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# Copyright 2024 Xi Zhang
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import torchvision.ops as ops
import re
class TAC(nn.Module):
def __init__(self, config):
super(TAC,self).__init__()
self.mm_hidden_size = config.mm_hidden_size
self.hidden_size = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.dropout = 0.1
self.layers_number = 12 # RAD-DINO hidden layers
# LFE
self.LFE = nn.Sequential(
ops.SqueezeExcitation(self.layers_number,self.layers_number // 2,activation=nn.GELU),
nn.Conv2d(self.layers_number,self.layers_number // 2,kernel_size=1,bias=False),
ops.SqueezeExcitation(self.layers_number // 2,self.layers_number // 4,activation=nn.GELU),
nn.Conv2d(self.layers_number // 2,self.layers_number // 4,kernel_size=1,bias=False),
ops.SqueezeExcitation(self.layers_number // 4,1,activation=nn.GELU),
nn.Conv2d(self.layers_number // 4,1,kernel_size=1,bias=False)
)
self.LFE_prior_bias = nn.Parameter(torch.tensor(0.0, device=torch.device("cuda" if torch.cuda.is_available() else "cpu")))
self.LFE_cos = nn.CosineSimilarity(dim=-1, eps=1e-6)
# self-attention
self.cur_self_attention = nn.MultiheadAttention(embed_dim=(self.mm_hidden_size), num_heads=self.num_attention_heads,batch_first=True,add_bias_kv=True)
self.prior_self_attention = nn.MultiheadAttention(embed_dim=(self.mm_hidden_size), num_heads=self.num_attention_heads,batch_first=True,add_bias_kv=True)
self.cros_attention = nn.MultiheadAttention(embed_dim=(self.mm_hidden_size), num_heads=self.num_attention_heads,batch_first=True,add_bias_kv=True)
self.norm1 = nn.LayerNorm(self.mm_hidden_size)
self.norm2 = nn.LayerNorm(self.mm_hidden_size)
self.norm3 = nn.LayerNorm(self.mm_hidden_size)
self.norm4 = nn.LayerNorm(self.mm_hidden_size)
self.mlp_attn = nn.Sequential(
nn.Linear(self.mm_hidden_size, self.mm_hidden_size),
nn.GELU(),
nn.Dropout(self.dropout),
nn.Linear(self.mm_hidden_size, self.mm_hidden_size),
nn.Dropout(self.dropout)
)
self.mlp_final = nn.Sequential(
nn.Linear(self.mm_hidden_size, self.hidden_size),
nn.GELU(),
nn.Linear(self.hidden_size, self.hidden_size),
nn.GELU(),
nn.Linear(self.hidden_size, self.hidden_size),
nn.GELU(),
nn.Linear(self.hidden_size, self.hidden_size)
)
self.dropout1 = nn.Dropout(self.dropout)
self.dropout2 = nn.Dropout(self.dropout)
self.dropout3 = nn.Dropout(self.dropout)
def calculate_cosine_similarity(self, tensor1, tensor2):
assert tensor1.shape == tensor2.shape, "The shapes of the two tensors must be the same"
tensor1_flat = tensor1.view(tensor1.size(0), -1)
tensor2_flat = tensor2.view(tensor2.size(0), -1)
tensor1_flat_normalized = tensor1_flat / tensor1_flat.norm(dim=-1, keepdim=True)
tensor2_flat_normalized = tensor2_flat / tensor2_flat.norm(dim=-1, keepdim=True)
cosine_similarities = self.LFE_cos(tensor1_flat_normalized, tensor2_flat_normalized)
cosine_similarities_normalized = ((cosine_similarities + 1) / 2).pow(8)
cosine_similarities_normalized = cosine_similarities_normalized.view(-1, 1, 1)
return cosine_similarities_normalized
# self-attention block
def cur_self_att_block(self,x):
x = self.cur_self_attention(x,x,x)[0]
return self.dropout1(x)
# self-attention block
def prior_self_att_block(self,x):
x = self.prior_self_attention(x,x,x)[0]
return self.dropout2(x)
# cross attention block
def cros_att_block(self,x,y):
x = self.cros_attention(x,y,y)[0]
return self.dropout3(x)
#TFM
def TFM(self,cur_features,prev_features):
cur_features_temp = cur_features
prev_features_temp = prev_features
cos= self.calculate_cosine_similarity(cur_features_temp,prev_features_temp)
prev_weight = cos * self.LFE_prior_bias
prev_features_temp = prev_features_temp + prev_weight
cur_features = self.norm1(cur_features_temp + self.cur_self_att_block(cur_features_temp))
prev_features = self.norm2(prev_features_temp + self.prior_self_att_block(prev_features_temp))
combined_features = self.norm3(cur_features + self.cros_att_block(cur_features,prev_features))
output = self.norm4(cur_features_temp + self.mlp_attn(combined_features))
output = self.mlp_final(output)
return output
def forward(self, image_features, *args, **kwargs):
cur_features, prev_features = image_features
cur_features = self.LFE(cur_features).squeeze(1)
prev_features= self.LFE(prev_features).squeeze(1)
output = self.TFM(cur_features,prev_features)
return output
@property
def config(self):
return {"mm_projector_type": 'TAC'}
class Projector(nn.Module):
def __init__(self, base_projector):
super().__init__()
self.projector = base_projector
def forward(self, image_features, *args, **kwargs):
temp_features = image_features[0].squeeze(1)
return self.projector(temp_features)
def build_vision_projector(config, delay_load=False, *args,**kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
linear_layer = nn.Linear(config.mm_hidden_size, config.hidden_size)
return Projector(linear_layer)
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return Projector(nn.Sequential(*modules))
if projector_type == 'TAC':
return TAC(config)
raise ValueError(f'Unknown projector type: {projector_type}') |