_base_ = [
    '../_base_/datasets/zid_detection.py',
    '../_base_/schedules/schedule_1x.py', 
    '../_base_/default_runtime.py'
]
# model settings
model = dict(
    type='ZidRCNN',
    pretrained='torchvision://resnet50',
    neg_rpn=False,
    mode='det',
    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'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='OlnRPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            # Use a single anchor per location.
            scales=[8],
            ratios=[1.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='TBLRBBoxCoder',
            normalizer=1.0,),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.0),
        reg_decoded_bbox=True,
        loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0),
        objectness_type='Centerness',
        loss_objectness=dict(type='L1Loss', loss_weight=1.0),
        ),
    roi_head=dict(
        type='ZidRoIHead3DGConvMix',
        support_guidance=dict(
            in_channel3d=32,
            hidden_3d=32,
            in_channel2d=256,
            hidden_2d=512,
            learn_rot=True,
            num_r=1),
        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='Shared2FCBBoxSuperHead',
            neg_head=True,
            contrastive=dict(
                test_topk=10,
                avg_conrta=True,
                bg_conrta=False,
                num_conrta=10),
            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.],
                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),
            bbox_score_type='BoxIoU',  # 'BoxIoU' or 'Centerness'
            loss_bbox_score=dict(type='L1Loss', loss_weight=1.0),
            loss_contra=dict(
                type='CrossEntropyLoss', 
                use_sigmoid=False, 
                loss_weight=0.0,
                ),
            )),
    # 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,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            # Objectness assigner and sampler 
            objectness_assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.3,
                neg_iou_thr=0.1,
                min_pos_iou=0.3,
                ignore_iof_thr=-1),
            objectness_sampler=dict(
                type='RandomSampler',
                num=256,
                # Ratio 0 for negative samples.
                pos_fraction=1.,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_across_levels=False,
            nms_pre=2000,
            nms_post=2000,
            max_num=2000,
            nms_thr=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='InstanceSampler', # make sure gt is always input for ROI head
                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=2000,
            nms_post=2000,
            max_num=2000,
            nms_thr=0.0,  # No nms
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.0,
            nms=dict(type='nms', iou_threshold=0.7),
            # max_per_img should be greater enough than k of AR@k evaluation
            # because the cross-dataset AR evaluation does not count those
            # proposals on the 'seen' classes into the budget (k), to avoid
            # evaluating recall on seen-class objects. It's recommended to use
            # max_per_img=1500 or 2000 when evaluating upto AR@1000.
            max_per_img=1500,
            )
    ))

# Dataset
dataset_type = 'ZidDataset'
data_root = 'data/OWID/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    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=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=8,
    train=dict(
        type=dataset_type,
        img_scale=55000,
        ins_scale=180000,
        p1_path=data_root + 'P1/',
        ann_file=data_root + 'P2/train_annotations.json',
        img_prefix=data_root + '/P2/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        img_scale=500,
        ins_scale=500,
        p1_path=data_root + 'P1/',
        ann_file=data_root + 'P2/val_annotations_ins.json',
        img_prefix=data_root + '/P2/',
        pipeline=test_pipeline))

lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[6, 7])
total_epochs = 10
# optimizer
optimizer = dict(type='Sergery', lr=0.02, momentum=0.9, weight_decay=0.0001, fix_voxel=0.1)
optimizer_config = dict(grad_clip=None)

checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=10,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_layers = ['relate_3d']
resume_from = 'outputs/VoxDet_p1/model_final.pth'
workflow = [('train', 1)]

work_dir='outputs/VoxDet_p2_2'
