|
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.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) |
|
_, 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] |
|
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_] |
|
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'): |
|
|
|
nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) |
|
nn.init.zeros_(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) |
|
|