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-------------------------------------------------------------------------------- 1 | # PP-LCNet-Pytorch 2 | 3 | ## Pre-Trained Models 4 | 5 | - [Google Drive](https://drive.google.com/drive/folders/1mEgUtok2cUmBIp50Lg35gQU8-XPa8C-d?usp=sharing) 6 | - [p018](https://pan.baidu.com/s/1WmgLhImnPmk71uuLmSN64A) 7 | 8 | ## Accuracy 9 | 10 | | Models | Top1 | Top5 | 11 | | ----------------- | ------ | ------ | 12 | | PPLCNet_x0_25 | 0.5186 | 0.7565 | 13 | | PPLCNet_x0_35 | 0.5809 | 0.8083 | 14 | | PPLCNet_x0_5 | 0.6314 | 0.8466 | 15 | | PPLCNet_x0_75 | 0.6818 | 0.8830 | 16 | | PPLCNet_x1_0 | 0.7132 | 0.9003 | 17 | | PPLCNet_x1_5 | 0.7371 | 0.9153 | 18 | | PPLCNet_x2_0 | 0.7518 | 0.9227 | 19 | | PPLCNet_x2_5 | 0.7660 | 0.9300 | 20 | | PPLCNet_x0_5_ssld | 0.6610 | 0.8646 | 21 | | PPLCNet_x1_0_ssld | 0.7439 | 0.9209 | 22 | | PPLCNet_x2_5_ssld | 0.8082 | 0.9533 | 23 | 24 | ## Referneces 25 | 26 | - [PP-LCNet: A Lightweight CPU Convolutional Neural Network](https://arxiv.org/pdf/2109.15099.pdf) 27 | - [PP-LCNet](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/en/models/PPLCNet_en.md) -------------------------------------------------------------------------------- /pp_lcnet.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | __all__ = [ 6 | "PPLCNet_x0_25", "PPLCNet_x0_35", "PPLCNet_x0_5", "PPLCNet_x0_75", "PPLCNet_x1_0", 7 | "PPLCNet_x1_5", "PPLCNet_x2_0", "PPLCNet_x2_5" 8 | ] 9 | 10 | NET_CONFIG = { 11 | "blocks2": 12 | #k, in_c, out_c, s, use_se 13 | [[3, 16, 32, 1, False]], 14 | "blocks3": [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], 15 | "blocks4": [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], 16 | "blocks5": [[3, 128, 256, 2, False], [5, 256, 256, 1, False], 17 | [5, 256, 256, 1, False], [5, 256, 256, 1, False], 18 | [5, 256, 256, 1, False], [5, 256, 256, 1, False]], 19 | "blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True]] 20 | } 21 | 22 | 23 | def make_divisible(v, divisor=8, min_value=None): 24 | if min_value is None: 25 | min_value = divisor 26 | new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) 27 | if new_v < 0.9 * v: 28 | new_v += divisor 29 | return new_v 30 | 31 | class Hardswish(nn.Module): 32 | def __init__(self, inplace=True): 33 | super().__init__() 34 | self.inplace = inplace 35 | 36 | def forward(self, x): 37 | return x * F.relu6(x + 3., inplace=self.inplace) / 6. 38 | 39 | class Hardsigmoid(nn.Module): 40 | def __init__(self, inplace=True): 41 | super().__init__() 42 | self.inplace = inplace 43 | 44 | def forward(self, x): 45 | return F.relu6(x + 3., inplace=True) / 6. 46 | 47 | class ConvBNLayer(nn.Module): 48 | def __init__(self, 49 | num_channels, 50 | filter_size, 51 | num_filters, 52 | stride, 53 | num_groups=1): 54 | super().__init__() 55 | 56 | self.conv = nn.Conv2d( 57 | in_channels=num_channels, 58 | out_channels=num_filters, 59 | kernel_size=filter_size, 60 | stride=stride, 61 | padding=(filter_size - 1) // 2, 62 | groups=num_groups, 63 | bias=False) 64 | 65 | self.bn = nn.BatchNorm2d( 66 | num_filters, 67 | ) 68 | self.hardswish = Hardswish() 69 | 70 | def forward(self, x): 71 | x = self.conv(x) 72 | x = self.bn(x) 73 | x = self.hardswish(x) 74 | return x 75 | 76 | 77 | class DepthwiseSeparable(nn.Module): 78 | def __init__(self, 79 | num_channels, 80 | num_filters, 81 | stride, 82 | dw_size=3, 83 | use_se=False): 84 | super().__init__() 85 | self.use_se = use_se 86 | self.dw_conv = ConvBNLayer( 87 | num_channels=num_channels, 88 | num_filters=num_channels, 89 | filter_size=dw_size, 90 | stride=stride, 91 | num_groups=num_channels) 92 | if use_se: 93 | self.se = SEModule(num_channels) 94 | self.pw_conv = ConvBNLayer( 95 | num_channels=num_channels, 96 | filter_size=1, 97 | num_filters=num_filters, 98 | stride=1) 99 | 100 | def forward(self, x): 101 | x = self.dw_conv(x) 102 | if self.use_se: 103 | x = self.se(x) 104 | x = self.pw_conv(x) 105 | return x 106 | 107 | 108 | class SEModule(nn.Module): 109 | def __init__(self, channel, reduction=4): 110 | super().