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# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# 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 torch.nn.functional as F
class MinAttention(nn.Module):
def __init__(self, q_dim: int, kv_dim: int, dim_head=64, heads=8):
super().__init__()
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
self.norm1 = nn.LayerNorm(q_dim)
self.norm2 = nn.LayerNorm(kv_dim)
self.to_q = nn.Linear(q_dim, inner_dim, bias=False)
self.to_k = nn.Linear(kv_dim, inner_dim, bias=False)
self.to_v = nn.Linear(kv_dim, inner_dim, bias=False)
def forward(self, local_fea, global_fea):
global_fea = self.norm1(global_fea)
local_fea = self.norm2(local_fea)
b, l, _ = global_fea.shape
q = self.to_q(global_fea)
k = self.to_k(local_fea)
v = self.to_v(local_fea)
q = q.view(b, -1, self.heads, self.dim_head).transpose(1, 2)
k = k.view(b, -1, self.heads, self.dim_head).transpose(1, 2)
v = v.view(b, -1, self.heads, self.dim_head).transpose(1, 2)
hidden_states = F.scaled_dot_product_attention(
q,k,v, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(b, -1, self.heads*self.dim_head)
hidden_states = hidden_states.to(q.dtype)
return hidden_states
class CustomParameter(nn.Module):
def __init__(self, init_value):
super().__init__()
self.init_value = init_value
self.value = nn.Parameter(torch.tensor(init_value))
def forward(self):
return self.value
class ProjectorHighResMinAttn(nn.Module):
def __init__(self, vision_dim, out_dim, dim_head=64, adaptive_scale=False, scale_value=1.0, **kwargs):
super().__init__()
self.initial_projection_dim = vision_dim * 4
heads = vision_dim // dim_head
self.min_attention = MinAttention(q_dim=vision_dim, kv_dim=vision_dim, dim_head=dim_head, heads=heads)
self.projector = nn.Sequential(
nn.Linear(vision_dim, self.initial_projection_dim, bias=True),
nn.GELU(),
nn.Linear(self.initial_projection_dim, out_dim, bias=True),
nn.GELU(),
nn.Linear(out_dim, out_dim, bias=True),
nn.LayerNorm(out_dim)
)
self.projector_base = nn.Linear(vision_dim, out_dim, bias=True)
self.adaptive_scale = adaptive_scale
if self.adaptive_scale:
self.scale_value = CustomParameter(scale_value)
def forward(self, vision_input_dict, time_emb=None, **kwargs):
"""
vision_input_dict: here, this is not a dict, just for the unity of naming
"""
img_patch_features = vision_input_dict
deep_features, deep_features_local = img_patch_features
fused_img_features = self.min_attention(deep_features_local, deep_features)
fused_img_features = self.projector(fused_img_features)
deep_img_features = self.projector_base(deep_features)
if self.adaptive_scale:
output = deep_img_features + fused_img_features * self.scale_value()
else:
output = deep_img_features + fused_img_features
return output
class ProjectorHighResShallowMinAttnV1(nn.Module):
def __init__(self, vision_dim, out_dim, dim_head=64, **kwargs):
super().__init__()
self.initial_projection_dim = vision_dim * 4
heads = vision_dim // dim_head
self.min_attention = MinAttention(q_dim=vision_dim, kv_dim=vision_dim, dim_head=dim_head, heads=heads)
self.projector = nn.Sequential(
nn.Linear(vision_dim, self.initial_projection_dim, bias=True),
nn.GELU(),
nn.Linear(self.initial_projection_dim, out_dim, bias=True),
nn.GELU(),
nn.Linear(out_dim, out_dim, bias=True),
nn.LayerNorm(out_dim)
)
self.projector_base = nn.Linear(vision_dim, out_dim, bias=True)
self.min_attention2 = MinAttention(q_dim=vision_dim, kv_dim=vision_dim, dim_head=dim_head, heads=heads)
self.projector2 = nn.Sequential(
nn.Linear(vision_dim, self.initial_projection_dim, bias=True),
nn.GELU(),
nn.Linear(self.initial_projection_dim, out_dim, bias=True),
nn.GELU(),
nn.Linear(out_dim, out_dim, bias=True),
nn.LayerNorm(out_dim)
)
def forward(self, vision_input_dict, time_emb=None, **kwargs):
"""
vision_input_dict: here, this is not a dict, just for the unity of naming
"""
img_patch_features = vision_input_dict
shallow_features1, shallow_features2, shallow_features3, deep_features, deep_features_local = img_patch_features
shallow_features = torch.cat([shallow_features1, shallow_features2, shallow_features3], dim=1) # token concat
# original code
fused_img_features = self.min_attention(deep_features_local, deep_features)
fused_img_features = self.projector(fused_img_features)
deep_img_features = self.projector_base(deep_features)
output = deep_img_features + fused_img_features
# new code part
fused_img_features2 = self.min_attention2(shallow_features, deep_features)
fused_img_features2 = self.projector2(fused_img_features2)
output = torch.cat([deep_img_features, fused_img_features2], dim=1)
return output |