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import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
class MaskExtractor(nn.Module):
def __init__(self, config, mm_hidden_size, depth=2):
super(MaskExtractor, self).__init__()
self.mask_pooling = MaskPooling()
modules = [nn.Linear(mm_hidden_size, config.hidden_size)]
for _ in range(1, depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
self.feat_linear = nn.Sequential(*modules)
def forward(self, feats, masks):
query_feats = []
if masks is None: #infer
return None
num_imgs = len(masks)
region_token_nums = []
image_idx = 0
for idx in range(num_imgs):
if masks[idx]==None:
continue
for mask_idx in range(len(masks[idx])):
mask = masks[idx][mask_idx].unsqueeze(0).unsqueeze(0).float()
if len(mask[0])==0:
mask = torch.zeros((1, 1, 336, 336)).to(feats.device).float()
feat = feats[image_idx].unsqueeze(0)
image_idx+=1
# h, w = feat.shape[1:3]
feat = feat.permute(0,3,1,2)
raw_dtype = feat.dtype
feat = feat.to(mask.dtype)
mask_feat_raw = self.mask_pooling(feat, mask) # [n, 1024]
query_feats.append(mask_feat_raw)
if len(query_feats)==0:
return None
mask_feats = torch.cat(query_feats, dim=0)
mask_feats = mask_feats.to(feats[0].dtype)
mask_feats_linear = self.feat_linear(mask_feats)
return mask_feats_linear
class MaskPooling(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, mask):
if not x.shape[-2:] == mask.shape[-2:]:
# reshape mask to x
mask = F.interpolate(mask, size=x.shape[-2:], mode='bilinear', align_corners=False)
# b, c, h ,w = x.shape
# b, q, h, w = mask.shape
mask = (mask > 0).to(mask.dtype)
mask = mask.permute(1,0,2,3)
denorm = mask.sum(dim=(-1, -2), keepdim=True) + 1e-8
mask_pooled_x = (x * mask/denorm).sum(-1).sum(-1)
return mask_pooled_x
def build_region_encoder(config, mm_hidden_size):
return MaskExtractor(config, mm_hidden_size)
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