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import torch
import torch.nn as nn
import re
from .honeybee import CAbstractor
from functools import partial
import numpy as np
from torch.nn.init import trunc_normal_
from torch.nn import functional as F
import math
class IdentityMap(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, *args, **kwargs):
return x
@property
def config(self):
return {"mm_projector_type": 'identity'}
class SimpleResBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.pre_norm = nn.LayerNorm(channels)
self.proj = nn.Sequential(
nn.Linear(channels, channels),
nn.GELU(),
nn.Linear(channels, channels)
)
def forward(self, x):
x = self.pre_norm(x)
return x + self.proj(x)
def build_vision_projector(config, delay_load=False, **kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_hidden_size, config.hidden_size)
if projector_type == "cabstract":
n_query = getattr(config, 'mm_projector_n_query', None)
image_size = getattr(config, 'image_size', None)
if not n_query:
n_query = kwargs.get("mm_projector_n_query",144)
if not image_size:
image_size = kwargs.get("image_size",336)
vokens = int(image_size/14*image_size/14)
print ("n_query",n_query)
print ("image_size",image_size)
print ("vokens",vokens)
return CAbstractor(vokens, config.mm_hidden_size, config.hidden_size, num_queries=n_query)
if projector_type == "tokenpacker":
#TokenPacker(hidden_size=config.hidden_size, scale_factor=config.scale_factor)
image_size = kwargs.get("image_size",448)
return TokenPacker(hidden_size=config.hidden_size, mm_hidden_size=config.mm_hidden_size, raw_grid=int(image_size/14))
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
def build_vision_projector_aux(config, delay_load=False, **kwargs):
projector_type = getattr(config, 'mm_projector_aux_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_region_hidden_size, config.hidden_size)
if projector_type == "cabstract":
n_query = getattr(config, 'mm_projector_n_query', None)
image_size = getattr(config, 'image_size', None)
if not n_query:
n_query = kwargs.get("mm_projector_n_query",144)
if not image_size:
image_size = kwargs.get("image_size",336)
vokens = int(image_size/14*image_size/14)
print ("n_query",n_query)
print ("image_size",image_size)
print ("vokens",vokens)
return CAbstractor(vokens, config.mm_region_hidden_size, config.hidden_size, num_queries=n_query)
if projector_type == "tokenpacker":
#TokenPacker(hidden_size=config.hidden_size, scale_factor=config.scale_factor)
image_size = kwargs.get("image_size",448)
return TokenPacker(hidden_size=config.hidden_size, mm_hidden_size=config.mm_region_hidden_size, raw_grid=int(image_size/14))
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_region_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 nn.Sequential(*modules)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
class TokenPacker(nn.Module):
def __init__(
self,
raw_grid=32,
embed_dim=1024,
num_heads=1024//128,
hidden_size=4096,
mm_hidden_size=3200,
scale_factor=2,
norm_layer=partial(nn.LayerNorm, eps=1e-6)
):
super().__init__()
if raw_grid%scale_factor!=0:
raise ValueError("scale_factor must be divisible by grid size")
self.raw_grid = raw_grid
self.grid_size = raw_grid//scale_factor
self.num_queries = self.grid_size ** 2
self.embed_dim = embed_dim
self.num_heads = num_heads
self.scale_factor = scale_factor
kv_dim = mm_hidden_size
self.q_proj_1 = nn.Linear(kv_dim, embed_dim, bias=False)
k_modules = [nn.Linear(mm_hidden_size*4, 1024)]
for _ in range(1,2):
k_modules.append(nn.GELU())
k_modules.append(nn.Linear(1024, 1024))
self.k_proj_1 = nn.Sequential(*k_modules)
v_modules = [nn.Linear(mm_hidden_size*4, 1024)]
for _ in range(1,2):
v_modules.append(nn.GELU())
v_modules.append(nn.Linear(1024, 1024))
self.v_proj_1 = nn.Sequential(*v_modules)
self.ln_q_1 = norm_layer(embed_dim)
self.ln_k_1 = norm_layer(embed_dim)
self.ln_v_1 = norm_layer(embed_dim)
self.clip_attn = nn.MultiheadAttention(embed_dim, num_heads)
modules = [nn.Linear(1024, hidden_size)]
for _ in range(1, 2):
modules.append(nn.GELU())
modules.append(nn.Linear(hidden_size, hidden_size))
self.mlp = nn.Sequential(*modules)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def divide_feature(self, x, kernel_size, token_num, N, c):
h = w = int(token_num**0.5)
#print (x.shape)
reshape_x = x.reshape(h, w, N, c).reshape(h//kernel_size, kernel_size, w, N, c)
reshape_x = reshape_x.permute(0,2,1,3,4)
reshape_x = reshape_x.reshape(h//kernel_size, w//kernel_size, kernel_size, kernel_size, N, c)
reshape_x = reshape_x.permute(0,1,3,2,4,5).reshape(h//kernel_size, w//kernel_size, kernel_size*kernel_size, N, c)
reshape_x = reshape_x.permute(2,0,1,3,4).reshape(kernel_size*kernel_size, -1, c)
return reshape_x
def forward(self, x, attn_mask=None):
x_multi = x[1] # mulit-level
x = x[0] # original single-level
key = self.ln_k_1(self.k_proj_1(x_multi)).permute(1, 0, 2)
value = self.ln_v_1(self.v_proj_1(x_multi)).permute(1, 0, 2)
token_num, N, c = key.shape
q = F.interpolate(x.reshape(x.shape[0],self.raw_grid,self.raw_grid,-1).float().permute(0,3,1,2), size=(self.grid_size, self.grid_size), mode='bilinear').permute(0,2,3,1) ## fix
q = q.reshape(q.shape[0], -1, q.shape[-1]).to(x.dtype)
query = self.ln_q_1(self.q_proj_1(q)).permute(1, 0, 2)
reshape_query = self.divide_feature(query, 1, self.num_queries, N, c)
reshape_key = self.divide_feature(key, self.scale_factor, token_num, N, c)
reshape_value = self.divide_feature(value, self.scale_factor, token_num, N, value.shape[-1])
out = self.clip_attn(
reshape_query,
reshape_key,
reshape_value,
attn_mask=attn_mask)[0]
x = out
x = x.reshape(self.num_queries, N, -1)
x = x.permute(1, 0, 2)
x = self.mlp(x)
return x
def _repeat(self, query, N: int):
return query.unsqueeze(1).repeat(1, N, 1)
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