hughperkins
commented
9 years ago I'm porting some cunn code over to clnn
Awesome!
Is there any plans on implementing those cases?
I sort of implement stuff as and when it becomes necessary. If you have a moment to implement one of the commented out cases that would be great! :-)
Also, some CNN noob questions: what those cases stand for?
In max-pooling, we take a square of pooling width (kW) by pooling height (kH) pixels, and find the maximum value in that grid. The output from that grid is this max value. Then, we take the next square of pixels, and do the same thing. Useful web pages: http://ufldl.stanford.edu/wiki/index.php/Pooling and http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/
When we back-propagate, we take the gradient from the gradOutput pixel, and send it back to the one that produced the max value on the way forwards. So we have to store which pixel that was, when we do the forwards propagation.
The pooling areas can be exactly contiguous, which is the easy situation. In this case each gradOutput value maps to exactly one gradInput pixel. Well, that's always true. But the point is, each gradInput pixel will take input from exactly 0 or 1 gradOutput pixels. But rather than making the pooling areas contiguous, we can make them overlap. And then some gradInput pixels need to be updated from the sum of several gradOutput pixels. This is a bit tricky to do, if done from parallel threads, so we consider this case separately. The stride, dW and dH decides the horizontal and vertical distance between each pooling area (kW by kH). When dW == kW and dH == kH, this is the contiguous case. When dW is less than kW, or dH is less than kH, then we have overlapping pools.
Actually, you might not need to handle this, because:
if (input->nDimension == 3) {Spatial max pooling is over a 2d image, so that is 2 dimensions, W ('width') and H (height). But typically, there will be more than one image plane for each incoming example, so we have nInputPlane image planes per example, three dimensions. Finally, we can provide a mini-batch of multiple examples, so that would be 4 dimensions.
Currently, implementation is done for mini-batches, but not for single non-batched examples.
- if you can shoe-horn your current code into mini-batches, eg by
reshapeing to have an extra dimension of length 1, then you dont need to change the SpatialMaxPooling code at all - alternatively, perhaps we can simply modify the
clnn_SpatialMaxPooling_updateGradInputmethod toreshapethe incoming tensor to have an extra dimension? In which case, we can reuse the rest of theupdateGradInputcode as-is
As far as this bit:
} else if((kW>>1) <= dW && (kH>>1) <= dH) {
To handle overlapping pools, I noticed that mostly we only have 3 x 3 pools, with 2 x 2 stride. So, as a first simplification, I only consider this case. Actually, let's consider an even simpler case for now, which is an input image with one single row, with 1 x 3 pools, and 1 x 2 stride. In this case, the pools overlap horizontally:
|pool 1|
|pool 2|
|pool 3|
... etcBut no more than 2 pools overlap for any input/gradInput pixel. So, we can do the pooling in two batches. In the first batch, we do the odd pools:
|pool 1|
|pool 3|These dont overlap :-)
Then we do the even pools:
|pool 2|
|pool 4|... dont overlap either. And we add their results to the gradInput results from the first batch of pools.
In 2 dimensions, we'll need 4 such batches, like we will have these pools first:
X . X . X .
. . . . . .
X . X . X .
. . . . . .(where 'X' are the pools we are calculating, and '.' are the ones we skip for now)
Next batch will be:
. X . X . X
. . . . . .
. X . X . X
. . . . . .Then:
. . . . . .
X . X . X .
. . . . . .
X . X . X .Finally
. . . . . .
. X . X . X
. . . . . .
. X . X . XSo, 4 times.
This will actually generalize to any case where the pooling size is no smaller than half the stride, or something like this, hence that weird looking if condition earlier.
It would be easy enough to handle the fully general case though. eg, if stride is 1, and pooling size is 3, then we'd simply need to do the backpropagation 9 times, adding the results to the output of the earlier updateGradInput batches.
fmassa
brunoro
I'm porting some cunn code over to clnn and stumbled over the following error:
Browsing through
SpatialMaxPooling.cppI found out that there's actually some commented code just above the line that throws that exception. Is there any plans on implementing those cases?Also, some CNN noob questions: what those cases stand for? I'd be happy to implement those if anyone points out some reference to what actually this method is doing.