model = dict(
    type='FasterRCNN',
    pretrained='/netscratch/minouei/ga/pretrained/model_torch.pth',
    backbone=dict(
        type='ResNeXt',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=0,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        groups=64,
        base_width=4),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='CascadeRPNHead',
        num_stages=2,
        stages=[
            dict(
                type='StageCascadeRPNHead',
                in_channels=256,
                feat_channels=256,
                anchor_generator=dict(
                    type='AnchorGenerator',
                    scales=[8],
                    ratios=[1.0],
                    strides=[4, 8, 16, 32, 64]),
                adapt_cfg=dict(type='dilation', dilation=3),
                bridged_feature=True,
                sampling=False,
                with_cls=False,
                reg_decoded_bbox=True,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=(0.0, 0.0, 0.0, 0.0),
                    target_stds=(0.1, 0.1, 0.5, 0.5)),
                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=7.0)),
            dict(
                type='StageCascadeRPNHead',
                in_channels=256,
                feat_channels=256,
                adapt_cfg=dict(type='offset'),
                bridged_feature=False,
                sampling=True,
                with_cls=True,
                reg_decoded_bbox=True,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=(0.0, 0.0, 0.0, 0.0),
                    target_stds=(0.05, 0.05, 0.1, 0.1)),
                loss_cls=dict(
                    type='CrossEntropyLoss', use_sigmoid=True,
                    loss_weight=0.7),
                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=7.0))
        ]),
    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=5,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.04, 0.04, 0.08, 0.08]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
            loss_bbox=dict(type='SmoothL1Loss', loss_weight=1.0, beta=1.0))))
train_cfg = dict(
    rpn=[
        dict(
            assigner=dict(
                type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.7,
                min_pos_iou=0.3,
                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_across_levels=False,
        nms_pre=2000,
        nms_post=1000,
        max_num=300,
        nms_thr=0.8,
        min_bbox_size=0),
    rcnn=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.65,
            neg_iou_thr=0.65,
            min_pos_iou=0.65,
            match_low_quality=False,
            ignore_iof_thr=-1),
        sampler=dict(
            type='RandomSampler',
            num=256,
            pos_fraction=0.25,
            neg_pos_ub=-1,
            add_gt_as_proposals=True),
        pos_weight=-1,
        debug=False))
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=1000,
        nms_post=1000,
        max_num=300,
        nms_thr=0.8,
        min_bbox_size=0),
    rcnn=dict(
        score_thr=0.001,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=100))
dataset_type = 'CocoDataset'
data_root = '/ds/documents/PubLayNet/publaynet/'
num_classes = 5
classes = ('text', 'title', 'list', 'table', 'figure')
img_norm_cfg = dict(
    mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(704, 704), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.0),
    dict(
        type='Normalize',
        mean=[103.53, 116.28, 123.675],
        std=[1.0, 1.0, 1.0],
        to_rgb=False),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(704, 704),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[103.53, 116.28, 123.675],
                std=[1.0, 1.0, 1.0],
                to_rgb=False),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=8,
    train=dict(
        type='CocoDataset',
        ann_file='/ds/documents/PubLayNet/publaynet/train.json',
        img_prefix='/ds/documents/PubLayNet/publaynet/train/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', img_scale=(704, 704), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.0),
            dict(
                type='Normalize',
                mean=[103.53, 116.28, 123.675],
                std=[1.0, 1.0, 1.0],
                to_rgb=False),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        classes=('text', 'title', 'list', 'table', 'figure')),
    val=dict(
        type='CocoDataset',
        ann_file='/ds/documents/PubLayNet/publaynet/val.json',
        img_prefix='/ds/documents/PubLayNet/publaynet/val/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(704, 704),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[1.0, 1.0, 1.0],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('text', 'title', 'list', 'table', 'figure')),
    test=dict(
        type='CocoDataset',
        ann_file='/ds/documents/PubLayNet/publaynet/val.json',
        img_prefix='/ds/documents/PubLayNet/publaynet/val/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(704, 704),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[1.0, 1.0, 1.0],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('text', 'title', 'list', 'table', 'figure')))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    gamma=0.5,
    step=[1, 2, 3])
total_epochs = 3
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook')
    ])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/netscratch/minouei/report/work_dirs/crpn_faster_rcnn_rx101_freeze_fpn_1x_publay/epoch_1.pth'
resume_from = None
workflow = [('train', 1)]
rpn_weight = 0.7
work_dir = '/netscratch/minouei/report/work_dirs/crpn_faster_rcnn_rx101_fpn_1x_publay'
gpu_ids = range(0, 1)
