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4985e90 | 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 | """LW-Vit"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class HSwish(nn.Module):
"""Hard Swish activation function"""
def forward(self, x):
out = x * F.relu6(x + 3, inplace=True) / 6
return out
class MV2_Block(nn.Module):
"""MobileNetV2 Inverted Residual Block with h-swish activation"""
def __init__(self, in_channels, expand_channels, out_channels, stride):
super().__init__()
self.stride = stride
self.use_res_connect = self.stride == 1 and in_channels == out_channels
self.expand = nn.Sequential(
nn.Conv2d(in_channels, expand_channels, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(expand_channels),
HSwish()
)
self.depthwise = nn.Sequential(
nn.Conv2d(expand_channels, expand_channels, kernel_size=3, stride=stride,
padding=1, groups=expand_channels, bias=False),
nn.BatchNorm2d(expand_channels),
HSwish()
)
self.project = nn.Sequential(
nn.Conv2d(expand_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
if self.use_res_connect:
return x + self.project(self.depthwise(self.expand(x)))
else:
return self.project(self.depthwise(self.expand(x)))
class LW_ViT_Transformer_Block(nn.Module):
"""Lightweight Vision Transformer Block"""
def __init__(self, in_channels, out_channels, patch_size=2, heads=4, dim=128):
super().__init__()
self.patch_size = patch_size
self.in_channels = in_channels
self.out_channels = out_channels
self.dim = dim
# Downsampling and channel adjustment
self.downsample = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=1)
self.channel_adjust = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1)
# Calculate patch dimension
patch_dim = out_channels * patch_size * patch_size
# Add projection layer if needed
self.projection = nn.Identity() if patch_dim == dim else nn.Linear(patch_dim, dim)
# Transformer components
self.norm1 = nn.LayerNorm(dim)
self.attn = nn.MultiheadAttention(embed_dim=dim, num_heads=heads, batch_first=True)
self.norm2 = nn.LayerNorm(dim)
self.ffn = nn.Sequential(
nn.Linear(dim, dim * 4),
nn.GELU(),
nn.Linear(dim * 4, dim)
)
# Final processing
self.final_conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.norm_out = nn.BatchNorm2d(out_channels)
self.act = HSwish()
def forward(self, x):
# Initial downsampling and channel adjustment
x = self.downsample(x)
x = self.channel_adjust(x)
B, C, H, W = x.shape
# Convert feature map to patches and embed
# Reshape to [B, C, H/P, P, W/P, P]
x_reshaped = x.reshape(B, C, H // self.patch_size, self.patch_size,
W // self.patch_size, self.patch_size)
# Permute to [B, H/P, W/P, C, P, P]
x_permuted = x_reshaped.permute(0, 2, 4, 1, 3, 5)
# Reshape to [B, H/P * W/P, C * P * P]
patches = x_permuted.reshape(B, (H // self.patch_size) * (W // self.patch_size), -1)
# Apply projection if needed
patches = self.projection(patches)
# Apply transformer operations
normed_patches = self.norm1(patches)
attn_out, _ = self.attn(normed_patches, normed_patches, normed_patches)
patches = patches + attn_out
normed_patches = self.norm2(patches)
ffn_out = self.ffn(normed_patches)
patches = patches + ffn_out
# Reshape back to feature map
# Reshape to [B, H/P, W/P, C, P, P]
x_back = patches.reshape(B, H // self.patch_size, W // self.patch_size,
C, self.patch_size, self.patch_size)
# Permute to [B, C, H/P, P, W/P, P]
x_back = x_back.permute(0, 3, 1, 4, 2, 5)
# Reshape to [B, C, H, W]
x_out = x_back.reshape(B, C, H, W)
# Final processing
out = self.final_conv(x_out)
out = self.norm_out(out)
out = self.act(out)
return out
class LW_ViT(nn.Module):
"""Lightweight Vision Transformer for Chinese Character Recognition"""
def __init__(self, base_channels=16, num_classes=3892):
super().__init__()
# Stem layer
self.stem = nn.Sequential(
nn.Conv2d(3, base_channels, 3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(base_channels),
HSwish()
)
# Feature extraction layers
self.features = nn.Sequential(
# MV2 blocks
MV2_Block(base_channels, 4*base_channels, base_channels, stride=1),
MV2_Block(base_channels, 4*base_channels, base_channels, stride=2),
MV2_Block(base_channels, 4*base_channels, base_channels, stride=1),
MV2_Block(base_channels, 4*base_channels, base_channels, stride=2),
# Transformer block
LW_ViT_Transformer_Block(
in_channels=base_channels,
out_channels=2*base_channels,
patch_size=2,
heads=4,
dim=128
),
# Final convolutional layer
nn.Conv2d(2*base_channels, 4*base_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(4*base_channels),
HSwish()
)
# Global pooling and classifier
self.global_pool = nn.AdaptiveAvgPool2d(1)
self.dropout = nn.Dropout(0.1) # Added dropout for regularization
self.classifier = nn.Sequential(
nn.Linear(4*base_channels, 8*base_channels),
nn.BatchNorm1d(8*base_channels),
HSwish(),
nn.Dropout(0.1), # Added dropout for regularization
nn.Linear(8*base_channels, num_classes)
)
def forward(self, x):
x = self.stem(x)
x = self.features(x)
x = self.global_pool(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.classifier(x)
return x
num_cls = 3892
num_epochs = 100
user_device = "cuda" if torch.cuda.is_available() else "cpu"
LW_model = LW_ViT(base_channels=16, num_classes=num_cls) |