SYMBOL INDEX (12494 symbols across 1403 files) FILE: benchmarks/benchmarks/api/bench_add_self_loop.py function track_time (line 15) | def track_time(graph_name, format): FILE: benchmarks/benchmarks/api/bench_batch.py function track_time (line 12) | def track_time(batch_size): FILE: benchmarks/benchmarks/api/bench_builtin_apply_edges.py function track_time (line 17) | def track_time(graph_name, format, feat_size, reduce_type): FILE: benchmarks/benchmarks/api/bench_builtin_apply_edges_hetero.py function track_time (line 17) | def track_time(num_relations, format, feat_size, reduce_type): FILE: benchmarks/benchmarks/api/bench_builtin_multi_update_all.py function track_time (line 15) | def track_time(feat_size, num_relations, multi_reduce_type): FILE: benchmarks/benchmarks/api/bench_builtin_update_all_coo.py function track_time (line 18) | def track_time(graph_name, format, feat_size, msg_type, reduce_type): FILE: benchmarks/benchmarks/api/bench_builtin_update_all_csc.py function track_time (line 18) | def track_time(graph_name, format, feat_size, msg_type, reduce_type): FILE: benchmarks/benchmarks/api/bench_edge_ids.py function track_time (line 19) | def track_time(graph_name, format, fraction, return_uv): FILE: benchmarks/benchmarks/api/bench_edge_subgraph.py function track_time (line 16) | def track_time(graph_name, format, seed_egdes_num): FILE: benchmarks/benchmarks/api/bench_find_edges.py function track_time (line 16) | def track_time(graph_name, format, fraction): FILE: benchmarks/benchmarks/api/bench_format_conversion.py function track_time (line 27) | def track_time(graph_name, format): FILE: benchmarks/benchmarks/api/bench_fused_sample_neighbors.py function track_time (line 18) | def track_time(graph_name, format, seed_nodes_num, fanout): FILE: benchmarks/benchmarks/api/bench_heterograph_construction.py function track_time (line 14) | def track_time(num_relations): FILE: benchmarks/benchmarks/api/bench_homograph_edge_construction.py function track_time (line 15) | def track_time(size): FILE: benchmarks/benchmarks/api/bench_homograph_scipy_construction.py function track_time (line 16) | def track_time(size, scipy_format): FILE: benchmarks/benchmarks/api/bench_in_degrees.py function track_time (line 18) | def track_time(graph_name, format, fraction): FILE: benchmarks/benchmarks/api/bench_in_edges.py function track_time (line 18) | def track_time(graph_name, format, fraction): FILE: benchmarks/benchmarks/api/bench_in_subgraph.py function track_time (line 16) | def track_time(graph_name, format, seed_nodes_num): FILE: benchmarks/benchmarks/api/bench_khop.py function track_time (line 15) | def track_time(graph_name, format, k): FILE: benchmarks/benchmarks/api/bench_knn_graph.py function track_time (line 22) | def track_time(size, dim, k, algorithm): FILE: benchmarks/benchmarks/api/bench_metis_partition.py function track_time (line 15) | def track_time(graph_name, k): FILE: benchmarks/benchmarks/api/bench_nn_graphconv.py function track_time (line 19) | def track_time(graph_name, feat_dim, aggr_type): FILE: benchmarks/benchmarks/api/bench_nn_heterographconv.py function track_time (line 18) | def track_time(feat_dim, num_relations): FILE: benchmarks/benchmarks/api/bench_node_subgraph.py function track_time (line 16) | def track_time(graph_name, format, seed_nodes_num): FILE: benchmarks/benchmarks/api/bench_random_walk.py function _random_walk (line 10) | def _random_walk(g, seeds, length): function _node2vec (line 14) | def _node2vec(g, seeds, length): function track_time (line 24) | def track_time(graph_name, num_seeds, length, algorithm): FILE: benchmarks/benchmarks/api/bench_readout.py function track_time (line 15) | def track_time(batch_size, feat_size, readout_op, type): FILE: benchmarks/benchmarks/api/bench_reverse.py function track_time (line 15) | def track_time(graph_name, format): FILE: benchmarks/benchmarks/api/bench_sample_neighbors.py function track_time (line 18) | def track_time(graph_name, format, seed_nodes_num, fanout): FILE: benchmarks/benchmarks/api/bench_to_block.py function track_time (line 16) | def track_time(graph_name, num_seed_nodes, fanout): FILE: benchmarks/benchmarks/api/bench_udf_apply_edges.py function track_time (line 17) | def track_time(graph_name, format, feat_size, reduce_type): FILE: benchmarks/benchmarks/api/bench_udf_multi_update_all.py function track_time (line 16) | def track_time(feat_size, num_relations, multi_reduce_type): FILE: benchmarks/benchmarks/api/bench_udf_update_all.py function track_time (line 18) | def track_time(graph_name, format, feat_size, msg_type, reduce_type): FILE: benchmarks/benchmarks/api/bench_unbatch.py function track_time (line 12) | def track_time(batch_size): FILE: benchmarks/benchmarks/kernel/bench_edgesoftmax.py function track_time (line 14) | def track_time(graph, num_heads): FILE: benchmarks/benchmarks/kernel/bench_gsddmm_u_dot_v.py function calc_gflops (line 10) | def calc_gflops(graph, feat_size, num_heads, time): function track_flops (line 27) | def track_flops(graph, feat_size, num_heads): FILE: benchmarks/benchmarks/kernel/bench_gspmm_copy_u.py function calc_gflops (line 10) | def calc_gflops(graph, feat_size, time): function track_flops (line 18) | def track_flops(graph, feat_size, reducer): FILE: benchmarks/benchmarks/kernel/bench_gspmm_u_mul_e_sum.py function calc_gflops (line 10) | def calc_gflops(graph, feat_size, num_heads, time): function track_flops (line 27) | def track_flops(graph, feat_size, num_heads): FILE: benchmarks/benchmarks/model_acc/bench_gat.py class GAT (line 10) | class GAT(nn.Module): method __init__ (line 11) | def __init__( method forward (line 70) | def forward(self, g, inputs): function evaluate (line 79) | def evaluate(model, g, features, labels, mask): function track_acc (line 92) | def track_acc(data): FILE: benchmarks/benchmarks/model_acc/bench_gcn.py class GCN (line 10) | class GCN(nn.Module): method __init__ (line 11) | def __init__( method forward (line 27) | def forward(self, g, features): function evaluate (line 36) | def evaluate(model, g, features, labels, mask): function track_acc (line 49) | def track_acc(data): FILE: benchmarks/benchmarks/model_acc/bench_gcn_udf.py class GraphConv (line 9) | class GraphConv(nn.Module): method __init__ (line 10) | def __init__(self, in_dim, out_dim, activation=None): method forward (line 20) | def forward(self, graph, feat): method mfunc (line 36) | def mfunc(self, edges): method rfunc (line 39) | def rfunc(self, nodes): class GCN (line 45) | class GCN(nn.Module): method __init__ (line 46) | def __init__( method forward (line 62) | def forward(self, g, features): function evaluate (line 71) | def evaluate(model, g, features, labels, mask): function track_acc (line 84) | def track_acc(data): FILE: benchmarks/benchmarks/model_acc/bench_rgcn_base.py function track_acc (line 12) | def track_acc(dataset, ns_mode): FILE: benchmarks/benchmarks/model_acc/bench_rgcn_ns.py class EntityClassify (line 17) | class EntityClassify(nn.Module): method __init__ (line 41) | def __init__( method forward (line 110) | def forward(self, blocks, feats, norm=None): class RelGraphEmbedLayer (line 121) | class RelGraphEmbedLayer(nn.Module): method __init__ (line 142) | def __init__( method forward (line 177) | def forward(self, node_ids, node_tids, type_ids, features): function evaluate (line 212) | def evaluate(model, embed_layer, eval_loader, node_feats): function track_acc (line 239) | def track_acc(data): FILE: benchmarks/benchmarks/model_acc/bench_sage.py class GraphSAGE (line 10) | class GraphSAGE(nn.Module): method __init__ (line 11) | def __init__( method forward (line 36) | def forward(self, graph, inputs): function evaluate (line 46) | def evaluate(model, g, features, labels, mask): function track_acc (line 59) | def track_acc(data): FILE: benchmarks/benchmarks/model_acc/bench_sage_ns.py class SAGE (line 15) | class SAGE(nn.Module): method __init__ (line 16) | def __init__( method forward (line 31) | def forward(self, blocks, x): method inference (line 40) | def inference(self, g, x, batch_size, device): function compute_acc (line 87) | def compute_acc(pred, labels): function evaluate (line 95) | def evaluate(model, g, inputs, labels, val_nid, batch_size, device): function load_subtensor (line 112) | def load_subtensor(g, seeds, input_nodes, device): function track_acc (line 123) | def track_acc(data): FILE: benchmarks/benchmarks/model_speed/bench_gat.py class GAT (line 13) | class GAT(nn.Module): method __init__ (line 14) | def __init__( method forward (line 73) | def forward(self, g, inputs): function track_time (line 84) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_gat_ns.py class GAT (line 16) | class GAT(nn.Module): method __init__ (line 17) | def __init__( method forward (line 68) | def forward(self, blocks, x): function load_subtensor (line 78) | def load_subtensor(g, seeds, input_nodes, device): function track_time (line 89) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_gcn_udf.py class GraphConv (line 12) | class GraphConv(nn.Module): method __init__ (line 13) | def __init__(self, in_dim, out_dim, activation=None): method forward (line 23) | def forward(self, graph, feat): method mfunc (line 39) | def mfunc(self, edges): method rfunc (line 42) | def rfunc(self, nodes): class GCN (line 48) | class GCN(nn.Module): method __init__ (line 49) | def __init__( method forward (line 65) | def forward(self, g, features): function track_time (line 76) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_pinsage.py function _init_input_modules (line 17) | def _init_input_modules(g, ntype, textset, hidden_dims): class BagOfWordsPretrained (line 52) | class BagOfWordsPretrained(nn.Module): method __init__ (line 53) | def __init__(self, field, hidden_dims): method forward (line 69) | def forward(self, x, length): class BagOfWords (line 78) | class BagOfWords(nn.Module): method __init__ (line 79) | def __init__(self, field, hidden_dims): method forward (line 89) | def forward(self, x, length): class WeightedSAGEConv (line 93) | class WeightedSAGEConv(nn.Module): method __init__ (line 94) | def __init__(self, input_dims, hidden_dims, output_dims, act=F.relu): method reset_parameters (line 103) | def reset_parameters(self): method forward (line 110) | def forward(self, g, h, weights): class SAGENet (line 133) | class SAGENet(nn.Module): method __init__ (line 134) | def __init__(self, hidden_dims, n_layers): method forward (line 150) | def forward(self, blocks, h): class LinearProjector (line 157) | class LinearProjector(nn.Module): method __init__ (line 162) | def __init__(self, full_graph, ntype, textset, hidden_dims): method forward (line 170) | def forward(self, ndata): class ItemToItemScorer (line 190) | class ItemToItemScorer(nn.Module): method __init__ (line 191) | def __init__(self, full_graph, ntype): method _add_bias (line 197) | def _add_bias(self, edges): method forward (line 202) | def forward(self, item_item_graph, h): class PinSAGEModel (line 215) | class PinSAGEModel(nn.Module): method __init__ (line 216) | def __init__(self, full_graph, ntype, textsets, hidden_dims, n_layers): method forward (line 223) | def forward(self, pos_graph, neg_graph, blocks): method get_repr (line 229) | def get_repr(self, blocks): function compact_and_copy (line 235) | def compact_and_copy(frontier, seeds): class ItemToItemBatchSampler (line 244) | class ItemToItemBatchSampler(IterableDataset): method __init__ (line 245) | def __init__(self, g, user_type, item_type, batch_size): method __iter__ (line 253) | def __iter__(self): class NeighborSampler (line 271) | class NeighborSampler(object): method __init__ (line 272) | def __init__( method sample_blocks (line 301) | def sample_blocks(self, seeds, heads=None, tails=None, neg_tails=None): method sample_from_item_pairs (line 323) | def sample_from_item_pairs(self, heads, tails, neg_tails): function assign_simple_node_features (line 339) | def assign_simple_node_features(ndata, g, ntype, assign_id=False): function assign_textual_node_features (line 350) | def assign_textual_node_features(ndata, textset, ntype): function assign_features_to_blocks (line 385) | def assign_features_to_blocks(blocks, g, textset, ntype): class PinSAGECollator (line 394) | class PinSAGECollator(object): method __init__ (line 395) | def __init__(self, sampler, g, ntype, textset): method collate_train (line 401) | def collate_train(self, batches): method collate_test (line 411) | def collate_test(self, samples): function track_time (line 420) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_rgcn_base.py function track_time (line 12) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_rgcn_hetero_ns.py class RelGraphConvLayer (line 17) | class RelGraphConvLayer(nn.Module): method __init__ (line 42) | def __init__( method forward (line 102) | def forward(self, g, inputs): class RelGraphEmbed (line 149) | class RelGraphEmbed(nn.Module): method __init__ (line 152) | def __init__( method forward (line 188) | def forward(self, block=None): class EntityClassify (line 217) | class EntityClassify(nn.Module): method __init__ (line 218) | def __init__( method forward (line 281) | def forward(self, h, blocks): function track_time (line 289) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_rgcn_homogeneous_ns.py class EntityClassify (line 17) | class EntityClassify(nn.Module): method __init__ (line 41) | def __init__( method forward (line 110) | def forward(self, blocks, feats, norm=None): class RelGraphEmbedLayer (line 121) | class RelGraphEmbedLayer(nn.Module): method __init__ (line 142) | def __init__( method forward (line 177) | def