* mx tf relconv
* use method instead of private attr
* src and dst have different fc for gat
* update edgeconv
* change sage and sgconv
* no degree check on gin
* add remainding API doc
* fix pylint
* infer fc_src and fc_dst, only one tensor for block
* fix pytest
* go through gcn, relgcn
* fix tagconv formula
* fix doc in sageconv
* fix sgconv doc
* replace hat with tilde
* more comments on gmmconv
* fix agnnconv chebconv doc
* modify nnconv doc
* remove &
* add nn conv examples
* Rebase master
* More merge conflicts
* check homo
* add back self loop for some convs, check homo in tranform
* add example for denseconv
* add example and doc for dotgat and cfconv
* check in-degree for graphconv
* add language fix
* gconv address all comments
* another round of change based on api template
* change agnn
* go through agnn, appnp, atomic, cf, cheb, dense, gat, sage modules
* finish pytorch part of nn conv
* mxnet graphconv done
* tensorflow graphconv works
* add new modules into doc
* add comments to not split code
* refine doc
* resr
* more comments
* more fix
* finish conv and dense conv part api
* pylint fix
* fix pylink
* fix pylint
* more fix
* fix
* fix test fail because zere in degree
* fix test fail
* sage is not update for mxnet tf
Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-81.us-east-2.compute.internal>
* slice dstdata from srcdata within nn module
* a bunch of fixes
* add comment
* fix gcmc layer
* repr for blocks
* fix
* fix context
* fix
* do not copy internal columns
* docstring
* New version for the convolutional layer
* Minor changes
* Minor changes
* Update python/dgl/nn/pytorch/conv/chebconv.py
Co-authored-by: Zihao Ye <zihaoye.cs@gmail.com>
* Resolved variable miss-naming, import simplifying and raising warning
* Added dg_warnings instead of warnings.warn
* add doc
* upd
* upd
Co-authored-by: Zihao Ye <zihaoye.cs@gmail.com>
Co-authored-by: yzh119 <expye@outlook.com>
* remove edge and to bipartite and graphsage with sampling
* fixes
* fixes
* fixes
* reenable multigpu training
* fixes
* compatibility in DGLGraph
* rename to compact_as_bipartite
* bugfix
* lint
* add offline inference
* skip GPU tests
* fix
* addresses comments
* fix
* fix
* fix
* more tests
* more docs and unit tests
* workaround for empty slice on empty data
* Fix RelGraphConv when num_bases is None
* Improve error message in when num_bases is wrong
* Fix integer modulo by zero exception when num_bases is 0
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
* upd
* fig edgebatch edges
* add test
* trigger
* Update README.md for pytorch PinSage example.
Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.
* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.
1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
test/pytorch/test_nn.py work on both CPU and GPU
* Fix style
* Delete unused code
* Make agnostic test only related to tests/backend
1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu
* Fix code style
* fix
* doc
* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.
* Fix syntex
* Remove rand
* Add TAGCN nn.module and example
* Now tagcn can run on CPU.
* Add unitest for TGConv
* Fix style
* For pubmed dataset, using --lr=0.005 can achieve better acc
* Fix style
* Fix some descriptions
* trigger
* Fix doc
* Add nn.TGConv and example
* Fix bug
* Update data in mxnet.tagcn test acc.
* Fix some comments and code
* delete useless code
* Fix namming
* Fix bug
* Fix bug
* Add test code for mxnet TAGCov
* Update some docs
* Fix some code
* Update docs dgl.nn.mxnet
* Update weight init
* Fix
* upd
* fig edgebatch edges
* add test
* trigger
* Update README.md for pytorch PinSage example.
Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.
* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.
1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
test/pytorch/test_nn.py work on both CPU and GPU
* Fix style
* Delete unused code
* Make agnostic test only related to tests/backend
1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu
* Fix code style
* fix
* doc
* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.
* Fix syntex
* Remove rand
* Add TAGCN nn.module and example
* Now tagcn can run on CPU.
* Add unitest for TGConv
* Fix style
* For pubmed dataset, using --lr=0.005 can achieve better acc
* Fix style
* Fix some descriptions
* trigger
* Fix doc
* upd
* fig edgebatch edges
* add test
* trigger
* Update README.md for pytorch PinSage example.
Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.
* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.
1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
test/pytorch/test_nn.py work on both CPU and GPU
* Fix style
* Delete unused code
* Make agnostic test only related to tests/backend
1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu
* Fix code style
* fix
* doc
* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.
* Fix syntex
* Remove rand
Tianjun Xiao