-
trick2-mobilenetv1、mobilenetv2、mobilenetv3替换YOLO主干
文章目录
前言
以YOLOV4为例，分别用Mobilenetv1,Mobilenetv2,Mobilenetv3替换YOLOV4主干。
一、YOLOV4主干网络
二、Mobilenetv1,Mobilenetv2,Mobilenetv3构建
1.Mobilenetv1构建（深度可分离卷积）
代码如下（示例）：
import torch import torch.nn as nn def conv_bn(inp, oup, stride = 1): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True) ) def conv_dw(inp, oup, stride = 1): return nn.Sequential( # part1 nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), nn.BatchNorm2d(inp), nn.ReLU6(inplace=True), # part2 nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True), ) class MobileNetV1(nn.Module): def __init__(self): super(MobileNetV1, self).__init__() self.stage1 = nn.Sequential( # 416,416,3 -> 208,208,32 conv_bn(3, 32, 2), # 208,208,32 -> 208,208,64 conv_dw(32, 64, 1), # 208,208,64 -> 104,104,128 conv_dw(64, 128, 2), conv_dw(128, 128, 1), # 104,104,128 -> 52,52,256 conv_dw(128, 256, 2), conv_dw(256, 256, 1), ) # 52,52,256 -> 26,26,512 self.stage2 = nn.Sequential( conv_dw(256, 512, 2), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), ) # 26,26,512 -> 13,13,1024 self.stage3 = nn.Sequential( conv_dw(512, 1024, 2), conv_dw(1024, 1024, 1), ) self.avg = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Linear(1024, 1000) def forward(self, x): x = self.stage1(x) x = self.stage2(x) x = self.stage3(x) x = self.avg(x) # x = self.model(x) x = x.view(-1, 1024) x = self.fc(x) return x def mobilenet_v1(pretrained=False, progress=True): model = MobileNetV1() if pretrained: state_dict = torch.load('./model_data/mobilenet_v1_weights.pth') model.load_state_dict(state_dict, strict=True) return model if __name__ == "__main__": import torch from torchsummary import summary # 需要使用device来指定网络在GPU还是CPU运行 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = mobilenet_v1().to(device) summary(model, input_size=(3, 416, 416))- 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
2.Mobilenetv2构建(倒残差结构)
代码如下（示例）：
from torch import nn from torchvision.models.utils import load_state_dict_from_url model_urls = { 'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth', } def _make_divisible(v, divisor, min_value=None): 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 class ConvBNReLU(nn.Sequential): def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1): padding = (kernel_size - 1) // 2 super(ConvBNReLU, self).__init__( nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False), nn.BatchNorm2d(out_planes), nn.ReLU6(inplace=True) ) class InvertedResidual(nn.Module): def __init__(self, inp, oup, stride, expand_ratio): super(InvertedResidual, self).__init__() self.stride = stride assert stride in [1, 2] hidden_dim = int(round(inp * expand_ratio)) self.use_res_connect = self.stride == 1 and inp == oup layers = [] if expand_ratio != 1: layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) layers.extend([ ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), ]) self.conv = nn.Sequential(*layers) def forward(self, x): if self.use_res_connect: return x + self.conv(x) else: return self.conv(x) class MobileNetV2(nn.Module): def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8): super(MobileNetV2, self).