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I recently came across a method in Pytorch when I try to implement AlexNet. I don't understand how it works. Please explain the idea behind it with some examples. And how it is different from Maxpooling or Average poling in terms of Neural Network functionality
nn.AdaptiveAvgPool2d((6, 6))
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AdaptiveAvgPool2d (output_size)[source] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size.
Average pooling method smooths out the image and hence the sharp features may not be identified when this pooling method is used. Max pooling selects the brighter pixels from the image. It is useful when the background of the image is dark and we are interested in only the lighter pixels of the image.
There are two types of Pooling: Max Pooling and Average Pooling . Max Pooling returns the maximum value from the portion of the image covered by the Kernel. On the other hand, Average Pooling returns the average of all the values from the portion of the image covered by the Kernel.
In average-pooling or max-pooling, you essentially set the stride and kernel-size by your own, setting them as hyper-parameters. You will have to re-configure them if you happen to change your input size.
In Adaptive Pooling on the other hand, we specify the output size instead. And the stride and kernel-size are automatically selected to adapt to the needs. The following equations are used to calculate the value in the source code.
Stride = (input_size//output_size) Kernel size = input_size - (output_size-1)*stride Padding = 0 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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