Upload 7 files

mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py/20230419_203137/vis_data/config.py

@@ -0,0 +1,824 @@
+model = dict(
+    type='MixPL',
+    detector=dict(
+        type='FasterRCNN',
+        data_preprocessor=dict(
+            type='DetDataPreprocessor',
+            mean=[103.53, 116.28, 123.675],
+            std=[1.0, 1.0, 1.0],
+            bgr_to_rgb=False,
+            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=False),
+            norm_eval=True,
+            style='caffe',
+            init_cfg=dict(
+                type='Pretrained',
+                checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+        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, 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=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0.0, 0.0, 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))),
+        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))),
+    data_preprocessor=dict(
+        type='MultiBranchDataPreprocessor',
+        data_preprocessor=dict(
+            type='DetDataPreprocessor',
+            mean=[103.53, 116.28, 123.675],
+            std=[1.0, 1.0, 1.0],
+            bgr_to_rgb=False,
+            pad_size_divisor=32)),
+    semi_train_cfg=dict(
+        least_num=1,
+        cache_size=8,
+        mixup=True,
+        mosaic=True,
+        mosaic_shape=[(400, 400), (800, 800)],
+        mosaic_weight=0.5,
+        erase=True,
+        erase_patches=(1, 20),
+        erase_ratio=(0, 0.1),
+        erase_thr=0.7,
+        cls_pseudo_thr=0.7,
+        freeze_teacher=True,
+        sup_weight=1.0,
+        unsup_weight=2.0,
+        min_pseudo_bbox_wh=(0.01, 0.01)),
+    semi_test_cfg=dict(predict_on='teacher'))
+default_scope = 'mmdet'
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=10000,
+        by_epoch=False,
+        max_keep_ckpts=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='DetVisualizationHook'))
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'))
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='DetLocalVisualizer',
+    vis_backends=[dict(type='LocalVisBackend')],
+    name='visualizer')
+log_processor = dict(type='LogProcessor', window_size=50, by_epoch=False)
+log_level = 'INFO'
+load_from = None
+resume = True
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+file_client_args = dict(backend='disk')
+color_space = [[{
+    'type': 'ColorTransform'
+}], [{
+    'type': 'AutoContrast'
+}], [{
+    'type': 'Equalize'
+}], [{
+    'type': 'Sharpness'
+}], [{
+    'type': 'Posterize'
+}], [{
+    'type': 'Solarize'
+}], [{
+    'type': 'Color'
+}], [{
+    'type': 'Contrast'
+}], [{
+    'type': 'Brightness'
+}]]
+geometric = [[{
+    'type': 'Rotate'
+}], [{
+    'type': 'ShearX'
+}], [{
+    'type': 'ShearY'
+}], [{
+    'type': 'TranslateX'
+}], [{
+    'type': 'TranslateY'
+}]]
+scale = [(1333, 400), (1333, 1200)]
+branch_field = ['sup', 'unsup_teacher', 'unsup_student']
+sup_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='RandomResize',
+        scale=[(1333, 400), (1333, 1200)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='RandAugment',
+        aug_space=[[{
+            'type': 'ColorTransform'
+        }], [{
+            'type': 'AutoContrast'
+        }], [{
+            'type': 'Equalize'
+        }], [{
+            'type': 'Sharpness'
+        }], [{
+            'type': 'Posterize'
+        }], [{
+            'type': 'Solarize'
+        }], [{
+            'type': 'Color'
+        }], [{
+            'type': 'Contrast'
+        }], [{
+            'type': 'Brightness'
+        }]],
+        aug_num=1),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+    dict(
+        type='MultiBranch',
+        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+        sup=dict(type='PackDetInputs'))
+]
+weak_pipeline = [
+    dict(
+        type='RandomResize',
+        scale=[(1333, 400), (1333, 1200)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'flip', 'flip_direction',
+                   'homography_matrix'))
+]
+strong_pipeline = [
+    dict(
+        type='RandomResize',
+        scale=[(1333, 400), (1333, 1200)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='RandomOrder',
+        transforms=[
+            dict(
+                type='RandAugment',
+                aug_space=[[{
+                    'type': 'ColorTransform'
+                }], [{
+                    'type': 'AutoContrast'
+                }], [{
+                    'type': 'Equalize'
+                }], [{
+                    'type': 'Sharpness'
+                }], [{
+                    'type': 'Posterize'
+                }], [{
+                    'type': 'Solarize'
+                }], [{
+                    'type': 'Color'
+                }], [{
+                    'type': 'Contrast'
+                }], [{
+                    'type': 'Brightness'
+                }]],
+                aug_num=1),
+            dict(
+                type='RandAugment',
+                aug_space=[[{
+                    'type': 'Rotate'
+                }], [{
+                    'type': 'ShearX'
+                }], [{
+                    'type': 'ShearY'
+                }], [{
+                    'type': 'TranslateX'
+                }], [{
+                    'type': 'TranslateY'
+                }]],
+                aug_num=1)
+        ]),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'flip', 'flip_direction',