forward(self, node_ids, node_tids, type_ids, features): function track_time (line 214) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_sage.py class GraphSAGE (line 13) | class GraphSAGE(nn.Module): method __init__ (line 14) | def __init__( method forward (line 39) | def forward(self, graph, inputs): function track_time (line 51) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_sage_ns.py class SAGE (line 15) | class SAGE(nn.Module): method __init__ (line 16) | def __init__( method forward (line 31) | def forward(self, blocks, x): function load_subtensor (line 41) | def load_subtensor(g, seeds, input_nodes, device): function track_time (line 52) | def track_time(data): FILE: benchmarks/benchmarks/model_speed/bench_sage_unsupervised_ns.py class NegativeSampler (line 16) | class NegativeSampler(object): method __init__ (line 17) | def __init__(self, g, k, neg_share=False): method __call__ (line 22) | def __call__(self, g, eids): function load_subtensor (line 34) | def load_subtensor(g, input_nodes, device): class SAGE (line 42) | class SAGE(nn.Module): method __init__ (line 43) | def __init__( method forward (line 58) | def forward(self, blocks, x): function load_subtensor (line 68) | def load_subtensor(g, input_nodes, device): class CrossEntropyLoss (line 76) | class CrossEntropyLoss(nn.Module): method forward (line 77) | def forward(self, block_outputs, pos_graph, neg_graph): function track_time (line 99) | def track_time(data, num_negs, batch_size): FILE: benchmarks/benchmarks/multigpu/bench_multigpu_rgcn.py class EntityClassify (line 31) | class EntityClassify(nn.Module): method __init__ (line 32) | def __init__( method forward (line 101) | def forward(self, blocks, feats, norm=None): function gen_norm (line 112) | def gen_norm(g): class NeighborSampler (line 123) | class NeighborSampler: method __init__ (line 124) | def __init__(self, g, target_idx, fanouts): method sample_blocks (line 129) | def sample_blocks(self, seeds): function run (line 149) | def run(proc_id, n_gpus, n_cpus, args, devices, dataset, split, queue=No... function track_time (line 363) | def track_time(data, dgl_sparse): function config (line 539) | def config(): FILE: benchmarks/benchmarks/multigpu/bench_multigpu_sage.py class SAGE (line 20) | class SAGE(nn.Module): method __init__ (line 21) | def __init__( method forward (line 36) | def forward(self, blocks, x): function load_subtensor (line 46) | def load_subtensor(nfeat, labels, seeds, input_nodes, dev_id): function run (line 57) | def run(result_queue, proc_id, n_gpus, args, devices, data): function track_time (line 150) | def track_time(data): FILE: benchmarks/benchmarks/multigpu/rgcn_model.py class BaseRGCN (line 6) | class BaseRGCN(nn.Module): method __init__ (line 7) | def __init__( method build_model (line 33) | def build_model(self): method build_input_layer (line 48) | def build_input_layer(self): method build_hidden_layer (line 51) | def build_hidden_layer(self, idx): method build_output_layer (line 54) | def build_output_layer(self): method forward (line 57) | def forward(self, g, h, r, norm): function initializer (line 63) | def initializer(emb): class RelGraphEmbedLayer (line 68) | class RelGraphEmbedLayer(nn.Module): method __init__ (line 89) | def __init__( method dgl_emb (line 132) | def dgl_emb(self): method forward (line 140) | def forward(self, node_ids, node_tids, type_ids, features): FILE: benchmarks/benchmarks/rgcn.py class RGCN (line 10) | class RGCN(nn.Module): method __init__ (line 11) | def __init__( method forward (line 42) | def forward(self, g, nids=None): function load_data (line 58) | def load_data(data_name, get_norm=False, inv_target=False): FILE: benchmarks/benchmarks/utils.py function _download (line 20) | def _download(url, path, filename): function thread_wrapped_func (line 42) | def thread_wrapped_func(func): function get_graph (line 71) | def get_graph(name, format=None): function get_ogb_graph (line 118) | def get_ogb_graph(name): function get_livejournal (line 124) | def get_livejournal(): function get_friendster (line 145) | def get_friendster(): class OGBDataset (line 166) | class OGBDataset(object): method __init__ (line 167) | def __init__(self, g, num_labels, predict_category=None): method num_labels (line 173) | def num_labels(self): method num_classes (line 177) | def num_classes(self): method predict_category (line 181) | def predict_category(self): method __getitem__ (line 184) | def __getitem__(self, idx): function load_ogb_product (line 188) | def load_ogb_product(): function load_ogb_mag (line 224) | def load_ogb_mag(): class PinsageDataset (line 258) | class PinsageDataset: method __init__ (line 259) | def __init__(self, g, user_ntype, item_ntype, textset): method user_ntype (line 266) | def user_ntype(self): method item_ntype (line 270) | def item_ntype(self): method textset (line 274) | def textset(self): method __getitem__ (line 277) | def __getitem__(self, idx): function load_nowplaying_rs (line 281) | def load_nowplaying_rs(): function process_data (line 311) | def process_data(name): function get_bench_device (line 336) | def get_bench_device(): function setup_track_time (line 344) | def setup_track_time(*args, **kwargs): function setup_track_acc (line 350) | def setup_track_acc(*args, **kwargs): function setup_track_flops (line 356) | def setup_track_flops(*args, **kwargs): function parametrize (line 375) | def parametrize(param_name, params): function noop_decorator (line 440) | def noop_decorator(param_name, params): class TestFilter (line 449) | class TestFilter: method __init__ (line 450) | def __init__(self): method check (line 471) | def check(self, func): function skip_if_gpu (line 501) | def skip_if_gpu(): function _cuda_device_count (line 514) | def _cuda_device_count(q): function get_num_gpu (line 520) | def get_num_gpu(): function skip_if_not_4gpu (line 533) | def skip_if_not_4gpu(): function benchmark (line 546) | def benchmark(track_type, timeout=60): class Timer (line 587) | class Timer: method __init__ (line 588) | def __init__(self, device=None): method __enter__ (line 595) | def __enter__(self): method __exit__ (line 604) | def __exit__(self, type, value, traceback): FILE: benchmarks/scripts/fix_ram_info.py function main (line 5) | def main(): FILE: benchmarks/scripts/generate_excel.py function get_branch_name_from_hash (line 8) | def get_branch_name_from_hash(hash): function main (line 23) | def main(): function dict_to_csv (line 60) | def dict_to_csv(output_results_dict): function side_by_side_view (line 89) | def side_by_side_view(df): FILE: benchmarks/scripts/replace_branch.py function json_minify (line 7) | def json_minify(string, strip_space=True): function add_prefix (line 63) | def add_prefix(branch_name): function change_branch (line 70) | def change_branch(branch_str: str): FILE: dgl_sparse/include/sparse/elementwise_op.h function namespace (line 11) | namespace dgl { FILE: dgl_sparse/include/sparse/matrix_ops.h function namespace (line 13) | namespace dgl { FILE: dgl_sparse/include/sparse/reduction.h function namespace (line 13) | namespace dgl { FILE: dgl_sparse/include/sparse/sddmm.h function namespace (line 12) | namespace dgl { FILE: dgl_sparse/include/sparse/softmax.h function namespace (line 11) | namespace dgl { FILE: dgl_sparse/include/sparse/sparse_format.h function namespace (line 19) | namespace dgl { FILE: dgl_sparse/include/sparse/sparse_matrix.h function namespace (line 22) | namespace dgl { FILE: dgl_sparse/include/sparse/spmm.h function namespace (line 12) | namespace dgl { FILE: dgl_sparse/include/sparse/spspmm.h function namespace (line 12) | namespace dgl { FILE: dgl_sparse/src/cpu/matrix_ops_impl.cc type dgl (line 8) | namespace dgl { type sparse (line 9) | namespace sparse {} FILE: dgl_sparse/src/elemenwise_op.cc type dgl (line 16) | namespace dgl { type sparse (line 17) | namespace sparse { function SpSpAdd (line 21) | c10::intrusive_ptr SpSpAdd( class SpSpMulAutoGrad (line 36) | class SpSpMulAutoGrad : public Function { function variable_list (line 46) | variable_list SpSpMulAutoGrad::forward( function tensor_list (line 77) | tensor_list SpSpMulAutoGrad::backward( function SpSpMul (line 102) | c10::intrusive_ptr SpSpMul( function SpSpDiv (line 121) | c10::intrusive_ptr SpSpDiv( FILE: dgl_sparse/src/matmul.cc type dgl (line 17) | namespace dgl { type sparse (line 18) | namespace sparse { function SpMMNoAutoGrad (line 20) | torch::Tensor SpMMNoAutoGrad( function SDDMMNoAutoGrad (line 77) | torch::Tensor SDDMMNoAutoGrad( function BroadcastOpNoAutoGrad (line 113) | torch::Tensor BroadcastOpNoAutoGrad( function BroadcastSubNoAutoGrad (line 146) | torch::Tensor BroadcastSubNoAutoGrad( function BroadcastDivNoAutoGrad (line 152) | torch::Tensor BroadcastDivNoAutoGrad( function BroadcastMulNoAutoGrad (line 158) | torch::Tensor BroadcastMulNoAutoGrad( function SpSpMMNoAutoGrad (line 164) | c10::intrusive_ptr SpSpMMNoAutoGrad( FILE: dgl_sparse/src/matmul.h function namespace (line 14) | namespace dgl { FILE: dgl_sparse/src/matrix_ops.cc type dgl (line 9) | namespace dgl { type sparse (line 10) | namespace sparse { function COOIntersection (line 22) | std::tuple, torch::Tensor, torch::Tensor> COOIn... function RevertPermutation (line 59) | static torch::Tensor RevertPermutation(const torch::Tensor& perm) { function CompactIndices (line 75) | static std::tuple CompactIndices( function CompactCOO (line 112) | static std::tuple, torch::Tensor> C... function CompactCSR (line 138) | static std::tuple, torch::Tensor> C... function Compact (line 167) | std::tuple, torch::Tensor> Compact( FILE: dgl_sparse/src/matrix_ops_impl.h function namespace (line 17) | namespace dgl { FILE: dgl_sparse/src/python_binding.cc type dgl (line 21) | namespace dgl { type sparse (line 22) | namespace sparse { function TORCH_LIBRARY (line 24) | TORCH_LIBRARY(dgl_sparse, m) { FILE: dgl_sparse/src/reduction.cc type dgl (line 18) | namespace dgl { type sparse (line 19) | namespace sparse { function ReduceAlong (line 23) | torch::Tensor ReduceAlong( function ReduceAll (line 70) | torch::Tensor ReduceAll( function Reduce (line 90) | torch::Tensor Reduce( FILE: dgl_sparse/src/sddmm.cc type dgl (line 15) | namespace dgl { type sparse (line 16) | namespace sparse { class SDDMMAutoGrad (line 20) | class SDDMMAutoGrad : public Function { function _SDDMMSanityCheck (line 29) | void _SDDMMSanityCheck( function tensor_list (line 88) | tensor_list SDDMMAutoGrad::backward( function SDDMM (line 108) | c10::intrusive_ptr SDDMM( FILE: dgl_sparse/src/softmax.cc type dgl (line 14) | namespace dgl { type sparse (line 15) | namespace sparse { class SoftmaxAutoGrad (line 19) | class SoftmaxAutoGrad : public Function { function tensor_list (line 52) | tensor_list SoftmaxAutoGrad::backward( function Softmax (line 76) | c10::intrusive_ptr Softmax( FILE: dgl_sparse/src/sparse_format.cc type dgl (line 14) | namespace dgl { type sparse (line 15) | namespace sparse { function COOFromOldDGLCOO (line 17) | std::shared_ptr COOFromOldDGLCOO(const aten::COOMatrix& dgl_coo) { function COOToOldDGLCOO (line 27) | aten::COOMatrix COOToOldDGLCOO(const std::shared_ptr& coo) { function CSRFromOldDGLCSR (line 35) | std::shared_ptr CSRFromOldDGLCSR(const aten::CSRMatrix& dgl_csr) { function CSRToOldDGLCSR (line 44) | aten::CSRMatrix CSRToOldDGLCSR(const std::shared_ptr& csr) { function COOToTorchCOO (line 52) | torch::Tensor COOToTorchCOO( function CSRToCOO (line 64) | std::shared_ptr CSRToCOO(const std::shared_ptr& csr) { function CSCToCOO (line 70) | std::shared_ptr CSCToCOO(const std::shared_ptr& csc) { function COOToCSR (line 77) | std::shared_ptr COOToCSR(const std::shared_ptr& coo) { function CSCToCSR (line 83) | std::shared_ptr CSCToCSR(const std::shared_ptr& csc) { function COOToCSC (line 89) | std::shared_ptr COOToCSC(const std::shared_ptr& coo) { function CSRToCSC (line 96) | std::shared_ptr CSRToCSC(const std::shared_ptr& csr) { function DiagToCOO (line 102) | std::shared_ptr DiagToCOO( function DiagToCSR (line 111) | std::shared_ptr DiagToCSR( function DiagToCSC (line 124) | std::shared_ptr DiagToCSC( function COOTranspose (line 137) | std::shared_ptr COOTranspose(const std::shared_ptr& coo) { function COOSort (line 143) | std::pair, torch::Tensor> COOSort( FILE: dgl_sparse/src/sparse_matrix.cc type dgl (line 17) | namespace dgl { type sparse (line 18) | namespace sparse { FILE: dgl_sparse/src/sparse_matrix_coalesce.cc type dgl (line 14) | namespace dgl { type sparse (line 15) | namespace sparse { FILE: dgl_sparse/src/spmm.cc type dgl (line 17) | namespace dgl { type sparse (line 18) | namespace sparse { class SpMMAutoGrad (line 22) | class SpMMAutoGrad : public Function { function _SpMMSanityCheck (line 31) | void _SpMMSanityCheck( function tensor_list (line 88) | tensor_list SpMMAutoGrad::backward( function SpMM (line 114) | torch::Tensor SpMM( FILE: dgl_sparse/src/spspmm.cc type dgl (line 15) | namespace dgl { type sparse (line 16) | namespace sparse { class SpSpMMAutoGrad (line 20) | class SpSpMMAutoGrad : public Function { function _SpSpMMSanityCheck (line 30) | void _SpSpMMSanityCheck( function _CSRMask (line 62) | torch::Tensor _CSRMask( function variable_list (line 73) | variable_list SpSpMMAutoGrad::forward( function tensor_list (line 93) | tensor_list SpSpMMAutoGrad::backward( function DiagSpSpMM (line 119) | c10::intrusive_ptr DiagSpSpMM( function SpSpMM (line 155) | c10::intrusive_ptr SpSpMM( FILE: dgl_sparse/src/utils.h function namespace (line 18) | namespace dgl { FILE: dglgo/dglgo/apply_pipeline/graphpred/gen.py class ApplyGraphpredPipelineCfg (line 26) | class ApplyGraphpredPipelineCfg(BaseModel): class ApplyGraphpredPipeline (line 33) | class