_base_ = [
    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]

plugin = True
plugin_dir = 'projects/instance_segment_anything/'

model = dict(
    type='DetWrapperInstanceSAM',
    det_wrapper_type='focalnet_dino',
    det_wrapper_cfg=dict(num_classes=91,
                   param_dict_type='default',
                   ddetr_lr_param=False,
                   onecyclelr=False,
                   modelname='dino',
                   frozen_weights=None,
                   backbone='focalnet_L_384_22k_fl4',
                   focal_levels=4,
                   focal_windows=3,
                   use_checkpoint=False,
                   dilation=False,
                   position_embedding='sine',
                   pe_temperatureH=20,
                   pe_temperatureW=20,
                   return_interm_indices=[0, 1, 2, 3],
                   backbone_freeze_keywords=None,
                   enc_layers=6,
                   dec_layers=6,
                   unic_layers=0,
                   pre_norm=False,
                   dim_feedforward=2048,
                   hidden_dim=256,
                   dropout=0.0,
                   nheads=8,
                   num_queries=900,
                   query_dim=4,
                   num_patterns=0,
                   pdetr3_bbox_embed_diff_each_layer=False,
                   pdetr3_refHW=-1,
                   random_refpoints_xy=False,
                   fix_refpoints_hw=-1,
                   dabdetr_yolo_like_anchor_update=False,
                   dabdetr_deformable_encoder=False,
                   dabdetr_deformable_decoder=False,
                   use_deformable_box_attn=False,
                   box_attn_type='roi_align',
                   dec_layer_number=None,
                   num_feature_levels=5,
                   enc_n_points=4,
                   dec_n_points=4,
                   decoder_layer_noise=False,
                   dln_xy_noise=0.2,
                   dln_hw_noise=0.2,
                   add_channel_attention=False,
                   add_pos_value=False,
                   two_stage_type='standard',
                   two_stage_pat_embed=0,
                   two_stage_add_query_num=0,
                   two_stage_bbox_embed_share=False,
                   two_stage_class_embed_share=False,
                   two_stage_learn_wh=False,
                   two_stage_default_hw=0.05,
                   two_stage_keep_all_tokens=False,
                   num_select=300,
                   transformer_activation='relu',
                   batch_norm_type='FrozenBatchNorm2d',
                   masks=False,
                   aux_loss=True,
                   set_cost_class=2.0,
                   set_cost_bbox=5.0,
                   set_cost_giou=2.0,
                   no_interm_box_loss=False,
                   focal_alpha=0.25,
                   decoder_sa_type='sa',  # ['sa', 'ca_label', 'ca_content']
                   matcher_type='HungarianMatcher',  # or SimpleMinsumMatcher
                   decoder_module_seq=['sa', 'ca', 'ffn'],
                   nms_iou_threshold=-1,
                   dec_pred_bbox_embed_share=True,
                   dec_pred_class_embed_share=True,
                   use_dn=False,
                   dn_number=100,
                   dn_box_noise_scale=0.4,
                   dn_label_noise_ratio=0.5,
                   embed_init_tgt=True,
                   dn_labelbook_size=91,
                   match_unstable_error=True,
                   # for ema
                   use_ema=False,
                   ema_decay=0.9997,
                   ema_epoch=0,
                   use_detached_boxes_dec_out=False),
    det_model_ckpt='ckpt/focalnet_l_dino.pth',
    num_classes=80,
    model_type='vit_h',
    sam_checkpoint='ckpt/sam_vit_h_4b8939.pth',
    use_sam_iou=True,
)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# 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'])
        ])
]

dataset_type = 'CocoDataset'
data_root = 'data/coco/'

data = dict(
    samples_per_gpu=1,
    workers_per_gpu=1,
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline))