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c2aaa6b | 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 | import torch
import torch.nn as nn
from einops import rearrange
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
nn.SiLU()
)
def conv_nxn_bn(inp, oup, kernel_size=3, stride=1):
return nn.Sequential(
nn.Conv2d(inp, oup, kernel_size, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.SiLU()
)
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout=0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -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, dim_head, mlp_dim, dropout=0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PreNorm(dim, Attention(dim, heads, dim_head, dropout)),
PreNorm(dim, FeedForward(dim, mlp_dim, dropout))
]))
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return x
class MV2Block(nn.Module):
def __init__(self, inp, oup, stride=1, expansion=4):
super().__init__()
self.stride = stride
hidden_dim = int(inp * expansion)
self.use_res_connect = self.stride == 1 and inp == oup
if expansion == 1:
self.conv = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
else:
self.conv = nn.Sequential(
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileViTBlock(nn.Module):
def __init__(self, dim, depth, channel, kernel_size, patch_size, mlp_dim, dropout=0.):
super().__init__()
self.ph, self.pw = patch_size
self.conv1 = conv_nxn_bn(channel, channel, kernel_size)
self.conv2 = conv_1x1_bn(channel, dim)
self.transformer = Transformer(dim, depth, 1, 32, mlp_dim, dropout)
self.conv3 = conv_1x1_bn(dim, channel)
self.conv4 = conv_nxn_bn(2 * channel, channel, kernel_size)
def forward(self, x):
y = x.clone()
x = self.conv1(x)
x = self.conv2(x)
_, _, h, w = x.shape
x = rearrange(x, 'b d (h ph) (w pw) -> b (ph pw) (h w) d', ph=self.ph, pw=self.pw)
x = self.transformer(x)
x = rearrange(x, 'b (ph pw) (h w) d -> b d (h ph) (w pw)', h=h//self.ph, w=w//self.pw, ph=self.ph, pw=self.pw)
x = self.conv3(x)
x = torch.cat((x, y), 1)
x = self.conv4(x)
return x
class MobileViTv3_Small(nn.Module):
def __init__(self, image_size=(224, 224), num_classes=10):
super().__init__()
ih, iw = image_size
ph, pw = 2, 2
dims = [144, 192, 240]
channels = [16, 32, 64, 64, 96, 96, 128, 128, 160, 160, 640]
self.conv1 = conv_nxn_bn(3, channels[0], stride=2)
self.mv2 = nn.ModuleList([])
self.mv2.append(MV2Block(channels[0], channels[1], 1, 4))
self.mv2.append(MV2Block(channels[1], channels[2], 2, 4))
self.mv2.append(MV2Block(channels[2], channels[3], 1, 4))
self.mv2.append(MV2Block(channels[3], channels[4], 2, 4))
self.mvit = nn.ModuleList([])
self.mvit.append(MobileViTBlock(dims[0], 2, channels[5], 3, (ph, pw), int(dims[0]*2)))
self.mv2_2 = nn.ModuleList([])
self.mv2_2.append(MV2Block(channels[5], channels[6], 2, 4))
self.mvit_2 = nn.ModuleList([])
self.mvit_2.append(MobileViTBlock(dims[1], 4, channels[7], 3, (ph, pw), int(dims[1]*2)))
self.mv2_3 = nn.ModuleList([])
self.mv2_3.append(MV2Block(channels[7], channels[8], 2, 4))
self.mvit_3 = nn.ModuleList([])
self.mvit_3.append(MobileViTBlock(dims[2], 3, channels[9], 3, (ph, pw), int(dims[2]*2)))
self.conv2 = conv_1x1_bn(channels[9], channels[10])
self.pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(channels[10], num_classes)
def forward(self, x):
x = self.conv1(x)
for conv in self.mv2: x = conv(x)
for m in self.mvit: x = m(x)
for conv in self.mv2_2: x = conv(x)
for m in self.mvit_2: x = m(x)
for conv in self.mv2_3: x = conv(x)
for m in self.mvit_3: x = m(x)
x = self.conv2(x)
x = self.pool(x).view(-1, x.shape[1])
return self.fc(x) |