ApplyGraphpredPipeline(PipelineBase): method __init__ (line 34) | def __init__(self): method setup_user_cfg_cls (line 38) | def setup_user_cfg_cls(cls): method user_cfg_cls (line 52) | def user_cfg_cls(self): method get_cfg_func (line 55) | def get_cfg_func(self): method gen_script (line 116) | def gen_script(cls, user_cfg_dict): method get_description (line 155) | def get_description() -> str: FILE: dglgo/dglgo/apply_pipeline/nodepred/gen.py class ApplyNodepredPipeline (line 21) | class ApplyNodepredPipeline(PipelineBase): method __init__ (line 22) | def __init__(self): method setup_user_cfg_cls (line 26) | def setup_user_cfg_cls(cls): method user_cfg_cls (line 37) | def user_cfg_cls(self): method get_cfg_func (line 40) | def get_cfg_func(self): method gen_script (line 98) | def gen_script(cls, user_cfg_dict): method get_description (line 138) | def get_description() -> str: FILE: dglgo/dglgo/apply_pipeline/nodepred_sample/gen.py class ApplyNodepredNsPipeline (line 21) | class ApplyNodepredNsPipeline(PipelineBase): method __init__ (line 22) | def __init__(self): method setup_user_cfg_cls (line 26) | def setup_user_cfg_cls(cls): method user_cfg_cls (line 37) | def user_cfg_cls(self): method get_cfg_func (line 40) | def get_cfg_func(self): method gen_script (line 98) | def gen_script(cls, user_cfg_dict): method get_description (line 138) | def get_description() -> str: FILE: dglgo/dglgo/cli/apply_cli.py function apply (line 11) | def apply(cfg: str = typer.Option(..., help="config yaml file name")): FILE: dglgo/dglgo/cli/cli.py function main (line 25) | def main(): FILE: dglgo/dglgo/cli/export_cli.py function export (line 14) | def export( FILE: dglgo/dglgo/cli/recipe_cli.py function list_recipes (line 10) | def list_recipes(): function get_recipe (line 43) | def get_recipe( FILE: dglgo/dglgo/cli/train_cli.py function train (line 13) | def train( FILE: dglgo/dglgo/model/edge_encoder/bilinear.py class BilinearPredictor (line 6) | class BilinearPredictor(nn.Module): method __init__ (line 7) | def __init__( method forward (line 37) | def forward(self, h_src, h_dst): FILE: dglgo/dglgo/model/edge_encoder/dot.py class DotPredictor (line 6) | class DotPredictor(nn.Module): method __init__ (line 7) | def __init__( method forward (line 23) | def forward(self, h_src, h_dst): FILE: dglgo/dglgo/model/edge_encoder/ele.py class ElementWiseProductPredictor (line 6) | class ElementWiseProductPredictor(nn.Module): method __init__ (line 7) | def __init__( method forward (line 38) | def forward(self, h_src, h_dst): FILE: dglgo/dglgo/model/graph_encoder/gin_ogbg.py class MLP (line 9) | class MLP(nn.Module): method __init__ (line 10) | def __init__(self, feat_size: int): method forward (line 21) | def forward(self, h): class OGBGGIN (line 25) | class OGBGGIN(nn.Module): method __init__ (line 26) | def __init__( method forward (line 89) | def forward(self, graph, node_feat, edge_feat): FILE: dglgo/dglgo/model/graph_encoder/pna.py function aggregate_mean (line 12) | def aggregate_mean(h): function aggregate_max (line 17) | def aggregate_max(h): function aggregate_min (line 22) | def aggregate_min(h): function aggregate_sum (line 27) | def aggregate_sum(h): function aggregate_var (line 32) | def aggregate_var(h): function aggregate_std (line 40) | def aggregate_std(h): function scale_identity (line 55) | def scale_identity(h, D, delta): function scale_amplification (line 60) | def scale_amplification(h, D, delta): function scale_attenuation (line 65) | def scale_attenuation(h, D, delta): class MLP (line 77) | class MLP(nn.Module): method __init__ (line 78) | def __init__( method forward (line 105) | def forward(self, h): class SimplePNAConv (line 113) | class SimplePNAConv(nn.Module): method __init__ (line 116) | def __init__( method reduce (line 145) | def reduce(self, nodes): method forward (line 154) | def forward(self, g, h): class PNA (line 172) | class PNA(nn.Module): method __init__ (line 173) | def __init__( method forward (line 279) | def forward(self, graph, node_feat, edge_feat=None): FILE: dglgo/dglgo/model/node_encoder/gat.py class GAT (line 10) | class GAT(nn.Module): method __init__ (line 11) | def __init__( method forward (line 82) | def forward(self, graph, node_feat, edge_feat=None): method forward_block (line 96) | def forward_block(self, blocks, node_feat, edge_feat=None): FILE: dglgo/dglgo/model/node_encoder/gcn.py class GCN (line 7) | class GCN(nn.Module): method __init__ (line 8) | def __init__( method forward (line 66) | def forward(self, g, node_feat, edge_feat=None): method forward_block (line 82) | def forward_block(self, blocks, node_feat, edge_feat=None): FILE: dglgo/dglgo/model/node_encoder/gin.py class GIN (line 6) | class GIN(nn.Module): method __init__ (line 7) | def __init__( method forward (line 55) | def forward(self, graph, node_feat, edge_feat=None): FILE: dglgo/dglgo/model/node_encoder/sage.py class GraphSAGE (line 6) | class GraphSAGE(nn.Module): method __init__ (line 7) | def __init__( method forward (line 57) | def forward(self, graph, node_feat, edge_feat=None): method forward_block (line 73) | def forward_block(self, blocks, node_feat, edge_feat=None): FILE: dglgo/dglgo/model/node_encoder/sgc.py class SGC (line 8) | class SGC(nn.Module): method __init__ (line 9) | def __init__(self, data_info: dict, embed_size: int = -1, bias=True, k... method forward (line 43) | def forward(self, g, node_feat, edge_feat=None): method normalize (line 54) | def normalize(h): FILE: dglgo/dglgo/pipeline/graphpred/gen.py class GraphpredPipelineCfg (line 28) | class GraphpredPipelineCfg(BaseModel): class GraphpredPipeline (line 43) | class GraphpredPipeline(PipelineBase): method __init__ (line 44) | def __init__(self): method setup_user_cfg_cls (line 48) | def setup_user_cfg_cls(cls): method user_cfg_cls (line 63) | def user_cfg_cls(self): method get_cfg_func (line 66) | def get_cfg_func(self): method gen_script (line 114) | def gen_script(cls, user_cfg_dict): method get_description (line 160) | def get_description() -> str: FILE: dglgo/dglgo/pipeline/linkpred/gen.py class LinkpredPipelineCfg (line 25) | class LinkpredPipelineCfg(BaseModel): class LinkpredPipeline (line 49) | class LinkpredPipeline(PipelineBase): method __init__ (line 54) | def __init__(self): method setup_user_cfg_cls (line 58) | def setup_user_cfg_cls(cls): method user_cfg_cls (line 79) | def user_cfg_cls(self): method get_cfg_func (line 82) | def get_cfg_func(self): method gen_script (line 154) | def gen_script(cls, user_cfg_dict): method get_description (line 225) | def get_description() -> str: FILE: dglgo/dglgo/pipeline/nodepred/gen.py class NodepredPipelineCfg (line 33) | class NodepredPipelineCfg(BaseModel): class NodepredPipeline (line 44) | class NodepredPipeline(PipelineBase): method __init__ (line 48) | def __init__(self): method setup_user_cfg_cls (line 52) | def setup_user_cfg_cls(cls): method user_cfg_cls (line 67) | def user_cfg_cls(self): method get_cfg_func (line 70) | def get_cfg_func(self): method gen_script (line 116) | def gen_script(cls, user_cfg_dict): method get_description (line 162) | def get_description() -> str: FILE: dglgo/dglgo/pipeline/nodepred_sample/gen.py class SamplerConfig (line 26) | class SamplerConfig(BaseModel): class Config (line 34) | class Config: class NodepredNSPipelineCfg (line 57) | class NodepredNSPipelineCfg(BaseModel): class NodepredNsPipeline (line 69) | class NodepredNsPipeline(PipelineBase): method __init__ (line 70) | def __init__(self): method setup_user_cfg_cls (line 75) | def setup_user_cfg_cls(cls): method user_cfg_cls (line 91) | def user_cfg_cls(self): method get_cfg_func (line 94) | def get_cfg_func(self): method gen_script (line 152) | def gen_script(user_cfg_dict): method get_description (line 203) | def get_description() -> str: FILE: dglgo/dglgo/utils/base_model.py class DeviceEnum (line 15) | class DeviceEnum(str, Enum): class DGLBaseModel (line 20) | class DGLBaseModel(PydanticBaseModel): class Config (line 21) | class Config: method with_fields (line 26) | def with_fields(cls, model_name, **field_definitions): function get_literal_value (line 30) | def get_literal_value(type_): function extract_name (line 38) | def extract_name(union_type): class EarlyStopConfig (line 47) | class EarlyStopConfig(DGLBaseModel): FILE: dglgo/dglgo/utils/early_stop.py class EarlyStopping (line 4) | class EarlyStopping: method __init__ (line 5) | def __init__( method step (line 14) | def step(self, acc, model): method save_checkpoint (line 32) | def save_checkpoint(self, model): method load_checkpoint (line 36) | def load_checkpoint(self, model): FILE: dglgo/dglgo/utils/enter_config.py class PipelineConfig (line 16) | class PipelineConfig(DGLBaseModel): class UserConfig (line 25) | class UserConfig(DGLBaseModel): FILE: dglgo/dglgo/utils/factory.py class PipelineBase (line 21) | class PipelineBase(ABC): method __init__ (line 23) | def __init__(self) -> None: method get_cfg_func (line 27) | def get_cfg_func(self): method gen_script (line 31) | def gen_script(user_cfg_dict: dict): method get_description (line 35) | def get_description() -> str: class DataFactoryClass (line 39) | class DataFactoryClass: method __init__ (line 40) | def __init__(self): method register (line 45) | def register( method get_dataset_enum (line 66) | def get_dataset_enum(self): method get_dataset_classname (line 72) | def get_dataset_classname(self, name): method get_constructor_arg_type (line 75) | def get_constructor_arg_type(self, model_name): method get_pydantic_config (line 82) | def get_pydantic_config(self): method get_import_code (line 105) | def get_import_code(self, name): method get_import_code (line 108) | def get_import_code(self, name): method get_extra_args (line 111) | def get_extra_args(self, name): method get_class_name (line 114) | def get_class_name(self, name): method get_generated_code_dict (line 117) | def get_generated_code_dict(self, name, args='**cfg["data"]'): method filter (line 127) | def filter(self, pipeline_name): method get_base_class (line 138) | def get_base_class(dataset_name, pipeline_name): class PipelineFactory (line 250) | class PipelineFactory: method register (line 258) | def register(cls, name: str) -> Callable: method register_default_config_generator (line 270) | def register_default_config_generator(cls, name: str) -> Callable: method call_default_config_generator (line 282) | def call_default_config_generator( method call_generator (line 290) | def call_generator(cls, generator_name, cfg): method get_pipeline_enum (line 294) | def get_pipeline_enum(cls): class ApplyPipelineFactory (line 301) | class ApplyPipelineFactory: method register (line 308) | def register(cls, name: str) -> Callable: class ModelFactory (line 323) | class ModelFactory: method __init__ (line 326) | def __init__(self): method get_model_enum (line 332) | def get_model_enum(self): method register (line 338) | def register(self, model_name: str) -> Callable: method get_source_code (line 352) | def get_source_code(self, model_name): method get_constructor_default_args (line 355) | def get_constructor_default_args(self, model_name): method get_pydantic_constructor_arg_type (line 362) | def get_pydantic_constructor_arg_type(self, model_name: str): method get_constructor_doc_dict (line 381) | def get_constructor_doc_dict(self, name): method get_pydantic_model_config (line 390) | def get_pydantic_model_config(self): method get_model_class_name (line 399) | def get_model_class_name(self, model_name): method get_constructor_arg_type (line 402) | def get_constructor_arg_type(self, model_name): method filter (line 409) | def filter(self, filter_func): class SamplerFactory (line 418) | class SamplerFactory: method __init__ (line 421) | def __init__(self): method get_model_enum (line 424) | def get_model_enum(self): method register (line 430) | def register(self, sampler_name: str) -> Callable: method get_constructor_default_args (line 441) | def get_constructor_default_args(self, sampler_name): method get_pydantic_constructor_arg_type (line 448) | def get_pydantic_constructor_arg_type(self, sampler_name: str): method get_pydantic_model_config (line 470) | def get_pydantic_model_config(self): method get_model_class_name (line 479) | def get_model_class_name(self, model_name): method get_constructor_arg_type (line 482) | def get_constructor_arg_type(self, model_name): method get_constructor_doc_dict (line 489) | def get_constructor_doc_dict(self, name): FILE: dglgo/dglgo/utils/yaml_dump.py function deep_convert_dict (line 4) | def deep_convert_dict(layer): function merge_comment (line 20) | def merge_comment(d, comment_dict, column=30): FILE: dglgo/tests/test_pipeline.py class ExperimentSpec (line 12) | class ExperimentSpec(NamedTuple): function test_train (line 28) | def test_train(spec): function setup_recipe_folder (line 67) | def setup_recipe_folder(): function test_recipe (line 78) | def test_recipe(file, setup_recipe_folder): FILE: examples/advanced/cugraph/graphsage.py class SAGE (line 18) | class SAGE(nn.Module): method __init__ (line 19) | def __init__(self, in_size, hid_size, out_size): method forward (line 30) | def forward(self, blocks, x): method inference (line 39) | def inference(self, g, device, batch_size): function evaluate (line 76) | def evaluate(model, graph, dataloader): function layerwise_infer (line 94) | def layerwise_infer(device, graph, nid, model, batch_size): function train (line 108) | def train(args, device, g, dataset, model): FILE: examples/advanced/cugraph/rgcn.py class RGCN (line 23) | class RGCN(nn.Module): method __init__ (line 24) | def __init__(self, num_nodes, h_dim, out_dim, num_rels, num_bases): method