stride | Stride Game Engine | Game Engine library

 by   stride3d C# Version: releases/ License: MIT

kandi X-RAY | stride Summary

kandi X-RAY | stride Summary

stride is a C# library typically used in Telecommunications, Media, Media, Entertainment, Gaming, Game Engine applications. stride has no bugs, it has a Permissive License and it has medium support. However stride has 1 vulnerabilities. You can download it from GitHub.

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              stride has a medium active ecosystem.
              It has 4574 star(s) with 748 fork(s). There are 173 watchers for this library.
              It had no major release in the last 6 months.
              There are 408 open issues and 483 have been closed. On average issues are closed in 145 days. There are 49 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of stride is releases/

            kandi-Quality Quality

              stride has 0 bugs and 0 code smells.

            kandi-Security Security

              stride has 1 vulnerability issues reported (0 critical, 1 high, 0 medium, 0 low).
              stride code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              stride is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              stride releases are not available. You will need to build from source code and install.
              stride saves you 21136 person hours of effort in developing the same functionality from scratch.
              It has 41545 lines of code, 70 functions and 6904 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

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            stride Key Features

            No Key Features are available at this moment for stride.

            stride Examples and Code Snippets

            Compute the mesh stride .
            pythondot img1Lines of Code : 6dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _compute_mesh_strides(mesh_dims: List[MeshDimension]) -> List[int]:
              strides = [1]
              for idx, dim in enumerate(reversed(mesh_dims[1:])):
                strides.append(strides[idx] * dim.size)
              return strides  

            Community Discussions


            variadic template: SFINAE on last argument
            Asked 2022-Mar-11 at 11:03

            I have an array (of any rank), and I would like to have an index operator that:

            1. Allows for missing indices, such that the following is equivalent



            Answered 2022-Mar-11 at 11:03

            In earlier version of this answer I didn't provided full implementation since something was not adding up for me.

            If this index should calculate index for flattened multidimensional array then your example implementation is invalid. Problem is hidden since you are comparing two results for index with all indexes provided and shorten version where zero padding is assumed.
            Sadly I flowed this pattern in first versions of test in Catch2.

            Here is proper test for index of flattened multidimensional array, where last index matches flattened index:



            Convolution Function Latency Bottleneck
            Asked 2022-Mar-10 at 13:57

            I have implemented a Convolutional Neural Network in C and have been studying what parts of it have the longest latency.

            Based on my research, the massive amounts of matricial multiplication required by CNNs makes running them on CPUs and even GPUs very inefficient. However, when I actually profiled my code (on an unoptimized build) I found out that something other than the multiplication itself was the bottleneck of the implementation.

            After turning on optimization (-O3 -march=native -ffast-math, gcc cross compiler), the Gprof result was the following:

            Clearly, the convolution2D function takes the largest amount of time to run, followed by the batch normalization and depthwise convolution functions.

            The convolution function in question looks like this:



            Answered 2022-Mar-10 at 13:57

            Looking at the result of Cachegrind, it doesn't look like the memory is your bottleneck. The NN has to be stored in memory anyway, but if it's too large that your program's having a lot of L1 cache misses, then it's worth thinking to try to minimize L1 misses, but 1.7% of L1 (data) miss rate is not a problem.

            So you're trying to make this run fast anyway. Looking at your code, what's happening at the most inner loop is very simple (load-> multiply -> add -> store), and it doesn't have any side effect other than the final store. This kind of code is easily parallelizable, for example, by multithreading or vectorizing. I think you'll know how to make this run in multiple threads seeing that you can write code with some complexity, and you asked in comments how to manually vectorize the code.

            I will explain that part, but one thing to bear in mind is that once you choose to manually vectorize the code, it will often be tied to certain CPU architectures. Let's not consider non-AMD64 compatible CPUs like ARM. Still, you have the option of MMX, SSE, AVX, and AVX512 to choose as an extension for vectorized computation, and each extension has multiple versions. If you want maximum portability, SSE2 is a reasonable choice. SSE2 appeared with Pentium 4, and it supports 128-bit vectors. For this post I'll use AVX2, which supports 128-bit and 256-bit vectors. It runs fine on your CPU, and has reasonable portability these days, supported from Haswell (2013) and Excavator (2015).

