Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,344 Bytes
d9c19b7 |
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 |
import torch
import torch.nn as nn
import re
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)
class SimpleMlp(nn.Module):
def __init__(self, in_channels, out_channels, twoview=False):
super().__init__()
self.proj = nn.Sequential(
nn.Linear(in_channels, out_channels),
nn.GELU(),
nn.Linear(out_channels, out_channels)
)
embed_std = 1 / math.sqrt(out_channels)
self.image_newline = nn.Parameter(
torch.randn(out_channels) * embed_std
)
self.image_begin = nn.Parameter(
torch.randn(out_channels) * embed_std
)
self.image_end = nn.Parameter(
torch.randn(out_channels) * embed_std
)
if twoview:
self.image_sep = nn.Parameter(
torch.randn(out_channels) * embed_std
)
def forward(self, x, size=(16,16), x2=None, size2=(16, 16), modalities='image'):
if modalities in ['image', 'text']:
h, w = size
dtype = x.dtype
x = x.reshape(x.shape[0], h, w, -1)
x = self.proj(x) #b,h,w, c
b, h, w, c = x.shape
x = torch.cat([
x,
self.image_newline.reshape(1, 1, 1, c).expand(b, h, 1, c).to(dtype)
], dim=2)
x = x.reshape(b, -1, c)
if x2 is not None:
h2, w2 = size2
x2 = x2.reshape(x2.shape[0], h2, w2, -1)
x2 = self.proj(x2) #b,h,w, c
b2, h2, w2, c2 = x2.shape
x2 = torch.cat([
x2,
self.image_newline.reshape(1, 1, 1, c).expand(b, h2, 1, c).to(dtype)
], dim=2)
x2 = x2.reshape(b, -1, c)
sep = self.image_sep.reshape(1, 1, -1).expand(b, 1, c2).to(dtype)
x = torch.cat([x, sep, x2], dim=1)
assert b == 1
assert b2 == 1 # only support batch size 1
begin = self.image_begin.reshape(1, 1, -1).expand(b, 1, c).to(dtype)
end = self.image_end.reshape(1, 1, -1).expand(b, 1, c).to(dtype)
x = torch.cat([begin, x, end], dim=1)
return x
elif modalities in ['video', 'video_long']:
# x2 is the true feature, ignore x
h, w = size
dtype = x.dtype
x = x.reshape(x.shape[0], h, w, -1)
x = self.proj(x).mean() * 0.0
h2, w2 = size2
x2 = x2.reshape(x2.shape[0], h2, w2, -1)
x2 = self.proj(x2) + x #b, h, w, c
b2, h2, w2, c = x2.shape
x2 = torch.cat([
x2,
self.image_newline.reshape(1, 1, 1, c).expand(b2, h2, 1, c).to(dtype)
], dim=2)
x2 = x2.reshape(b2, -1, c)
sep = self.image_sep.reshape(1, 1, -1).expand(b2, 1, c).to(dtype)
x2 = torch.cat([x2, sep], dim=1)
x2 = x2.flatten(0, 1)
begin = self.image_begin.reshape(1, -1).expand(1, c).to(dtype)
end = self.image_end.reshape(1, -1).expand(1, c).to(dtype)
x2 = torch.cat([begin, x2, end], dim=0)
x2 = x2.unsqueeze(0)
return x2
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)
elif projector_type == 'simple_mlp_twoview':
return SimpleMlp(config.mm_hidden_size, config.hidden_size, twoview=True)
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
mlp_gelu_resnet_match = re.match(r'^mlp(\d+)x_res(\d+)x_gelu$', projector_type)
if mlp_gelu_resnet_match:
mlp_depth = int(mlp_gelu_resnet_match.group(1))
res_depth = int(mlp_gelu_resnet_match.group(2))
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))
for _ in range(res_depth):
modules.append(SimpleResBlock(config.hidden_size))
return nn.Sequential(*modules)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
|