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Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This is a port of our original code from Tensorflow to PyTorch.
The code is a lot faster and cleaner compared to the original code base. The results are a little different from the ones reported in the paper. In particular, the performance is a little lower for low occlusion and higher for stronger occlusion.
On average the results are slightly better than reported in the paper. Training CompositionalNets for other backbones and layers should be possible but has not been extensively tested so far.
The code uses Python 3. Download pretrained CompNet weights from here and copy them inside the models folder. The repositroy contains a few images for the demo script. If you want to evaluate on the full datasets used in our paper you need to download the data here and copy it inside the data folder. CompNets require a tight crop of the object in the image. Our demo script classifies the images from the demo folder, extracts the predicted location of occluders, and writes the results back into the demo folder.
This will output qualitative occlusion localization results for each image and a quantitative analysis over all images as ROC curve. We initialize CompositionalNets i. In particular, we initialize the vMF kernels by clustering the feature vectors:.
Furthermore, we initialize the mixture models by EM-type learning. The initial cluster assignment for the EM-type learning is computed based on the similarity of the vMF encodings of the training images. To compute the similarity matrices use:.
Afterwards you can compute the initialization of the mixture models by executing:.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
If nothing happens, download the GitHub extension for Visual Studio and try again. This is largely unsupported software, no extensions are planned and development has stopped. It is however fully functional and has served as a test-bed for compositional models between and Designing a Python library like this that is performant, easy to debug and elegant is difficult. The main author has now switched to using Julia for most of his research, but it is not entirely unlikely that he may take another stab at a library similar to nerv, if time permits it, over the next few years.
We will use a Stanford Sentiment Treebank -like example where we have the phrase "This burger isn't bad" and annotate each sub-phrase of the sentence with a sentiment label. Once we have created this compositional structure, we will show how to train a model to predict the composition and sentiment of the example sentence.
On a reasonably modern Debian-based system, use the following to install all of the required dependencies:. Deep Learning, in particular feature learning, allows for an amasing new set of models for composition, especially for Natural Language Processing tasks. However, a vast majority of the community is used to the standard feature engineering, then throwing things into a Support Vector Machine or linear classifier development cycle.
We want to change this, while it may never become as easy as generating a simple sparse matrix that is fed into an external linear classifier, we will do our utmost to bring the rest of the community on-board without having to read highly optimised research code or deriving methods from papers. If you find this library useful in some way and want to provide academic credit, the best way to do so is probably to cite.
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We use analytics cookies to understand how you use our websites so we can make them better, e.We have moved all code to a git repository, which provides many advantages in the development process. The SVN repository will no longer be maintained. Added a new module which provides an interface for pyCPA. This new interface allows to perform worst-case timing analysis to acquire metrics such as worst-case response times of tasks in SMFF models.
This can be used within the generation process to e. Another option is to use the pyCPA analysis as reference for the evaluation of timing analysis approaches.
You could also use SMFF and pyCPA to develop optimization algorithms for real-time systems without having to implement your own timing analysis. Evaluation of scheduling, allocation or performance verification algorithms requires either analytical performance estimations or a large number of testcases.
In many cases, e. Oftentimes realistic models of such systems are not available to the developer in large numbers. The generated system models can be used for evaluation of scheduling, allocation or performance verification algorithms. As requirements for the generated systems are domain-specific the framework is implemented in a modular way, such that the model is extendible and each step of the model generation can be exchanged by a custom implementation.
During the development of e. If formal proofs of correctness or analytically derived performance estimations can be given a small set of such systems is sufficient. However, in many cases this is not possible e. In this case the algorithm has to be tested with an extensive set of testcases. For many algorithm developers, especially in academia, system models are not available in large numbers.
Manually creating such system models is very time-consuming and might not respect requirements on randomness. Consider the following example. A developer has implemented a heuristic algorithm for optimized priority assignment in distributed real-time systems.
As the algorithm is based on a heuristic, no analytical estimation of the performance of the algorithm can be given. Thus the developer has to evaluate the algorithm against a set of testcases. As no extensive set of testcases is available to the developer, the system models have to be generated automatically. These models however need to resemble real-world systems, as typical for the targeted domain. Furthermore, they need to be sufficiently random, in order not to bias the evaluation.
In this paper we address this issue and present SMFF - a framework for parameter-driven generation of models of distributed real-time systems. The generated models incorporate a description of the platform, of the software applications mapped onto the platform and the associated scheduling and timing parameters, thus covering the entire model specification.
As system models, that are used for algorithm evaluation, have to resemble real-world systems, requirements on testcase systems may be highly domain- and problem-specific. The presented framework provides a high degree of modularity, allowing the user to extend the system-model and to replace algorithms for system model generation, thus making the framework a universal tool for testcase generation.
