# dataset settings
dataset_type = 'VrdDataset'
data_root = 'data/vrd/'
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, with_rel=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', 'gt_rels', 'gt_relmaps']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    # Since the forward process may need gt info, annos must be loaded.
    dict(type='LoadAnnotations', with_bbox=True, with_rel=True),
    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),
            # NOTE: Do not change the img to DC.
            dict(type='ImageToTensor', keys=['img']),
            dict(type='ToTensor', keys=['gt_bboxes', 'gt_labels']),
            dict(type='ToDataContainer', fields=(dict(key='gt_bboxes'), dict(key='gt_labels'))),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
        ])
]
data = dict(
    imgs_per_gpu=8,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'sg_annotations/train_sgs.json',
        dict_file=data_root + 'sg_annotations/labels.json',
        image_file=data_root + 'sg_annotations/train_images.json',
        pipeline=train_pipeline,
        num_im=-1,
        split='train',
        img_prefix=data_root + 'sg_dataset/sg_train_images'),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'sg_annotations/test_sgs.json',
        dict_file=data_root + 'sg_annotations/labels.json',
        image_file=data_root + 'sg_annotations/test_images.json',
        pipeline=test_pipeline,
        num_im=-1,
        split='val',
        img_prefix=data_root + 'sg_dataset/sg_test_images/'),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'sg_annotations/test_sgs.json',
        dict_file=data_root + 'sg_annotations/labels.json',
        image_file=data_root + 'sg_annotations/test_images.json',
        pipeline=test_pipeline,
        num_im=-1,
        split='test',
        img_prefix=data_root + 'sg_dataset/sg_test_images/'))
# model settings
dataset_config = data['train'].copy()
dataset_config.update(dict(cache=data_root + 'VRD_statistics.cache',
                           cooccur_cache=data_root + 'VRD_cooccur_statistics.cache'))
model = dict(
    type='FasterRCNN',
    pretrained='checkpoints/mmlab/imnet/resnext101_64x4d-ee2c6f71.pth',
    backbone=dict(
        type='ResNeXt',
        depth=101,
        groups=64,
        base_width=4,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        style='pytorch'),
    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_scales=[8],
        anchor_ratios=[0.5, 1.0, 2.0],
        anchor_strides=[4, 8, 16, 32, 64],
        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='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
    bbox_roi_extractor=dict(
        type='SingleRoIExtractor',
        roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
        out_channels=256,
        featmap_strides=[4, 8, 16, 32]),
    bbox_head=dict(
        type='SharedFCBBoxHead',
        num_fcs=2,
        in_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=101,
        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='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
    relation_head=dict(
        type='KERNHead',
        dataset_config=dataset_config,
        num_classes=101,
        num_predicates=71,
        use_bias=True,
        head_config=dict(
            use_gt_box=True,
            use_gt_label=False,
            use_vision=True,
            embed_dim=200,
            hidden_dim=512,
            roi_dim=1024,
            context_pooling_dim=4096,
            dropout_rate=0.2,
            time_step=3,
            glove_dir='data/glove/',
            causal_effect_analysis=False),
        bbox_roi_extractor=dict(
            type='VisualSpatialExtractor',
            bbox_roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
            # mask_roi_layer=dict(type='ShapeAwareRoIAlign', out_size=7, sample_num=2),
            with_visual_bbox=True,
            with_visual_mask=False,
            with_visual_point=False,
            with_spatial=False,
            in_channels=256,
            fc_out_channels=1024,
            featmap_strides=[4, 8, 16, 32]),
        relation_roi_extractor=dict(
            type='VisualSpatialExtractor',
            bbox_roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
            # mask_roi_layer=dict(type='ShapeAwareRoIAlign', out_size=7, sample_num=2),
            with_visual_bbox=True,
            with_visual_mask=False,
            with_visual_point=False,
            with_spatial=True,
            separate_spatial=False,
            in_channels=256,
            fc_out_channels=1024,
            featmap_strides=[4, 8, 16, 32]),
        relation_sampler=dict(
            type='Motif',
            pos_iou_thr=0.5,
            require_overlap=False,  # for sgdet training, not require
            num_sample_per_gt_rel=4,
            num_rel_per_image=1024,
            pos_fraction=0.25,
            test_overlap=True    # for testing
        ),
        loss_object=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_relation=dict(
            type='CrossEntropyLoss', use_sigmoid=False, 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,
            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=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,
            ignore_iof_thr=-1),
        sampler=dict(
            type='RandomSampler',
            num=512,
            pos_fraction=0.25,
            neg_pos_ub=-1,
            add_gt_as_proposals=True),
        mask_size=28,
        pos_weight=-1,
        debug=False))
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=1000,
        nms_post=1000,
        max_num=1000,
        nms_thr=0.7,
        min_bbox_size=0),
    rcnn=dict(
        score_thr=0.05,
        nms=dict(type='nms', iou_thr=0.5),
        max_per_img=50,  # Follow the setting in TDE, 80 Bboxes are selected.
        mask_thr_binary=0.5,
        rle_mask_encode=False,  # do not transform the mask into rle.
        crop_mask=True,  # so that the mask shape is the same as bbox, instead of image shape
        format_mask_result=False,  # do not transform to the result format like bbox
        to_tensor=True))
find_unused_parameters = True
evaluation = dict(interval=1, metric='sgcls', relation_mode=True, classwise=True, nogc_thres_num=[10, 70])
# optimizer
optimizer = dict(type='SGD', lr=0.002, momentum=0.9, weight_decay=0.0001,
                 freeze_modules=['backbone', 'neck', 'rpn_head', 'bbox_head', 'mask_head'])
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=50,
    warmup_ratio=1.0 / 3,
    step=[7, 10])
checkpoint_config = dict(interval=1)

# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './new_experiments/VRD_SgCls_kern_faster_rcnn_x101_64x4d_fpn_1x'
load_from = './experiments/VRD_Detection_faster_rcnn_x101_64x4d_fpn_1x_ftCOCO/latest.pth'
# load_mapping = dict(align_dict={'relation_head.bbox_roi_extractor.visual_bbox_head': 'bbox_head.shared_fcs',
#                                'relation_head.relation_roi_extractor.visual_bbox_head': 'bbox_head.shared_fcs'})
resume_from = None
workflow = [('train', 1), ('val', 1)]

# yapf:disable
log_config = dict(
    interval=10,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook'),
        dict(type='WandbLoggerHook',
             init_kwargs=dict(
                 project=work_dir.split('/')[-1],
                 name='train-1',
                 config=work_dir + '/cfg.yaml'))
    ])
