future-image-similarity | This repository is for our CoRL 2019 paper

 by   anwu21 Python Version: Current License: No License

kandi X-RAY | future-image-similarity Summary

kandi X-RAY | future-image-similarity Summary

future-image-similarity is a Python library. future-image-similarity has no bugs, it has no vulnerabilities and it has low support. However future-image-similarity build file is not available. You can download it from GitHub.

This repository is for our CoRL 2019 paper:.
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              future-image-similarity has a low active ecosystem.
              It has 8 star(s) with 0 fork(s). There are 3 watchers for this library.
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              It had no major release in the last 6 months.
              future-image-similarity has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of future-image-similarity is current.

            kandi-Quality Quality

              future-image-similarity has no bugs reported.

            kandi-Security Security

              future-image-similarity has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              future-image-similarity does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              future-image-similarity releases are not available. You will need to build from source code and install.
              future-image-similarity has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed future-image-similarity and discovered the below as its top functions. This is intended to give you an instant insight into future-image-similarity implemented functionality, and help decide if they suit your requirements.
            • Load data
            • Train the model
            • Computes the KL divergence criterion
            • Plot the model
            • Construct a tensor from input images
            • Convert a tensor
            • Return True if arg is a sequence
            • Test function
            • Evaluate the similarity function of a sequence
            • Mean squared error
            • Perform a forward computation
            • Reparameters
            • Saves text with given text
            • Draw a text tensor
            • Saves tensors to a gif file
            • Creates a set of images
            • Load OOM from directory
            • Clears the progress bar
            • Get a generator of training data
            • Normalize data
            • Get a batch from the test loader
            Get all kandi verified functions for this library.

            future-image-similarity Key Features

            No Key Features are available at this moment for future-image-similarity.

            future-image-similarity Examples and Code Snippets

            No Code Snippets are available at this moment for future-image-similarity.

            Community Discussions

            No Community Discussions are available at this moment for future-image-similarity.Refer to stack overflow page for discussions.

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

            Vulnerabilities

            No vulnerabilities reported

            Install future-image-similarity

            Download the code git clone https://github.com/anwu21/future-image-similarity.git. Download the dataset from https://iu.box.com/s/m34dam93h1wxpu237ireq3kyh0oucc5c and place in the "data" folder to unzip. train_predictor.py contains the code to train the stochastic future image predictor. You will need to choose to train on the real life lab dataset or the simulated dataset: set the --dataset flag to either "lab_pose" or "gaz_pose" (ex. python3 train_predictor.py --dataset lab_pose). train_critic.py contains the code to train the critic. You may use either your newly trained predictor model or the pretrained predictor model contained in the "logs" folder. Make sure to set the --dataset flag to either "lab_value" or "gaz_value" (ex. python3 train_critic.py --dataset lab_value). Once you have trained a predictor and a critic, you can obtain the robot action by feeding an image and an array of N action candidates to the predictor. The optimal action is the candidate that leads to the highest value from the critic. action_example.py provides an example of obtaining the action.
            Download the code git clone https://github.com/anwu21/future-image-similarity.git
            Download the dataset from https://iu.box.com/s/m34dam93h1wxpu237ireq3kyh0oucc5c and place in the "data" folder to unzip.
            train_predictor.py contains the code to train the stochastic future image predictor. You will need to choose to train on the real life lab dataset or the simulated dataset: set the --dataset flag to either "lab_pose" or "gaz_pose" (ex. python3 train_predictor.py --dataset lab_pose).
            train_critic.py contains the code to train the critic. You may use either your newly trained predictor model or the pretrained predictor model contained in the "logs" folder. Make sure to set the --dataset flag to either "lab_value" or "gaz_value" (ex. python3 train_critic.py --dataset lab_value).
            Once you have trained a predictor and a critic, you can obtain the robot action by feeding an image and an array of N action candidates to the predictor. The optimal action is the candidate that leads to the highest value from the critic. action_example.py provides an example of obtaining the action.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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