+                   'homography_matrix'))
+]
+unsup_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='MultiBranch',
+        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+        unsup_teacher=[
+            dict(
+                type='RandomResize',
+                scale=[(1333, 400), (1333, 1200)],
+                keep_ratio=True),
+            dict(type='RandomFlip', prob=0.5),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor', 'flip', 'flip_direction',
+                           'homography_matrix'))
+        ],
+        unsup_student=[
+            dict(
+                type='RandomResize',
+                scale=[(1333, 400), (1333, 1200)],
+                keep_ratio=True),
+            dict(type='RandomFlip', prob=0.5),
+            dict(
+                type='RandomOrder',
+                transforms=[
+                    dict(
+                        type='RandAugment',
+                        aug_space=[[{
+                            'type': 'ColorTransform'
+                        }], [{
+                            'type': 'AutoContrast'
+                        }], [{
+                            'type': 'Equalize'
+                        }], [{
+                            'type': 'Sharpness'
+                        }], [{
+                            'type': 'Posterize'
+                        }], [{
+                            'type': 'Solarize'
+                        }], [{
+                            'type': 'Color'
+                        }], [{
+                            'type': 'Contrast'
+                        }], [{
+                            'type': 'Brightness'
+                        }]],
+                        aug_num=1),
+                    dict(
+                        type='RandAugment',
+                        aug_space=[[{
+                            'type': 'Rotate'
+                        }], [{
+                            'type': 'ShearX'
+                        }], [{
+                            'type': 'ShearY'
+                        }], [{
+                            'type': 'TranslateX'
+                        }], [{
+                            'type': 'TranslateY'
+                        }]],
+                        aug_num=1)
+                ]),
+            dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor', 'flip', 'flip_direction',
+                           'homography_matrix'))
+        ])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+batch_size = 8
+num_workers = 8
+labeled_dataset = dict(
+    type='CocoDataset',
+    data_root='data/',
+    ann_file='coco_semi_anns/instances_train2017.1@10.json',
+    data_prefix=dict(img='coco/train2017/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=[
+        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+        dict(type='LoadAnnotations', with_bbox=True),
+        dict(
+            type='RandomResize',
+            scale=[(1333, 400), (1333, 1200)],
+            keep_ratio=True),
+        dict(type='RandomFlip', prob=0.5),
+        dict(
+            type='RandAugment',
+            aug_space=[[{
+                'type': 'ColorTransform'
+            }], [{
+                'type': 'AutoContrast'
+            }], [{
+                'type': 'Equalize'
+            }], [{
+                'type': 'Sharpness'
+            }], [{
+                'type': 'Posterize'
+            }], [{
+                'type': 'Solarize'
+            }], [{
+                'type': 'Color'
+            }], [{
+                'type': 'Contrast'
+            }], [{
+                'type': 'Brightness'
+            }]],
+            aug_num=1),
+        dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+        dict(
+            type='MultiBranch',
+            branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+            sup=dict(type='PackDetInputs'))
+    ])
+unlabeled_dataset = dict(
+    type='CocoDataset',
+    data_root='data/',
+    ann_file='coco_semi_anns/instances_train2017.1@10-unlabeled.json',
+    data_prefix=dict(img='coco/train2017/'),
+    filter_cfg=dict(filter_empty_gt=False),
+    pipeline=[
+        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+        dict(type='LoadAnnotations', with_bbox=True),
+        dict(
+            type='MultiBranch',
+            branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+            unsup_teacher=[
+                dict(
+                    type='RandomResize',
+                    scale=[(1333, 400), (1333, 1200)],
+                    keep_ratio=True),
+                dict(type='RandomFlip', prob=0.5),
+                dict(
+                    type='PackDetInputs',
+                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                               'scale_factor', 'flip', 'flip_direction',
+                               'homography_matrix'))
+            ],
+            unsup_student=[
+                dict(
+                    type='RandomResize',
+                    scale=[(1333, 400), (1333, 1200)],
+                    keep_ratio=True),
+                dict(type='RandomFlip', prob=0.5),
+                dict(
+                    type='RandomOrder',
+                    transforms=[
+                        dict(
+                            type='RandAugment',
+                            aug_space=[[{
+                                'type': 'ColorTransform'
