pytorch_scatter | PyTorch Extension Library of Optimized Scatter Operations | Machine Learning library

 by   rusty1s Python Version: 2.1.1 License: MIT

kandi X-RAY | pytorch_scatter Summary

kandi X-RAY | pytorch_scatter Summary

pytorch_scatter is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. pytorch_scatter has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However pytorch_scatter has 10 bugs. You can install using 'pip install pytorch_scatter' or download it from GitHub, PyPI.

PyTorch Extension Library of Optimized Scatter Operations
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            kandi-support Support

              pytorch_scatter has a medium active ecosystem.
              It has 1286 star(s) with 154 fork(s). There are 17 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 17 open issues and 299 have been closed. On average issues are closed in 138 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch_scatter is 2.1.1

            kandi-Quality Quality

              OutlinedDot
              pytorch_scatter has 10 bugs (1 blocker, 0 critical, 9 major, 0 minor) and 5 code smells.

            kandi-Security Security

              pytorch_scatter has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pytorch_scatter code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pytorch_scatter 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

              pytorch_scatter releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              pytorch_scatter saves you 576 person hours of effort in developing the same functionality from scratch.
              It has 1344 lines of code, 75 functions and 27 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pytorch_scatter and discovered the below as its top functions. This is intended to give you an instant insight into pytorch_scatter implemented functionality, and help decide if they suit your requirements.
            • Calculate timing
            • Scatters src by index
            • Execute the given function
            • Scatter the maximum value of src
            • Return a list of all supported extensions
            • Test whether a given dataset is consistent
            • Download a dataset
            • Calculate the scatter sum
            Get all kandi verified functions for this library.

            pytorch_scatter Key Features

            No Key Features are available at this moment for pytorch_scatter.

            pytorch_scatter Examples and Code Snippets

            Point-GNN,Getting Started,Prerequisites
            Pythondot img1Lines of Code : 7dot img1License : Permissive (MIT)
            copy iconCopy
            conda install pytorch torchvision
            
            
            pip3 install --user opencv-python
            pip3 install --user open3d-python==0.7.0.0
            pip3 install --user scikit-learn
            pip3 install --user tqdm
            pip3 install --user shapely
              
            Dynamic Plane Convolutional Occupancy Networks,Installation
            C++dot img2Lines of Code : 4dot img2License : Permissive (MIT)
            copy iconCopy
            conda env create -f environment.yaml
            conda activate dyn_conv_onet
            
            pip install torch-scatter==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
            
            python setup.py build_ext --inplace
              
            GoMatch: A Geometric-Only Matcher for Visual Localization,Installation
            Jupyter Notebookdot img3Lines of Code : 3dot img3License : Permissive (MIT)
            copy iconCopy
            pip install .
            
            pip install ".[full]"
            
            pip uninstall gomatch
              
            RunTimeError during one hot encoding
            Pythondot img4Lines of Code : 49dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # Learn with Label Propagation
            label_propagation = LabelPropagation(adj_matrix_t)
            print("Label Propagation: ", end="")
            label_propagation.fit(labels_t)
            label_propagation_output_labels = label_propagation.predict_classes()
            
            PyTorch torch_sparse installation without CUDA
            Pythondot img5Lines of Code : 6dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
            pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
            pip install --no-index torch-cluster -f https://py
            Pytorch: Node2Vec: TypeError: tuple indices must be integers or slices, not tuple
            Pythondot img6Lines of Code : 27dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def pos_sample(self, batch):
                batch = batch.repeat(self.walks_per_node)
                rowptr, col, _ = self.adj.csr()
                rw = random_walk(rowptr, col, batch, self.walk_length, self.p, self.q)
                if not isinstance(rw, torch.Tensor):
                    rw =
            Changing a Dockerfile from CUDA9 to CUDA10
            Pythondot img7Lines of Code : 16dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04
            
            RUN apt-get update && apt-get install -y --no-install-recommends \
                    apt-utils \
                    python3.6 \
                    python-dev \
                    python-pip \
                    python-setuptools \
                  

            Community Discussions

            QUESTION

            how to vectorize the scatter-matmul operation
            Asked 2020-Jan-08 at 08:43

            I have many matrices w1, w2, w3...wn with shapes (k*n1, k*n2, k*n3...k*nn) and x1, x2, x3...xn with shapes (n1*m, n2*m, n3*m...nn*m).
            I want to get w1@x1, w2@x2, w3@x3 ... respectively.

            The resulting matrix is multiple k*m matrices and can be concatenated into a large matrix with shape (k*n)*m.

            Multiply them one by one will be slow. How to vectorize this operation?

            Note: The input can be a k*(n1+n2+n3+...+nn) matrix and a (n1+n2+n3+...+nn)*m matrix, and we may use a batch index to indicate those submatrices.

            This operation is related to the scatter operations implemented in pytorch_scatter, so I refer it as "scatter_matmul".

            ...

            ANSWER

            Answered 2020-Jan-08 at 07:33

            You can vectorize your operation by creating a large block-diagonal matrix W of shape n*kx(n1+..+nn) where the w_i matrices are the blocks on the diagonal. Then you can vertically stack all x matrices into an X matrix of shape (n1+..+nn)xm. Multiplying the block diagonal W with the vertical stack of all x matrices, X:

            Source https://stackoverflow.com/questions/59640574

            QUESTION

            Pytorch differentiable conditional (index-based) sum
            Asked 2019-Sep-19 at 10:49

            I have an idx array like [0, 1, 0, 2, 3, 1] and another 2d array data like the following:

            ...

            ANSWER

            Answered 2019-Sep-19 at 09:54

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

            Vulnerabilities

            No vulnerabilities reported

            Install pytorch_scatter

            We provide pip wheels for all major OS/PyTorch/CUDA combinations, see here. To install the binaries for PyTorch 1.8.0, simply run. where ${CUDA} should be replaced by either cpu, cu101, cu102, or cu111 depending on your PyTorch installation. To install the binaries for PyTorch 1.7.0 and 1.7.1, simply run. where ${CUDA} should be replaced by either cpu, cu92, cu101, cu102, or cu110 depending on your PyTorch installation. Note: Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0 and PyTorch 1.6.0 (following the same procedure).

            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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            gh repo clone rusty1s/pytorch_scatter

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            git@github.com:rusty1s/pytorch_scatter.git

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