mobilenetv3 NL
时间: 2023-10-19 16:09:40 浏览: 49
MobilenetV3 NL是一种基于深度学习的神经网络模型,它是MobilenetV3的一种变体。其中的NL代表着“non-linearities”,即非线性激活函数。相比于传统的线性激活函数,非线性激活函数可以更好地拟合复杂的数据分布,从而提高模型的准确率。同时,MobilenetV3 NL还引入了一些新的结构和技术,如可分离卷积、注意力机制等,进一步提高了模型的性能和效率。
具体来说,MobilenetV3 NL相比于MobilenetV3,主要有以下改进:
1.引入了非线性激活函数,如h-swish和h-sigmoid,提高了模型的准确率;
2.加入了注意力机制(SE模块),使得模型可以更好地关注重要的特征;
3.优化了网络结构,
相关问题
mobilenetv3与yolov5结合代码
将MobileNetV3和YOLOv5结合起来需要进行以下步骤:
1. 首先,需要下载YOLOv5的代码和预训练权重,以及MobileNetV3的代码和预训练权重。
2. 接着,需要在YOLOv5中修改模型结构,以便将MobileNetV3作为YOLOv5的特征提取器。可以在YOLOv5的models/yolov5s.py文件中进行修改,将原来的卷积层替换为MobileNetV3的卷积层。
3. 在训练时,需要将YOLOv5的训练数据集进行转换,以适应MobileNetV3的输入尺寸。可以使用YOLOv5的datasets.py文件中的resize方法进行转换。
4. 最后,在训练时,需要将YOLOv5的训练代码中的特征提取器替换为MobileNetV3,并按照MobileNetV3的训练方式进行训练。
下面是一个简单的参考代码,仅供参考:
```python
import torch
import torch.nn as nn
import torchvision.models as models
from models.common import Conv, DWConv
class MobileNetV3(nn.Module):
def __init__(self, width_mult=1.0):
super(MobileNetV3, self).__init__()
self.inplanes = 16
self.cfgs = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', 2],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', 2],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hswish', 2],
[3, 200, 80, False, 'hswish', 1],
[3, 184, 80, False, 'hswish', 1],
[3, 184, 80, False, 'hswish', 1],
[3, 480, 112, True, 'hswish', 1],
[3, 672, 112, True, 'hswish', 1],
[5, 672, 160, True, 'hswish', 2],
[5, 960, 160, True, 'hswish', 1],
[5, 960, 160, True, 'hswish', 1],
]
# head
self.conv1 = Conv(3, self.inplanes, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.hswish = Hswish(inplace=True)
# body
self.features = nn.ModuleList([])
for k, exp, c, se, nl, s in self.cfgs:
outplanes = int(c * width_mult)
self.features.append(InvertedResidual(self.inplanes, outplanes, k, s, exp, se, nl))
self.inplanes = outplanes
# tail
self.conv2 = Conv(self.inplanes, 960, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(960)
self.hswish2 = Hswish(inplace=True)
def forward(self, x):
# head
x = self.conv1(x)
x = self.bn1(x)
x = self.hswish(x)
# body
for f in self.features:
x = f(x)
# tail
x = self.conv2(x)
x = self.bn2(x)
x = self.hswish2(x)
return x
class YOLOv5(nn.Module):
def __init__(self, num_classes=80):
super(YOLOv5, self).__init__()
self.head = Conv(960, 1024, 3, stride=1, padding=1)
self.body = nn.Sequential(
Residual(1024, 512, 1),
Conv(512, 256, 1),
nn.Upsample(scale_factor=2, mode='nearest'),
Residual(512, 256, 1),
Conv(256, 128, 1),
nn.Upsample(scale_factor=2, mode='nearest'),
Residual(256, 128, 1),
Conv(128, 64, 1),
nn.Upsample(scale_factor=2, mode='nearest'),
Residual(128, 64, 1),
)
self.tail = nn.Conv2d(64, 3 * (5 + num_classes), 1)
def forward(self, x):
x = self.head(x)
x = self.body(x)
x = self.tail(x)
return x
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# load MobileNetV3
mobilenetv3 = MobileNetV3().to(device)
mobilenetv3.load_state_dict(torch.load('mobilenetv3_weights.pth'))
# load YOLOv5
yolov5 = YOLOv5().to(device)
yolov5.load_state_dict(torch.load('yolov5_weights.pth'))
# replace feature extractor of YOLOv5 with MobileNetV3
yolov5.head = mobilenetv3.conv2
yolov5.body = mobilenetv3.features
# train YOLOv5 with MobileNetV3 feature extractor
train_dataset = ...
train_loader = ...
optimizer = ...
loss_fn = ...
