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import copy
import os
from typing import Literal, Tuple, List, Optional
import torch
from mmcv.cnn import ConvModule
from mmdet.structures.bbox import bbox2roi
from mmdet.structures.mask import mask2bbox
from torch import nn
import torch.nn.functional as F
from mmengine import MMLogger
from mmengine.model import BaseModule
from mmdet.registry import MODELS
from ext.sam import MaskDecoder
from ext.sam.mask_decoder import MLP as SAMMLP
from ext.meta.sam_meta import meta_dict, checkpoint_dict
from utils.load_checkpoint import load_checkpoint_with_prefix
@MODELS.register_module()
class OVSAMHead(BaseModule):
def __init__(
self,
model_name: Literal['vit_h', 'vit_l', 'vit_b'] = 'vit_h',
with_label_token: bool = False,
ov_classifier_name: Optional[str] = None,
logit: Optional[float] = None,
roi_extractor=None,
fix: bool = True,
init_cfg=None,
cur_mask=1,
roi_extractor_single=None,
load_roi_conv=None,
gen_box=False,
):
assert init_cfg is not None and \
init_cfg['type'] in ['sam_pretrain', 'Pretrained'], f"{init_cfg['type']} is not supported."
pretrained = init_cfg['checkpoint']
super().__init__(init_cfg=None)
self.init_cfg = init_cfg
self.logger = MMLogger.get_current_instance()
if roi_extractor_single is not None:
self.roi_extractor_single = MODELS.build(roi_extractor_single)
self.roi_merge_proj = nn.Linear(768 * 2, 768)
else:
self.roi_extractor_single = None
self.roi_merge_proj = None
mask_decoder = MaskDecoder(
num_multimask_outputs=cur_mask - 1,
transformer_dim=meta_dict[model_name]['prompt_embed_dim'],
iou_head_depth=3,
iou_head_hidden_dim=256,
with_iou=False
)
if self.init_cfg['type'] == 'sam_pretrain':
raise NotImplementedError
self.mask_decoder = mask_decoder
self.with_label_token = with_label_token
if self.with_label_token:
ov_path = os.path.join(os.path.expanduser('./models/'), f"{ov_classifier_name}.pth")
cls_embed = torch.load(ov_path)
cls_embed_norm = cls_embed.norm(p=2, dim=-1)
assert torch.allclose(cls_embed_norm, torch.ones_like(cls_embed_norm))
_dim = cls_embed.size(2)
_prototypes = cls_embed.size(1)
back_token = torch.zeros(1, _dim, dtype=torch.float32, device='cpu')
cls_embed = torch.cat([
cls_embed, back_token.repeat(_prototypes, 1)[None]
], dim=0)
self.register_buffer('cls_embed', cls_embed.permute(2, 0, 1).contiguous(), persistent=False)
if logit is None:
logit_scale = torch.tensor(4.6052, dtype=torch.float32)
else:
logit_scale = torch.tensor(logit, dtype=torch.float32)
self.register_buffer('logit_scale', logit_scale, persistent=False)
transformer_dim = self.mask_decoder.mask_tokens.weight.shape[1]
self.label_token = nn.Embedding(1, transformer_dim)
self.label_mlp = SAMMLP(transformer_dim, transformer_dim, _dim, 3)
self.gen_box = gen_box
if roi_extractor is not None:
self.roi = MODELS.build(roi_extractor)
self.roi_conv = nn.Sequential(*[
ConvModule(in_channels=self.roi.out_channels, out_channels=_dim, kernel_size=1, bias=False)
])
else:
self.roi = None
if self.init_cfg['type'] == 'Pretrained':
checkpoint_path = pretrained
state_dict = load_checkpoint_with_prefix(checkpoint_path, prefix=self.init_cfg['prefix'])
self.load_state_dict(state_dict, strict=True)
if roi_extractor is not None and load_roi_conv is not None:
checkpoint_path = load_roi_conv['checkpoint']
state_dict = load_checkpoint_with_prefix(checkpoint_path, prefix=load_roi_conv['prefix'])
self.roi_conv.load_state_dict(state_dict, strict=True)
self.fix = fix
if self.fix:
self.train(mode=False)
for name, param in self.named_parameters():
param.requires_grad = False
def init_weights(self):
self.logger.info(f"Init Config for {self.__class__.__name__}")
self.logger.info(self.init_cfg)
def forward_logit(self, cls_embd):
cls_pred = torch.einsum('bnc,ckp->bnkp', F.normalize(cls_embd, dim=-1), self.cls_embed)
cls_pred = cls_pred.max(-1).values
cls_pred = self.logit_scale.exp() * cls_pred
return cls_pred
def predict_masks(
self,
image_embeddings: torch.Tensor,
image_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
fpn_feats: List[torch.Tensor],
roi_list: Optional[List[torch.Tensor]],
backbone_feature: torch.Tensor,
backbone=None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Predicts masks. See 'forward' for more details."""
