缺陷

[zld-make]

目前，开源代码与论文所述的网络结构不完全一致，尤其以LS Block最为严重

[LQchen1]

确实，看了一下代码，LS Block只在前三阶段中每个奇数块的时候被调用，偶数块的时候调用的还是RepVGGDW块+SE。

[LQchen1]

而且在表8中替换掉DW和SE flops居然不发生变化，是如何替换的呢？

[zld-make]

而且在表8中替换掉DW和SE flops居然不发生变化，是如何替换的呢？

有可能四舍五入了，毕竟DW和SE相比较整个网络 FLOPs 增加不大，若能精确到小数点后四位也能直接看出差距。关键在于开源代码LS Block存在问题，无法进行复现，但核心的 LSConv 并没有发现缺陷，与论文描述一致。

[zld-make]

但论文核心创新点的代码目前看起来没有缺陷，即LSConv。

[Fortunate-ziye]

确实，看了一下代码，LS Block只在前三阶段中每个奇数块的时候被调用，偶数块的时候调用的还是RepVGGDW块+SE。

应该是这样

[xpbag]

LSTest(
(stem): Stem(
(stages): Sequential(
(0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(lsBlock1): LSBlock(
(lsBlock): Sequential(
(0): Block(
(mixer): RepVGGDW(
(conv): Conv2d_BN(
(c): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d_BN(
(c): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), groups=64, bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(se): SEModule(
(fc1): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
(bn): Identity()
(act): ReLU(inplace=True)
(fc2): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
)
)
(downsample1): Downsample(
(depthwise_conv): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(pointwise_conv): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(lsBlock2): LSBlock(
(lsBlock): Sequential(
(0): Block(
(mixer): RepVGGDW(
(conv): Conv2d_BN(
(c): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d_BN(
(c): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), groups=128, bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(se): SEModule(
(fc1): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(bn): Identity()
(act): ReLU(inplace=True)
(fc2): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(1): Block(
(se): Identity()
(mixer): LSConv(
(lkp): LKP(
(cv1): Conv2d_BN(
(c): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(cv2): Conv2d_BN(
(c): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=64, bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(cv3): Conv2d_BN(
(c): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(cv4): Conv2d(64, 144, kernel_size=(1, 1), stride=(1, 1))
(norm): GroupNorm(16, 144, eps=1e-05, affine=True)
)
(ska): SKA()
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
)
)
(downsample2): Downsample(
(depthwise_conv): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=128, bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(pointwise_conv): Sequential(
(0): Conv2d(128, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(lsBlock3): LSBlock(
(lsBlock): Sequential(
(0): Block(
(mixer): RepVGGDW(
(conv): Conv2d_BN(
(c): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d_BN(
(c): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), groups=192, bias=False)
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(se): SEModule(
(fc1): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1))
(bn): Identity()
(act): ReLU(inplace=True)
(fc2): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(192, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(1): Block(
(se): Identity()
(mixer): LSConv(
(lkp): LKP(
(cv1): Conv2d_BN(
(c): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(cv2): Conv2d_BN(
(c): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96, bias=False)
(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(cv3): Conv2d_BN(
(c): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(cv4): Conv2d(96, 216, kernel_size=(1, 1), stride=(1, 1))
(norm): GroupNorm(24, 216, eps=1e-05, affine=True)
)
(ska): SKA()
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(192, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(2): Block(
(mixer): RepVGGDW(
(conv): Conv2d_BN(
(c): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d_BN(
(c): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), groups=192, bias=False)
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(se): SEModule(
(fc1): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1))
(bn): Identity()
(act): ReLU(inplace=True)
(fc2): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(192, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
)
)
(downsample3): Downsample(
(depthwise_conv): Sequential(
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(pointwise_conv): Sequential(
(0): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(mas): LSBlock(
(lsBlock): Sequential(
(0): Block(
(mixer): RepVGGDW(
(conv): Conv2d_BN(
(c): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d_BN(
(c): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), groups=256, bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(se): SEModule(
(fc1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(bn): Identity()
(act): ReLU(inplace=True)
(fc2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(1): Block(
(se): Identity()
(mixer): Residual(
(m): Attention(
(qkv): Conv2d_BN(
(c): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(proj): Sequential(
(0): ReLU()
(1): Conv2d_BN(
(c): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(dw): Conv2d_BN(
(c): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(2): Block(
(mixer): RepVGGDW(
(conv): Conv2d_BN(
(c): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d_BN(
(c): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), groups=256, bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(se): SEModule(
(fc1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(bn): Identity()
(act): ReLU(inplace=True)
(fc2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(3): Block(
(se): Identity()
(mixer): Residual(
(m): Attention(
(qkv): Conv2d_BN(
(c): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(proj): Sequential(
(0): ReLU()
(1): Conv2d_BN(
(c): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(dw): Conv2d_BN(
(c): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(ffn): Residual(
(m): FFN(
(pw1): Conv2d_BN(
(c): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(act): ReLU()
(pw2): Conv2d_BN(
(c): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
)
)
)