for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader):
data, targets = data.to(device), targets.to(device)
optimizer.zero_grad()
outputs = yolov5(data)
loss = loss_fn(outputs, targets)
loss.backward()
optimizer.step()
```
给出一个mobilenetv3net代码示例
以下是一个使用 PyTorch 实现的 MobileNetV3 网络的代码示例:
```python
import torch
import torch.nn as nn
class SqueezeExcitation(nn.Module):
def __init__(self, in_channels, se_ratio=.25):
super(SqueezeExcitation, self).__init__()
self.pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(in_channels, in_channels // 4, kernel_size=1, bias=False)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(in_channels // 4, in_channels, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
self.se_ratio = se_ratio
def forward(self, x):
out = self.pool(x)
out = self.fc1(out)
out = self.relu(out)
out = self.fc2(out)
out = self.sigmoid(out)
return x * out
class MobileNetV3Block(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, use_se=True, nl='HS'):
super(MobileNetV3Block, self).__init__()
self.use_se = use_se
self.stride = stride
self.in_channels = in_channels
self.out_channels = out_channels
assert nl in ['HS', 'RE']
self.nl = nl
padding = (kernel_size - 1) // 2
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, groups=in_channels, bias=False)
self.bn1 = nn.BatchNorm2d(in_channels)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels, out_channels, 1, 1, , bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
if use_se:
self.se = SqueezeExcitation(out_channels)
self.relu2 = nn.ReLU(inplace=True)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
if self.nl == 'HS':
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
if self.use_se:
out = self.se(out)
if self.nl == 'RE':
out = self.relu1(out)
if self.stride == 1 and self.in_channels == self.out_channels:
out = x + out
return out
class MobileNetV3(nn.Module):
def __init__(self, num_classes=100, mode='large', multiplier=1.):
super(MobileNetV3, self).__init__()
assert mode in ['large', 'small']
if mode == 'large':
layers = [
# in_channels, out_channels, kernel_size, stride, use_se, nl
MobileNetV3Block(3, int(16 * multiplier), 3, 1, False, 'RE'),
MobileNetV3Block(int(16 * multiplier), int(16 * multiplier), 3, 2, False, 'RE'),
MobileNetV3Block(int(16 * multiplier), int(24 * multiplier), 3, 1, False, 'RE'),
MobileNetV3Block(int(24 * multiplier), int(24 * multiplier), 3, 1, False, 'RE'),
MobileNetV3Block(int(24 * multiplier), int(40 * multiplier), 5, 2, True, 'RE'),
MobileNetV3Block(int(40 * multiplier), int(40 * multiplier), 5, 1, True, 'RE'),
MobileNetV3Block(int(40 * multiplier), int(40 * multiplier), 5, 1, True, 'RE'),
MobileNetV3Block(int(40 * multiplier), int(80 * multiplier), 3, 2, False, 'HS'),
MobileNetV3Block(int(80 * multiplier), int(80 * multiplier), 3, 1, False, 'HS'),
MobileNetV3Block(int(80 * multiplier), int(80 * multiplier), 3, 1, False, 'HS'),
MobileNetV3Block(int(80 * multiplier), int(112 * multiplier), 3, 1, True, 'HS'),
MobileNetV3Block(int(112 * multiplier), int(112 * multiplier), 3, 1, True, 'HS'),
MobileNetV3Block(int(112 * multiplier), int(160 * multiplier), 5, 2, True, 'HS'),
MobileNetV3Block(int(160 * multiplier), int(160 * multiplier), 5, 1, True, 'HS'),
MobileNetV3Block(int(160 * multiplier), int(160 * multiplier), 5, 1, True, 'HS'),
MobileNetV3Block(int(160 * multiplier), int(320 * multiplier), 3, 1, False, 'HS'),
nn.Conv2d(int(320 * multiplier), int(128 * multiplier), 1, 1, , bias=False),
nn.BatchNorm2d(int(128 * multiplier)),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(int(128 * multiplier), num_classes, 1, 1, , bias=True),
]
else:
layers = [
MobileNetV3Block(3, int(16 * multiplier), 3, 2, True, 'RE'),
MobileNetV3Block(int(16 * multiplier), int(24 * multiplier), 3, 2, False, 'RE'),
MobileNetV3Block(int(24 * multiplier), int(24 * multiplier), 3, 1, False, 'RE'),
MobileNetV3Block(int(24 * multiplier), int(40 * multiplier), 5, 2, True, 'RE'),
MobileNetV3Block(int(40 * multiplier), int(40 * multiplier), 5, 1, True, 'RE'),
MobileNetV3Block(int(40 * multiplier), int(40 * multiplier), 5, 1, True, 'RE'),
MobileNetV3Block(int(40 * multiplier), int(48 * multiplier), 5, 1, True, 'HS'),
MobileNetV3Block(int(48 * multiplier), int(48 * multiplier), 5, 1, True, 'HS'),
MobileNetV3Block(int(48 * multiplier), int(96 * multiplier), 5, 2, True, 'HS'),
MobileNetV3Block(int(96 * multiplier), int(96 * multiplier), 5, 1, True, 'HS'),
MobileNetV3Block(int(96 * multiplier), int(96 * multiplier), 5, 1, True, 'HS'),
nn.Conv2d(int(96 * multiplier), int(576 * multiplier), 1, 1, , bias=False),
nn.BatchNorm2d(int(576 * multiplier)),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(int(576 * multiplier), num_classes, 1, 1, , bias=True),
]
self.layers = nn.Sequential(*layers)
def forward(self, x):
out = self.layers(x)
out = out.view(out.size(), -1)
return out
```
希望这个代码示例能对你有所帮助!