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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.models.layers import coordinate_to_encoding
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.structures import SampleList, TrackDataSample
from mmdet.utils import (ConfigType, OptConfigType, OptMultiConfig)
from mmdet.models.dense_heads.anchor_free_head import AnchorFreeHead
from mmcv.cnn.bricks.transformer import MultiheadAttention
from .mask2former_vid import Mask2FormerVideoHead
from .yoso_head import CrossAttenHead, KernelUpdator
@MODELS.register_module()
class RapSAMVideoHead(Mask2FormerVideoHead):
def __init__(self,
frozen_head=False,
frozen_pred=False,
use_adaptor=False,
prompt_with_kernel_updator=False,
panoptic_with_kernel_updator=False,
num_mask_tokens = 1,
num_stages = 3,
use_kernel_updator=False,
sphere_cls = False,
ov_classifier_name = None,
temperature=0.1,
feat_channels=256,
num_things_classes: int = 80,
num_stuff_classes: int = 53,
num_queries: int = 100,
loss_cls: ConfigType = None,
loss_mask: ConfigType = None,
loss_dice: ConfigType = None,
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
init_cfg: OptMultiConfig = None,
matching_whole_map: bool = False,
enable_box_query: bool = False,
**kwargs) -> None:
super(AnchorFreeHead, self).__init__(init_cfg=init_cfg)
self.prompt_with_kernel_updator = prompt_with_kernel_updator
self.panoptic_with_kernel_updator = panoptic_with_kernel_updator
self.use_adaptor = use_adaptor
self.num_mask_tokens = num_mask_tokens
self.mask_tokens = nn.Embedding(num_mask_tokens, feat_channels)
self.pb_embedding = nn.Embedding(2, feat_channels)
self.pos_linear = nn.Linear(2 * feat_channels, feat_channels)
self.matching_whole_map = matching_whole_map
self.enable_box_query = enable_box_query
self.num_things_classes = num_things_classes
self.num_stuff_classes = num_stuff_classes
self.num_classes = self.num_things_classes + self.num_stuff_classes
self.num_queries = num_queries
self.feat_channels = feat_channels
self.num_stages = num_stages
self.kernels = nn.Embedding(self.num_queries, feat_channels)
self.mask_heads = nn.ModuleList()
for _ in range(self.num_stages):
self.mask_heads.append(CrossAttenHead(
self.num_classes, self.feat_channels, self.num_queries,
use_kernel_updator=use_kernel_updator,
frozen_head=frozen_head, frozen_pred=frozen_pred,
sphere_cls=sphere_cls,
ov_classifier_name=ov_classifier_name, with_iou_pred=True))
self.temperature = temperature
if use_adaptor:
cross_attn_cfg = dict(embed_dims=256, batch_first=True, num_heads=8)
if self.panoptic_with_kernel_updator:
self.panoptic_attn = KernelUpdator(feat_channels=256)
self.panoptic_norm = nn.Identity()
if sphere_cls:
cls_embed_dim = self.mask_heads[0].fc_cls.size(0)
self.panoptic_cls = nn.Sequential(
nn.Linear(feat_channels, cls_embed_dim)
)
else:
raise NotImplementedError
self.panoptic_cls = nn.Linear(256, self.num_classes+1)
else:
self.panoptic_attn = MultiheadAttention(**cross_attn_cfg)
self.panoptic_norm = nn.LayerNorm(256)
if sphere_cls:
cls_embed_dim = self.mask_heads[0].fc_cls.size(0)
self.panoptic_cls = nn.Sequential(
nn.Linear(feat_channels, cls_embed_dim)
)
else:
raise NotImplementedError
self.panoptic_cls = nn.Linear(256, self.num_classes+1)
if self.prompt_with_kernel_updator:
self.prompt_attn = KernelUpdator(feat_channels=256)
self.prompt_norm = nn.Identity()
self.prompt_iou = nn.Linear(256, 1)
else:
self.prompt_attn = MultiheadAttention(**cross_attn_cfg)
self.prompt_norm = nn.LayerNorm(256)
self.prompt_iou = nn.Linear(256, 1)
self.test_cfg = test_cfg
self.train_cfg = train_cfg
if train_cfg:
self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
self.sampler = TASK_UTILS.build(
self.train_cfg['sampler'], default_args=dict(context=self))
self.num_points = self.train_cfg.get('num_points', 12544)
self.oversample_ratio = self.train_cfg.get('oversample_ratio', 3.0)
self.importance_sample_ratio = self.train_cfg.get(
'importance_sample_ratio', 0.75)
self.class_weight = loss_cls.class_weight
self.loss_cls = MODELS.build(loss_cls)
