pytorch_scatter | PyTorch Extension Library of Optimized Scatter Operations | Machine Learning library
kandi X-RAY | pytorch_scatter Summary
kandi X-RAY | pytorch_scatter Summary
PyTorch Extension Library of Optimized Scatter Operations
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Top functions reviewed by kandi - BETA
- 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
pytorch_scatter Key Features
pytorch_scatter Examples and Code Snippets
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
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
pip install .
pip install ".[full]"
pip uninstall gomatch
# 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()
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
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 =
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
Trending Discussions on pytorch_scatter
QUESTION
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 07:33You can vectorize your operation by creating a large block-diagonal matrix W
of shape n*k
x(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
:
QUESTION
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 09:54You are looking for index_add_
:
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