+                            }], [{
+                                'type': 'AutoContrast'
+                            }], [{
+                                'type': 'Equalize'
+                            }], [{
+                                'type': 'Sharpness'
+                            }], [{
+                                'type': 'Posterize'
+                            }], [{
+                                'type': 'Solarize'
+                            }], [{
+                                'type': 'Color'
+                            }], [{
+                                'type': 'Contrast'
+                            }], [{
+                                'type': 'Brightness'
+                            }]],
+                            aug_num=1),
+                        dict(
+                            type='RandAugment',
+                            aug_space=[[{
+                                'type': 'Rotate'
+                            }], [{
+                                'type': 'ShearX'
+                            }], [{
+                                'type': 'ShearY'
+                            }], [{
+                                'type': 'TranslateX'
+                            }], [{
+                                'type': 'TranslateY'
+                            }]],
+                            aug_num=1)
+                    ]),
+                dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+                dict(
+                    type='PackDetInputs',
+                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                               'scale_factor', 'flip', 'flip_direction',
+                               'homography_matrix'))
+            ])
+    ])
+train_dataloader = dict(
+    batch_size=5,
+    num_workers=5,
+    persistent_workers=True,
+    sampler=dict(
+        type='GroupMultiSourceSampler', batch_size=5, source_ratio=[1, 4]),
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=[
+            dict(
+                type='CocoDataset',
+                data_root='data/',
+                ann_file='coco_semi_anns/instances_train2017.1@10.json',
+                data_prefix=dict(img='coco/train2017/'),
+                filter_cfg=dict(filter_empty_gt=True, min_size=32),
+                pipeline=[
+                    dict(
+                        type='LoadImageFromFile',
+                        file_client_args=dict(backend='disk')),
+                    dict(type='LoadAnnotations', with_bbox=True),
+                    dict(
+                        type='RandomResize',
+                        scale=[(1333, 400), (1333, 1200)],
+                        keep_ratio=True),
+                    dict(type='RandomFlip', prob=0.5),
+                    dict(
+                        type='RandAugment',
+                        aug_space=[[{
+                            'type': 'ColorTransform'
+                        }], [{
+                            'type': 'AutoContrast'
+                        }], [{
+                            'type': 'Equalize'
+                        }], [{
+                            'type': 'Sharpness'
+                        }], [{
+                            'type': 'Posterize'
+                        }], [{
+                            'type': 'Solarize'
+                        }], [{
+                            'type': 'Color'
+                        }], [{
+                            'type': 'Contrast'
+                        }], [{
+                            'type': 'Brightness'
+                        }]],
+                        aug_num=1),
+                    dict(
+                        type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+                    dict(
+                        type='MultiBranch',
+                        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+                        sup=dict(type='PackDetInputs'))
+                ]),
+            dict(
+                type='CocoDataset',
+                data_root='data/',
+                ann_file=
+                'coco_semi_anns/instances_train2017.1@10-unlabeled.json',
+                data_prefix=dict(img='coco/train2017/'),
+                filter_cfg=dict(filter_empty_gt=False),
+                pipeline=[
+                    dict(
+                        type='LoadImageFromFile',
+                        file_client_args=dict(backend='disk')),
+                    dict(type='LoadAnnotations', with_bbox=True),
+                    dict(
+                        type='MultiBranch',
+                        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+                        unsup_teacher=[
+                            dict(
+                                type='RandomResize',
+                                scale=[(1333, 400), (1333, 1200)],
+                                keep_ratio=True),
+                            dict(type='RandomFlip', prob=0.5),
+                            dict(
+                                type='PackDetInputs',
+                                meta_keys=('img_id', 'img_path', 'ori_shape',
+                                           'img_shape', 'scale_factor', 'flip',
+                                           'flip_direction',
+                                           'homography_matrix'))
+                        ],
+                        unsup_student=[