__init__() block = InvertedResidual input_channel = 32 last_channel = 1280 if inverted_residual_setting is None: inverted_residual_setting = [ # t, c, n, s # 208,208,32 -> 208,208,16 [1, 16, 1, 1], # 208,208,16 -> 104,104,24 [6, 24, 2, 2], # 104,104,24 -> 52,52,32 [6, 32, 3, 2], # 52,52,32 -> 26,26,64 [6, 64, 4, 2], # 26,26,64 -> 26,26,96 [6, 96, 3, 1], # 26,26,96 -> 13,13,160 [6, 160, 3, 2], # 13,13,160 -> 13,13,320 [6, 320, 1, 1], ] if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4: raise ValueError("inverted_residual_setting should be non-empty " "or a 4-element list, got {}".format(inverted_residual_setting)) input_channel = _make_divisible(input_channel * width_mult, round_nearest) self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) # 416,416,3 -> 208,208,32 features = [ConvBNReLU(3, input_channel, stride=2)] for t, c, n, s in inverted_residual_setting: output_channel = _make_divisible(c * width_mult, round_nearest) for i in range(n): stride = s if i == 0 else 1 features.append(block(input_channel, output_channel, stride, expand_ratio=t)) input_channel = output_channel features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1)) self.features = nn.Sequential(*features) self.classifier = nn.Sequential( nn.Dropout(0.2), nn.Linear(self.last_channel, num_classes), ) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.BatchNorm2d): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.zeros_(m.bias) def forward(self, x): x = self.features(x) x = x.mean([2, 3]) x = self.classifier(x) return x def mobilenet_v2(pretrained=False, progress=True): model = MobileNetV2() if pretrained: state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'], model_dir="model_data", progress=progress) model.load_state_dict(state_dict) return model if __name__ == "__main__": print(mobilenet_v2())- 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
3.Mobilenetv3构建（bneck结构）
代码如下（示例）：
import math import torch import torch.nn as nn def _make_divisible(v, divisor, min_value=None): if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_v < 0.9 * v: new_v += divisor return new_v class h_sigmoid(nn.Module): def __init__(self, inplace=True): super(h_sigmoid, self).__init__() self.relu = nn.ReLU6(inplace=inplace) def forward(self, x): return self.relu(x + 3) / 6 class h_swish(nn.Module): def __init__(self, inplace=True): super(h_swish, self).__init__() self.sigmoid = h_sigmoid(inplace=inplace) def forward(self, x): return x * self.sigmoid(x) class SELayer(nn.Module): def __init__(self, channel, reduction=4): super(SELayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, _make_divisible(channel // reduction, 8)), nn.ReLU(inplace=True), nn.Linear(_make_divisible(channel // reduction, 8), channel), h_sigmoid() ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return x * y def conv_3x3_bn(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), h_swish() ) def conv_1x1_bn(inp, oup): return nn.Sequential( nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), h_swish() ) class InvertedResidual(nn.Module): def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs): super(InvertedResidual, self).