[xpbag]

RuntimeError: The size of tensor a (16) must match the size of tensor b (4) at non-singleton dimension 3我跑源码不知道为什么遇到这个问题

[LQchen1]

@Fortunate-ziye 是的，最后一个stage使用Attention还可以理解，但是前面交替使用LSConv和RepVGG+SE，这个算是一个较大的结构改变了，想知道全部使用LSConv或者LSConv+RepVGG+SE的效果怎么样，如果作者能给出这部分的消融就好了，总的来说创新点是很好的。

[Fortunate-ziye]

你跑的源码，为什么要贴个结构图呢，而且我记得这个报错挺详细的，实在不行你把报错的地方复制给ai，让ai判断一下什么问题> RuntimeError: The size of tensor a (16) must match the size of tensor b (4) at non-singleton dimension 3我跑源码不知道为什么遇到这个问题

[xpbag]

嗯嗯我知道，这个结构图不重要，主要我好奇作者自己使用没有遇到相关问题吗

[Fortunate-ziye]

结构图划的手累，哈哈哈哈，你跑的分类吗，我好像没遇到过> 嗯嗯我知道，这个结构图不重要，主要我好奇作者自己使用没有遇到相关问题吗

[xpbag]

class LSTest(torch.nn.Module):
"""
i = 0, ed = 64, kd = 16, dpth = 1, nh = 4, ar = 1.0
i = 1, ed = 128, kd = 16, dpth = 2, nh = 4, ar = 2.0
i = 2, ed = 192, kd = 16, dpth = 3, nh = 4, ar = 3.0
i = 3, ed = 256, kd = 16, dpth = 4, nh = 4, ar = 4.0
"""

def __init__(self, img_size=224,
             patch_size=16,
             in_chans=3,
             num_classes=1000,
             embed_dim=[64, 128, 192, 256],
             key_dim=[16, 16, 16, 16],
             depth=[1, 2, 3, 4],
             num_heads=[4, 4, 4, 4],
             distillation=False, ):
    super().__init__()
    resolution = img_size // patch_size
    self.stem = lsnet.Stem(3, 64)
    self.lsBlock1 = lsnet.LSBlock(ed=64, kd=16, dpth=1, nh=4, ar=1.0, stage=0, resolution=resolution)
    resolution = (resolution - 1) // 2 + 1
    self.downsample1 = lsnet.Downsample(64, 128, stride=2)
    self.lsBlock2 = lsnet.LSBlock(ed=128, kd=16, dpth=2, nh=4, ar=2.0, stage=1, resolution=resolution)
    resolution = (resolution - 1) // 2 + 1
    self.downsample2 = lsnet.Downsample(128, 192, stride=2)
    self.lsBlock3 = lsnet.LSBlock(ed=192, kd=16, dpth=3, nh=4, ar=3.0, stage=2, resolution=resolution)
    resolution = (resolution - 1) // 2 + 1
    self.downsample3 = lsnet.Downsample(192, 256, stride=2)
    self.mas = lsnet.LSBlock(ed=256, kd=16, dpth=4, nh=4, ar=4.0, stage=3, resolution=resolution)
    self.to(device)
def forward(self, x):
    x = self.stem(x)
    x = self.lsBlock1(x)
    x = self.downsample1(x)
    x = self.lsBlock2(x)
    x = self.downsample2(x)
    x = self.lsBlock3(x)
    x = self.downsample3(x)
    x = self.mas(x)
    return x

if name == 'main':

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = lsnet.LSNet()
# model = LSTest()

model.to(device)
input = torch.randn(1, 3, 224, 224).to(device)
# print(model)
output = model(input)
print(output.shape)我就是简单的自己模块化后测试下原模型，然后发现有问题，然后直接使用原模型测试也有这个错，看之前也有个老哥遇到这问题，不是很明白hhh，但是改好了能跑了

[xpbag]

(q.transpose(-2, -1) @ k) * self.scale

RuntimeError: The size of tensor a (16) must match the size of tensor b (4) at non-singleton dimension 3

[xpbag]

参数维度为
q.shape = torch.Size([1, 4, 16, 16])
k.shape = torch.Size([1, 4, 16, 16])
v.shape = torch.Size([1, 4, 64, 16])
q.transpose(-2, -1).shape = torch.Size([1, 4, 16, 16])