RPN | Code Repository for Regression Planning Networks | Artificial Intelligence library

You have the requirement

Division between two integers should truncate toward zero.

Hence, you need to:

okay, found the solution, turns out the github solution does work, particularly this one. so i just added these lines on exporter.py:

Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) means the normalization process is applied to all the 3 channels of the input image. 0.485 is applied to the R channel, 0.456 is applied to the G channel and 0.406 is applied to the B channel. The same goes for the standard deviation values.

The 1st Conv. layer of the backbone expects a 1 channel input and that's the reason you get this error.

You could do the following to solve the issue.

Re-define the GeneralizedRCNNTransform and attach it to your model. You could do something like this:

In a typical RPN language, you can't have the same token - interpreted as either a unary or binary operator depending on context, because there is no context. It has to always be one or the other. So commonly - is kept as the binary subtraction operator, and some other token is used for the unary negation operator. Forth, for instance, called it NEGATE. Thus in Forth, your -190 + 20 could be coded as 190 NEGATE 20 +, and your -(9+9) as 9 9 + NEGATE.

Forth could also parse negative numbers, so your -190 + 20 could also be coded -190 20 +. However the - is not an operator in this instance, but merely part of the single token -190. The only operator being used in this example is +.

If you write 190 - 20 + in a typical RPN language, you will get a stack underflow (or else whatever happened to be on the stack, minus 190, plus 20) since - is unconditionally interpreted as the binary operator.

RPN has no concept of precedence nor associativity - those serve to resolve ambiguity in the evaluation of expressions, and RPN has no such ambiguity in the first place.

You could take an object for all operators and check if the value of input is an operator, then perform the operation with the reversed popped values of the stack or push the value to the stack.

Your heart of your question seems to be:

Given a String of characters, how can I check that all those characters exist in another String

There are a couple of ways to do this:

Regex:

The regex for detecting all of "abc" in a string is "^(?=.*a)(?=.*b)(?=.*c).*". You could either hardcode the regex, or build it from the string like this:

This was more difficult when Object Detection was only compatible with TF1, but is now pretty simple in TF2. There's a good example in this colab.

1. I dont know, how my program will know that I use matrix, vector or scalar? For example I have

   matrix_A matrix_B * how my program will know that name matrix_A and matrix_B means the multiplication of 2 matrices and use function multMatrices()

you need a dictionary associating name (char*) to Element, because you will have very probably few definitions there is no performance problem and it is useless to use complex structures and to implement a map using hashing, or balanced tree etc.

Then having for instance a binary operation the valid case is to have at least 2 Element pushed in the stack and you just have to check their type to decide what function to call. Note you can also use 0, 1, 2 for the type allowing to use an array of pointer to function.

Are the type of the operand must be the same ? It may be allowed to multiply/add/divide/substract a vector/matrix by a scalar for instance, applying the operation on each cell ?

In Vector the attribute keyWordV, in Scalar the attribute keyWordS is useless, in Matrix the attribute keyWordM is useless.

It is also useless to have the attribute name in Vector/Scalar/Matrix, you just need to have it in Element at the same level of type (out of the union)

To use fixed size array in Vector/Scalar/Matrix is practical but that use more memory than necessary and what you will do if a number of element is greater than MAX_ARRAY_DIM or if a name is greater than MAX_NAME ? Same for Element elements[10];.

1. My writing functions aren't working, but my functions with showing on the screen works perfect

how is it possible, the writeX and showX functions are exactly the same and in fact showX(x) can be defined as writeX(stdout, x)

1. In this algorithm I dont have unary operations ...

Frankly the reverse polish notation is so simple to implement you can do that by yourself from scratch, and doing that you will learn more ;-)

Whatever n an nnary operator pops its n operand(s) from the stack (of course check there are at least n element else indicate the error), computes the result, then pushes the result, trivial

Feature Pyramid Networks(FPN) for Object Detection is not an RPN.

FPN is just a better way to do feature extraction. It incorporates features from several stages together which gives better features for the rest of the object detection pipeline (specifically because it incorporates features from the first stages which gives better features for detection of small/medium size objects).

As the original paper states: "Our goal is to leverage a ConvNet’s pyramidal feature hierarchy, which has semantics from low to high levels, and build a feature pyramid with high-level semantics throughout. The resulting Feature Pyramid Network is general purpose and in this paper we focus on sliding window proposers (Region Proposal Network, RPN for short) [29] and region-based detectors (Fast R-CNN)"

So they use it to check "Two stage" object detection pipeline. The first stage is the RPN and this is what they check in section 5.1 and then they check it for the classification stage in section 5.2.

Fast R-CNN Faster R-CNN etc.. are region based object detectors and not sliding window detectors. They get a fixed set of regions from the RPN to classify and thats it.

A good explanation on the differences you can see at https://medium.com/@jonathan_hui/what-do-we-learn-from-region-based-object-detectors-faster-r-cnn-r-fcn-fpn-7e354377a7c9.