+                            dict(
+                                type='RandomResize',
+                                scale=[(1333, 400), (1333, 1200)],
+                                keep_ratio=True),
+                            dict(type='RandomFlip', prob=0.5),
+                            dict(
+                                type='RandomOrder',
+                                transforms=[
+                                    dict(
+                                        type='RandAugment',
+                                        aug_space=[[{
+                                            'type': 'ColorTransform'
+                                        }], [{
+                                            'type': 'AutoContrast'
+                                        }], [{
+                                            'type': 'Equalize'
+                                        }], [{
+                                            'type': 'Sharpness'
+                                        }], [{
+                                            'type': 'Posterize'
+                                        }], [{
+                                            'type': 'Solarize'
+                                        }], [{
+                                            'type': 'Color'
+                                        }], [{
+                                            'type': 'Contrast'
+                                        }], [{
+                                            'type': 'Brightness'
+                                        }]],
+                                        aug_num=1),
+                                    dict(
+                                        type='RandAugment',
+                                        aug_space=[[{
+                                            'type': 'Rotate'
+                                        }], [{
+                                            'type': 'ShearX'
+                                        }], [{
+                                            'type': 'ShearY'
+                                        }], [{
+                                            'type': 'TranslateX'
+                                        }], [{
+                                            'type': 'TranslateY'
+                                        }]],
+                                        aug_num=1)
+                                ]),
+                            dict(
+                                type='FilterAnnotations',
+                                min_gt_bbox_wh=(0.01, 0.01)),
+                            dict(
+                                type='PackDetInputs',
+                                meta_keys=('img_id', 'img_path', 'ori_shape',
+                                           'img_shape', 'scale_factor', 'flip',
+                                           'flip_direction',
+                                           'homography_matrix'))
+                        ])
+                ])
+        ]))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type='CocoDataset',
+        data_root='data/coco/',
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=[
+            dict(
+                type='LoadImageFromFile',
+                file_client_args=dict(backend='disk')),
+            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor'))
+        ]))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type='CocoDataset',
+        data_root='data/coco/',
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=[
+            dict(
+                type='LoadImageFromFile',
+                file_client_args=dict(backend='disk')),
+            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor'))
+        ]))
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file='data/coco/annotations/instances_val2017.json',
+    metric='bbox',
+    format_only=False)
+test_evaluator = dict(
+    type='CocoMetric',
+    ann_file='data/coco/annotations/instances_val2017.json',
+    metric='bbox',
+    format_only=False)
+detector = dict(
+    type='FasterRCNN',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[103.53, 116.28, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        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=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    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, 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=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 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))),
+    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)))
+work_dir = '/mnt/petrelfs/chenzeming/mixpl/mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py'
+train_cfg = dict(
+    type='IterBasedTrainLoop', max_iters=180000, val_interval=5000)
+val_cfg = dict(type='TeacherStudentValLoop')
+test_cfg = dict(type='TestLoop')
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500)
+]
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001),
+    clip_grad=dict(max_norm=20, norm_type=2))
+custom_hooks = [dict(type='AnnealMeanTeacherHook', momentum=0.0002, gamma=4)]
+launcher = 'slurm'

mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py/iter_180000.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a014249c67c6c8278de2e795e9ed33f5c3d6788b1b60131de97541d0b43cbc3
+size 567531777

mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py/last_checkpoint

