dingxiaoh / repmlp Goto Github PK

把merge CNN into FC是完全等价的吗？
还是像formulation部分说的一种输入输出维度相同的替换？
如果是完全等价，可否在训练阶段也去掉CNN？

您好，请问下，您们的RepMLP用于人脸识别时是否加CosFace、ArcFace之类的？输入只用了9696，是否用过112112？
谢谢！

您好，想问下你们的RepMLP pretrain模型 训练的时候是跑了多久，用了几张卡呢？谢谢！

https://github.com/DingXiaoH/RepMLP/blob/main/repmlpnet.py#L49

def forward(self, inputs):
        x = F.adaptive_avg_pool2d(inputs, output_size=(1, 1))
        x = self.fc1(x)

according to the paper, it may should be (h, w)?

self.conv_embedding = conv_bn_relu(in_channels, channels[0], kernel_size=patch_size, stride=patch_size, padding=0)

The first embedding layer is still CNN without conversion to MLP finally, have you tried to convert this embedding layer to MLP as well?

Can you add requirements ?!

Thank you very much for proposing an excellent model and sharing it publicly. Also congratulations on the publication of your results in CVPR. Since I want the RepMLP model should be on one-dimensional data, that is, the input is only [B, C, H]. Would like to ask if it is possible to provide a RepMLP model for such one-dimensional data?

丁学长您好,之前看到您在文章中写到使用Cifar数据集来进行一致性的验证。
我想复现一下这个结果,但是Readme中好像并没有告知关于Cifar训练集和测试集的切分细节,以及它的输入维度。
这个能告知一下吗?

This design of repmlp-resnet is different from the lastest repmlpnet, and it shows great face recognition accuracy.

why not keep repmlp-resnet in this repo?

关于在单位阵上做卷积，单位阵里有很多0啊，局部信息不会丢失嘛，（还是我理解错了）
比如这段代码里：

在这个上面做卷积，I的形状是(9,1,3,3)，每个(3,3)中只有一个值不为0，卷积后reshape回去，也只有对角元上不为0，这样做(9,9)x(9,1)的矩阵乘的话，相当与给(3,3)里的每一个元素乘了一个单独的值，也不是卷积吧。

Can you provide the implementation for sementaic segmentation? I am wodering how you split the feature map to fit MLP.

thank you.

Hi, I try python repmlpnet.py, and set the size of x at [100, 3, 224, 224]. But I find the deviation between the new_y and origin_y is 0.0019, which is not negligible. I wonder why it happens, thank you in advance.

Light Block is not fast as the paper says

def test(network, p=True):
    x = torch.ones(128, 3, 224, 224).cuda()
    model = network.cuda()
    if p: print(model)
    model.eval()
    with torch.no_grad(): 
        # warm iters
        for i in range(20):
            y = model(x)
        # inference test 
        iters = 50
        start = time.time()
        for i in range(iters):
            y = model(x)
        end = time.time()
        print((end-start)/iters, 's')
    print(y.shape)

if __name__ == "__main__":
    torch.backends.cudnn.benchmark=True
    test(create_RepMLPRes50_Base_224(deploy=True), False)
    test(create_RepMLPRes50_Light_224(deploy=True), False)
    test(create_RepMLPRes50_Bottleneck_224(deploy=True), False)

with Titan XP

Base: 17.1 ms
Light Block: 16.9 ms
Bottleneck: 18.6 ms