forward (line 47) | def forward(self, g, fanouts=[None, None]): function evaluate (line 54) | def evaluate(model, labels, dataloader, inv_target): function train (line 75) | def train(device, g, target_idx, labels, train_mask, model, fanouts): FILE: examples/core/Graphormer/dataset.py class MolHIVDataset (line 13) | class MolHIVDataset(th.utils.data.Dataset): method __init__ (line 14) | def __init__(self): method collate (line 31) | def collate(self, samples): FILE: examples/core/Graphormer/main.py function train_epoch (line 46) | def train_epoch(model, optimizer, data_loader, lr_scheduler): function evaluate_network (line 106) | def evaluate_network(model, data_loader): function train_val_pipeline (line 153) | def train_val_pipeline(params): FILE: examples/core/Graphormer/model.py class Graphormer (line 10) | class Graphormer(nn.Module): method __init__ (line 11) | def __init__( method reset_output_layer_parameters (line 78) | def reset_output_layer_parameters(self): method forward (line 82) | def forward( FILE: examples/core/gat/train.py class GAT (line 13) | class GAT(nn.Module): method __init__ (line 14) | def __init__(self, in_size, hid_size, out_size, heads): method forward (line 39) | def forward(self, g, inputs): function evaluate (line 50) | def evaluate(g, features, labels, mask, model): function train (line 61) | def train(g, features, labels, masks, model, num_epochs): FILE: examples/core/gated_gcn/train.py class GatedGCN (line 19) | class GatedGCN(nn.Module): method __init__ (line 20) | def __init__( method forward (line 54) | def forward(self, g, node_feat, edge_feat): function train (line 69) | def train(model, device, data_loader, opt, loss_fn): function evaluate (line 87) | def evaluate(model, device, data_loader, evaluator): FILE: examples/core/graphsage/node_classification.py class SAGE (line 33) | class SAGE(nn.Module): method __init__ (line 34) | def __init__(self, in_size, hidden_size, out_size): method forward (line 42) | def forward(self, graph, x): function evaluate (line 53) | def evaluate(g, features, labels, mask, model): function train (line 64) | def train(g, features, labels, masks, model): FILE: examples/core/rgcn/hetero_rgcn.py function prepare_data (line 71) | def prepare_data(args, device): function extract_embed (line 134) | def extract_embed(node_embed, input_nodes): function rel_graph_embed (line 141) | def rel_graph_embed(graph, embed_size): class RelGraphConvLayer (line 181) | class RelGraphConvLayer(nn.Module): method __init__ (line 182) | def __init__( method reset_parameters (line 248) | def reset_parameters(self): method forward (line 255) | def forward(self, g, inputs): class EntityClassify (line 311) | class EntityClassify(nn.Module): method __init__ (line 312) | def __init__(self, g, in_size, out_size): method reset_parameters (line 351) | def reset_parameters(self): method forward (line 356) | def forward(self, h, blocks): function extract_node_features (line 362) | def extract_node_features(name, g, input_nodes, node_embed, feats, device): function train (line 385) | def train( function evaluate (line 472) | def evaluate( function main (line 536) | def main(args): FILE: examples/distributed/graphsage/node_classification.py class DistSAGE (line 16) | class DistSAGE(nn.Module): method __init__ (line 36) | def __init__( method forward (line 51) | def forward(self, blocks, x): method inference (line 70) | def inference(self, g, x, batch_size, device): function compute_acc (line 139) | def compute_acc(pred, labels): function evaluate (line 159) | def evaluate(model, g, inputs, labels, val_nid, test_nid, batch_size, de... function run (line 198) | def run(args, device, data): function main (line 338) | def main(args): FILE: examples/distributed/graphsage/node_classification_unsupervised.py class DistSAGE (line 20) | class DistSAGE(nn.Module): method __init__ (line 21) | def __init__( method forward (line 36) | def forward(self, blocks, x): method inference (line 45) | def inference(self, g, x, batch_size, device): method join (line 108) | def join(self): function load_subtensor (line 113) | def load_subtensor(g, input_nodes, device): class CrossEntropyLoss (line 121) | class CrossEntropyLoss(nn.Module): method forward (line 122) | def forward(self, block_outputs, pos_graph, neg_graph): function generate_emb (line 140) | def generate_emb(model, g, inputs, batch_size, device): function compute_acc (line 155) | def compute_acc(emb, labels, train_nids, val_nids, test_nids): function run (line 187) | def run(args, device, data): function main (line 373) | def main(args): FILE: examples/distributed/graphsage/partition_graph.py function load_reddit (line 10) | def load_reddit(self_loop=True): function load_ogb (line 19) | def load_ogb(name, root="dataset"): FILE: examples/distributed/rgcn/lp_perf.py function run (line 16) | def run(args, g, train_eids): function rand_init_prob (line 133) | def rand_init_prob(shape, dtype): function rand_init_mask (line 139) | def rand_init_mask(shape, dtype): function main (line 145) | def main(args): FILE: examples/distributed/rgcn/node_classification.py class RelGraphConvLayer (line 38) | class RelGraphConvLayer(nn.Module): method __init__ (line 62) | def __init__( method forward (line 122) | def forward(self, g, inputs): class EntityClassify (line 167) | class EntityClassify(nn.Module): method __init__ (line 191) | def __init__( method forward (line 251) | def forward(self, blocks, feats, norm=None): function init_emb (line 262) | def init_emb(shape, dtype): class DistEmbedLayer (line 268) | class DistEmbedLayer(nn.Module): method __init__ (line 288) | def __init__( method forward (line 352) | def forward(self, node_ids): function compute_acc (line 378) | def compute_acc(results, labels): function evaluate (line 386) | def evaluate( function run (line 448) | def run(args, device, data): function main (line 707) | def main(args): FILE: examples/distributed/rgcn/partition_graph.py function load_ogb (line 11) | def load_ogb(dataset): FILE: examples/graphbolt/disk_based_feature/node_classification.py function accuracy (line 18) | def accuracy(out, labels): class SAGE (line 27) | class SAGE(nn.Module): method __init__ (line 28) | def __init__(self, in_size, hidden_size, out_size, num_layers, dropout): method set_layer_dtype (line 42) | def set_layer_dtype(self, _dtype): method forward (line 47) | def forward(self, blocks, x): method inference (line 57) | def inference(self, graph, features, dataloader, storage_device): function create_dataloader (line 88) | def create_dataloader( function train_step (line 139) | def train_step(minibatch, optimizer, model, loss_fn): function train_helper (line 151) | def train_helper( function train (line 191) | def train( function layerwise_infer (line 239) | def layerwise_infer( function evaluate_step (line 271) | def evaluate_step(minibatch, model): function evaluate (line 280) | def evaluate( function parse_args (line 307) | def parse_args(): function main (line 401) | def main(): FILE: examples/graphbolt/lightning/node_classification.py class SAGE (line 50) | class SAGE(LightningModule): method __init__ (line 51) | def __init__(self, in_feats, n_hidden, n_classes): method forward (line 64) | def forward(self, blocks, x): method log_node_and_edge_counts (line 73) | def log_node_and_edge_counts(self, blocks): method training_step (line 95) | def training_step(self, batch, batch_idx): method validation_step (line 112) | def validation_step(self, batch, batch_idx): method configure_optimizers (line 128) | def configure_optimizers(self): class DataModule (line 135) | class DataModule(LightningDataModule): method __init__ (line 136) | def __init__(self, dataset, fanouts, batch_size, num_workers): method create_dataloader (line 147) | def create_dataloader(self, node_set, is_train): method train_dataloader (line 171) | def train_dataloader(self): method val_dataloader (line 174) | def val_dataloader(self): FILE: examples/graphbolt/link_prediction.py class SAGE (line 57) | class SAGE(nn.Module): method __init__ (line 58) | def __init__(self, in_size, hidden_size): method forward (line 73) | def forward(self, blocks, x): method inference (line 82) | def inference(self, graph, features, dataloader, storage_device): function create_dataloader (line 113) | def create_dataloader(args, graph, features, itemset, is_train=True): function compute_mrr (line 250) | def compute_mrr(args, model, node_emb, seeds, labels, indexes): function evaluate (line 279) | def evaluate(args, model, graph, features, all_nodes_set, valid_set, tes... function train (line 305) | def train(args, model, graph, features, train_set): function parse_args (line 345) | def parse_args(): function main (line 381) | def main(args): FILE: examples/graphbolt/node_classification.py function create_dataloader (line 52) | def create_dataloader( class SAGE (line 168) | class SAGE(nn.Module): method __init__ (line 169) | def __init__(self, in_size, hidden_size, out_size): method set_layer_dtype (line 182) | def set_layer_dtype(self, _dtype): method forward (line 187) | def forward(self, blocks, x): method inference (line 197) | def inference(self, graph, features, dataloader, storage_device): function layerwise_infer (line 229) | def layerwise_infer( function evaluate (line 256) | def evaluate(args, model, graph, features, itemset, num_classes): function train (line 284) | def train(args, graph, features, train_set, valid_set, num_classes, model): function parse_args (line 332) | def parse_args(): function main (line 396) | def main(args): FILE: examples/graphbolt/pyg/hetero/node_classification.py function accuracy (line 17) | def accuracy(out, labels): function create_dataloader (line 26) | def create_dataloader( class RelGraphConvLayer (line 81) | class RelGraphConvLayer(nn.Module): method __init__ (line 82) | def __init__( method forward (line 124) | def forward(self, subgraph, x): class EntityClassify (line 162) | class EntityClassify(nn.Module): method __init__ (line 163) | def __init__(self, graph, in_size, hidden_size, out_size, n_layers): method forward (line 179) | def forward(self, subgraphs, h): function evaluate_step (line 186) | def evaluate_step(minibatch, model): function evaluate (line 200) | def evaluate( function train_step (line 229) | def train_step(minibatch, optimizer, model, loss_fn): function train_helper (line 248) | def train_helper( function train (line 286) | def train( function parse_args (line 322) | def parse_args(): function main (line 412) | def main(): FILE: examples/graphbolt/pyg/labor/load_dataset.py function load_dgl (line 4) | def load_dgl(name): function load_dataset (line 31) | def load_dataset(dataset_name, disk_based_feature_keys=None): FILE: examples/graphbolt/pyg/labor/node_classification.py function accuracy (line 24) | def accuracy(out, labels): class GraphSAGE (line 33) | class GraphSAGE(torch.nn.Module): method __init__ (line 34) | def __init__( method forward (line 56) | def forward(self, subgraphs, x): method inference (line 68) | def inference(self, graph, features, dataloader, storage_device): function create_dataloader (line 105) | def create_dataloader( function train_step (line 155) | def train_step(minibatch, optimizer, model, loss_fn, multilabel, eval_fn): function train_helper (line 168) | def train_helper( function train (line 210) | def train( function layerwise_infer (line 263) | def layerwise_infer( function evaluate_step (line 297) | def evaluate_step(minibatch, model, eval_fn): function evaluate (line 306) | def evaluate( function parse_args (line 334) | def parse_args(): function main (line 436) | def main(): FILE: examples/graphbolt/pyg/labor/sage_conv.py class SAGEConv (line 13) | class SAGEConv(MessagePassing): method __init__ (line 59) | def __init__( method reset_parameters (line 103) | def reset_parameters(self): method forward (line 110) | def forward( method message (line 133) | def message(self, x_j: Tensor) -> Tensor: method message_and_aggregate (line 136) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: method __repr__ (line 141) | def __repr__(self) -> str: FILE: examples/graphbolt/pyg/link_prediction.py class GraphSAGE (line 52) | class GraphSAGE(torch.nn.Module): method __init__ (line 60) | def __init__(self, in_size, hidden_size, n_layers): method forward (line 75) | def forward(self, subgraphs, x): method inference (line 91) | def inference(self, graph, features, dataloader, storage_device): function create_dataloader (line 124) | def create_dataloader( function predictions_step (line 188) | def predictions_step(model, h_src, h_dst): function compute_predictions (line 192) | def compute_predictions(model, node_emb, seeds, device): function evaluate (line 218) | def evaluate(model, graph, features, all_nodes_set, valid_set, test_set): function train_step (line 250) | def train_step(minibatch, optimizer, model): function train_helper (line 265) | def train_helper(dataloader, model, optimizer, device): function train (line 281) | def train(dataloader, model, device): function parse_args (line 308) | def parse_args(): function main (line 390) | def main(): FILE: examples/graphbolt/pyg/multigpu/node_classification.py function accuracy (line 72) | def accuracy(out, labels): class GraphSAGE (line 81) | class GraphSAGE(torch.nn.Module): method __init__ (line 91) | def __init__(self, in_size, hidden_size, out_size, n_layers, cooperati... method forward (line 101) | def forward(self, minibatch, x): function create_dataloader (line 124) | def create_dataloader( function weighted_reduce (line 184) | def weighted_reduce(tensor, weight, dst=0): function train_step (line 202) | def