@@ -0,0 +1 @@
+/mnt/petrelfs/chenzeming/mixpl/mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py/iter_180000.pth

mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py/mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py

@@ -0,0 +1,824 @@
+model = dict(
+    type='MixPL',
+    detector=dict(
+        type='FasterRCNN',
+        data_preprocessor=dict(
+            type='DetDataPreprocessor',
+            mean=[103.53, 116.28, 123.675],
+            std=[1.0, 1.0, 1.0],
+            bgr_to_rgb=False,
+            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=False),
+            norm_eval=True,
+            style='caffe',
+            init_cfg=dict(
+                type='Pretrained',
+                checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+        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, 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=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0.0, 0.0, 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))),
+        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))),
+    data_preprocessor=dict(
+        type='MultiBranchDataPreprocessor',
+        data_preprocessor=dict(
+            type='DetDataPreprocessor',
+            mean=[103.53, 116.28, 123.675],
+            std=[1.0, 1.0, 1.0],
+            bgr_to_rgb=False,
+            pad_size_divisor=32)),
+    semi_train_cfg=dict(
+        least_num=1,
+        cache_size=8,
+        mixup=True,
+        mosaic=True,
+        mosaic_shape=[(400, 400), (800, 800)],
+        mosaic_weight=0.5,
+        erase=True,
+        erase_patches=(1, 20),
+        erase_ratio=(0, 0.1),
+        erase_thr=0.7,
+        cls_pseudo_thr=0.7,
+        freeze_teacher=True,
+        sup_weight=1.0,
+        unsup_weight=2.0,
+        min_pseudo_bbox_wh=(0.01, 0.01)),
+    semi_test_cfg=dict(predict_on='teacher'))
+default_scope = 'mmdet'
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=10000,
+        by_epoch=False,
+        max_keep_ckpts=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='DetVisualizationHook'))
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'))
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='DetLocalVisualizer',
+    vis_backends=[dict(type='LocalVisBackend')],
+    name='visualizer')
+log_processor = dict(type='LogProcessor', window_size=50, by_epoch=False)
+log_level = 'INFO'
+load_from = None
+resume = True
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+file_client_args = dict(backend='disk')
+color_space = [[{
+    'type': 'ColorTransform'
+}], [{
+    'type': 'AutoContrast'
+}], [{
+    'type': 'Equalize'
+}], [{
+    'type': 'Sharpness'
+}], [{
+    'type': 'Posterize'
+}], [{
+    'type': 'Solarize'
+}], [{
+    'type': 'Color'
+}], [{
+    'type': 'Contrast'
+}], [{
+    'type': 'Brightness'
+}]]
+geometric = [[{
+    'type': 'Rotate'
+}], [{
+    'type': 'ShearX'
+}], [{
+    'type': 'ShearY'
+}], [{
+    'type': 'TranslateX'
+}], [{
+    'type': 'TranslateY'
+}]]
+scale = [(1333, 400), (1333, 1200)]
+branch_field = ['sup', 'unsup_teacher', 'unsup_student']
+sup_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='RandomResize',
+        scale=[(1333, 400), (1333, 1200)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='RandAugment',
+        aug_space=[[{
+            'type': 'ColorTransform'
+        }], [{
+            'type': 'AutoContrast'
+        }], [{
+            'type': 'Equalize'
+        }], [{
+            'type': 'Sharpness'
+        }], [{
+            'type': 'Posterize'
+        }], [{
+            'type': 'Solarize'
+        }], [{
+            'type': 'Color'
+        }], [{
+            'type': 'Contrast'
+        }], [{
+            'type': 'Brightness'
+        }]],
+        aug_num=1),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+    dict(
+        type='MultiBranch',
+        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+        sup=dict(type='PackDetInputs'))
+]
+weak_pipeline = [
+    dict(
+        type='RandomResize',
+        scale=[(1333, 400), (1333, 1200)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'flip', 'flip_direction',
+                   'homography_matrix'))
+]
+strong_pipeline = [
+    dict(
+        type='RandomResize',
+        scale=[(1333, 400), (1333, 1200)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='RandomOrder',
+        transforms=[
+            dict(
+                type='RandAugment',
+                aug_space=[[{
+                    'type': 'ColorTransform'
+                }], [{
+                    'type': 'AutoContrast'
+                }], [{
+                    'type': 'Equalize'
+                }], [{
+                    'type': 'Sharpness'
+                }], [{
+                    'type': 'Posterize'
+                }], [{
+                    'type': 'Solarize'
+                }], [{
+                    'type': 'Color'
+                }], [{
+                    'type': 'Contrast'
+                }], [{
+                    'type': 'Brightness'
+                }]],
+                aug_num=1),
+            dict(
+                type='RandAugment',
+                aug_space=[[{
+                    'type': 'Rotate'
+                }], [{
+                    'type': 'ShearX'
+                }], [{
+                    'type': 'ShearY'
+                }], [{
+                    'type': 'TranslateX'
+                }], [{
+                    'type': 'TranslateY'