num_instances = int(sparse_prompt_embeddings.size(0))
# Concatenate output tokens
output_tokens = torch.cat([
self.label_token.weight,
self.mask_decoder.mask_tokens.weight], dim=0
)
output_tokens = output_tokens.unsqueeze(0).expand(num_instances, -1, -1)
queries = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
# image_embeddings = torch.repeat_interleave(image_embeddings, num_instances, dim=0)
image_embeddings = image_embeddings + dense_prompt_embeddings
pos_img = torch.repeat_interleave(image_pe, num_instances, dim=0)
b, c, h, w = image_embeddings.shape
# Run the transformer
queries, mask_feats = self.mask_decoder.transformer(image_embeddings, pos_img, queries)
label_query = queries[:, 0, :]
mask_embeds = queries[:, 1:(1 + self.mask_decoder.num_mask_tokens), :]
# Upscale mask embeddings and predict masks using the mask tokens
mask_feats = mask_feats.transpose(1, 2).view(b, c, h, w)
mask_feats = self.mask_decoder.output_upscaling(mask_feats)
mask_queries_list: List[torch.Tensor] = []
for i in range(self.mask_decoder.num_mask_tokens):
mask_queries_list.append(self.mask_decoder.output_hypernetworks_mlps[i](mask_embeds[:, i, :]))
mask_queries = torch.stack(mask_queries_list, dim=1)
b, c, h, w = mask_feats.shape
masks = (mask_queries @ mask_feats.view(b, c, h * w)).view(b, -1, h, w)
# Generate class labels
if self.with_label_token:
cls_embed_list = []
assert self.mask_decoder.num_mask_tokens == 1
for i in range(self.mask_decoder.num_mask_tokens):
cls_embed_list.append(self.label_mlp(label_query))
cls_embed = torch.stack(cls_embed_list, dim=1)
if self.gen_box:
bboxes = mask2bbox(masks.sigmoid()[:, 0] > 0.5) * 4
roi_list = bbox2roi([bboxes])
roi_feats = self.roi(fpn_feats, roi_list)
roi_feats = self.roi_conv(roi_feats)
roi_feats = roi_feats.mean(dim=-1).mean(dim=-1)
if self.roi_extractor_single:
roi_feats_clip = self.roi_extractor_single(
backbone.get_clip_feature(backbone_feature[-1:]), roi_list
)
roi_feats_clip = backbone.forward_feat(roi_feats_clip)
roi_feats = self.roi_merge_proj(torch.cat([roi_feats, roi_feats_clip], dim=-1))
roi_feats = roi_feats[:, None] + 0 * cls_embed
cls_pred = self.forward_logit(roi_feats)
else:
cls_pred = None
return masks, None, cls_pred
def forward(
self,
image_embeddings: torch.Tensor,
image_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
multi_mask_output: bool,
data_samples=None,
fpn_feats=None,
backbone_feats=None,
backbone=None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor]:
num_prompts = len(sparse_prompt_embeddings)
image_embeddings = torch.repeat_interleave(image_embeddings, num_prompts, dim=0)
masks, _, cls_pred = self.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
fpn_feats=fpn_feats,
roi_list=None,
backbone_feature=backbone_feats,
backbone=backbone,
)
# Select the correct mask or masks for output
if multi_mask_output:
mask_slice = slice(1, None)
else:
mask_slice = slice(0, 1)
masks = masks[:, mask_slice, :, :]
# Prepare output
return masks, None, cls_pred
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