Delete FasterRCNN/configs/faster_rcnn/faster-rcnn_r50_fpn_2x_coco_ReLINZ_112px_RndSmpl_Imgs:all_Anno:small-only_Augm_Val:LINZ.py

FasterRCNN/configs/faster_rcnn/faster-rcnn_r50_fpn_2x_coco_ReLINZ_112px_RndSmpl_Imgs:all_Anno:small-only_Augm_Val:LINZ.py

@@ -1,412 +0,0 @@
-_base_ = [
-    '../_base_/models/faster-rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
-]
-
-
-
-# TRAIN DATASET
-data_root_train = '/var/storage/Common/SatelliteVehicles/Datasets/Real/Real-LINZ_112px_0.125m_RndSmpl_Imgs:all_Anno:small-only/train/'
-
-# 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/'
-
-# 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/'
-
-
-max_epochs = 1000 # 40
-train_batch_size_per_gpu = 64
-validation_batch_size_per_gpu = 64
-test_batch_size_per_gpu = 64
-num_workers = 8
-
-
-class_name = ('small',)
-num_classes = len(class_name)
-metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
-
-img_scale = (128, 128)
-
-affine_scale = 0.9
-
-load_from = 'https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50_fpn_2x_coco'
-
-
-# model settings
-model = dict(
-    type='FasterRCNN',
-    data_preprocessor=dict(
-        type='DetDataPreprocessor',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        bgr_to_rgb=True,
-        pad_size_divisor=32),
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch',
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        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],
-            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='StandardRoIHead',
-        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='Shared2FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=num_classes,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
-    # model training and testing settings
-    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=256,
-                pos_fraction=0.5,
-                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=False,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            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)
-        # soft-nms is also supported for rcnn testing
-        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
-    ))
-
-
-
-dataset_type = 'CocoDataset'
-
-backend_args = None
-
-# Original
-# train_pipeline = [
-#     dict(type='LoadImageFromFile', backend_args=backend_args),
-#     dict(type='LoadAnnotations', with_bbox=True),
-#     dict(type='Resize', scale=img_scale, keep_ratio=True),
-#     dict(type='RandomFlip', prob=0.5),
-#     dict(type='PackDetInputs')
-# ]
-
-pre_transform = [
-    dict(type='LoadImageFromFile', backend_args=backend_args),
-    dict(type='LoadAnnotations', with_bbox=True)
-]
-
-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),
-#     batch_sampler=dict(type='AspectRatioBatchSampler'),
-#     dataset=dict(
-#         type=dataset_type,
-#         data_root=data_root_train,
-#         ann_file='annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500_BalancedRatio:0.2000.json',
-#         data_prefix=dict(img='images/'),
-#         filter_cfg=dict(filter_empty_gt=False, min_size=32),
-#         pipeline=train_pipeline,
-#         metainfo=metainfo,
-#         backend_args=backend_args
-#     )
-# )
-
-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='annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-            ann_file='annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500_BalancedRatio:0.2000.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),
-    # If you don't have a gt annotation, delete the pipeline
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PackDetInputs',
-        meta_keys=(
-            'img_id', 'img_path', 'ori_shape', 'img_shape',
-            'scale_factor'
-        )
-    )
-]
-
-
-val_dataloader = dict(
-    batch_size=validation_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='annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-        data_prefix=dict(img='images/'),
-        filter_cfg=dict(filter_empty_gt=False, min_size=32),
-        test_mode=True,
-        pipeline=test_pipeline,
-        metainfo=metainfo,
-        backend_args=backend_args
-    )
-)
-
-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='annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-        data_prefix=dict(img='images/'),
-        filter_cfg=dict(filter_empty_gt=False, min_size=32),
-        test_mode=True,
-        pipeline=test_pipeline,
-        metainfo=metainfo,
-        backend_args=backend_args
-    )
-)
-
-# test_dataloader = val_dataloader
-
-val_evaluator = dict(
-    type='CocoMetric',
-    ann_file=data_root_val + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-    metric='bbox',
-    format_only=False,
-    backend_args=backend_args
-)
-
-test_evaluator = dict(
-    type='CocoMetric',
-    ann_file=data_root_test + 'annotations_coco_FakeBBoxes:42.36px_ForIoU:0.500.json',
-    metric='bbox',
-    format_only=False,
-    backend_args=backend_args
-)
-
-# test_evaluator = val_evaluator
-
-
-
-# training schedule for 2x
-train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')
-
-# learning rate
-param_scheduler = [
-    dict(
-        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
-    dict(
-        type='MultiStepLR',
-        begin=0,
-        end=max_epochs,
-        by_epoch=True,
-        milestones=[16, 22],
-        gamma=0.1)
-]
-
-# optimizer
-optim_wrapper = dict(
-    type='OptimWrapper',
-    optimizer=dict(
-        type='SGD',
-        lr=0.2,
-        momentum=0.9,
-        weight_decay=0.0001
-    )
-)
-
-# Default setting for scaling LR automatically
-#   - `enable` means enable scaling LR automatically
-#       or not by default.
-#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU)
-auto_scale_lr = dict(enable=False, base_batch_size=train_batch_size_per_gpu)
-
-
-default_hooks = dict(
-    checkpoint=dict(
-        interval=1,
-        max_keep_ckpts=1, 
-        save_best=['coco/bbox_mAP', 'coco/bbox_mAP_50']
-    ),
-    # The warmup_mim_iter parameter is critical.
-    # The default value is 1000 which is not suitable for cat datasets.
-    # param_scheduler=dict(
-    #     max_epochs=max_epochs, 
-    #     warmup_mim_iter=1000,
-    #     lr_factor=lr_factor
-    # ),
-    logger=dict(type='LoggerHook', interval=5))
-
-vis_backends = [dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend')]
-visualizer = dict(
-    type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
-