File size: 7,294 Bytes
bd0580b e67ed29 bd0580b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
import torch
import torch.nn as nn
import re
import math
import os
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
def build_vision_tower():
vision_tower = "internlm/internlm2-wqx-vl-clip"
return CLIPVisionTower(vision_tower)
def build_vision_projector():
projector_type = 'mlp2x_gelu'
mm_hidden_size = 4096
mid_hidden_size = 6144
hidden_size = 6144
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(mm_hidden_size, mid_hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(mid_hidden_size, mid_hidden_size))
return nn.Sequential(*modules)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
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 CLIPVisionTower(nn.Module):
def __init__(self, vision_tower):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
#self.conv_dim = 8192
#self.conv = torch.nn.Conv2d(1024, self.conv_dim,3,2,1)
self.select_layer = -1
self.select_feature = 'patch'
self.load_model()
def load_model(self):
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def resize_pos(self):
print ('Dummy Resized')
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
if self.select_feature == 'patch':
image_features = image_features[:, 1:]
elif self.select_feature == 'cls_patch':
image_features = image_features
else:
raise ValueError(f'Unexpected select feature: {self.select_feature}')
return image_features
def forward(self, images, glb_GN, sub_GN):
if not self.is_loaded:
self.load_model()
assert type(images) is list
shapes = []
input_imgs = []
for img in images:
_, C, H, W = img.shape
shapes.append([H//560, W//560])
sub_img = img.reshape(1,3,H//560,560,W//560,560).permute(0,2,4,1,3,5).reshape(-1,3,560,560).contiguous()
glb_img = torch.nn.functional.interpolate(img.float(), size=(560,560), mode='bicubic',).to(sub_img.dtype)
input_imgs.append(glb_img)
input_imgs.append(sub_img)
input_imgs = torch.cat(input_imgs, dim=0)
image_forward_outs = self.vision_tower(input_imgs.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(input_imgs.dtype) ### B*?, N, C
_, N, C = image_features.shape
H = int(math.sqrt(N))
assert N == 40 ** 2
output_imgs = []
output_len = []
for [h, w] in shapes:
B_ = h*w
glb_img = image_features[:1] ### 1, N, C
glb_img = glb_img.reshape(1,H,H,C).reshape(1,H//2,2,H//2,2,C).contiguous().permute(0,1,3,2,4,5).reshape(1,H//2,H//2,4*C).contiguous()
temp_glb_GN = sub_GN.repeat(1, H//2, 1, 1)
glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(1,-1,4*C)
sub_img = image_features[1:1+B_] ### ?, N, C
sub_img = sub_img.reshape(B_,H,H,C).reshape(B_,H//2,2,H//2,2,C).contiguous().permute(0,1,3,2,4,5).reshape(B_,-1,4*C).contiguous()
sub_img = sub_img.reshape(1, h, w, 20, 20, -1).permute(0,1,3,2,4,5).reshape(1,h*20,w*20,4*C)
temp_sub_GN = sub_GN.repeat(1, h*20, 1, 1)
sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(1,-1,4*C)
output_imgs.append(torch.cat([glb_img, glb_GN, sub_img], dim=1))
temp_len = int((h*w+1)*400 + 1 + (h+1)*20)
assert temp_len == output_imgs[-1].shape[1]
output_len.append(temp_len)
image_features = image_features[1+h*w:]
output_imgs = torch.cat(output_imgs, dim=1)
return output_imgs, output_len
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
class PLoRA(nn.Linear):
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
device=None,
dtype=None,
lora_r=8,
lora_alpha=16,
lora_dropout=0.05,
lora_len=0,
**kwargs) -> None:
super().__init__(in_features, out_features, bias, device, dtype)
self.lora_r = lora_r
self.lora_alpha = lora_alpha
self.lora_len = lora_len
if lora_dropout > 0.:
self.lora_dropout = nn.Dropout(p=lora_dropout)
else:
self.lora_dropout = lambda x: x
self.lora_scaling = self.lora_alpha / self.lora_r
self.Plora_A = nn.Linear(in_features,
self.lora_r,
bias=False,
device=device,
dtype=dtype)
self.Plora_B = nn.Linear(self.lora_r,
out_features,
bias=False,
device=device,
dtype=dtype)
self.reset_parameters()
def reset_parameters(self):
if hasattr(self, 'lora_A'):
# initialize A the same way as the default for nn.Linear and B to zero
nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
nn.init.zeros_(self.lora_B.weight)
#print ("lora weight init {} {}".format(torch.mean(self.lora_A.weight), torch.mean(self.lora_B.weight)))
def forward(self, x, im_mask=None):
B, N, C = x.shape
x = x.reshape(-1, C)
res = super().forward(x)
if im_mask is not None:
if torch.sum(im_mask) > 0:
part_x = x[im_mask]
res[im_mask] += self.Plora_B(self.Plora_A(
self.lora_dropout(part_x))) * self.lora_scaling
else:
part_x = x[:1]
res[:1] += self.Plora_B(self.Plora_A(
self.lora_dropout(part_x))) * 0
return res.reshape(B, N, -1)
|