+                }]],
+                aug_num=1)
+        ]),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'flip', 'flip_direction',
+                   'homography_matrix'))
+]
+unsup_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='MultiBranch',
+        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+        unsup_teacher=[
+            dict(
+                type='RandomResize',
+                scale=[(1333, 400), (1333, 1200)],
+                keep_ratio=True),
+            dict(type='RandomFlip', prob=0.5),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor', 'flip', 'flip_direction',
+                           'homography_matrix'))
+        ],
+        unsup_student=[
+            dict(
+                type='RandomResize',
+                scale=[(1333, 400), (1333, 1200)],
+                keep_ratio=True),
+            dict(type='RandomFlip', prob=0.5),
+            dict(
+                type='RandomOrder',
+                transforms=[
+                    dict(
+                        type='RandAugment',
+                        aug_space=[[{
+                            'type': 'ColorTransform'
+                        }], [{
+                            'type': 'AutoContrast'
+                        }], [{
+                            'type': 'Equalize'
+                        }], [{
+                            'type': 'Sharpness'
+                        }], [{
+                            'type': 'Posterize'
+                        }], [{
+                            'type': 'Solarize'
+                        }], [{
+                            'type': 'Color'
+                        }], [{
+                            'type': 'Contrast'
+                        }], [{
+                            'type': 'Brightness'
+                        }]],
+                        aug_num=1),
+                    dict(
+                        type='RandAugment',
+                        aug_space=[[{
+                            'type': 'Rotate'
+                        }], [{
+                            'type': 'ShearX'
+                        }], [{
+                            'type': 'ShearY'
+                        }], [{
+                            'type': 'TranslateX'
+                        }], [{
+                            'type': 'TranslateY'
+                        }]],
+                        aug_num=1)
+                ]),
+            dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor', 'flip', 'flip_direction',
+                           'homography_matrix'))
+        ])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+batch_size = 8
+num_workers = 8
+labeled_dataset = dict(
+    type='CocoDataset',
+    data_root='data/',
+    ann_file='coco_semi_anns/instances_train2017.1@10.json',
+    data_prefix=dict(img='coco/train2017/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=[
+        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+        dict(type='LoadAnnotations', with_bbox=True),
+        dict(
+            type='RandomResize',
+            scale=[(1333, 400), (1333, 1200)],
+            keep_ratio=True),
+        dict(type='RandomFlip', prob=0.5),
+        dict(
+            type='RandAugment',
+            aug_space=[[{
+                'type': 'ColorTransform'
+            }], [{
+                'type': 'AutoContrast'
+            }], [{
+                'type': 'Equalize'
+            }], [{
+                'type': 'Sharpness'
+            }], [{
+                'type': 'Posterize'
+            }], [{
+                'type': 'Solarize'
+            }], [{
+                'type': 'Color'
+            }], [{
+                'type': 'Contrast'
+            }], [{
+                'type': 'Brightness'
+            }]],
+            aug_num=1),
+        dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+        dict(
+            type='MultiBranch',
+            branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+            sup=dict(type='PackDetInputs'))
+    ])
+unlabeled_dataset = dict(
+    type='CocoDataset',
+    data_root='data/',
+    ann_file='coco_semi_anns/instances_train2017.1@10-unlabeled.json',
+    data_prefix=dict(img='coco/train2017/'),
+    filter_cfg=dict(filter_empty_gt=False),
+    pipeline=[
+        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+        dict(type='LoadAnnotations', with_bbox=True),
+        dict(
+            type='MultiBranch',
+            branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+            unsup_teacher=[
+                dict(
+                    type='RandomResize',
+                    scale=[(1333, 400), (1333, 1200)],
+                    keep_ratio=True),
+                dict(type='RandomFlip', prob=0.5),
+                dict(
+                    type='PackDetInputs',
+                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                               'scale_factor', 'flip', 'flip_direction',
+                               'homography_matrix'))
+            ],
+            unsup_student=[
+                dict(
+                    type='RandomResize',
+                    scale=[(1333, 400), (1333, 1200)],
+                    keep_ratio=True),
+                dict(type='RandomFlip', prob=0.5),
+                dict(
+                    type='RandomOrder',
+                    transforms=[
+                        dict(