            The pattern you're using in the inner loop is called FMA (fused multiply and add). AVX2 has an instruction for this. Have a look at this function and the compiled output.



            Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)
            Asked 2022-Mar-08 at 14:22

            I am training a Unet segmentation model for binary class. The dataset is loaded in tensorflow data pipeline. The images are in (512, 512, 3) shape, masks are in (512, 512, 1) shape. The model expects the input in (512, 512, 3) shape. But I am getting the following error. Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)

            Here are the images in metadata dataframe.

            Randomly sampling the indices to select the training and validation set



            Answered 2022-Mar-08 at 13:38

            Use train_batches in and not train_images. Also, you do not need to use repeat(), which causes an infinite dataset if you do not specify how many times you want to repeat your dataset. Regarding your labels error, try rewriting your model like this:



            How can I return a multidimensional array from a shared library in C to python as an np.array?
            Asked 2022-Feb-21 at 10:17

            I am currently trying to figure a way to return a mutidimensional array (of doubles) from a shared library in C to python and make it an np.array. My current approach looks like this:

            shared library ("utils.c")



            Answered 2022-Feb-21 at 10:17

            First of all, a scoped C array allocated on the stack (like in somefunction) must never be returned by a function. The space of the stack will be reused by other function like the one of CPython for example. The returned array must be allocated on the heap instead.

            Moreover, writing a function working with Numpy arrays using ctypes is pretty cumbersome. As you found out, you need to pass the full shape in parameter. But the thing is you also need to pass the strides for each dimension and for each input arrays in parameter of the function since they may not be contiguous in memory (for example np.transpose change this). That being said, we can assume that the input array is contiguous for sake of performance and sanity. This can be enforced with np.ascontiguousarray. The pointer of the views a and b can be extracted using numpy.ctypeslib.as_ctypes, but hopefully ctype can do that automatically. Furthermore, the returned array is currently a C pointer and not a Numpy array. Thus, you need to create a Numpy array with the right shape and strides from it numpy.ctypeslib.as_array. Because the resulting shape is not known from the caller, you need to retrieve it from the callee function using several integer pointers (one per dimension). In the end, this results in a pretty-big ugly highly-bug-prone code (which will often silently crash if anything goes wrong not to mention the possible memory leaks if you do not pay attention to that). You can use Cython to do most of this work for you.

            Assuming you do not want to use Cython or you cannot, here is an example code with ctypes:



            Saving model on Tensorflow 2.7.0 with data augmentation layer
            Asked 2022-Feb-04 at 17:25

            I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.

            Here is the code of data augmentation:



            Answered 2022-Feb-04 at 17:25

            This seems to be a bug in Tensorflow 2.7 when using combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format"test", save_format='h5'):



            Why is creating a temp array with Stride illegal in an Array extension
            Asked 2022-Jan-21 at 20:20

            This code throws a compiler error: "Argument type 'StrideTo' expected to be an instance of a class or class-constrained type"



            Answered 2022-Jan-21 at 20:20

            That is a bug: SR-13847 Wrong generic used in extensions:

            For some reason when calling initializer in extension, compiler tries to match unrelated generics.

            In your case, Array is interpreted as Array. As a workaround, you can specify the type of the index array explicitly:



            Why would a much lighter Keras model run at the same speed at inference as the much larger original model?
            Asked 2022-Jan-11 at 21:51

            I trained a Keras model with the following architecture:



            Answered 2022-Jan-11 at 21:51

            The number of parameters is at most and indication how fast a model trains or runs inference. It might depend on many other factors.