train_step(minibatch, optimizer, model, loss_fn): function train_helper (line 214) | def train_helper(rank, dataloader, model, optimizer, loss_fn, device): function train (line 236) | def train(args, rank, train_dataloader, valid_dataloader, model, device): function evaluate_step (line 276) | def evaluate_step(minibatch, model): function evaluate (line 285) | def evaluate(rank, model, dataloader, device): function parse_args (line 299) | def parse_args(): function run (line 390) | def run(rank, world_size, args, dataset): FILE: examples/graphbolt/pyg/node_classification.py class GraphSAGE (line 64) | class GraphSAGE(torch.nn.Module): method __init__ (line 76) | def __init__(self, in_size, hidden_size, out_size): method forward (line 83) | def forward(self, x, edge_index): method inference (line 91) | def inference(self, dataloader, x_all, device): function create_dataloader (line 110) | def create_dataloader( function train (line 134) | def train(model, dataloader, optimizer): function evaluate (line 167) | def evaluate(model, dataloader, num_classes): function layerwise_infer (line 187) | def layerwise_infer( function main (line 204) | def main(): FILE: examples/graphbolt/pyg/node_classification_advanced.py function accuracy (line 69) | def accuracy(out, labels): class GraphSAGE (line 78) | class GraphSAGE(torch.nn.Module): method __init__ (line 88) | def __init__(self, in_size, hidden_size, out_size, n_layers): method forward (line 97) | def forward(self, subgraphs, x): method inference (line 113) | def inference(self, graph, features, dataloader, storage_device): function create_dataloader (line 146) | def create_dataloader( function train_step (line 204) | def train_step(minibatch, optimizer, model, loss_fn): function train_helper (line 216) | def train_helper(dataloader, model, optimizer, loss_fn, device): function train (line 238) | def train(train_dataloader, valid_dataloader, model, device): function layerwise_infer (line 272) | def layerwise_infer(args, graph, features, test_set, all_nodes_set, model): function evaluate_step (line 291) | def evaluate_step(minibatch, model): function evaluate (line 300) | def evaluate(model, dataloader, device): function parse_args (line 312) | def parse_args(): function main (line 401) | def main(): FILE: examples/graphbolt/quickstart/link_prediction.py function create_dataloader (line 21) | def create_dataloader(dataset, device, is_train=True): class GraphSAGE (line 57) | class GraphSAGE(nn.Module): method __init__ (line 58) | def __init__(self, in_size, hidden_size=16): method forward (line 69) | def forward(self, blocks, x): function evaluate (line 80) | def evaluate(model, dataset, device): function train (line 117) | def train(model, dataset, device): FILE: examples/graphbolt/quickstart/node_classification.py function create_dataloader (line 16) | def create_dataloader(dataset, itemset, device): class GCN (line 35) | class GCN(nn.Module): method __init__ (line 36) | def __init__(self, in_size, out_size, hidden_size=16): method forward (line 42) | def forward(self, blocks, x): function evaluate (line 53) | def evaluate(model, dataset, itemset, device): function train (line 72) | def train(model, dataset, device): FILE: examples/graphbolt/rgcn/hetero_rgcn.py function load_dataset (line 66) | def load_dataset(dataset_name): function create_dataloader (line 93) | def create_dataloader( function extract_embed (line 152) | def extract_embed(node_embed, input_nodes): function extract_node_features (line 159) | def extract_node_features(name, block, data, node_embed, device): function rel_graph_embed (line 185) | def rel_graph_embed(graph, embed_size): class RelGraphConvLayer (line 230) | class RelGraphConvLayer(nn.Module): method __init__ (line 231) | def __init__( method reset_parameters (line 297) | def reset_parameters(self): method forward (line 304) | def forward(self, g, inputs): class EntityClassify (line 359) | class EntityClassify(nn.Module): method __init__ (line 360) | def __init__(self, graph, in_size, out_size): method reset_parameters (line 401) | def reset_parameters(self): method forward (line 406) | def forward(self, blocks, h): function evaluate (line 413) | def evaluate( function train (line 478) | def train( function main (line 560) | def main(args): FILE: examples/graphbolt/sparse/graphsage.py class SAGEConv (line 42) | class SAGEConv(nn.Module): method __init__ (line 47) | def __init__( method reset_parameters (line 60) | def reset_parameters(self): method forward (line 65) | def forward(self, A, feat): class SAGE (line 81) | class SAGE(nn.Module): method __init__ (line 82) | def __init__(self, in_size, hid_size, out_size): method forward (line 93) | def forward(self, sampled_matrices, x): class SparseNeighborSampler (line 106) | class SparseNeighborSampler(SubgraphSampler): method __init__ (line 107) | def __init__(self, datapipe, matrix, fanouts): method sample_subgraphs (line 117) | def sample_subgraphs(self, seeds, seeds_timestamp=None): function create_dataloader (line 141) | def create_dataloader(A, fanouts, ids, features, device): function evaluate (line 152) | def evaluate(model, dataloader, num_classes): function validate (line 172) | def validate(device, dataset, model, num_classes): function train (line 181) | def train(device, A, features, dataset, num_classes, model): FILE: examples/graphbolt/temporal_link_prediction.py class CategoricalEncoder (line 61) | class CategoricalEncoder(nn.Module): method __init__ (line 62) | def __init__( method reset_parameters (line 71) | def reset_parameters(self): method forward (line 74) | def forward(self, input_feat: torch.Tensor): class HeteroSAGE (line 78) | class HeteroSAGE(nn.Module): method __init__ (line 79) | def __init__(self, in_size, hidden_size): method forward (line 105) | def forward(self, blocks, X_node_dict): function create_dataloader (line 117) | def create_dataloader(args, graph, features, itemset, is_train=True): function train (line 163) | def train(args, model, graph, features, train_set, encoders): function parse_args (line 221) | def parse_args(): function download_datasets (line 269) | def download_datasets(name, root="datasets"): function main (line 282) | def main(args): FILE: examples/legacy/link_prediction.py function to_bidirected_with_reverse_mapping (line 61) | def to_bidirected_with_reverse_mapping(g): class SAGE (line 133) | class SAGE(nn.Module): method __init__ (line 134) | def __init__(self, in_size, hidden_size): method forward (line 150) | def forward(self, pair_graph, neg_pair_graph, blocks, x): method inference (line 163) | def inference(self, g, device, batch_size): function compute_mrr (line 234) | def compute_mrr( function evaluate (line 265) | def evaluate(device, graph, edge_split, model, batch_size): function train (line 287) | def train( function parse_args (line 391) | def parse_args(): function main (line 439) | def main(args): FILE: examples/legacy/node_classification.py class SAGE (line 56) | class SAGE(nn.Module): method __init__ (line 57) | def __init__(self, in_size, hidden_size, out_size): method forward (line 68) | def forward(self, blocks, x): method inference (line 78) | def inference(self, g, device, batch_size, fused_sampling: bool = True): function evaluate (line 155) | def evaluate(model, graph, dataloader, num_classes): function layerwise_infer (line 172) | def layerwise_infer( function train (line 184) | def train(device, g, dataset, model, num_classes, use_uva, fused_sampling): FILE: examples/multigpu/graphbolt/node_classification.py class SAGE (line 56) | class SAGE(nn.Module): method __init__ (line 57) | def __init__(self, in_size, hidden_size, out_size): method set_layer_dtype (line 70) | def set_layer_dtype(self, dtype): method forward (line 75) | def forward(self, blocks, x): function create_dataloader (line 86) | def create_dataloader( function weighted_reduce (line 153) | def weighted_reduce(tensor, weight, dst=0): function evaluate (line 171) | def evaluate(rank, model, dataloader, num_classes, device): function train (line 192) | def train( function run (line 275) | def run(rank, world_size, args, devices, dataset): function parse_args (line 370) | def parse_args(): FILE: examples/multigpu/node_classification_sage.py class SAGE (line 61) | class SAGE(nn.Module): method __init__ (line 62) | def __init__(self, in_size, hid_size, out_size): method forward (line 73) | def forward(self, blocks, x): method inference (line 82) | def inference(self, g, device, batch_size, use_uva): function evaluate (line 126) | def evaluate(device, model, g, num_classes, dataloader): function layerwise_infer (line 144) | def layerwise_infer( function train (line 161) | def train( function run (line 250) | def run(proc_id, nprocs, devices, g, data, args): FILE: examples/mxnet/appnp/appnp.py class APPNP (line 19) | class APPNP(nn.Block): method __init__ (line 20) | def __init__( method forward (line 51) | def forward(self, features): function evaluate (line 64) | def evaluate(model, features, labels, mask): function main (line 70) | def main(args): FILE: examples/mxnet/gat/gat.py class GAT (line 15) | class GAT(nn.Block): method __init__ (line 16) | def __init__( method forward (line 70) | def forward(self, inputs): FILE: examples/mxnet/gat/train.py function elu (line 30) | def elu(data): function evaluate (line 34) | def evaluate(model, features, labels, mask): function main (line 43) | def main(args): FILE: examples/mxnet/gat/utils.py class EarlyStopping (line 4) | class EarlyStopping: method __init__ (line 5) | def __init__(self, patience=10): method step (line 11) | def step(self, acc, model): method save_checkpoint (line 29) | def save_checkpoint(self, model): FILE: examples/mxnet/gcn/gcn.py class GCN (line 14) | class GCN(gluon.Block): method __init__ (line 15) | def __init__( method forward (line 32) | def forward(self, features): FILE: examples/mxnet/gcn/gcn_concat.py class GCNLayer (line 23) | class GCNLayer(gluon.Block): method __init__ (line 24) | def __init__(self, g, out_feats, activation, dropout): method forward (line 30) | def forward(self, h): class GCN (line 44) | class GCN(gluon.Block): method __init__ (line 45) | def __init__(self, g, n_hidden, n_classes, n_layers, activation, dropo... method forward (line 54) | def forward(self, features): function evaluate (line 65) | def evaluate(model, features, labels, mask): function main (line 71) | def main(args): FILE: examples/mxnet/gcn/gcn_mp.py function gcn_msg (line 12) | def gcn_msg(edge): function gcn_reduce (line 17) | def gcn_reduce(node): class NodeUpdate (line 22) | class NodeUpdate(gluon.Block): method __init__ (line 23) | def __init__(self, out_feats, activation=None, bias=True): method forward (line 34) | def forward(self, node): class GCNLayer (line 43) | class GCNLayer(gluon.Block): method __init__ (line 44) | def __init__(self, g, in_feats, out_feats, activation, dropout, bias=T... method forward (line 54) | def forward(self, h): class GCN (line 64) | class GCN(gluon.Block): method __init__ (line 65) | def __init__( method forward (line 80) | def forward(self, features): FILE: examples/mxnet/gcn/train.py function evaluate (line 17) | def evaluate(model, features, labels, mask): function main (line 23) | def main(args): FILE: examples/mxnet/gin/dataloader.py class SubsetRandomSampler (line 15) | class SubsetRandomSampler(Sampler): method __init__ (line 16) | def __init__(self, indices): method __iter__ (line 19) | def __iter__(self): method __len__ (line 24) | def __len__(self): function collate (line 29) | def collate(samples): class GraphDataLoader (line 43) | class GraphDataLoader: method __init__ (line 44) | def __init__( method train_valid_loader (line 87) | def train_valid_loader(self): method _split_fold10 (line 90) | def _split_fold10(self, labels, fold_idx=0, seed=0, shuffle=True): method _split_rand (line 108) | def _split_rand(self, labels, split_ratio=0.7, seed=0, shuffle=True): FILE: examples/mxnet/gin/gin.py class ApplyNodeFunc (line 16) | class ApplyNodeFunc(nn.Block): method __init__ (line 19) | def __init__(self, mlp): method forward (line 25) | def forward(self, h): class MLP (line 32) | class MLP(nn.Block): method __init__ (line 35) | def __init__(self, num_layers, input_dim, hidden_dim, output_dim): method forward (line 73) | def forward(self, x): class GIN (line 83) | class GIN(nn.Block): method __init__ (line 86) | def __init__( method forward (line 173) | def forward(self, g, h): FILE: examples/mxnet/gin/main.py function train (line 15) | def train(args, net, trainloader, trainer, criterion, epoch): function eval_net (line 47) | def eval_net(args, net, dataloader, criterion): function main (line 73) | def main(args): FILE: examples/mxnet/gin/parser.py class Parser (line 8) | class Parser: method __init__ (line 9) | def __init__(self, description): method _parse (line 17) | def _parse(self): FILE: examples/mxnet/graphsage/main.py class GraphSAGE (line 26) | class GraphSAGE(nn.Block): method __init__ (line 27) | def __init__( method forward (line 75) | def forward(self, features): function evaluate (line 82) | def evaluate(model, features, labels, mask): function main (line 88) | def main(args): FILE: examples/mxnet/monet/citation.py class MoNet (line 20) | class MoNet(nn.Block): method __init__ (line 21) | def __init__( method forward (line 55) | def forward(self, feat, pseudo): function evaluate (line 64) | def evaluate(model, features, pseudo, labels, mask): function main (line 70) | def main(args): FILE: examples/mxnet/rgcn/entity_classify.py class EntityClassify (line 26) | class EntityClassify(BaseRGCN): method build_input_layer (line 27) | def build_input_layer(self): method