-
Notifications
You must be signed in to change notification settings - Fork 1.9k
/
MobileViT.py
237 lines (187 loc) · 7.55 KB
/
MobileViT.py
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
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
from torch import nn
import torch
from torch.nn.modules import conv
from torch.nn.modules.conv import Conv2d
from einops import rearrange
def conv_bn(inp,oup,kernel_size=3,stride=1):
return nn.Sequential(
nn.Conv2d(inp,oup,kernel_size=kernel_size,stride=stride,padding=kernel_size//2),
nn.BatchNorm2d(oup),
nn.SiLU()
)
class PreNorm(nn.Module):
def __init__(self,dim,fn):
super().__init__()
self.ln=nn.LayerNorm(dim)
self.fn=fn
def forward(self,x,**kwargs):
return self.fn(self.ln(x),**kwargs)
class FeedForward(nn.Module):
def __init__(self,dim,mlp_dim,dropout) :
super().__init__()
self.net=nn.Sequential(
nn.Linear(dim,mlp_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Linear(mlp_dim,dim),
nn.Dropout(dropout)
)
def forward(self,x):
return self.net(x)
class Attention(nn.Module):
def __init__(self,dim,heads,head_dim,dropout):
super().__init__()
inner_dim=heads*head_dim
project_out=not(heads==1 and head_dim==dim)
self.heads=heads
self.scale=head_dim**-0.5
self.attend=nn.Softmax(dim=-1)
self.to_qkv=nn.Linear(dim,inner_dim*3,bias=False)
self.to_out=nn.Sequential(
nn.Linear(inner_dim,dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self,x):
qkv=self.to_qkv(x).chunk(3,dim=-1)
q,k,v=map(lambda t:rearrange(t,'b p n (h d) -> b p h n d',h=self.heads),qkv)
dots=torch.matmul(q,k.transpose(-1,-2))*self.scale
attn=self.attend(dots)
out=torch.matmul(attn,v)
out=rearrange(out,'b p h n d -> b p n (h d)')
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self,dim,depth,heads,head_dim,mlp_dim,dropout=0.):
super().__init__()
self.layers=nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PreNorm(dim,Attention(dim,heads,head_dim,dropout)),
PreNorm(dim,FeedForward(dim,mlp_dim,dropout))
]))
def forward(self,x):
out=x
for att,ffn in self.layers:
out=out+att(out)
out=out+ffn(out)
return out
class MobileViTAttention(nn.Module):
def __init__(self,in_channel=3,dim=512,kernel_size=3,patch_size=7,depth=3,mlp_dim=1024):
super().__init__()
self.ph,self.pw=patch_size,patch_size
self.conv1=nn.Conv2d(in_channel,in_channel,kernel_size=kernel_size,padding=kernel_size//2)
self.conv2=nn.Conv2d(in_channel,dim,kernel_size=1)
self.trans=Transformer(dim=dim,depth=depth,heads=8,head_dim=64,mlp_dim=mlp_dim)
self.conv3=nn.Conv2d(dim,in_channel,kernel_size=1)
self.conv4=nn.Conv2d(2*in_channel,in_channel,kernel_size=kernel_size,padding=kernel_size//2)
def forward(self,x):
y=x.clone() #bs,c,h,w
## Local Representation
y=self.conv2(self.conv1(x)) #bs,dim,h,w
## Global Representation
_,_,h,w=y.shape
y=rearrange(y,'bs dim (nh ph) (nw pw) -> bs (ph pw) (nh nw) dim',ph=self.ph,pw=self.pw) #bs,h,w,dim
y=self.trans(y)
y=rearrange(y,'bs (ph pw) (nh nw) dim -> bs dim (nh ph) (nw pw)',ph=self.ph,pw=self.pw,nh=h//self.ph,nw=w//self.pw) #bs,dim,h,w
## Fusion
y=self.conv3(y) #bs,dim,h,w
y=torch.cat([x,y],1) #bs,2*dim,h,w
y=self.conv4(y) #bs,c,h,w
return y
class MV2Block(nn.Module):
def __init__(self,inp,out,stride=1,expansion=4):
super().__init__()
self.stride=stride
hidden_dim=inp*expansion
