global soft pooling?

[hiyyg]

Can soft pool be used as global soft pooling which can replace global average pooling?

[shuaizzZ]

and AdaptivePool？

[alexandrosstergiou]

@hiyyg We have not tested if SoftPool can be used as a Global Pooling operation. You could try this however yourself as the results would definitely be interesting! In the paper, we mainly focused on sub-sampling activations along spatial/spatio-temporal dimensions. Activation vectorisation through Global pooling operations is a bit beyond that scope.

@shuaizzZ You can do adaptive pooling operations in your model's forward() or even create your own custom class. A very simplified version would be:

class AdaptiveSoftPoolxd:
    def __init__(self,output_size):
        self.out_size = output_size

    def forward(self,x)

        # Get size
        inp_shape = list(x.size())
        # Stride
        stride = (inp_shape[2]//self.out_size)
        # Kernel
        kernel = inp_shape[2] - (self.out_size-1)*stride

        # Apply pooling
        x = soft_pool2d(x,kernel_size=kernel,stride=stride)
        return x

[dengfenglai321]

@hiyyg We have not tested if SoftPool can be used as a Global Pooling operation. You could try this however yourself as the results would definitely be interesting! In the paper, we mainly focused on sub-sampling activations along spatial/spatio-temporal dimensions. Activation vectorisation through Global pooling operations is a bit beyond that scope.

@shuaizzZ You can do adaptive pooling operations in your model's forward() or even create your own custom class. A very simplified version would be:

class AdaptiveSoftPoolxd:
    def __init__(self,output_size):
        self.out_size = output_size

    def forward(self,x)

        # Get size
        inp_shape = list(x.size())
        # Stride
        stride = (inp_shape[2]//self.out_size)
        # Kernel
        kernel = inp_shape[2] - (self.out_size-1)*stride

        # Apply pooling
        x = soft_pool2d(x,kernel_size=kernel,stride=stride)
        return x

hi I want to use AdaptiveSoftPoolxd but not global average pooling, and I have some error: TypeError: 'AdaptiveSoftPoolxd' object is not callable

the AdaptiveSoftPoolxd is :

# AdaptiveSoftPoolxd
class AdaptiveSoftPoolxd:
    def __init__(self, output_size):
        self.out_size = output_size
    def forward(self, x):
        # Get size
        inp_shape = list(x.size())
        # Stride
        stride = (inp_shape[2] // self.out_size)
        # Kernel
        kernel = inp_shape[2] - (self.out_size - 1) * stride
        # Apply pooling
        x = soft_pool2d(x, kernel_size=kernel, stride=stride)
        return x

and the SE is ：

# SE attention
class SE(nn.Module):
    def __init__(self, in_channels, channels, se_ratio=12):
        super(SE, self).__init__()
        # self.avg_pool = nn.AdaptiveAvgPool2d(1) # Avgpool
        # self.avg_pool = SoftPool2d(kernel_size=(2,2), stride=(2,2)) # softpool
        self.avg_pool = AdaptiveSoftPoolxd(1) # AdaptiveSoftPoolxd
        self.fc = nn.Sequential(
            nn.Conv2d(in_channels, channels // se_ratio, kernel_size=1, padding=0),
            nn.BatchNorm2d(channels // se_ratio),
            nn.ReLU(inplace=True),
            nn.Conv2d(channels // se_ratio, channels, kernel_size=1, padding=0),
            nn.Sigmoid()
        )

    def forward(self, x):
        print('x is {}'.format(x))
        y = self.avg_pool(x)
        y = self.fc(y)
        return x * y

anything is wrong??

why I can't call AdaptiveSoftPoolxd

[duanzhiihao]

@cendelian I guess you forget to inherit torch.nn.Module when defining AdaptiveSoftPoolxd:

class AdaptiveSoftPoolxd: should be

class AdaptiveSoftPoolxd(nn.Module):
    def __init__(self, output_size):
        super().__init__()
        ...

[dengfenglai321]

@cendelian I guess you forget to inherit torch.nn.Module when defining AdaptiveSoftPoolxd:

class AdaptiveSoftPoolxd: should be

class AdaptiveSoftPoolxd(nn.Module):
    def __init__(self, output_size):
        super().__init__()
        ...

thanks But I found another error: THCudaCheck FAIL file=/pytorch/aten/src/THC/THCCachingHostAllocator.cpp line=278 error=700 : an illegal memory access was encountered

class AdaptiveSoftPoolxd(nn.Module):
    def __init__(self, output_size):
        super(AdaptiveSoftPoolxd, self).__init__()
        self.out_size = output_size
    def forward(self, x):
        # Get size
        inp_shape = list(x.size())
        # Stride
        stride = (inp_shape[2] // self.out_size)
        # Kernel
        kernel = inp_shape[2] - (self.out_size - 1) * stride
        # Apply pooling
        x = soft_pool2d(x, kernel_size=kernel, stride=stride)
        return x