__init__() assert stride in [1, 2] self.identity = stride == 1 and inp == oup if inp == hidden_dim: self.conv = nn.Sequential( # dw nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim, bias=False), nn.BatchNorm2d(hidden_dim), h_swish() if use_hs else nn.ReLU(inplace=True), # Squeeze-and-Excite SELayer(hidden_dim) if use_se else nn.Identity(), # 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), h_swish() if use_hs else nn.ReLU(inplace=True), # dw nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim, bias=False), nn.BatchNorm2d(hidden_dim), # Squeeze-and-Excite SELayer(hidden_dim) if use_se else nn.Identity(), h_swish() if use_hs else nn.ReLU(inplace=True), # pw-linear nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), ) def forward(self, x): if self.identity: return x + self.conv(x) else: return self.conv(x) class MobileNetV3(nn.Module): def __init__(self, num_classes=1000, width_mult=1.): super(MobileNetV3, self).__init__() # setting of inverted residual blocks self.cfgs = [ #` k, t, c, SE,HS,s # 208,208,16 -> 208,208,16 [3, 1, 16, 0, 0, 1], # 208,208,16 -> 104,104,24 [3, 4, 24, 0, 0, 2], [3, 3, 24, 0, 0, 1], # 104,104,24 -> 52,52,40 [5, 3, 40, 1, 0, 2], [5, 3, 40, 1, 0, 1], [5, 3, 40, 1, 0, 1], # 52,52,40 -> 26,26,80 [3, 6, 80, 0, 1, 2], [3, 2.5, 80, 0, 1, 1], [3, 2.3, 80, 0, 1, 1], [3, 2.3, 80, 0, 1, 1], # 26,26,80 -> 26,26,112 [3, 6, 112, 1, 1, 1], [3, 6, 112, 1, 1, 1], # 26,26,112 -> 13,13,160 [5, 6, 160, 1, 1, 2], [5, 6, 160, 1, 1, 1], [5, 6, 160, 1, 1, 1] ] input_channel = _make_divisible(16 * width_mult, 8) # 416,416,3 -> 208,208,16 layers = [conv_3x3_bn(3, input_channel, 2)] block = InvertedResidual for k, t, c, use_se, use_hs, s in self.cfgs: output_channel = _make_divisible(c * width_mult, 8) exp_size = _make_divisible(input_channel * t, 8) layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs)) input_channel = output_channel self.features = nn.Sequential(*layers) self.conv = conv_1x1_bn(input_channel, exp_size) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) output_channel = _make_divisible(1280 * width_mult, 8) if width_mult > 1.0 else 1280 self.classifier = nn.Sequential( nn.Linear(exp_size, output_channel), h_swish(), nn.Dropout(0.2), nn.Linear(output_channel, num_classes), ) self._initialize_weights() def forward(self, x): x = self.features(x) x = self.conv(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): n = m.weight.size(1) m.weight.data.normal_(0, 0.01) m.bias.data.zero_() def mobilenet_v3(pretrained=False, **kwargs): model = MobileNetV3(**kwargs) if pretrained: state_dict = torch.load('./model_data/mobilenetv3-large-1cd25616.pth') model.load_state_dict(state_dict, strict=True) return model- 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
三、获得主干网络中的三个有效特征层（yolo4.py)
1.导入库
import torch import torch.nn as nn from collections import OrderedDict from nets.mobilenet_v1 import mobilenet_v1 from nets.mobilenet_v2 import mobilenet_v2 from nets.mobilenet_v3 import mobilenet_v3- 1
- 2
- 3
- 4
- 5
- 6
2.Mobilenetv1
class MobileNetV1(nn.Module): def __init__(self, pretrained = False): super(MobileNetV1, self).__init__() self.model = mobilenet_v1(pretrained=pretrained) def forward(self, x): out3 = self.model.stage1(x) out4 = self.model.stage2(out3) out5 = self.model.stage3(out4) return out3, out4, out5- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
3.Mobilenetv2
class MobileNetV2(nn.Module): def __init__(self, pretrained = False): super(MobileNetV2, self).__init__() self.model = mobilenet_v2(pretrained=pretrained) def forward(self, x): out3 = self.model.features[:7](x) out4 = self.model.features[7:14](out3) out5 = self.model.features[14:18](out4) return out3, out4, out5- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
4.Mobilenetv3
