Is it possible to invert an avgPool2d operation in PyTorch, like maxunpool2d for a maxpool2d operation, and if so, how could that be done?
I've already checked the documentation, and there isn't an option to return the indices, like in the maxpool2d operation, so I assume the unpooling won't be possible in a similar way.
EDIT:
I found a document by Intel which describes how the unpooling works. After checking the math regarding the avgpool2d function the unpooling seems to be pretty straight forward, basically mirroring every input element onto multiple output elements, and apply padding in order to get a correct output size.
I think you are looking for ConvTransposed2d, aka deconvolution: This function allows you to "upsample" the pooled layer.
Using fixed weights you can replicate the averged pooled values. You can also train this layer hopefully getting something better.
Related
I am simulating the case "Cavity driven lid" and I try to get all the stream lines with the stream tracer of paraview, but I only get the ones that intersect the reference line, and because of that there are vortices that are not visible. How can I see all the stream-lines in the domain?
Thanks a lot in adavance.
To add a little bit to Mathieu's answer, if you really want streamlines everywhere, then you can create a Stream Tracer With Custom Source (as Mathieu suggested) and set your data to both the Input and the Seed Source. That will create a streamline originating from every point in your dataset, which is pretty much what you asked for.
However, while you can do this, you will probably not be happy with the results. First of all, unless your data is trivially small, this will take a long time to compute and create a large amount of data. Even worse, the result will be so dense that you won't be able to see anything. You will get all those interesting streamlines through vortices, but they will be completely hidden by all the boring streamlines around them.
Thus, you are better off with trying to derive a data set that contains seed points that are likely to trace a stream through the vortices that you are interested in. One thing you might want to try is to compute the vorticity of your vector field (Gradient Of Unstructured Data Set when turning on advanced option Compute Vorticity), find the magnitude of that (Calculator), and then use the Threshold filter to pull out the cells with large vorticity. Then use that as your Seed Source.
Another (probably better) option if your data is 2D or you can extract an interesting surface along the flow of your data is to use the Surface LIC plugin. Details can be found at https://www.paraview.org/Wiki/ParaView/Line_Integral_Convolution.
You have to choose a representative source for your streamline.
You could use a "Sphere Source", so in the StreamTracer properties.
If that fails, you can use a StreamTracerWithCustomSource and use your own source that you will have to create yourself first.
I thought this should be a simple task, I just can't find the way to do it:
I am using 'imregister' (MATLAB) to register two medical X-ray images.
To ensure I get the best registration outcome as possible, I use some image processing techniques such as contrast enhancement, blackening of objects that are different between images and even cropping.
The outcome of this seems to be quite satisfying.
Now, I want to perform the exact same registaration on the original images, so that I can display the two ORIGINAL images automatically in alignment.
I think that an output parameter such as tform serves this purpose of performing a certain registration on any two images, but unfortunately 'imregister' does not provide such a parameter, as far as I know.
It does provide as an output the spatial referencing object R_reg which might be the answer, but I still haven't figured out how to use it to re-preform the registration.
I should mention that since I am dealing with medical X-ray images on which non of the feature-detecting algorithms seem to work well enough to perform registration, I can only use intensity-based (as opposed to feature-based) registration, and therefore am using 'imregister'.
Does anyone know how I can accomplish this?
Thanks!
Noga
So to make an answer out of my comment, there are 2 things you can do depending on the Matlab release you are using:
Option 1: R2013a and earlier
I suggest modifying the built-in imregister function by forcing tform to be an output and save that function under another name.
For example:
function [movingReg,Rreg,tform] = imregister2(varargin)
save that, add it to your path and you're good to go. If you type edit imregister you will notice that the 1st line calls imregtform to get the geometric transformation required, while the last line calls imwarp, to apply that geometric transformation. Which leads us to Option 2.
Option 2: R2013b and later
Well in that case you can directly use imregtform to get the tform object and then use imwarpto apply it. Easy isn't it?
Hope that makes things clearer!
I have vectors of data that I feed through the filter() function -- said filter was constructed to emit a reasonable approximation of the original signal that is then used to identify "bad" elements in the original data (said elements are typically caused by infrequent short-duration sensor malfunctions and are quite distinct from good data). After identifying these bad elements, I want to go back and replace them with something reasonable.
One approach would be to replace the bad values with the filtered output; however, the output was generated with the bad values, so it has some amount of undesired distortion.
Ideally, I'd like a way to tell filter() to assume that the bad element[s] are missing and that it should instead generate a reasonable interpolation of the missing value[s] (e.g., based on the surrounding values and the properties of the filter) for use when constructing the output.
I've been told that certain toolboxes allow insertion of special values (e.g., NaN) to indicate missing (but assumed to be well-behaved) data.
I looked at the source code for Octave's filter() and nothing obvious leapt out at me -- is there a special value (or other mechanism) to tell filter() to assume that well-behaved data is missing (and should be inserted as needed)?
Inserting NaN won't work for this. The filter function is pretty simple--it simply implements an IIR filter.
If your signal is smooth and slowly-changing, you might get away with simply using interp1 to interpolate new values for the bad stretches based on the good data on either side.
If your signal has more complicated spectral content, I think "Wiener interpolation" is the phase to google for. For extrapolation you can use linear predictive coding.
I have a picture.1200*1175 pixel.I want to train a net(mlp or hopfield) to learn a specific part of it(201*111pixel) to save its weight to use in a new net(with the same previous feature)only without train it to find that specific part.now there are this questions :what kind of nets is useful;mlp or hopfield,if mlp;the number of hidden layers;the trainlm function is unuseful because "out of memory" error.I convert the picture to a binary image,is it useful?
What exactly do you need the solution to do? Find an object with an image (like "Where's Waldo"?). Will the target object always be the same size and orientation? Might it look different because of lighting changes?
If you just need to find a fixed pattern of pixels within a larger image, I suggest using a straightforward correlation measure, such as crosscorrelation to find it efficiently.
If you need to contend with any of the issues mentioned above, then there are two basic solutions: 1. Build a model using examples of the object in different poses, scalings, etc. so that the model will recognize any of them, or 2. Develop a way to normalize the patch of pixels being examined, to minimize the effect of those distortions (like Hu's invariant moments). If nothing else, yuo'll want to perform some sort of data reduction to get the number of inputs down. Technically, you could also try a model which is invariant to rotations, etc., but I don't know how well those work. I suspect that they are more tempermental than traditional approaches.
I found AdaBoost to be helpful in picking out only important bits of an image. That, and resizing the image to something very tiny (like 40x30) using a Gaussian filter will speed it up and put weight on more of an area of the photo rather than on a tiny insignificant pixel.
I have written matlab codes for two different block matching algorithms, extensive search and three step search, but i am not sure how i can check whether i am getting the correct results. Is there any standard way to check these or any standard code which i can run and compare my result with.I read somewhere that JM software can be used but i didnt find any way to use it.
You can always use the results produced by your algorithms to create the next frame of video and then analyze its quality by either visually inspecting it (which is rather subjective, and we like to deal in numbers) or calculating the mean square error between the produced image and the one you're trying to estimate. Mean square error of the exhaustive (extensive) search should be lower than the one three-step gives you.
Well, did you try to plot it? I mean,after the block-matching you have a new image, right?.
A way to know if you result if true or not is to check the sum of the difference of 2 frames.
A - pre_frame
B - post_frame
C - Compensated frame
If abs(abs(A-B)) is lower than abs(abs(A-C))) that mean it could be true.
Next time, try to specify your algoritm. Also, put your code here to help you more.