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Add MobileViT
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""" | ||
An implementation of MobileViT Model as defined in: | ||
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer | ||
Arxiv: https://arxiv.org/abs/2110.02178 | ||
Origin Code: https://github.com/murufeng/awesome_lightweight_networks | ||
""" | ||
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import torch | ||
import torch.nn as nn | ||
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from einops import rearrange | ||
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def _make_divisible(v, divisor, min_value=None): | ||
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if min_value is None: | ||
min_value = divisor | ||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) | ||
if new_v < 0.9 * v: | ||
new_v += divisor | ||
return new_v | ||
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def Conv_BN_ReLU(inp, oup, kernel, stride=1): | ||
return nn.Sequential( | ||
nn.Conv2d(inp, oup, kernel_size=kernel, stride=stride, padding=1, bias=False), | ||
nn.BatchNorm2d(oup), | ||
nn.ReLU6(inplace=True) | ||
) | ||
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def conv_1x1_bn(inp, oup): | ||
return nn.Sequential( | ||
nn.Conv2d(inp, oup, 1, 1, 0, bias=False), | ||
nn.BatchNorm2d(oup), | ||
nn.ReLU6(inplace=True) | ||
) | ||
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class PreNorm(nn.Module): | ||
def __init__(self, dim, fn): | ||
super().__init__() | ||
self.norm = nn.LayerNorm(dim) | ||
self.fn = fn | ||
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def forward(self, x, **kwargs): | ||
return self.fn(self.norm(x), **kwargs) | ||
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class FeedForward(nn.Module): | ||
def __init__(self, dim, hidden_dim, dropout=0.): | ||
super().__init__() | ||
self.ffn = nn.Sequential( | ||
nn.Linear(dim, hidden_dim), | ||
nn.SiLU(), | ||
nn.Dropout(dropout), | ||
nn.Linear(hidden_dim, dim), | ||
nn.Dropout(dropout) | ||
) | ||
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def forward(self, x): | ||
return self.ffn(x) | ||
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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) | ||
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self.heads = heads | ||
self.scale = dim_head ** -0.5 | ||
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self.attend = nn.Softmax(dim=-1) | ||
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False) | ||
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self.to_out = nn.Sequential( | ||
nn.Linear(inner_dim, dim), | ||
nn.Dropout(dropout) | ||
) if project_out else nn.Identity() | ||
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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) | ||
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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) | ||
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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 = heads, dim_head = dim_head, dropout = dropout)), | ||
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)) | ||
])) | ||
def forward(self, x): | ||
for attn, ff in self.layers: | ||
x = attn(x) + x | ||
x = ff(x) + x | ||
return x | ||
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class MV2Block(nn.Module): | ||
def __init__(self, inp, oup, stride=1, expand_ratio=4): | ||
super(MV2Block, self).__init__() | ||
assert stride in [1, 2] | ||
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hidden_dim = round(inp * expand_ratio) | ||
self.identity = stride == 1 and inp == oup | ||
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if expand_ratio == 1: | ||
self.conv = nn.Sequential( | ||
# dw | ||
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), | ||
nn.BatchNorm2d(hidden_dim), | ||
nn.SiLU(), | ||
# pw-linear | ||
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), | ||
nn.BatchNorm2d(oup), | ||
) | ||
else: | ||
self.conv = nn.Sequential( | ||
# pw | ||
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), | ||
nn.BatchNorm2d(hidden_dim), | ||
nn.SiLU(), | ||
# dw | ||
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), | ||
nn.BatchNorm2d(hidden_dim), | ||
nn.SiLU(), | ||
# pw-linear | ||
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), | ||
nn.BatchNorm2d(oup), | ||
) | ||
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def forward(self, x): | ||
if self.identity: | ||
return x + self.conv(x) | ||
else: | ||
return self.conv(x) | ||
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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 | ||
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self.conv1 = Conv_BN_ReLU(channel, channel, kernel_size) | ||
self.conv2 = conv_1x1_bn(channel, dim) | ||
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self.transformer = Transformer(dim, depth, 1, 32, mlp_dim, dropout) | ||
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self.conv3 = conv_1x1_bn(dim, channel) | ||
self.conv4 = Conv_BN_ReLU(2 * channel, channel, kernel_size) | ||
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def forward(self, x): | ||
y = x.clone() | ||
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# Local representations | ||
x = self.conv1(x) | ||
x = self.conv2(x) | ||
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# Global representations | ||
_, _, 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) | ||
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# Fusion | ||
x = self.conv3(x) | ||
x = torch.cat((x, y), 1) | ||
x = self.conv4(x) | ||
return x | ||
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class MobileViT(nn.Module): | ||
def __init__(self, image_size, dims, channels, num_classes, expansion=4, kernel_size=3, patch_size=(2, 2)): | ||
super().__init__() | ||
ih, iw = image_size | ||
ph, pw = patch_size | ||
assert ih % ph == 0 and iw % pw == 0 | ||
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L = [2, 4, 3] | ||
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self.conv1 = Conv_BN_ReLU(3, channels[0], kernel=3, stride=2) | ||
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self.mv2 = nn.ModuleList([]) | ||
self.mv2.append(MV2Block(channels[0], channels[1], 1, expansion)) | ||
self.mv2.append(MV2Block(channels[1], channels[2], 2, expansion)) | ||
self.mv2.append(MV2Block(channels[2], channels[3], 1, expansion)) | ||
self.mv2.append(MV2Block(channels[2], channels[3], 1, expansion)) | ||
self.mv2.append(MV2Block(channels[3], channels[4], 2, expansion)) | ||
self.mv2.append(MV2Block(channels[5], channels[6], 2, expansion)) | ||
self.mv2.append(MV2Block(channels[7], channels[8], 2, expansion)) | ||
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self.mvit = nn.ModuleList([]) | ||
self.mvit.append(MobileViTBlock(dims[0], L[0], channels[5], kernel_size, patch_size, int(dims[0] * 2))) | ||
self.mvit.append(MobileViTBlock(dims[1], L[1], channels[7], kernel_size, patch_size, int(dims[1] * 4))) | ||
self.mvit.append(MobileViTBlock(dims[2], L[2], channels[9], kernel_size, patch_size, int(dims[2] * 4))) | ||
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self.conv2 = conv_1x1_bn(channels[-2], channels[-1]) | ||
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self.pool = nn.AvgPool2d(ih // 32, 1) | ||
self.fc = nn.Linear(channels[-1], num_classes, bias=False) | ||
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def forward(self, x): | ||
x = self.conv1(x) | ||
x = self.mv2[0](x) | ||
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x = self.mv2[1](x) | ||
x = self.mv2[2](x) | ||
x = self.mv2[3](x) | ||
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x = self.mv2[4](x) | ||
x = self.mvit[0](x) | ||
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x = self.mv2[5](x) | ||
x = self.mvit[1](x) | ||
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x = self.mv2[6](x) | ||
x = self.mvit[2](x) | ||
x = self.conv2(x) | ||
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x = self.pool(x).view(-1, x.shape[1]) | ||
x = self.fc(x) | ||
return x | ||
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