build_hidden_layer (line 39) | def build_hidden_layer(self, idx): method build_output_layer (line 51) | def build_output_layer(self): function main (line 63) | def main(args): FILE: examples/mxnet/rgcn/model.py class BaseRGCN (line 5) | class BaseRGCN(gluon.Block): method __init__ (line 6) | def __init__( method build_model (line 32) | def build_model(self): method build_input_layer (line 47) | def build_input_layer(self): method build_hidden_layer (line 50) | def build_hidden_layer(self): method build_output_layer (line 53) | def build_output_layer(self): method forward (line 56) | def forward(self, g, h, r, norm): FILE: examples/mxnet/scenegraph/data/dataloader.py function dgl_mp_batchify_fn (line 7) | def dgl_mp_batchify_fn(data): FILE: examples/mxnet/scenegraph/data/object.py class VGObject (line 16) | class VGObject(COCODetection): method __init__ (line 170) | def __init__(self, **kwargs): method annotation_dir (line 174) | def annotation_dir(self): method _parse_image_path (line 177) | def _parse_image_path(self, entry): FILE: examples/mxnet/scenegraph/data/prepare_visualgenome.py function parse_args (line 16) | def parse_args(): function download_vg (line 42) | def download_vg(path, overwrite=False): function download_json (line 72) | def download_json(path, overwrite=False): FILE: examples/mxnet/scenegraph/data/relation.py class VGRelation (line 22) | class VGRelation(VisionDataset): method __init__ (line 23) | def __init__( method __len__ (line 68) | def __len__(self): method _hash_bbox (line 71) | def _hash_bbox(self, object): method __getitem__ (line 75) | def __getitem__(self, idx): FILE: examples/mxnet/scenegraph/demo_reldn.py function parse_args (line 14) | def parse_args(): FILE: examples/mxnet/scenegraph/model/faster_rcnn.py class FasterRCNN (line 30) | class FasterRCNN(RCNN): method __init__ (line 170) | def __init__( method target_generator (line 275) | def target_generator(self): method reset_class (line 286) | def reset_class(self, classes, reuse_weights=None): method _pyramid_roi_feats (line 317) | def _pyramid_roi_feats( method hybrid_forward (line 416) | def hybrid_forward(self, F, x, gt_box=None, gt_label=None, m_rpn_box=N... function get_faster_rcnn (line 697) | def get_faster_rcnn( function faster_rcnn_resnet50_v1b_coco (line 747) | def faster_rcnn_resnet50_v1b_coco( function faster_rcnn_resnet50_v1b_custom (line 836) | def faster_rcnn_resnet50_v1b_custom( function faster_rcnn_resnet101_v1d_coco (line 941) | def faster_rcnn_resnet101_v1d_coco( function faster_rcnn_resnet101_v1d_custom (line 1029) | def faster_rcnn_resnet101_v1d_custom( FILE: examples/mxnet/scenegraph/model/reldn.py class EdgeConfMLP (line 16) | class EdgeConfMLP(nn.Block): method __init__ (line 19) | def __init__(self): method forward (line 22) | def forward(self, edges): class EdgeBBoxExtend (line 32) | class EdgeBBoxExtend(nn.Block): method __init__ (line 35) | def __init__(self): method bbox_delta (line 38) | def bbox_delta(self, bbox_a, bbox_b): method forward (line 53) | def forward(self, edges): class EdgeFreqPrior (line 72) | class EdgeFreqPrior(nn.Block): method __init__ (line 75) | def __init__(self, prior_pkl): method forward (line 81) | def forward(self, edges): class EdgeSpatial (line 90) | class EdgeSpatial(nn.Block): method __init__ (line 93) | def __init__(self, n_classes): method forward (line 102) | def forward(self, edges): class EdgeVisual (line 113) | class EdgeVisual(nn.Block): method __init__ (line 116) | def __init__(self, n_classes, vis_feat_dim=7 * 7 * 3): method forward (line 129) | def forward(self, edges): class RelDN (line 142) | class RelDN(nn.Block): method __init__ (line 145) | def __init__(self, n_classes, prior_pkl, semantic_only=False): method forward (line 160) | def forward(self, g): FILE: examples/mxnet/scenegraph/train_faster_rcnn.py function parse_args (line 43) | def parse_args(): function get_dataset (line 273) | def get_dataset(dataset, args): function get_dataloader (line 317) | def get_dataloader( function save_params (line 373) | def save_params( function split_and_load (line 399) | def split_and_load(batch, ctx_list): function validate (line 411) | def validate(net, val_data, ctx, eval_metric, args): function get_lr_at_iter (line 456) | def get_lr_at_iter(alpha, lr_warmup_factor=1.0 / 3.0): class ForwardBackwardTask (line 460) | class ForwardBackwardTask(Parallelizable): method __init__ (line 461) | def __init__( method forward_backward (line 480) | def forward_backward(self, x): function train (line 569) | def train(net, train_data, val_data, eval_metric, batch_size, ctx, args): FILE: examples/mxnet/scenegraph/train_freq_prior.py function parse_args (line 9) | def parse_args(): function with_overlap (line 33) | def with_overlap(boxA, boxB): function box_ious (line 47) | def box_ious(boxes): FILE: examples/mxnet/scenegraph/train_reldn.py function parse_args (line 17) | def parse_args(): function get_data_batch (line 193) | def get_data_batch(g_list, img_list, ctx_list): function batch_print (line 233) | def batch_print( FILE: examples/mxnet/scenegraph/utils/build_graph.py function bbox_improve (line 6) | def bbox_improve(bbox): function extract_edge_bbox (line 12) | def extract_edge_bbox(g): function build_graph_train (line 26) | def build_graph_train( function build_graph_validate_gt_obj (line 167) | def build_graph_validate_gt_obj( function build_graph_validate_gt_bbox (line 222) | def build_graph_validate_gt_bbox( function build_graph_validate_pred (line 284) | def build_graph_validate_pred( FILE: examples/mxnet/scenegraph/utils/metric.py function iou (line 17) | def iou(boxA, boxB): function object_iou_thresh (line 37) | def object_iou_thresh(gt_object, pred_object, iou_thresh=0.5): function triplet_iou_thresh (line 44) | def triplet_iou_thresh(pred_triplet, gt_triplet, iou_thresh=0.5): class AUCMetric (line 55) | class AUCMetric(mx.metric.EvalMetric): method __init__ (line 56) | def __init__(self, name="auc", eps=1e-12): method update (line 60) | def update(self, labels, preds): class PredCls (line 91) | class PredCls(mx.metric.EvalMetric): method __init__ (line 94) | def __init__(self, topk=20, iou_thresh=0.99): method update (line 99) | def update(self, labels, preds): class PhrCls (line 127) | class PhrCls(mx.metric.EvalMetric): method __init__ (line 130) | def __init__(self, topk=20, iou_thresh=0.99): method update (line 135) | def update(self, labels, preds): class SGDet (line 165) | class SGDet(mx.metric.EvalMetric): method __init__ (line 168) | def __init__(self, topk=20, iou_thresh=0.5): method update (line 173) | def update(self, labels, preds): class SGDetPlus (line 203) | class SGDetPlus(mx.metric.EvalMetric): method __init__ (line 206) | def __init__(self, topk=20, iou_thresh=0.5): method update (line 211) | def update(self, labels, preds): function extract_gt (line 266) | def extract_gt(g, img_size): function extract_pred (line 308) | def extract_pred(g, topk=100, joint_preds=False): FILE: examples/mxnet/scenegraph/utils/sampling.py function l0_sample (line 7) | def l0_sample(g, positive_max=128, negative_ratio=3): FILE: examples/mxnet/scenegraph/utils/viz.py function plot_sg (line 6) | def plot_sg(img, preds, obj_classes, rel_classes, topk=1): FILE: examples/mxnet/scenegraph/validate_reldn.py function parse_args (line 16) | def parse_args(): function get_data_batch (line 163) | def get_data_batch(g_list, img_list, ctx_list): FILE: examples/mxnet/sgc/sgc.py function evaluate (line 27) | def evaluate(model, g, features, labels, mask): function main (line 33) | def main(args): FILE: examples/mxnet/tagcn/tagcn.py class TAGCN (line 13) | class TAGCN(gluon.Block): method __init__ (line 14) | def __init__( method forward (line 29) | def forward(self, features): FILE: examples/mxnet/tagcn/train.py function evaluate (line 19) | def evaluate(model, features, labels, mask): function main (line 25) | def main(args): FILE: examples/mxnet/tree_lstm/train.py function batcher (line 23) | def batcher(ctx): function prepare_glove (line 36) | def prepare_glove(): function main (line 60) | def main(args): FILE: examples/mxnet/tree_lstm/tree_lstm.py class _TreeLSTMCellNodeFunc (line 16) | class _TreeLSTMCellNodeFunc(gluon.HybridBlock): method hybrid_forward (line 17) | def hybrid_forward(self, F, iou, b_iou, c): class _TreeLSTMCellReduceFunc (line 27) | class _TreeLSTMCellReduceFunc(gluon.HybridBlock): method __init__ (line 28) | def __init__(self, U_iou, U_f): method hybrid_forward (line 33) | def hybrid_forward(self, F, h, c): class _TreeLSTMCell (line 41) | class _TreeLSTMCell(gluon.HybridBlock): method __init__ (line 42) | def __init__(self, h_size): method message_func (line 49) | def message_func(self, edges): method apply_node_func (line 52) | def apply_node_func(self, nodes): class TreeLSTMCell (line 59) | class TreeLSTMCell(_TreeLSTMCell): method __init__ (line 60) | def __init__(self, x_size, h_size): method reduce_func (line 68) | def reduce_func(self, nodes): class ChildSumTreeLSTMCell (line 74) | class ChildSumTreeLSTMCell(_TreeLSTMCell): method __init__ (line 75) | def __init__(self, x_size, h_size): method reduce_func (line 81) | def reduce_func(self, nodes): class TreeLSTM (line 88) | class TreeLSTM(gluon.nn.Block): method __init__ (line 89) | def __init__( method forward (line 113) | def forward(self, batch, h, c): FILE: examples/pytorch/GATNE-T/src/main.py function get_graph (line 20) | def get_graph(network_data, vocab): class NeighborSampler (line 53) | class NeighborSampler(object): method __init__ (line 54) | def __init__(self, g, num_fanouts): method sample (line 58) | def sample(self, pairs): class DGLGATNE (line 77) | class DGLGATNE(nn.Module): method __init__ (line 78) | def __init__( method reset_parameters (line 113) | def reset_parameters(self): method forward (line 127) | def forward(self, block): class NSLoss (line 201) | class NSLoss(nn.Module): method __init__ (line 202) | def __init__(self, num_nodes, num_sampled, embedding_size): method reset_parameters (line 222) | def reset_parameters(self): method forward (line 225) | def forward(self, input, embs, label): function train_model (line 242) | def train_model(network_data): FILE: examples/pytorch/GATNE-T/src/main_sparse.py function get_graph (line 20) | def get_graph(network_data, vocab): class NeighborSampler (line 53) | class NeighborSampler(object): method __init__ (line 54) | def __init__(self, g, num_fanouts): method sample (line 58) | def sample(self, pairs): class DGLGATNE (line 78) | class DGLGATNE(nn.Module): method __init__ (line 79) | def __init__( method reset_parameters (line 114) | def reset_parameters(self): method forward (line 128) | def forward(self, block): class NSLoss (line 201) | class NSLoss(nn.Module): method __init__ (line 202) | def __init__(self, num_nodes, num_sampled, embedding_size): method reset_parameters (line 222) | def reset_parameters(self): method forward (line 227) | def forward(self, input, embs, label): function train_model (line 248) | def train_model(network_data): FILE: examples/pytorch/GATNE-T/src/main_sparse_multi_gpus.py function setup_seed (line 23) | def setup_seed(seed): function get_graph (line 31) | def get_graph(network_data, vocab): class NeighborSampler (line 64) | class NeighborSampler(object): method __init__ (line 65) | def __init__(self, g, num_fanouts): method sample (line 69) | def sample(self, pairs): class DGLGATNE (line 89) | class DGLGATNE(nn.Module): method __init__ (line 90) | def __init__( method reset_parameters (line 125) | def reset_parameters(self): method forward (line 139) | def forward(self, block): class NSLoss (line 212) | class NSLoss(nn.Module): method __init__ (line 213) | def __init__(self, num_nodes, num_sampled, embedding_size): method reset_parameters (line 233) | def reset_parameters(self): method forward (line 238) | def forward(self, input, embs, label): function run (line 259) | def run(proc_id, n_gpus, args, devices, data): function train_model (line 505) | def train_model(network_data): FILE: examples/pytorch/GATNE-T/src/utils.py function parse_args (line 15) | def parse_args(): function load_training_data (line 135) | def load_training_data(f_name): function load_testing_data (line 154) | def load_testing_data(f_name): function load_node_type (line 178) | def load_node_type(f_name): function generate_pairs_parallel (line 188) | def generate_pairs_parallel(walks, skip_window=None, layer_id=None): function generate_pairs (line 201) | def generate_pairs(all_walks, window_size, num_workers): function generate_vocab (line 236) | def generate_vocab(network_data): function get_score (line 258) | def get_score(local_model, edge): function evaluate (line 270) | def evaluate(model, true_edges, false_edges, num_workers): FILE: examples/pytorch/GNN-FiLM/data_loader.py function batcher (line 12) | def batcher(device): function load_PPI (line 23) | def load_PPI(batch_size=1, device="cpu"): FILE: examples/pytorch/GNN-FiLM/main.py class GNNFiLMLayer (line 15) | class GNNFiLMLayer(nn.Module): method __init__ (line 16) | def __init__(self, in_size, out_size, etypes, dropout=0.1): method forward (line 40) | def forward(self, g, feat_dict): class GNNFiLM (line 76) | class GNNFiLM(nn.Module): method __init__ (line 77) | def __init__( method forward (line 91) | def forward(self, g, out_key): function main (line 104) | def main(args): FILE: examples/pytorch/GNN-FiLM/utils.py function evaluate_f1_score (line 6) | def evaluate_f1_score(pred, label): FILE: examples/pytorch/NGCF/NGCF/main.py function main (line 11) | def main(args): FILE: examples/pytorch/NGCF/NGCF/model.py class NGCFLayer (line 7) | class