+                            type='RandAugment',
+                            aug_space=[[{
+                                'type': 'ColorTransform'
+                            }], [{
+                                'type': 'AutoContrast'
+                            }], [{
+                                'type': 'Equalize'
+                            }], [{
+                                'type': 'Sharpness'
+                            }], [{
+                                'type': 'Posterize'
+                            }], [{
+                                'type': 'Solarize'
+                            }], [{
+                                'type': 'Color'
+                            }], [{
+                                'type': 'Contrast'
+                            }], [{
+                                'type': 'Brightness'
+                            }]],
+                            aug_num=1),
+                        dict(
+                            type='RandAugment',
+                            aug_space=[[{
+                                'type': 'Rotate'
+                            }], [{
+                                'type': 'ShearX'
+                            }], [{
+                                'type': 'ShearY'
+                            }], [{
+                                'type': 'TranslateX'
+                            }], [{
+                                'type': 'TranslateY'
+                            }]],
+                            aug_num=1)
+                    ]),
+                dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+                dict(
+                    type='PackDetInputs',
+                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                               'scale_factor', 'flip', 'flip_direction',
+                               'homography_matrix'))
+            ])
+    ])
+train_dataloader = dict(
+    batch_size=5,
+    num_workers=5,
+    persistent_workers=True,
+    sampler=dict(
+        type='GroupMultiSourceSampler', batch_size=5, source_ratio=[1, 4]),
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=[
+            dict(
+                type='CocoDataset',
+                data_root='data/',
+                ann_file='coco_semi_anns/instances_train2017.1@10.json',
+                data_prefix=dict(img='coco/train2017/'),
+                filter_cfg=dict(filter_empty_gt=True, min_size=32),
+                pipeline=[
+                    dict(
+                        type='LoadImageFromFile',
+                        file_client_args=dict(backend='disk')),
+                    dict(type='LoadAnnotations', with_bbox=True),
+                    dict(
+                        type='RandomResize',
+                        scale=[(1333, 400), (1333, 1200)],
+                        keep_ratio=True),
+                    dict(type='RandomFlip', prob=0.5),
+                    dict(
+                        type='RandAugment',
+                        aug_space=[[{
+                            'type': 'ColorTransform'
+                        }], [{
+                            'type': 'AutoContrast'
+                        }], [{
+                            'type': 'Equalize'
+                        }], [{
+                            'type': 'Sharpness'
+                        }], [{
+                            'type': 'Posterize'
+                        }], [{
+                            'type': 'Solarize'
+                        }], [{
+                            'type': 'Color'
+                        }], [{
+                            'type': 'Contrast'
+                        }], [{
+                            'type': 'Brightness'
+                        }]],
+                        aug_num=1),
+                    dict(
+                        type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
+                    dict(
+                        type='MultiBranch',
+                        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+                        sup=dict(type='PackDetInputs'))
+                ]),
+            dict(
+                type='CocoDataset',
+                data_root='data/',
+                ann_file=
+                'coco_semi_anns/instances_train2017.1@10-unlabeled.json',
+                data_prefix=dict(img='coco/train2017/'),
+                filter_cfg=dict(filter_empty_gt=False),
+                pipeline=[
+                    dict(
+                        type='LoadImageFromFile',
+                        file_client_args=dict(backend='disk')),
+                    dict(type='LoadAnnotations', with_bbox=True),
+                    dict(
+                        type='MultiBranch',
+                        branch_field=['sup', 'unsup_teacher', 'unsup_student'],
+                        unsup_teacher=[
+                            dict(
+                                type='RandomResize',
+                                scale=[(1333, 400), (1333, 1200)],
+                                keep_ratio=True),
+                            dict(type='RandomFlip', prob=0.5),
+                            dict(
+                                type='PackDetInputs',
+                                meta_keys=('img_id', 'img_path', 'ori_shape',
+                                           'img_shape', 'scale_factor', 'flip',
+                                           'flip_direction',
