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Pytorch implementation of cnn network
class LWbottleneck(nn.Module):
def init(self, in_channels,out_channels,stride):
super(LWbottleneck, self).init()
self.stride = stride
self.pyramid_list = nn.ModuleList()
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=[5,1], stride=stride, padding=[2,0]))
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=[1,5], stride=stride, padding=[0,2]))
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=[3,1], stride=stride, padding=[1,0]))
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=[1,3], stride=stride, padding=[0,1]))
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=[2,1], stride=stride, padding=[1,0]))
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=[1,2], stride=stride, padding=[0,1]))
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=2, stride=stride, padding=1))
self.pyramid_list.append(ConvBNReLU(in_channels, in_channels, kernel_size=3, stride=stride, padding=1))
self.shrink = Conv1x1BN(in_channels*8,out_channels)
def forward(self, x):
b,c,w,h = x.shape
if self.stride>1:
w, h = w//self.stride,h//self.stride
outputs = []
for pyconv in self.pyramid_list:
pyconv_x = pyconv(x)
if x.shape[2:] != pyconv_x.shape[2:]:
pyconv_x = pyconv_x[:,:,:w,:h]
outputs.append(pyconv_x)
out = torch.cat(outputs, 1)
return self.shrink(out)
In the original paper, They used shotcut and add, which seems not to be in your code
厉害了,老哥~
I find your InceptionV4 code is not finished, so could you tell me which are finished and which are not?
大佬你好,有怎么训练LFFD代码吗,或者预训练权重,谢谢啊
Hey Author,
the original version in your repo:
def forward(self, x):
# [N, C, H, W]
b, c, h, w = x.size()
# [N, C/2, H * W]
x_phi = self.conv_phi(x).view(b, c, -1)
I guess it should be revised to:
x_phi = self.conv_phi(x).view(b, c//2, -1)
Best,
Zewei
You forgot to put the ECA_modul into Bottleneck
As title, do you have the project of YOLT?
Thank you a lot!
h
Hello, may I ask if there are weights for the MobileneTxt network pre trained on the ImageNet dataset?
Alexnet模型的各个conv层的通道数是不是错了呢?看论文中各个conv层的通道数应该分别是96, 256, 384, 384, 256啊。
没有DCN的代码?
您好,我是从csdn上的IA YOLO文章找到您的仓库的。但是我没有找到IA YOLO的代码,请问是在哪里呢?
SolveMixnet:MDConv class self.layers add to the network by self.layers = nn.Sequential(*self.layers【Better change the name or something】)
Otherwise an error:RuntimeError: Expected object of device type cuda but got device type cpu for argument #2 'weight' in call to _thnn_conv_depthwise2d_forward
Is there any pre-trained model on ImageNet dataset?
大佬您好,我在您的仓库里面没有找到LFFD的训练代码,不知道您方不方便上传一下。感谢!!!
Hello and so happy to see you use Pytorch-Lightning! 🎉
Just wondering if you already heard about quite the new Pytorch Lightning (PL) ecosystem CI where we would like to invite you to... You can check out our blog post about it: Stay Ahead of Breaking Changes with the New Lightning Ecosystem CI ⚡
As you use PL framework for your cool project, we would like to enhance your experience and offer you safe updates to our future releases. At this moment, you run tests with a particular PL version, but it may accidentally happen that the next version will be incompatible with your project... 😕 We do not intend to change anything on our project side, but still here we have a solution - ecosystem CI with testing both - your and our latest development head we can find it very early and prevent releasing eventually bad version... 👍
What is needed to do?
What will you get?
你好,关于MobileNetOne代码没找到,是在哪个路径下的呢?
@shanglianlm0525 请问大佬您能否提供一下LPN的预训练模型吗
Darknet19 is for YOLOv2, YOLOv3 needs Darknet53 and upsample.
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