NGCFLayer(nn.Module): method __init__ (line 8) | def __init__(self, in_size, out_size, norm_dict, dropout): method forward (line 32) | def forward(self, g, feat_dict): class NGCF (line 70) | class NGCF(nn.Module): method __init__ (line 71) | def __init__(self, g, in_size, layer_size, dropout, lmbd=1e-5): method create_bpr_loss (line 114) | def create_bpr_loss(self, users, pos_items, neg_items): method rating (line 130) | def rating(self, u_g_embeddings, pos_i_g_embeddings): method forward (line 133) | def forward(self, g, user_key, item_key, users, pos_items, neg_items): FILE: examples/pytorch/NGCF/NGCF/utility/batch_test.py function ranklist_by_heapq (line 25) | def ranklist_by_heapq(user_pos_test, test_items, rating, Ks): function get_auc (line 43) | def get_auc(item_score, user_pos_test): function ranklist_by_sorted (line 59) | def ranklist_by_sorted(user_pos_test, test_items, rating, Ks): function get_performance (line 77) | def get_performance(user_pos_test, r, auc, Ks): function test_one_user (line 95) | def test_one_user(x): function test (line 120) | def test(model, g, users_to_test, batch_test_flag=False): FILE: examples/pytorch/NGCF/NGCF/utility/helper.py function txt2list (line 13) | def txt2list(file_src): function ensureDir (line 19) | def ensureDir(dir_path): function uni2str (line 25) | def uni2str(unicode_str): function hasNumbers (line 29) | def hasNumbers(inputString): function delMultiChar (line 33) | def delMultiChar(inputString, chars): function merge_two_dicts (line 39) | def merge_two_dicts(x, y): function early_stopping (line 45) | def early_stopping( FILE: examples/pytorch/NGCF/NGCF/utility/load_data.py class Data (line 11) | class Data(object): method __init__ (line 12) | def __init__(self, path, batch_size): method sample (line 94) | def sample(self): method get_num_users_items (line 138) | def get_num_users_items(self): method print_statistics (line 141) | def print_statistics(self): FILE: examples/pytorch/NGCF/NGCF/utility/metrics.py function recall (line 15) | def recall(rank, ground_truth, N): function precision_at_k (line 21) | def precision_at_k(r, k): function average_precision (line 34) | def average_precision(r, cut): function mean_average_precision (line 47) | def mean_average_precision(rs): function dcg_at_k (line 56) | def dcg_at_k(r, k, method=1): function ndcg_at_k (line 74) | def ndcg_at_k(r, k, method=1): function recall_at_k (line 87) | def recall_at_k(r, k, all_pos_num): function hit_at_k (line 92) | def hit_at_k(r, k): function F1 (line 100) | def F1(pre, rec): function auc (line 107) | def auc(ground_truth, prediction): FILE: examples/pytorch/NGCF/NGCF/utility/parser.py function parse_args (line 7) | def parse_args(): FILE: examples/pytorch/P-GNN/main.py function get_loss (line 16) | def get_loss(p, data, out, loss_func, device, get_auc=True): function train_model (line 59) | def train_model(data, model, loss_func, optimizer, device, g_data): function eval_model (line 73) | def eval_model(data, g_data, model, loss_func, device): function main (line 90) | def main(args): FILE: examples/pytorch/P-GNN/model.py class PGNN_layer (line 7) | class PGNN_layer(nn.Module): method __init__ (line 8) | def __init__(self, input_dim, output_dim): method forward (line 17) | def forward(self, graph, feature, anchor_eid, dists_max): class PGNN (line 46) | class PGNN(nn.Module): method __init__ (line 47) | def __init__(self, input_dim, feature_dim=32, dropout=0.5): method forward (line 55) | def forward(self, data): FILE: examples/pytorch/P-GNN/utils.py function get_communities (line 11) | def get_communities(remove_feature): function to_single_directed (line 59) | def to_single_directed(edges): function split_edges (line 71) | def split_edges(p, edges, data, non_train_ratio=0.2): function to_bidirected (line 86) | def to_bidirected(edges): function get_negative_edges (line 90) | def get_negative_edges(positive_edges, num_nodes, num_negative_edges): function get_pos_neg_edges (line 112) | def get_pos_neg_edges(data, infer_link_positive=True): function shortest_path (line 128) | def shortest_path(graph, node_range, cutoff): function merge_dicts (line 137) | def merge_dicts(dicts): function all_pairs_shortest_path (line 144) | def all_pairs_shortest_path(graph, cutoff=None, num_workers=4): function precompute_dist_data (line 167) | def precompute_dist_data(edge_index, num_nodes, approximate=0): function get_dataset (line 191) | def get_dataset(args): function get_anchors (line 220) | def get_anchors(n): function get_dist_max (line 233) | def get_dist_max(anchor_set_id, dist): function get_a_graph (line 249) | def get_a_graph(dists_max, dists_argmax): function get_graphs (line 272) | def get_graphs(data, anchor_sets): function merge_result (line 288) | def merge_result(outputs): function preselect_anchor (line 303) | def preselect_anchor(data, args, num_workers=4): FILE: examples/pytorch/TAHIN/TAHIN.py class SemanticAttention (line 10) | class SemanticAttention(nn.Module): method __init__ (line 11) | def __init__(self, in_size, hidden_size=128): method forward (line 20) | def forward(self, z): class HANLayer (line 36) | class HANLayer(nn.Module): method __init__ (line 37) | def __init__( method forward (line 66) | def forward(self, g, h): class RelationalAGG (line 88) | class RelationalAGG(nn.Module): method __init__ (line 89) | def __init__(self, g, in_size, out_size, dropout=0.1): method forward (line 113) | def forward(self, g, feat_dict): class TAHIN (line 156) | class TAHIN(nn.Module): method __init__ (line 157) | def __init__( method forward (line 205) | def forward(self, g, user_key, item_key, user_idx, item_idx): FILE: examples/pytorch/TAHIN/data_loader.py function split_data (line 13) | def split_data(hg, etype_name): function process_amazon (line 47) | def process_amazon(root_path): function process_movielens (line 169) | def process_movielens(root_path): class MyDataset (line 302) | class MyDataset(Dataset): method __init__ (line 303) | def __init__(self, triple): method __getitem__ (line 307) | def __getitem__(self, index): method __len__ (line 314) | def __len__(self): function load_data (line 318) | def load_data(dataset, batch_size=128, num_workers=10, root_path="./data"): FILE: examples/pytorch/TAHIN/main.py function main (line 20) | def main(args): FILE: examples/pytorch/TAHIN/utils.py function evaluate_auc (line 4) | def evaluate_auc(pred, label): function evaluate_acc (line 9) | def evaluate_acc(pred, label): function evaluate_f1_score (line 16) | def evaluate_f1_score(pred, label): function evaluate_logloss (line 23) | def evaluate_logloss(pred, label): FILE: examples/pytorch/appnp/appnp.py class APPNP (line 13) | class APPNP(nn.Module): method __init__ (line 14) | def __init__( method reset_parameters (line 44) | def reset_parameters(self): method forward (line 48) | def forward(self, features): FILE: examples/pytorch/appnp/train.py function evaluate (line 19) | def evaluate(model, features, labels, mask): function main (line 30) | def main(args): FILE: examples/pytorch/argo/argo.py function transform (line 22) | def transform(self, X): function inverse_transform (line 48) | def inverse_transform(self, X): class ARGO (line 66) | class ARGO: method __init__ (line 67) | def __init__( method core_binder (line 104) | def core_binder( method auto_tuning (line 154) | def auto_tuning(self, train: Callable, args) -> List[int]: method mp_engine (line 190) | def mp_engine(self, x: List[int], train: Callable, args, ep: int) -> f... method run (line 251) | def run(self, train, args): FILE: examples/pytorch/argo/main.py class GNN (line 23) | class GNN(nn.Module): method __init__ (line 24) | def __init__( method forward (line 57) | def forward(self, blocks, x): function _train (line 74) | def _train(**kwargs): function train (line 117) | def train( FILE: examples/pytorch/argo/ogb_example.py class SAGE (line 20) | class SAGE(nn.Module): method __init__ (line 21) | def __init__( method forward (line 36) | def forward(self, blocks, x): method inference (line 52) | def inference(self, g, x, device): function compute_acc (line 100) | def compute_acc(pred, labels): function evaluate (line 107) | def evaluate(model, g, nfeat, labels, val_nid, test_nid, device): function load_subtensor (line 127) | def load_subtensor(nfeat, labels, seeds, input_nodes): function train (line 137) | def train(args, device, data): FILE: examples/pytorch/argo/ogb_example_ARGO.py class SAGE (line 31) | class SAGE(nn.Module): method __init__ (line 32) | def __init__( method forward (line 47) | def forward(self, blocks, x): method inference (line 63) | def inference(self, g, x, device): function compute_acc (line 111) | def compute_acc(pred, labels): function evaluate (line 118) | def evaluate(model, g, nfeat, labels, val_nid, test_nid, device): function load_subtensor (line 138) | def load_subtensor(nfeat, labels, seeds, input_nodes): function train (line 148) | def train( FILE: examples/pytorch/arma/citation.py function main (line 16) | def main(args): FILE: examples/pytorch/arma/model.py function glorot (line 10) | def glorot(tensor): function zeros (line 16) | def zeros(tensor): class ARMAConv (line 21) | class ARMAConv(nn.Module): method __init__ (line 22) | def __init__( method reset_parameters (line 72) | def reset_parameters(self): method forward (line 79) | def forward(self, g, feats): class ARMA4NC (line 114) | class ARMA4NC(nn.Module): method __init__ (line 115) | def __init__( method forward (line 147) | def forward(self, g, feats): FILE: examples/pytorch/bgnn/BGNN.py class BGNNPredictor (line 15) | class BGNNPredictor: method __init__ (line 56) | def __init__( method init_gbdt_model (line 90) | def init_gbdt_model(self, num_epochs, epoch): method fit_gbdt (line 111) | def fit_gbdt(self, pool, trees_per_epoch, epoch): method append_gbdt_model (line 116) | def append_gbdt_model(self, new_gbdt_model, weights): method train_gbdt (line 121) | def train_gbdt( method update_node_features (line 139) | def update_node_features(self, node_features, X, original_X): method update_gbdt_targets (line 168) | def update_gbdt_targets( method init_node_features (line 178) | def init_node_features(self, X): method init_optimizer (line 188) | def init_optimizer( method train_model (line 197) | def train_model(self, model_in, target_labels, train_mask, optimizer): method evaluate_model (line 221) | def evaluate_model(self, logits, target_labels, mask): method train_and_evaluate (line 246) | def train_and_evaluate( method update_early_stopping (line 279) | def update_early_stopping( method log_epoch (line 300) | def log_epoch( method fit (line 323) | def fit( method predict (line 486) | def predict(self, graph, X, test_mask): method plot_interactive (line 496) | def plot_interactive( FILE: examples/pytorch/bgnn/run.py class GNNModelDGL (line 23) | class GNNModelDGL(torch.nn.Module): method __init__ (line 24) | def __init__( method forward (line 86) | def forward(self, graph, features): function read_input (line 117) | def read_input(input_folder): function normalize_features (line 145) | def normalize_features(X, train_mask, val_mask, test_mask): function replace_na (line 153) | def replace_na(X, train_mask): function encode_cat_features (line 159) | def encode_cat_features(X, y, cat_features, train_mask, val_mask, test_m... FILE: examples/pytorch/bgrl/eval_function.py function fit_logistic_regression (line 10) | def fit_logistic_regression(X, y, data_random_seed=1, repeat=1): function fit_logistic_regression_preset_splits (line 53) | def fit_logistic_regression_preset_splits( function fit_ppi_linear (line 103) | def fit_ppi_linear( FILE: examples/pytorch/bgrl/main.py function train (line 29) | def train( function eval (line 76) | def eval(model, dataset, device, args, train_data, val_data, test_data): function main (line 123) | def main(args): FILE: examples/pytorch/bgrl/model.py class LayerNorm (line 12) | class LayerNorm(nn.Module): method __init__ (line 13) | def __init__(self, in_channels, eps=1e-5, affine=True): method reset_parameters (line 27) | def reset_parameters(self): method forward (line 31) | def forward(self, x, batch=None): method __repr__ (line 68) | def __repr__(self): class MLP_Predictor (line 72) | class MLP_Predictor(nn.Module): method __init__ (line 80) | def __init__(self, input_size, output_size, hidden_size=512): method forward (line 90) | def forward(self, x): method reset_parameters (line 93) | def reset_parameters(self): class GCN (line 100) | class GCN(nn.Module): method __init__ (line 101) | def __init__(self, layer_sizes, batch_norm_mm=0.99): method forward (line 110) | def forward(self, g): method reset_parameters (line 119) | def reset_parameters(self): class GraphSAGE_GCN (line 125) | class GraphSAGE_GCN(nn.Module): method __init__ (line 126) | def __init__(self, layer_sizes): method forward (line 162) | def forward(self, g): method reset_parameters (line 184) | def reset_parameters(self): class BGRL (line 195) | class BGRL(nn.Module): method __init__ (line 205) | def __init__(self, encoder, predictor): method trainable_parameters (line 221) | def trainable_parameters(self): method update_target_network (line 228) | def update_target_network(self, mm): method forward (line 238) | def forward(self, online_x, target_x): function compute_representations (line 251) | def compute_representations(net, dataset, device): FILE: examples/pytorch/bgrl/utils.py class CosineDecayScheduler (line 18) | class CosineDecayScheduler: method __init__ (line 19) | def __init__(self, max_val, warmup_steps, total_steps): method get (line 24) | def get(self, step): function get_graph_drop_transform (line 48) | def