self.use_res_connection=stride==1 and inp==out
if expansion==1:
self.conv=nn.Sequential(
nn.Conv2d(hidden_dim,hidden_dim,kernel_size=3,stride=self.stride,padding=1,groups=hidden_dim,bias=False),
nn.BatchNorm2d(hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim,out,kernel_size=1,stride=1,bias=False),
nn.BatchNorm2d(out)
)
else:
self.conv=nn.Sequential(
nn.Conv2d(inp,hidden_dim,kernel_size=1,stride=1,bias=False),
nn.BatchNorm2d(hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim,hidden_dim,kernel_size=3,stride=1,padding=1,groups=hidden_dim,bias=False),
nn.BatchNorm2d(hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim,out,kernel_size=1,stride=1,bias=False),
nn.SiLU(),
nn.BatchNorm2d(out)
)
def forward(self,x):
if(self.use_res_connection):
out=x+self.conv(x)
else:
out=self.conv(x)
return out
class MobileViT(nn.Module):
def __init__(self,image_size,dims,channels,num_classes,depths=[2,4,3],expansion=4,kernel_size=3,patch_size=2):
super().__init__()
ih,iw=image_size,image_size
ph,pw=patch_size,patch_size
assert iw%pw==0 and ih%ph==0
self.conv1=conv_bn(3,channels[0],kernel_size=3,stride=patch_size)
self.mv2=nn.ModuleList([])
self.m_vits=nn.ModuleList([])
self.mv2.append(MV2Block(channels[0],channels[1],1))
self.mv2.append(MV2Block(channels[1],channels[2],2))
self.mv2.append(MV2Block(channels[2],channels[3],1))
self.mv2.append(MV2Block(channels[2],channels[3],1)) # x2
self.mv2.append(MV2Block(channels[3],channels[4],2))
self.m_vits.append(MobileViTAttention(channels[4],dim=dims[0],kernel_size=kernel_size,patch_size=patch_size,depth=depths[0],mlp_dim=int(2*dims[0])))
self.mv2.append(MV2Block(channels[4],channels[5],2))
self.m_vits.append(MobileViTAttention(channels[5],dim=dims[1],kernel_size=kernel_size,patch_size=patch_size,depth=depths[1],mlp_dim=int(4*dims[1])))
self.mv2.append(MV2Block(channels[5],channels[6],2))
self.m_vits.append(MobileViTAttention(channels[6],dim=dims[2],kernel_size=kernel_size,patch_size=patch_size,depth=depths[2],mlp_dim=int(4*dims[2])))
self.conv2=conv_bn(channels[-2],channels[-1],kernel_size=1)
self.pool=nn.AvgPool2d(image_size//32,1)
self.fc=nn.Linear(channels[-1],num_classes,bias=False)
def forward(self,x):
y=self.conv1(x) #
y=self.mv2[0](y)
y=self.mv2[1](y) #
y=self.mv2[2](y)
y=self.mv2[3](y)
y=self.mv2[4](y) #
y=self.m_vits[0](y)
y=self.mv2[5](y) #
y=self.m_vits[1](y)
y=self.mv2[6](y) #
y=self.m_vits[2](y)
y=self.conv2(y)
y=self.pool(y).view(y.shape[0],-1)
y=self.fc(y)
return y
def mobilevit_xxs():
dims=[60,80,96]
channels= [16, 16, 24, 24, 48, 64, 80, 320]
return MobileViT(224,dims,channels,num_classes=1000)
def mobilevit_xs():
dims = [96, 120, 144]
channels = [16, 32, 48, 48, 64, 80, 96, 384]
return MobileViT(224, dims, channels, num_classes=1000)
def mobilevit_s():
dims = [144, 192, 240]
channels = [16, 32, 64, 64, 96, 128, 160, 640]
return MobileViT(224, dims, channels, num_classes=1000)
def count_paratermeters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
if __name__ == '__main__':
input=torch.randn(1,3,224,224)
### mobilevit_xxs
mvit_xxs=mobilevit_xxs()
out=mvit_xxs(input)
print(out.shape)
### mobilevit_xs
mvit_xs=mobilevit_xs()
out=mvit_xs(input)
print(out.shape)
### mobilevit_s
mvit_s=mobilevit_s()
out=mvit_s(input)
print(out.shape)