File size: 7,227 Bytes
24df943
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import torch
import torch.nn as nn
import re
import math
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig


def build_vision_tower():
    vision_tower = 'openai/clip-vit-large-patch14-336'
    return CLIPVisionTower(vision_tower)


def build_vision_projector():
    projector_type = 'mlp2x_gelu'
    mm_hidden_size = 4096
    mid_hidden_size = 4096
    hidden_size = 4096

    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//336, W//336])
            sub_img = img.reshape(1,3,H//336,336,W//336,336).permute(0,2,4,1,3,5).reshape(-1,3,336,336).contiguous()
            glb_img = torch.nn.functional.interpolate(img.float(), size=(336,336), 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 == 24 ** 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, 12, 12, -1).permute(0,1,3,2,4,5).reshape(1,h*12,w*12,4*C)
            temp_sub_GN = sub_GN.repeat(1, h*12, 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)*144 + 1 + (h+1)*12)
            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)
        im_mask = im_mask.view(-1)
        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)