Delete YOLOv8/configs/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco-ReLINZ-112px-RndSmpl_Imgs:all_Anno:small-only_FakeBBoxes:42.36px_IoU:0.500_Val:ReLINZ.py

YOLOv8/configs/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco-ReLINZ-112px-RndSmpl_Imgs:all_Anno:small-only_FakeBBoxes:42.36px_IoU:0.500_Val:ReLINZ.py

@@ -1,245 +0,0 @@
-_base_ = './yolov8_s_syncbn_fast_8xb16-500e_coco.py'
-
-# ========================modified parameters======================
-deepen_factor = 0.67
-widen_factor = 0.75
-last_stage_out_channels = 768
-
-affine_scale = 0.9
-mixup_prob = 0.1
-
-
-img_scale = (128, 128) #_base_.img_scale
-# img_scale = (640, 640) #_base_.img_scale
-num_classes = 1
-class_name = ('small',)
-num_classes = len(class_name)
-metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
-
-train_batch_size_per_gpu = 512
-val_batch_size_per_gpu = 128
-test_batch_size_per_gpu = 128
-
-train_num_workers = 16
-val_num_workers = 16
-test_num_workers = 16
-
-# -----train val related-----
-# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
-base_lr = 0.001
-lr_factor = 0.01  # Learning rate scaling factor
-max_epochs = 1000  # Maximum training epochs
-
-# Disable mosaic augmentation for final 10 epochs (stage 2)
-close_mosaic_epochs = 10
-
-save_epoch_intervals = 1
-max_keep_ckpts = 2
-
-# validation intervals in stage 2
-val_interval_stage2 = 1
-
-# TRAIN DATASET
-data_root_train = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/train/'
-ann_file_train = 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json'
-
-# VAL DATASET
-# data_root_val = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/validation/'
-data_root_val = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/validation_subset025.0_seed0/'
-ann_file_val = 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json'
-
-# TEST DATASET
-## LINZ
-data_root_test  = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/test/'
-ann_file_test = 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json'
-
-## Utah
-# data_root_test  = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-Utah_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/test/'
-# ann_file_test = 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json'
-
-
-load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco/yolov8_m_syncbn_fast_8xb16-500e_coco_20230115_192200-c22e560a.pth'
-
-
-# =======================Unmodified in most cases==================
-pre_transform = _base_.pre_transform
-last_transform = _base_.last_transform
-
-model = dict(
-    backbone=dict(
-        last_stage_out_channels=last_stage_out_channels,
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor
-    ),
-    neck=dict(
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor,
-        in_channels=[256, 512, last_stage_out_channels],
-        out_channels=[256, 512, last_stage_out_channels]
-    ),
-    bbox_head=dict(
-        head_module=dict(
-            num_classes=num_classes,
-            widen_factor=widen_factor,
-            in_channels=[256, 512, last_stage_out_channels])
-    ),
-    train_cfg=dict(
-        assigner=dict(
-            num_classes=num_classes
-        )
-    )
-)
-
-mosaic_affine_transform = [
-    dict(
-        type='Mosaic',
-        img_scale=img_scale,
-        pad_val=114.0,
-        pre_transform=pre_transform),
-    dict(
-        type='YOLOv5RandomAffine',
-        max_rotate_degree=0.0,
-        max_shear_degree=0.0,
-        max_aspect_ratio=100,
-        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))
-]
-
-# enable mixup
-train_pipeline = [
-    *pre_transform, *mosaic_affine_transform,
-    dict(
-        type='YOLOv5MixUp',
-        prob=mixup_prob,
-        pre_transform=[*pre_transform, *mosaic_affine_transform]),
-    *last_transform
-]
-
-train_pipeline_stage2 = [
-    *pre_transform,
-    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
-    dict(
-        type='LetterResize',
-        scale=img_scale,
-        allow_scale_up=True,
-        pad_val=dict(img=114.0)
-    ),
-    dict(
-        type='YOLOv5RandomAffine',
-        max_rotate_degree=0.0,
-        max_shear_degree=0.0,
-        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
-        max_aspect_ratio=100,
-        border_val=(114, 114, 114)
-    ), 
-    *last_transform
-]
-
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu,
-    num_workers=train_num_workers,
-    dataset=dict(
-        data_root=data_root_train,
-        ann_file=data_root_train+ann_file_train,
-        data_prefix=dict(img='images/'),
-        filter_cfg=dict(filter_empty_gt=False),
-        metainfo=metainfo,
-        pipeline=train_pipeline
-    )
-)
-
-# _base_.test_pipeline[1].img_scale = img_scale
-# _base_.test_pipeline[2].scale = img_scale
-
-test_pipeline = [
-    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
-    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
-    dict(
-        type='LetterResize',
-        scale=img_scale,
-        allow_scale_up=False,
-        pad_val=dict(img=114)),
-    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
-    dict(
-        type='mmdet.PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
-                   'scale_factor', 'pad_param'))
-]
-
-val_dataloader = dict(
-    batch_size=val_batch_size_per_gpu,
-    num_workers=val_num_workers,
-    dataset=dict(
-        data_root=data_root_val,
-        ann_file=data_root_val+ann_file_val,
-        data_prefix=dict(img='images/'),
-        metainfo=metainfo,
-        # filter_cfg=dict(filter_empty_gt=False), # Does this make a change?
-        filter_cfg=dict(filter_empty_gt=True), # Does this make a change?
-        pipeline=test_pipeline,
-    )
-)
-
-test_dataloader = dict(
-    batch_size=test_batch_size_per_gpu,
-    num_workers=test_num_workers,
-    dataset=dict(
-        data_root=data_root_test,
-        ann_file=data_root_test+ann_file_test,
-        data_prefix=dict(img='images/'),
-        metainfo=metainfo,
-        filter_cfg=dict(filter_empty_gt=False),  # Does this make a change?
-        pipeline=test_pipeline,
-    )
-)
-
-
-optim_wrapper = dict(
-    optimizer=dict(
-        lr=base_lr,
-        batch_size_per_gpu=train_batch_size_per_gpu
-    ),
-)
-
-
-default_hooks = dict(
-    param_scheduler=dict(
-        lr_factor=lr_factor,
-        max_epochs=max_epochs
-    ),
-    checkpoint=dict(
-        interval=save_epoch_intervals,
-        max_keep_ckpts=max_keep_ckpts,
-        save_best=['coco/bbox_mAP', 'coco/bbox_mAP_50']
-    )
-)
-
-_base_.custom_hooks[1].switch_epoch = max_epochs - close_mosaic_epochs
-_base_.custom_hooks[1].switch_pipeline = train_pipeline_stage2
-
-val_evaluator = dict(
-    ann_file=data_root_val + ann_file_val,
-)
-
-test_evaluator = dict(
-    ann_file= data_root_test + ann_file_test,
-)
-
-train_cfg = dict(
-    max_epochs=max_epochs,
-    val_interval=save_epoch_intervals,
-    dynamic_intervals=[
-        ((max_epochs - close_mosaic_epochs),
-                        val_interval_stage2)
-    ]
-)
-
-
-visualizer = dict(
-    vis_backends=[
-        dict(type='LocalVisBackend'), 
-        dict(type='TensorboardVisBackend')
-    ]
-)