_base_ = [
    '../_base_/models/detr4seg_r101.py', '../_base_/datasets/psg.py',
    '../_base_/custom_runtime.py'
]

custom_imports = dict(imports=[
    'openpsg.models.frameworks.detr4seg',
    'openpsg.models.relation_heads.detr4seg_head', 'openpsg.datasets',
    'openpsg.datasets.pipelines.loading',
    'openpsg.datasets.pipelines.rel_randomcrop',
    'openpsg.models.relation_heads.approaches.matcher',
    'openpsg.models.losses.seg_losses'
],
                      allow_failed_imports=False)

object_classes = [
    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
    'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
    'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
    'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
    'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
    'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
    'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
    'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
    'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
    'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard',
    'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit',
    'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform',
    'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea',
    'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone',
    'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other',
    'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
    'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged',
    'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged',
    'food-other-merged', 'building-other-merged', 'rock-merged',
    'wall-other-merged', 'rug-merged'
]

model = dict(bbox_head=dict(
    num_classes=len(object_classes),
    object_classes=object_classes,
))

img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True)
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadSceneGraphAnnotations', with_bbox=True, with_rel=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='AutoAugment',
        policies=[
            [
                dict(type='Resize',
                     img_scale=[(480, 1333), (512, 1333), (544, 1333),
                                (576, 1333), (608, 1333), (640, 1333),
                                (672, 1333), (704, 1333), (736, 1333),
                                (768, 1333), (800, 1333)],
                     multiscale_mode='value',
                     keep_ratio=True)
            ],
            [
                dict(type='Resize',
                     img_scale=[(400, 1333), (500, 1333), (600, 1333)],
                     multiscale_mode='value',
                     keep_ratio=True),
                dict(type='RandomCrop',
                     crop_type='absolute_range',
                     crop_size=(384, 600),
                     allow_negative_crop=False),  # no empty relations
                dict(type='Resize',
                     img_scale=[(480, 1333), (512, 1333), (544, 1333),
                                (576, 1333), (608, 1333), (640, 1333),
                                (672, 1333), (704, 1333), (736, 1333),
                                (768, 1333), (800, 1333)],
                     multiscale_mode='value',
                     override=True,
                     keep_ratio=True)
            ]
        ]),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=1),
    dict(type='RelsFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
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=1),
             dict(type='ImageToTensor', keys=['img']),
             dict(type='Collect', keys=['img'])
         ])
]
data = dict(samples_per_gpu=2,
            workers_per_gpu=2,
            train=dict(pipeline=train_pipeline),
            val=dict(pipeline=test_pipeline),
            test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
    type='AdamW',
    lr=0.0001,
    weight_decay=0.0001,
    paramwise_cfg=dict(
        custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))

# learning policy
lr_config = dict(policy='step', step=110)
runner = dict(type='EpochBasedRunner', max_epochs=150)

project_name = 'detr4seg'
expt_name = 'detr4seg_r101_coco'
work_dir = f'./work_dirs/{expt_name}'

log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')],
)

load_from = '/mnt/ssd/gzj/test/OpenPSG/detr_r50_fb_origin.pth'