_args),
-    dict(type='Resize', scale=img_scale, keep_ratio=True),
-    dict(type='Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
-    dict(
-        type='LoadAnnotations', 
-        with_bbox=True,
-        # with_mask=True
-        with_mask=False
-    ),
-    dict(
-        type='PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
-                   'scale_factor'))
-]
-
-val_dataloader = dict(
-    batch_size=val_batch_size_per_gpu,
-    num_workers=num_workers,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type=dataset_type,
-        data_root=data_root_val,
-        ann_file=data_root_val + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-        data_prefix=dict(img='images/'),
-        test_mode=True,
-        pipeline=test_pipeline,
-        metainfo=metainfo,
-    )
-)
-# test_dataloader = val_dataloader
-test_dataloader = dict(
-    batch_size=test_batch_size_per_gpu,
-    num_workers=num_workers,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type=dataset_type,
-        data_root=data_root_test,
-        ann_file=data_root_test + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-        data_prefix=dict(img='images/'),
-        test_mode=True,
-        pipeline=test_pipeline,
-        metainfo=metainfo,
-    )
-)
-
-val_evaluator = dict(
-    type='CocoMetric',
-    ann_file=data_root_val + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-    metric='bbox',
-    format_only=False)
-# test_evaluator = val_evaluator
-test_evaluator = dict(
-    type='CocoMetric',
-    ann_file=data_root_test + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-    metric='bbox',
-    format_only=False
-)
-
-optim_wrapper = dict(
-    type='AmpOptimWrapper',
-    constructor='LayerDecayOptimizerConstructor',
-    paramwise_cfg={
-        'decay_rate': 0.7,
-        'decay_type': 'layer_wise',
-        'num_layers': 12,
-    },
-    optimizer=dict(
-        type='AdamW',
-        # lr=0.0001,
-        # lr=0.01,
-        lr=0.001,
-        betas=(0.9, 0.999),
-        weight_decay=0.1,
-    ))
-
-# 100 ep = 184375 iters * 64 images/iter / 118000 images/ep
-# max_iters = 184375
-# interval = 5000
-max_iters = 100000
-
-# interval = 2000
-interval = 1000
-
-dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
-param_scheduler = [
-    dict(
-        type='LinearLR', 
-        start_factor=0.001,
-        by_epoch=False, 
-        begin=0, 
-        end=250
-    ),
-    dict(
-        type='MultiStepLR',
-        begin=0,
-        
-        end=max_iters,
-        # end=max_epochs,
-        
-        by_epoch=False,
-        # by_epoch=True,
-        
-        # 88 ep = [163889 iters * 64 images/iter / 118000 images/ep
-        # 96 ep = [177546 iters * 64 images/iter / 118000 images/ep
-        # milestones=[20, 29],
-        # milestones=[5000, 6000],
-        milestones=[1000, 2000],
-        gamma=0.1
-    )
-]
-
-train_cfg = dict(
-    type='IterBasedTrainLoop',
-    max_iters=max_iters,
-    val_interval=interval,
-    dynamic_intervals=dynamic_intervals
-)
-# train_cfg = dict(
-#     type='EpochBasedTrainLoop', 
-#     max_epochs=max_epochs, 
-#     val_interval=1
-# )
-
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')
-
-default_hooks = dict(
-    logger=dict(
-        type='LoggerHook', 
-        interval=50,
-        log_metric_by_epoch=False
-    ),
-    checkpoint=dict(
-        type='CheckpointHook',
-        by_epoch=False,
-        # by_epoch=True,
-        save_last=True,
-        # interval=1,
-        interval=interval,
-        save_best=['coco/bbox_mAP', 'coco/bbox_mAP_50'],
-        max_keep_ckpts=2
-    )
-)
-
-vis_backends = [
-    dict(type='LocalVisBackend'),
-    dict(type='TensorboardVisBackend')
-]
-
-visualizer = dict(
-    type='DetLocalVisualizer', 
-    vis_backends=vis_backends, 
-    name='visualizer'
-)
-
-log_processor = dict(
-    type='LogProcessor', 
-    window_size=50, 
-    by_epoch=False
-    # by_epoch=True
-)
-
-auto_scale_lr = dict(base_batch_size=64)
-
-