class MobileNetV3(nn.Module): def __init__(self, pretrained = False): super(MobileNetV3, self).__init__() self.model = mobilenet_v3(pretrained=pretrained) def forward(self, x): out3 = self.model.features[:7](x) out4 = self.model.features[7:13](out3) out5 = self.model.features[13:16](out4) return out3, out4, out5- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
四、YOLOV4主干特征提取网络的替换（yolo4.py)
1.在YoloBody定义backbone=“mobilenetv2”
class YoloBody(nn.Module): def __init__(self, anchors_mask, num_classes, backbone="mobilenetv2", pretrained=False):- 1
- 2
2.判断backbone是否是上面预先定义的类别
if backbone == "mobilenetv1": #---------------------------------------------------# # 52,52,256；26,26,512；13,13,1024 #---------------------------------------------------# self.backbone = MobileNetV1(pretrained=pretrained) in_filters = [256, 512, 1024] elif backbone == "mobilenetv2": #---------------------------------------------------# # 52,52,32；26,26,92；13,13,320 #---------------------------------------------------# self.backbone = MobileNetV2(pretrained=pretrained) in_filters = [32, 96, 320] elif backbone == "mobilenetv3": #---------------------------------------------------# # 52,52,40；26,26,112；13,13,160 #---------------------------------------------------# self.backbone = MobileNetV3(pretrained=pretrained) in_filters = [40, 112, 160]- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
3.关于通道不匹配错误的问题，需要修改卷积使用的输入通道数。
3.1 首先定义三个有效特征层的输出通道数是多少。
52,52,256；26,26,512；13,13,1024（mobilenetv1） 52,52,32；26,26,92；13,13,320（mobilenetv2） 52,52,40；26,26,112；13,13,160（mobilenetv3）- 1
- 2
- 3
in_filters = [256, 512, 1024] in_filters = [32, 96, 320] in_filters = [40, 112, 160]- 1
- 2
- 3
3.2 然后需要修改卷积使用的输入通道数
self.conv1 = make_three_conv([512, 1024], in_filters[2]) #1024->in_filters[2] self.conv_for_P4 = conv2d(in_filters[1], 256,1) #512->in_filters[1] self.conv_for_P3 = conv2d(in_filters[0], 128,1) #256->in_filters[0]- 1
- 2
- 3
4.参数量（大量的参数是在PAnet里面）
五、PAnet加强特征提取网络修改，使参数量更小（yolo4.py)
5.1 思路：PAnet大部分使用了3x3卷积，而在mobilenetv1里面提到过可以将深度可分离卷积替换3x3卷积，即可实现参数量的大幅度缩小。将下面深度可分离卷积用在yolo4.py中。
def conv_dw(inp, oup, stride = 1): return nn.Sequential( # part1 nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), nn.BatchNorm2d(inp), nn.ReLU6(inplace=True), # part2 nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True), )- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
5.2 在三次卷积块和五次卷积块以及yolo-head都会用到3x3卷积，用深度可分离卷积进行替换。用下面的方式全部进行替换。
conv2d(filters_list[0],filters_list[1],3)修改为下面的代码块： conv_dw(filters_list[0], filters_list[1])- 1
- 2
5.3 参数量变化
六、训练参数详解（train.py）
6.1 backbone
backbone = "mobilenetv1"- 1
6.2 model_path(要和backbone相对应，比如采用主干是mobilenetv1,对应的权值文件也是mobilenetv1，即根骨不同主干和权值）
model_path = 'model_data/yolov4_mobilenet_v1_voc.pth'- 1
七、利用训练好的模型进行预测（predict.py）,在yolo.py文件中更改三个地方：model_path(训练好的权值文件logs),classes_path(类别文件),backbone(与训练好的权值文件logs主干特征提取网络相对应).
"model_path" : 'model_data/yolov4_mobilenet_v1_voc.pth', "classes_path" : 'model_data/voc_classes.txt', "backbone" : 'mobilenetv1',- 1
- 2
- 3
总结
完成了主干的修改和PAnet的修改，主干部分就是将yolo主干替换成mobilenet，PAnet部分是利用mobilenetv1的思想，利用深度可分离卷积3x3卷积＋1x1卷积替换普通的卷积块。根据这个思想可以进一步减少yolo的参数。
-
相关阅读:
【机器学习】机器学习学习笔记 - 监督学习 - 多项式回归决策树回归 - 03
计算机毕业设计之java+ssm基于个人需求和地域特色的外卖推荐系统
《计算机视觉基础知识蓝皮书》第2篇 深度学习基础
大数据之Hive-01.基础入门概念
常用的dos网络命令总结
MCE | 烟酰胺核苷酸转氢酶——独特色素沉着机制
FTPS 227 Entering Passive Mode
Android 打开通知中心（StatusBar）
MSPM0G3507 ——GPIO例程讲解2——simultaneous_interrupts
form表单基础
- 原文地址：https://blog.csdn.net/qq_45825952/article/details/127805857