shiqiyu / simplecnnbycpp Goto Github PK

First, in face_binary_cls.cpp, such as float conv0_weight[16*3*3*3],it means out_channels = 16, in_channels = 3, and size = 3.From teacher words, the weight data is RGB form.I want to know the data in the following 1-d vector, what is the form of data stored? Do it store data one out_channels after another,?And for every out_channel, it store data R,G,B alternately or store all R data first and following G data and B data in the end?
for example, one out_channel the data form is [r1,g1,b1,r2,g2,b2......,rn , gn ,bn] or [r1,r2.....,rn ,g1,g2.....,gn ,b1,b2.....,bn]?

Second, in modle.py, for this part

self.backbone = nn.Sequential(
            ConvBNReLU(3, 16, 3, 2, 1),    # downsampled by 2, 128 -> 64
            nn.MaxPool2d(2, 2),            # downsampled by 2, 64 -> 32
            ConvBNReLU(16, 32, 3, 1),      # keep
            nn.MaxPool2d(2, 2),            # downsampled by 2, 32 -> 16
            ConvBNReLU(32, 32, 3, 2, 1)    # downsampled by 2, 16 -> 8
        )

for the forth line , it stride = 1 and padding is default value but in this situation, the out picture size will be two less than in size(30) with 3x3 conv_size .If we want to make the picture size is still 32, perhaps need 1 padding? And from 128 to 64 with 1 padding and 2 stride, for calculate formula, out size is (W-F+2P)/S+1 with W picture size = W, conv_size = F ,padding = P, stride = S, it will be 64.5 with in picture we assume have a circle of 0s but we only use left and top ,the right and bottom will not be used as long as it becomes 64?

This error can be easily fixed using @torch.no_grad()

初步看了下，simpleCNN仍然使用的是pytorch深度学习库复现，我在去年也类似复现了一个简单的手写数字识别，不调用任何第三方框架，从数学原理上实现，逐个公式各个击破，有兴趣详看参考这里 https://github.com/cuixing158/DeeplearningPractice