self.loss_mask = MODELS.build(loss_mask)
self.loss_dice = MODELS.build(loss_dice)
def init_weights(self) -> None:
pass
def forward(self, x, batch_data_samples: SampleList) -> Tuple[List[Tensor]]:
batch_img_metas = []
if isinstance(batch_data_samples[0], TrackDataSample):
for track_sample in batch_data_samples:
cur_list = []
for det_sample in track_sample:
cur_list.append(det_sample.metainfo)
batch_img_metas.append(cur_list)
num_frames = len(batch_img_metas[0])
else:
for data_sample in batch_data_samples:
batch_img_metas.append(data_sample.metainfo)
num_frames = 0
bs = len(batch_img_metas)
all_cls_scores = []
all_masks_preds = []
all_iou_preds = []
if self.prompt_training:
input_query_label, input_query_bbox, self_attn_mask, mask_dict = self.prepare_for_dn_mo(
batch_data_samples)
pos_embed = coordinate_to_encoding(input_query_bbox.sigmoid())
pos_embed = self.pos_linear(pos_embed)
object_kernels = input_query_label + pos_embed
else:
object_kernels = self.kernels.weight[None].repeat(bs, 1, 1)
self_attn_mask = None
mask_features = x
if num_frames > 0: # (bs*num_frames, c, h, w) -> (bs, c, num_frames*h, w)
mask_features = mask_features.unflatten(0, (bs, num_frames))
mask_features = mask_features.transpose(1, 2).flatten(2, 3)
mask_preds = torch.einsum('bnc,bchw->bnhw', object_kernels, mask_features)
for stage in range(self.num_stages):
mask_head = self.mask_heads[stage]
cls_scores, mask_preds, iou_preds, object_kernels = mask_head(
mask_features, object_kernels, mask_preds, self_attn_mask)
cls_scores = cls_scores / self.temperature
all_iou_preds.append(iou_preds)
all_cls_scores.append(cls_scores)
if num_frames > 0:
#(bs,num_query, num_frames*h, w) --> (bs,num_query,num_frames,h,w)
all_masks_preds.append(mask_preds.unflatten(2, (num_frames, -1)))
else:
all_masks_preds.append(mask_preds)
if self.use_adaptor:
keys = mask_features.flatten(2).transpose(1, 2).contiguous()
if not self.prompt_training:
if self.panoptic_with_kernel_updator:
hard_sigmoid_masks = (mask_preds.sigmoid() > 0.5).float()
f = torch.einsum('bnhw,bchw->bnc', hard_sigmoid_masks, mask_features)
object_kernels = self.panoptic_attn(f, object_kernels)
object_kernels = self.panoptic_norm(object_kernels)
mask_preds = torch.einsum('bnc,bchw->bnhw', object_kernels, mask_features)
else:
object_kernels = self.panoptic_attn(object_kernels, keys)
object_kernels = self.panoptic_norm(object_kernels)
mask_preds = torch.einsum('bnc,bchw->bnhw', object_kernels, mask_features)
cls_embd = self.panoptic_cls(object_kernels)
cls_scores = torch.einsum('bnc,ckp->bnkp', F.normalize(cls_embd, dim=-1), self.mask_heads[0].fc_cls)
cls_scores = cls_scores.max(-1).values
cls_scores = self.mask_heads[0].logit_scale.exp() * cls_scores
if num_frames > 0:
all_masks_preds.append(mask_preds.unflatten(2, (num_frames, -1)))
else:
all_masks_preds.append(mask_preds)
all_cls_scores.append(cls_scores)
all_iou_preds.append(all_iou_preds[-1])
else:
if self.prompt_with_kernel_updator:
hard_sigmoid_masks = (mask_preds.sigmoid() > 0.5).float()
f = torch.einsum('bnhw,bchw->bnc', hard_sigmoid_masks, mask_features)
object_kernels = self.prompt_attn(f, object_kernels)
object_kernels = self.prompt_norm(object_kernels)
iou_preds = self.prompt_iou(object_kernels)
mask_preds = torch.einsum('bnc,bchw->bnhw', object_kernels, mask_features)
else:
object_kernels = self.prompt_attn(object_kernels, keys)
object_kernels = self.prompt_norm(object_kernels)
iou_preds = self.prompt_iou(object_kernels)
mask_preds = torch.einsum('bnc,bchw->bnhw', object_kernels, mask_features)
if num_frames > 0:
all_masks_preds.append(mask_preds.unflatten(2, (num_frames, -1)))
else:
all_masks_preds.append(mask_preds)
all_cls_scores.append(all_cls_scores[-1])
all_iou_preds.append(iou_preds)
return all_cls_scores, all_masks_preds, all_iou_preds, object_kernels
def get_targets(self, *args, **kwargs):
raise NotImplementedError
def loss_by_feat(self, *args, **kwargs):
raise NotImplementedError
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