+                                           'homography_matrix'))
+                        ],
+                        unsup_student=[
+                            dict(
+                                type='RandomResize',
+                                scale=[(1333, 400), (1333, 1200)],
+                                keep_ratio=True),
+                            dict(type='RandomFlip', prob=0.5),
+                            dict(
+                                type='RandomOrder',
+                                transforms=[
+                                    dict(
+                                        type='RandAugment',
+                                        aug_space=[[{
+                                            'type': 'ColorTransform'
+                                        }], [{
+                                            'type': 'AutoContrast'
+                                        }], [{
+                                            'type': 'Equalize'
+                                        }], [{
+                                            'type': 'Sharpness'
+                                        }], [{
+                                            'type': 'Posterize'
+                                        }], [{
+                                            'type': 'Solarize'
+                                        }], [{
+                                            'type': 'Color'
+                                        }], [{
+                                            'type': 'Contrast'
+                                        }], [{
+                                            'type': 'Brightness'
+                                        }]],
+                                        aug_num=1),
+                                    dict(
+                                        type='RandAugment',
+                                        aug_space=[[{
+                                            'type': 'Rotate'
+                                        }], [{
+                                            'type': 'ShearX'
+                                        }], [{
+                                            'type': 'ShearY'
+                                        }], [{
+                                            'type': 'TranslateX'
+                                        }], [{
+                                            'type': 'TranslateY'
+                                        }]],
+                                        aug_num=1)
+                                ]),
+                            dict(
+                                type='FilterAnnotations',
+                                min_gt_bbox_wh=(0.01, 0.01)),
+                            dict(
+                                type='PackDetInputs',
+                                meta_keys=('img_id', 'img_path', 'ori_shape',
+                                           'img_shape', 'scale_factor', 'flip',
+                                           'flip_direction',
+                                           'homography_matrix'))
+                        ])
+                ])
+        ]))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type='CocoDataset',
+        data_root='data/coco/',
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=[
+            dict(
+                type='LoadImageFromFile',
+                file_client_args=dict(backend='disk')),
+            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor'))
+        ]))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type='CocoDataset',
+        data_root='data/coco/',
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=[
+            dict(
+                type='LoadImageFromFile',
+                file_client_args=dict(backend='disk')),
+            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+            dict(
+                type='PackDetInputs',
+                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                           'scale_factor'))
+        ]))
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file='data/coco/annotations/instances_val2017.json',
+    metric='bbox',
+    format_only=False)
+test_evaluator = dict(
+    type='CocoMetric',
+    ann_file='data/coco/annotations/instances_val2017.json',
+    metric='bbox',
+    format_only=False)
+detector = dict(
+    type='FasterRCNN',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[103.53, 116.28, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        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=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    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, 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=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 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))),
+    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)))
+work_dir = '/mnt/petrelfs/chenzeming/mixpl/mixpl_faster-rcnn_r50-caffe_fpn_180k_coco-s1-p10.py'
+train_cfg = dict(
+    type='IterBasedTrainLoop', max_iters=180000, val_interval=5000)
+val_cfg = dict(type='TeacherStudentValLoop')
+test_cfg = dict(type='TestLoop')
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500)
+]
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001),
+    clip_grad=dict(max_norm=20, norm_type=2))
+custom_hooks = [dict(type='AnnealMeanTeacherHook', momentum=0.0002, gamma=4)]
+launcher = 'slurm'