            Here some examples, which might influence the throughput of your model:

            1. The activation function: ReLu activations are faster then e.g. ELU or GELU which have exponetial terms. Not only is computing an exponention number slower than a linear number, but also the gradient is much more complex to compute since in Case of Relu is constant number, the slope of the activation (e.g.1).
            2. the bit precission used for your data. Some HW accelerators can make faster computations in float16 than in float32 and also reading less bits decreses latency.
            3. Some layers might not have parameters but perform fixed calculations. Eventhough no parameter is added to the network's weight, a computation still is performed.
            4. The archetecture of your training HW. Certain filter sizes and batch sizes can be computed more efficiently than others.
            5. sometimes the speed of the computing HW is not the bottleneck, the input pipeline for loading and preprocessing your data

            It's hard to tell without testing but in your particular example I would guess, that the following might slow down your inference:

            1. large perceptive field with a 7x7 conv
            2. leaky_relu is slightly slower than relu
            3. Probably your data input pipeline is the bottleneck, not the inference speed. If the inference speed is much faster than the data preparation, it might appear that both models have the same speed. But in reality the HW is idle and waits for data.

            To understand whats going on, you could either change some parameters and evaluate the speed, or you could analyze your input pipeline by tracing your hardware using tensorboard. Here is a smal guide:

            Best, Sascha



            Multi Label Imbalanced dataset classification
            Asked 2022-Jan-09 at 21:17

            I am currently working on an multi label fashion item dataset which is highly imbalanced I tried using class_weights to tackle it, but still the accuracy is stuck at 0.7556 every epoch. Is there any way, I can avoid this problem. Did I implement the class weights in a wrong way? I tried using data augmentation too.

            I have like 224 unique classes in train set. And some of them have only one example which is very frustrating

            Tried to solve the problem with the help of this notebook as well, but I am unable to get the same accuracy score. Looks like, in this notebook the possibility of imbalance in the dataset is not considered.



            Answered 2022-Jan-09 at 21:17

            First of all, metrics such as Precision and Recall are focused on the positive class only, avoiding the problems encountered by multi-class focus metrics in the case of the class imbalance. Thus, we may not obtain enough information about the performance of the negative class if we keep considering all indicators. Haibo He et al suggest the metrics below to rate both items:

            1. Geometric Mean.
            2. F-Measure.
            3. Macro-Averaged Accuracy.
            4. Newer Combinations of Threshold Metrics: Mean-Class-Weighted Accuracy, Optimized Precision, Adjusted Geometric Mean, Index of Balanced Accuracy.

            My suggestions:

            1. Use the PR-curve and the F1-score.
            2. Try with geometric transformations, photometric transformations, random occlusions (to avoid overfitting), SMOTE, Tomek links (for undersampling majorities), etc.
            3. Random undersampling may delete relevant features of your dataset. Again, analyze your dataset using KNN and other similar techniques.
            4. Check this book: H. He and Y. Ma, Imbalanced Learning: Foundations, Algorithms, and Applications, Hoboken, New Jersey: Wiley-IEEE Press, 2013.



            Is it possible to use a collection of hyperspectral 1x1 pixels in a CNN model purposed for more conventional datasets (CIFAR-10/MNIST)?
            Asked 2021-Dec-17 at 09:08

            I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:



            Answered 2021-Dec-16 at 10:18

            If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

            I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

            Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

            That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

            Some further ideas:

            • If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
            • If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.



            How strides help in traversing an array in numpy?
            Asked 2021-Dec-16 at 09:27
            arr = np.arange(16).reshape((2, 2, 4))
            (32, 16, 4)


            Answered 2021-Dec-15 at 20:52

            In my experience working through the C code is too much work. Simply finding the relevant function(s) is the hardest part. strides work the same regardless of dimensions or 'transpose'.

            Start with something simpler, like a (2,3) array, whose transposed strides will be (8,24). Imagine stepping through the flat [0,1,2...].

            Sample array, with 1 byte size so the sequential stride will be just 1


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            include/imageupload.js in the MyFTPUploader module in Stride 1.0 contains sensitive information including FTP login credentials, which might allow remote attackers to gain unauthorized access to the FTP server being used by the module by viewing the source code.

            Install stride

            You can download it from GitHub.


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