The algorithms presented in this paper are example implementations and were developed for the evaluation of an algorithm to find execution priorities in static-priority-preemptively SPP scheduled systems under consideration of timing constraints.
The SMFF framework is no simulation or benchmarking environment. Thus, it does not address the issues of simulation or performance monitoring. It rather provides models as input for such tools. Important Notice We have moved all code to a git repository, which provides many advantages in the development process.
Latest Changes - Introduction Evaluation of scheduling, allocation or performance verification algorithms requires either analytical performance estimations or a large number of testcases.The full source code of the example is shown at the end of this section. It rather is a package of methods and classes which can be embedded into your pyhon application - the spp example is such an example application.
The architecture can be entered in two ways, either you provide it with the source code or you can use an XML loader such as the Symta or the SMFF loader. However, in most cases it is sufficient to code your architecture directly in a python file. For this example we assume that our architecture consists of two resources e.
CPUs scheduled by an static-priority-preemptive SPP scheduler and four tasks of which some communicate by event-triggering.
The environment stimulus e. The application graph is shown on on the right. Before we actually start with the program, we import all pycpa modules which are needed for this example.
The interesting module are pycpa. This will parse the pyCPA related options such as the propagation method, verbosity, maximum-busy window, etc. Conveniently, this also prints the options which will be used for your pyCPA session. This is handy, when you run some analyses in batch jobs and want are uncertain about the exact settings after a few weeks.
However, the explicit call of this function is not necessary most of the time, as it is being implicitly called at the beginning of the analysis. It can be useful to control the exact time where the initialization happens in case you want to manually override some options from your code. The next step is to create two resources R1 and R2 and bind them to the system via pycpa. When creating a resource via pycpa. Resourcethe first argument of the constructor sets the resource id a string and the second defines the scheduling policy.
The scheduling policy is defined by a reference to an instance of a scheduler class derived from pycpa. For SPP, this is pycpa. In this class, different functions are defined which for instance compute the multiple-event busy window on that resource or the stopping condition for that particular scheduling policy.
The stopping condition specifies how many activations of a task have to be considered for the analysis. The default implementations of these functions from pycpa. Scheduler can be used for certain schedulers, but generally should be overridden by scheduler-specific versions. For SPP we have to look at all activations which fall in the level-i busy window, thus we choose the spp stopping condition. The next part is to create tasks via pycpa.
Resource and bind them to a resource via pycpa.Sebastian Witowski - Writing faster Python
In case tasks communicate with each other through event propagation e. Then, we plot the taskgraph to a pdf file by using pycpa. The analysis is performed by calling pycpa. This will will find the fixed-point of the scheduling problem and terminate if a result was found or if the system is not feasible e.To browse Academia.
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Using the Performance Analysis of Logs (PAL) Tool
Philip Axer. Nevertheless, py- same time, many application fields such as safety-critical systems CPA is built in a modular fashion and can easily be extended require a verification of worst-case timing behavior.
Deriving sound guarantees is a complex task, which can be solved by to support such protocols, too. This approach formally maximum performance, and it is not overly fine-tuned to keep computes worst-case timing scenarios on each component of the the implementation simple and comprehensible. Only obvious system and derives end-to-end system timing from these local performance tweaks are included.
In this paper, we present pyCPA, an open-source imple- The remainder of the paper is organized as follows: In mentation of the Compositional Performance Analysis approach. Targeted towards academia, pyCPA offers features such as Section II, we give an overview of real-time analysis ap- support for the most common real-time schedulers, path analysis proaches and corresponding analysis tools.
Then, in Section for communicating tasks, import and export functionality, and III we elaborate on the system model as used in CPA and different visualizations. After the formal foundation to the research domain. Finally, we conclude the paper in are subject to hard real-time constraints where it must be Section VII. In most cases, it is not straightforward to show that There are different approaches for formal analysis of worst- all timing requirements are satisfied under all circumstances.
Exact approaches like Research in the field of real-time performance analysis and Uppaal  use model checking techniques to derive the worst- worst-case execution time analysis provided various formal case timing of a system.
This can be very expensive in terms approaches such as compositional performance analysis CPA of run-time and memory for larger realistic systems. Holistic  to solve this problem.
CPA breaks down the analysis approaches such as  have similar issues. Compositional complexity of large systems into separate local component approaches like Real-Time-Calculus  and Compositional analyses and provides a way to integrate local performance Performance Analysis CPA  solve this by decomposing analysis techniques into a system-level analysis. This papaer presents pyCPA1an easy-to-understand and the analysis of the system at component level. They use easy-to-extend Python implementation of CPA.
