inference/fasterrcnn_config.py

dataset_type = 'CocoDataset'
data_root = '/home/safouane/Downloads/benchmark_aircraft/data/'
backend_args = None
max_epochs = 500
metainfo = dict(
    classes=('airplane', ), palette=[
        (
            0,
            128,
            255,
        ),
    ])
num_classes = 1
model = 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=1,
            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)))
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(
        1333,
        800,
    ), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs'),
]
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(type='Resize', scale=(
        1333,
        800,
    ), keep_ratio=True),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=(
            'img_id',
            'img_path',
            'ori_shape',
            'img_shape',
            'scale_factor',
        )),
]
train_dataloader = dict(
    batch_size=32,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type='CocoDataset',
        metainfo=dict(classes=('airplane', ), palette=[
            (
                220,
                20,
                60,
            ),
        ]),
        data_root='/home/safouane/Downloads/benchmark_aircraft/data/',
        ann_file='train/__coco.json',
        data_prefix=dict(img='train/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', scale=(
                1333,
                800,
            ), keep_ratio=True),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PackDetInputs'),
        ],
        backend_args=None))
val_dataloader = dict(
    batch_size=32,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        metainfo=dict(classes=('airplane', ), palette=[
            (
                220,
                20,
                60,
            ),
        ]),
        data_root='/home/safouane/Downloads/benchmark_aircraft/data/',
        ann_file='val/__coco.json',
        data_prefix=dict(img='val/'),
        test_mode=True,
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(type='Resize', scale=(
                1333,
                800,
            ), keep_ratio=True),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='PackDetInputs',
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                )),
        ],
        backend_args=None))
test_dataloader = dict(
    batch_size=32,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        metainfo=dict(classes=('airplane', ), palette=[
            (
                220,
                20,
                60,
            ),
        ]),
        data_root='/home/safouane/Downloads/benchmark_aircraft/data/',
        ann_file='test/__coco.json',
        data_prefix=dict(img='test/'),
        test_mode=True,
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(type='Resize', scale=(
                1333,
                800,
            ), keep_ratio=True),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='PackDetInputs',
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                )),
        ],
        backend_args=None))
val_evaluator = dict(
    type='CocoMetric',
    ann_file='/home/safouane/Downloads/benchmark_aircraft/data/val/__coco.json',
    metric='bbox',
    format_only=False,
    backend_args=None)
test_evaluator = dict(
    type='CocoMetric',
    ann_file=
    '/home/safouane/Downloads/benchmark_aircraft/data/test/__coco.json',
    metric='bbox',
    format_only=False,
    backend_args=None)
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=12,
        by_epoch=True,
        milestones=[
            8,
            11,
        ],
        gamma=0.1),
]
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=0.0001))
auto_scale_lr = dict(enable=False, base_batch_size=32)
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=50, save_best='auto'),
    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'),
        dict(type='TensorboardVisBackend'),
    ],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = '/home/safouane/Downloads/benchmark_aircraft/mmlab_configs/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth'
resume = False
launcher = 'none'
work_dir = './work_dirs/faster-rcnn_r50-caffe_fpn_1x_coco'