from torch.nn import GroupNorm, ReLU

from mmdet.models import MSDeformAttnPixelDecoder, CrossEntropyLoss, DiceLoss, FocalLoss
from mmdet.models.task_modules.assigners import HungarianAssigner, ClassificationCost, CrossEntropyLossCost, DiceCost
from mmdet.models.task_modules.samplers import MaskPseudoSampler

from seg.models.detectors import Mask2formerVideo
from seg.models.fusion_head import OMGFusionHead
from seg.models.heads import Mask2FormerVideoHead
from seg.models.backbones import OpenCLIPBackbone

num_things_classes = 80
num_stuff_classes = 53

ov_model_name = 'convnext_large_d_320'
ov_datasets_name = 'CocoPanopticOVDataset'
model = dict(
    type=Mask2formerVideo,
    data_preprocessor=None,  # to fill
    backbone=dict(
        type=OpenCLIPBackbone,
        model_name='convnext_large_d_320',
        fix=True,
        init_cfg=dict(
            type='clip_pretrain',
            checkpoint='laion2b_s29b_b131k_ft_soup'
        )
    ),
    panoptic_head=dict(
        init_cfg=dict(
            type='Pretrained',
            checkpoint='./models/omg_seg_convl.pth',
            prefix='panoptic_head.'
        ),
        type=Mask2FormerVideoHead,
        sphere_cls=True,
        ov_classifier_name=f'{ov_model_name}_{ov_datasets_name}',
        logit=None,
        enable_box_query=True,
        in_channels=[192, 384, 768, 1536],  # pass to pixel_decoder inside
        strides=[4, 8, 16, 32],
        feat_channels=256,
        out_channels=256,
        num_things_classes=num_things_classes,
        num_stuff_classes=num_stuff_classes,
        num_queries=300,
        num_transformer_feat_level=3,
        pixel_decoder=dict(
            type=MSDeformAttnPixelDecoder,
            num_outs=3,
            norm_cfg=dict(type=GroupNorm, num_groups=32),
            act_cfg=dict(type=ReLU),
            encoder=dict(  # DeformableDetrTransformerEncoder
                num_layers=6,
                layer_cfg=dict(  # DeformableDetrTransformerEncoderLayer
                    self_attn_cfg=dict(  # MultiScaleDeformableAttention
                        embed_dims=256,
                        num_heads=8,
                        num_levels=3,
                        num_points=4,
                        dropout=0.0,
                        batch_first=True),
                    ffn_cfg=dict(
                        embed_dims=256,
                        feedforward_channels=1024,
                        num_fcs=2,
                        ffn_drop=0.0,
                        act_cfg=dict(type=ReLU, inplace=True)))),
            positional_encoding=dict(num_feats=128, normalize=True)),
        enforce_decoder_input_project=False,
        positional_encoding=dict(num_feats=128, normalize=True),
        transformer_decoder=dict(  # Mask2FormerTransformerDecoder
            return_intermediate=True,
            num_layers=9,
            layer_cfg=dict(  # Mask2FormerTransformerDecoderLayer
                self_attn_cfg=dict(  # MultiheadAttention
                    embed_dims=256,
                    num_heads=8,
                    dropout=0.0,
                    batch_first=True),
                cross_attn_cfg=dict(  # MultiheadAttention
                    embed_dims=256,
                    num_heads=8,
                    dropout=0.0,
                    batch_first=True),
                ffn_cfg=dict(
                    embed_dims=256,
                    feedforward_channels=2048,
                    num_fcs=2,
                    ffn_drop=0.0,
                    act_cfg=dict(type='ReLU', inplace=True))),
            init_cfg=None),
        loss_cls=dict(
            type=CrossEntropyLoss,
            use_sigmoid=False,
            loss_weight=2.0,
            reduction='mean',
            class_weight=None  # [1.0] * num_classes + [0.1]
        ),
        loss_mask=dict(
            type=CrossEntropyLoss,
            use_sigmoid=True,
            reduction='mean',
            loss_weight=5.0),
        loss_dice=dict(
            type=DiceLoss,
            use_sigmoid=True,
            activate=True,
            reduction='mean',
            naive_dice=True,
            eps=1.0,
            loss_weight=5.0),
        loss_iou=dict(
            type=FocalLoss,
            use_sigmoid=True,
            loss_weight=2.0,
            reduction='mean'
        )
    ),
    panoptic_fusion_head=dict(
        type=OMGFusionHead,
        num_things_classes=num_things_classes,
        num_stuff_classes=num_stuff_classes,
        loss_panoptic=None,
        init_cfg=None
    ),
    train_cfg=dict(
        num_points=12544,
        oversample_ratio=3.0,
        importance_sample_ratio=0.75,
        assigner=dict(
            type=HungarianAssigner,
            match_costs=[
                dict(type=ClassificationCost, weight=2.0),
                dict(
                    type=CrossEntropyLossCost, weight=5.0, use_sigmoid=True),
                dict(type=DiceCost, weight=5.0, pred_act=True, eps=1.0)
            ]),
        sampler=dict(type=MaskPseudoSampler)),
    test_cfg=dict(
        panoptic_on=True,
        semantic_on=False,
        instance_on=True,
        # max_per_image is for instance segmentation.
        max_per_image=100,
        iou_thr=0.8,
        # In Mask2Former's panoptic postprocessing,
        # it will filter mask area where score is less than 0.5 .
        filter_low_score=True,
        object_mask_thr=0.,
    ),
    init_cfg=None
)