To our knowl- abstract event models to describe the interaction of com- edge, pyCPA is the only free as in speech implementation of ponents in the worst- and best-case. Event models describe the CPA approach. This can lead to of CPA, or simple reference benchmarks for novel analysis pessimism in the analysis, but avoids the state space explosion methodologies. To ease interaction with other toolkits, pyCPA from which holistic approaches suffer. Here, forks are possible, i.
The opposite, i.
Computer-Assisted Composition with Python
Toolbox implementing Real-Time-Calculus.The PAL Performance Analysis of Logs tool reads in a performance monitor counter log any known format and analyzes it using complex, but known thresholds provided.
The tool generates an HTML based report that graphically charts important performance counters and throws alerts when thresholds are exceeded. The thresholds are originally based on thresholds defined by the Microsoft product teams, including BizTalk Server, and members of Microsoft support. This tool is not a replacement of traditional performance analysis, but it automates the analysis of performance counter logs enough to help save you time. The PAL tool:.
Identifies BizTalk Server and operating system performance counter bottlenecks by analyzing for thresholds. It requires Microsoft Log Parser. You may want to use this tool to query a significant amount of logging information. The PAL tool analyzes performance counter logs only in English language. To use the PAL tool with performance counter logs in other languages, you must first translate the logs to English language.
This topic is long so that comprehensive information about the PAL tool can be contained in one place for easy reference.
The most reliable way for Windows to detect a disk performance bottleneck is by measuring its response times. If the response times are greater than. Common causes of poor disk latency are disk fragmentation, performance cache, an over saturated SAN, and too much load on the disk.
Use the SPA tool to help identify the top files and processes using the disk. Keep in mind that performance monitor counters are unable to specify which files are involved. If this is true, then we should expect the disk transfers per second to be at or above If not, then the disk architecture needs to be investigated. The Virtual Memory Manager continually adjusts the space used in physical memory and on disk to maintain a minimum number of available bytes for the operating system and processes.
When available bytes are plentiful, the Virtual Memory Manager lets the working sets of processes grow, or keeps them stable by removing an old page for each new page added. When available bytes are few, the Virtual Memory Manager must trim the working sets of processes to maintain the minimum required.
This analysis checks to see whether the total available memory is low — Warning at 10 percent available and Critical at 5 percent available. Low physical memory can cause increased privileged mode CPU and system delays.
This analysis determines whether any of the processes are consuming a large amount of the system's memory and whether the process is increasing in memory consumption over time. A process consuming large portions of memory is okay as long as the process returns the memory back to the system.The closer the score is to 1, the more anomalous the instance being scored is.
That is, how much each value in the input data contributed to the score. You can also list all of your anomaly scores. You can use curl to customize new anomaly scores. Once an anomaly score has been successfully created it will have the following properties. Creating an anomaly score is a near real-time process that take just a few seconds depending on whether the corresponding anomaly has been used recently and the workload of BigML's systems.
The anomaly score goes through a number of states until its fully completed. Through the status field in the anomaly score you can determine when the anomaly score has been fully processed and ready to be used.
Most of the times anomaly scores are fully processed and the output returned in the first call. These are the properties that an anomaly score's status has:To update an anomaly score, you need to PUT an object containing the fields that you want to update to the anomaly score' s base URL. Once you delete an anomaly score, it is permanently deleted.
If you try to delete an anomaly score a second time, or an anomaly score that does not exist, you will receive a "404 not found" response. However, if you try to delete an anomaly score that is being used at the moment, then BigML.
To list all the anomaly scores, you can use the anomalyscore base URL. By default, only the 20 most recent anomaly scores will be returned. You can get your list of anomaly scores directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your anomaly scores. Association Sets are useful to know which items have stronger associations with a given set of values for your fields. The similarity score then is multiplied by the selected association measure (confidence, leverage, support, lift, or coverage) to create a similarity-weighted score and finally return a ranking of the predicted items.
You can also list all of your association sets. You can use curl to customize new association sets. Once an association set has been successfully created it will have the following properties. Creating an association set is a near real-time process that take just a few seconds depending on whether the corresponding association has been used recently and the workload of BigML's systems.
The association set goes through a number of states until its fully completed. Through the status field in the association set you can determine when the association set has been fully processed and ready to be used. Most of the times association sets are fully processed and the output returned in the first call.
These are the properties that an association set's status has:To update an association set, you need to PUT an object containing the fields that you want to update to the association set' s base URL. Once you delete an association set, it is permanently deleted.
If you try to delete an association set a second time, or an association set that does not exist, you will receive a "404 not found" response. However, if you try to delete an association set that is being used at the moment, then BigML. To list all the association sets, you can use the associationset base URL. By default, only the 20 most recent association sets will be returned. You can get your list of association sets directly in your browser using your own username and API key with the following links.
You can also paginate, filter, and order your association sets.