__init__() 111 | self.avg_pool = nn.AdaptiveAvgPool2d(1) 112 | self.conv1 = nn.Conv2d( 113 | in_channels=channel, 114 | out_channels=channel // reduction, 115 | kernel_size=1, 116 | stride=1, 117 | padding=0) 118 | self.relu = nn.ReLU() 119 | self.conv2 = nn.Conv2d( 120 | in_channels=channel // reduction, 121 | out_channels=channel, 122 | kernel_size=1, 123 | stride=1, 124 | padding=0) 125 | self.hardsigmoid = Hardsigmoid() 126 | 127 | def forward(self, x): 128 | identity = x 129 | x = self.avg_pool(x) 130 | x = self.conv1(x) 131 | x = self.relu(x) 132 | x = self.conv2(x) 133 | x = self.hardsigmoid(x) 134 | x = torch.mul(identity, x) 135 | return x 136 | 137 | 138 | class PPLCNet(nn.Module): 139 | def __init__(self, 140 | scale=1.0, 141 | class_num=1000, 142 | dropout_prob=0.0, 143 | class_expand=1280): 144 | super().__init__() 145 | self.scale = scale 146 | self.class_expand = class_expand 147 | 148 | self.conv1 = ConvBNLayer( 149 | num_channels=3, 150 | filter_size=3, 151 | num_filters=make_divisible(16 * scale), 152 | stride=2) 153 | 154 | self.blocks2 = nn.Sequential(*[ 155 | DepthwiseSeparable( 156 | num_channels=make_divisible(in_c * scale), 157 | num_filters=make_divisible(out_c * scale), 158 | dw_size=k, 159 | stride=s, 160 | use_se=se) 161 | for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"]) 162 | ]) 163 | 164 | self.blocks3 = nn.Sequential(*[ 165 | DepthwiseSeparable( 166 | num_channels=make_divisible(in_c * scale), 167 | num_filters=make_divisible(out_c * scale), 168 | dw_size=k, 169 | stride=s, 170 | use_se=se) 171 | for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"]) 172 | ]) 173 | 174 | self.blocks4 = nn.Sequential(*[ 175 | DepthwiseSeparable( 176 | num_channels=make_divisible(in_c * scale), 177 | num_filters=make_divisible(out_c * scale), 178 | dw_size=k, 179 | stride=s, 180 | use_se=se) 181 | for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"]) 182 | ]) 183 | 184 | self.blocks5 = nn.Sequential(*[ 185 | DepthwiseSeparable( 186 | num_channels=make_divisible(in_c * scale), 187 | num_filters=make_divisible(out_c * scale), 188 | dw_size=k, 189 | stride=s, 190 | use_se=se) 191 | for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"]) 192 | ]) 193 | 194 | self.blocks6 = nn.Sequential(*[ 195 | DepthwiseSeparable( 196 | num_channels=make_divisible(in_c * scale), 197 | num_filters=make_divisible(out_c * scale), 198 | dw_size=k, 199 | stride=s, 200 | use_se=se) 201 | for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"]) 202 | ]) 203 | 204 | self.avg_pool = nn.AdaptiveAvgPool2d(1) 205 | 206 | self.last_conv = nn.Conv2d( 207 | in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] * scale), 208 | out_channels=self.class_expand, 209 | kernel_size=1, 210 | stride=1, 211 | padding=0, 212 | bias=False) 213 | 214 | self.hardswish = Hardswish() 215 | self.dropout = nn.Dropout(dropout_prob) 216 | self.flatten = nn.Flatten(start_dim=1, end_dim=-1) 217 | 218 | self.fc = nn.Linear(self.class_expand, class_num) 219 | 220 | def forward(self, x): 221 | x = self.conv1(x) 222 | 223 | x = self.blocks2(x) 224 | x = self.blocks3(x) 225 | x = self.blocks4(x) 226 | x = self.blocks5(x) 227 | x = self.blocks6(x) 228 | 229 | x = self.avg_pool(x) 230 | x = self.last_conv(x) 231 | x = self.hardswish(x) 232 | x = self.dropout(x) 233 | x = self.flatten(x) 234 | x = self.fc(x) 235 | return x 236 | 237 | 238 | def PPLCNet_x0_25(**kwargs): 239 | """ 240 | PPLCNet_x0_25 241 | """ 242 | model = PPLCNet(scale=0.25, **kwargs) 243 | 244 | return model 245 | 246 | 247 | def PPLCNet_x0_35(**kwargs): 248 | """ 249 | PPLCNet_x0_35 250 | """ 251 | model = PPLCNet(scale=0.35, **kwargs) 252 | 253 | return model 254 | 255 | 256 | def PPLCNet_x0_5(**kwargs): 257 | """ 258 | PPLCNet_x0_5 259 | """ 260 | model = PPLCNet(scale=0.5, **kwargs) 261 | 262 | return model 263 | 264 | 265 | def PPLCNet_x0_75(**kwargs): 266 | """ 267 | PPLCNet_x0_75 268 | """ 269 | model = PPLCNet(scale=0.75, **kwargs) 270 | 271 | return model 272 | 273 | 274 | def PPLCNet_x1_0(**kwargs): 275 | """ 276 | PPLCNet_x1_0 277 | """ 278 | model = PPLCNet(scale=1.0, **kwargs) 279 | 280 | return model 281 | 282 | 283 | def PPLCNet_x1_5(**kwargs): 284 | """ 285 | PPLCNet_x1_5 286 | """ 287 | model = PPLCNet(scale=1.5, **kwargs) 288 | 289 | return model 290 | 291 | 292 | def PPLCNet_x2_0(**kwargs): 293 | """ 294 | PPLCNet_x2_0 295 | """ 296 | model = PPLCNet(scale=2.0, **kwargs) 297 | 298 | return model 299 | 300 | 301 | def PPLCNet_x2_5(**kwargs): 302 | """ 303 | PPLCNet_x2_5 304 | """ 305 | model = PPLCNet(scale=2.5, **kwargs) 306 | 307 | return model --------------------------------------------------------------------------------