get_graph_drop_transform(drop_edge_p, feat_mask_p): function get_wiki_cs (line 65) | def get_wiki_cs(transform=RowFeatNormalizer(subtract_min=True)): function get_ppi (line 74) | def get_ppi(): function get_dataset (line 88) | def get_dataset(name, transform=RowFeatNormalizer(subtract_min=True)): FILE: examples/pytorch/capsule/DGLDigitCapsule.py class DGLDigitCapsuleLayer (line 9) | class DGLDigitCapsuleLayer(nn.Module): method __init__ (line 10) | def __init__( method forward (line 26) | def forward(self, x): method compute_uhat (line 46) | def compute_uhat(self, x): FILE: examples/pytorch/capsule/DGLRoutingLayer.py class DGLRoutingLayer (line 7) | class DGLRoutingLayer(nn.Module): method __init__ (line 8) | def __init__(self, in_nodes, out_nodes, f_size, batch_size=0, device="... method forward (line 18) | def forward(self, u_hat, routing_num=1): function squash (line 63) | def squash(s, dim=1): function init_graph (line 70) | def init_graph(in_nodes, out_nodes, f_size, device="cpu"): FILE: examples/pytorch/capsule/main.py function train (line 9) | def train(args, model, device, train_loader, optimizer, epoch): function test (line 30) | def test(args, model, device, test_loader): function main (line 57) | def main(): FILE: examples/pytorch/capsule/model.py class Net (line 7) | class Net(nn.Module): method __init__ (line 8) | def __init__(self, device="cpu"): method forward (line 19) | def forward(self, x): method margin_loss (line 25) | def margin_loss(self, input, target): class PrimaryCapsuleLayer (line 44) | class PrimaryCapsuleLayer(nn.Module): method __init__ (line 45) | def __init__(self, in_channel=256, num_unit=8, device="cpu"): method forward (line 54) | def forward(self, x): FILE: examples/pytorch/caregnn/main.py function main (line 13) | def main(args): FILE: examples/pytorch/caregnn/main_sampling.py function evaluate (line 13) | def evaluate(model, loss_fn, dataloader, device="cpu"): function main (line 40) | def main(args): FILE: examples/pytorch/caregnn/model.py class CAREConv (line 7) | class CAREConv(nn.Module): method __init__ (line 10) | def __init__( method _calc_distance (line 42) | def _calc_distance(self, edges): method _top_p_sampling (line 52) | def _top_p_sampling(self, g, p): method forward (line 70) | def forward(self, g, feat): class CAREGNN (line 103) | class CAREGNN(nn.Module): method __init__ (line 104) | def __init__( method forward (line 176) | def forward(self, graph, feat): method RLModule (line 187) | def RLModule(self, graph, epoch, idx): FILE: examples/pytorch/caregnn/model_sampling.py function _l1_dist (line 8) | def _l1_dist(edges): class CARESampler (line 14) | class CARESampler(dgl.dataloading.BlockSampler): method __init__ (line 15) | def __init__(self, p, dists, num_layers): method sample_frontier (line 21) | def sample_frontier(self, block_id, g, seed_nodes, *args, **kwargs): method sample_blocks (line 49) | def sample_blocks(self, g, seed_nodes, exclude_eids=None): method __len__ (line 62) | def __len__(self): class CAREConv (line 66) | class CAREConv(nn.Module): method __init__ (line 69) | def __init__( method forward (line 101) | def forward(self, g, feat): class CAREGNN (line 124) | class CAREGNN(nn.Module): method __init__ (line 125) | def __init__( method forward (line 197) | def forward(self, blocks, feat): method RLModule (line 206) | def RLModule(self, graph, epoch, idx, dists): FILE: examples/pytorch/caregnn/utils.py class EarlyStopping (line 7) | class EarlyStopping: method __init__ (line 8) | def __init__(self, patience=10): method step (line 14) | def step(self, acc, model): method save_checkpoint (line 32) | def save_checkpoint(self, model): FILE: examples/pytorch/cluster_gcn/cluster_gcn.py class SAGE (line 14) | class SAGE(nn.Module): method __init__ (line 15) | def __init__(self, in_feats, n_hidden, n_classes): method forward (line 23) | def forward(self, sg, x): FILE: examples/pytorch/compGCN/data_loader.py class TrainDataset (line 11) | class TrainDataset(Dataset): method __init__ (line 25) | def __init__(self, triples, num_ent, lbl_smooth): method __len__ (line 31) | def __len__(self): method __getitem__ (line 34) | def __getitem__(self, idx): method collate_fn (line 47) | def collate_fn(data): method get_label (line 58) | def get_label(self, label): class TestDataset (line 65) | class TestDataset(Dataset): method __init__ (line 78) | def __init__(self, triples, num_ent): method __len__ (line 82) | def __len__(self): method __getitem__ (line 85) | def __getitem__(self, idx): method collate_fn (line 93) | def collate_fn(data): method get_label (line 104) | def get_label(self, label): class Data (line 111) | class Data(object): method __init__ (line 112) | def __init__(self, dataset, lbl_smooth, num_workers, batch_size): FILE: examples/pytorch/compGCN/main.py function predict (line 13) | def predict(model, graph, device, data_iter, split="valid", mode="tail"): function evaluate (line 57) | def evaluate(model, graph, device, data_iter, split="valid"): function main (line 94) | def main(args): FILE: examples/pytorch/compGCN/models.py class CompGraphConv (line 10) | class CompGraphConv(nn.Module): method __init__ (line 13) | def __init__( method forward (line 42) | def forward(self, g, n_in_feats, r_feats): class CompGCN (line 120) | class CompGCN(nn.Module): method __init__ (line 121) | def __init__( method forward (line 187) | def forward(self, graph): class CompGCN_ConvE (line 204) | class CompGCN_ConvE(nn.Module): method __init__ (line 205) | def __init__( method concat (line 272) | def concat(self, e1_embed, rel_embed): method forward (line 281) | def forward(self, graph, sub, rel): FILE: examples/pytorch/compGCN/utils.py function com_mult (line 9) | def com_mult(a, b): function conj (line 15) | def conj(a): function ccorr (line 20) | def ccorr(a, b): function in_out_norm (line 42) | def in_out_norm(graph): FILE: examples/pytorch/correct_and_smooth/main.py function evaluate (line 14) | def evaluate(y_pred, y_true, idx, evaluator): function main (line 18) | def main(): FILE: examples/pytorch/correct_and_smooth/model.py class MLPLinear (line 7) | class MLPLinear(nn.Module): method __init__ (line 8) | def __init__(self, in_dim, out_dim): method reset_parameters (line 13) | def reset_parameters(self): method forward (line 16) | def forward(self, x): class MLP (line 20) | class MLP(nn.Module): method __init__ (line 21) | def __init__(self, in_dim, hid_dim, out_dim, num_layers, dropout=0.0): method reset_parameters (line 38) | def reset_parameters(self): method forward (line 44) | def forward(self, x): class LabelPropagation (line 54) | class LabelPropagation(nn.Module): method __init__ (line 79) | def __init__(self, num_layers, alpha, adj="DAD"): method forward (line 87) | def forward( class CorrectAndSmooth (line 124) | class CorrectAndSmooth(nn.Module): method __init__ (line 155) | def __init__( method correct (line 178) | def correct(self, g, y_soft, y_true, mask): method smooth (line 217) | def smooth(self, g, y_soft, y_true, mask): FILE: examples/pytorch/dagnn/main.py class DAGNNConv (line 14) | class DAGNNConv(nn.Module): method __init__ (line 15) | def __init__(self, in_dim, k): method reset_parameters (line 23) | def reset_parameters(self): method forward (line 27) | def forward(self, graph, feats): class MLPLayer (line 51) | class MLPLayer(nn.Module): method __init__ (line 52) | def __init__(self, in_dim, out_dim, bias=True, activation=None, dropou... method reset_parameters (line 60) | def reset_parameters(self): method forward (line 68) | def forward(self, feats): class DAGNN (line 77) | class DAGNN(nn.Module): method __init__ (line 78) | def __init__( method forward (line 110) | def forward(self, graph, feats): function main (line 117) | def main(args): FILE: examples/pytorch/dagnn/utils.py function evaluate (line 8) | def evaluate(model, graph, feats, labels, idxs): function generate_random_seeds (line 22) | def generate_random_seeds(seed, nums): function set_random_state (line 27) | def set_random_state(seed): FILE: examples/pytorch/deepergcn/layers.py class GENConv (line 10) | class GENConv(nn.Module): method __init__ (line 43) | def __init__( method forward (line 83) | def forward(self, g, node_feats, edge_feats): FILE: examples/pytorch/deepergcn/main.py function train (line 13) | def train(model, device, data_loader, opt, loss_fn): function test (line 32) | def test(model, device, data_loader, evaluator): function main (line 48) | def main(): FILE: examples/pytorch/deepergcn/models.py class DeeperGCN (line 9) | class DeeperGCN(nn.Module): method __init__ (line 40) | def __init__( method forward (line 77) | def forward(self, g, edge_feats, node_feats=None): FILE: examples/pytorch/deepergcn/modules.py class MLP (line 6) | class MLP(nn.Sequential): method __init__ (line 14) | def __init__(self, channels, act="relu", dropout=0.0, bias=True): class MessageNorm (line 27) | class MessageNorm(nn.Module): method __init__ (line 40) | def __init__(self, learn_scale=False): method forward (line 46) | def forward(self, feats, msg, p=2): FILE: examples/pytorch/dgi/dgi.py class Encoder (line 17) | class Encoder(nn.Module): method __init__ (line 18) | def __init__(self, g, in_feats, n_hidden, n_layers, activation, dropout): method forward (line 25) | def forward(self, features, corrupt=False): class Discriminator (line 33) | class Discriminator(nn.Module): method __init__ (line 34) | def __init__(self, n_hidden): method uniform (line 39) | def uniform(self, size, tensor): method reset_parameters (line 44) | def reset_parameters(self): method forward (line 48) | def forward(self, features, summary): class DGI (line 53) | class DGI(nn.Module): method __init__ (line 54) | def __init__(self, g, in_feats, n_hidden, n_layers, activation, dropout): method forward (line 62) | def forward(self, features): class Classifier (line 76) | class Classifier(nn.Module): method __init__ (line 77) | def __init__(self, n_hidden, n_classes): method reset_parameters (line 82) | def reset_parameters(self): method forward (line 85) | def forward(self, features): FILE: examples/pytorch/dgi/gcn.py class GCN (line 10) | class GCN(nn.Module): method __init__ (line 11) | def __init__( method forward (line 28) | def forward(self, features): FILE: examples/pytorch/dgi/train.py function evaluate (line 14) | def evaluate(model, features, labels, mask): function main (line 25) | def main(args): FILE: examples/pytorch/dgmg/configure.py function dataset_based_configure (line 6) | def dataset_based_configure(opts): FILE: examples/pytorch/dgmg/cycles.py function get_previous (line 10) | def get_previous(i, v_max): function get_next (line 17) | def get_next(i, v_max): function is_cycle (line 24) | def is_cycle(g): function get_decision_sequence (line 45) | def get_decision_sequence(size): function generate_dataset (line 72) | def generate_dataset(v_min, v_max, n_samples, fname): class CycleDataset (line 82) | class CycleDataset(Dataset): method __init__ (line 83) | def __init__(self, fname): method __len__ (line 89) | def __len__(self): method __getitem__ (line 92) | def __getitem__(self, index): method collate_single (line 95) | def collate_single(self, batch): method collate_batch (line 99) | def collate_batch(self, batch): function dglGraph_to_adj_list (line 103) | def dglGraph_to_adj_list(g): class CycleModelEvaluation (line 112) | class CycleModelEvaluation(object): method __init__ (line 113) | def __init__(self, v_min, v_max, dir): method rollout_and_examine (line 121) | def rollout_and_examine(self, model, num_samples): method write_summary (line 181) | def write_summary(self): class CyclePrinting (line 210) | class CyclePrinting(object): method __init__ (line 211) | def __init__(self, num_epochs, num_batches): method update (line 218) | def update(self, epoch, metrics): FILE: examples/pytorch/dgmg/main.py function main (line 18) | def main(opts): FILE: examples/pytorch/dgmg/model.py class GraphEmbed (line 10) | class GraphEmbed(nn.Module): method __init__ (line 11) | def __init__(self, node_hidden_size): method forward (line 23) | def forward(self, g): class GraphProp (line 34) | class GraphProp(nn.Module): method __init__ (line 35) | def __init__(self, num_prop_rounds, node_hidden_size): method dgmg_msg (line 63) | def dgmg_msg(self, edges): method dgmg_reduce (line 67) | def dgmg_reduce(self, nodes, round): method forward (line 77) | def forward(self, g): function bernoulli_action_log_prob (line 90) | def bernoulli_action_log_prob(logit, action): class AddNode (line 99) | class AddNode(nn.Module): method __init__ (line 100) | def __init__(self, graph_embed_func, node_hidden_size): method _initialize_node_repr (line 117) | def _initialize_node_repr(self, g, node_type, graph_embed): method prepare_training (line 131) | def prepare_training(self): method forward (line 134) | def forward(self, g, action=None): class AddEdge (line 155) | class AddEdge(nn.Module): method __init__ (line 156) | def __init__(self, graph_embed_func, node_hidden_size): method prepare_training (line 164) | def prepare_training(self): method forward (line 167) | def forward(self, g, action=None): class ChooseDestAndUpdate (line 185) | class ChooseDestAndUpdate(nn.Module): method __init__ (line 186) | def __init__(self, graph_prop_func, node_hidden_size): method _initialize_edge_repr (line 192) | def _initialize_edge_repr(self, g, src_list, dest_list): method prepare_training (line 199) | def prepare_training(self): method forward (line 202) | def forward(self, g, dest): class DGMG (line 235) | class DGMG(nn.Module): method __init__ (line 236) | def __init__(self, v_max, node_hidden_size, num_prop_rounds): method init_weights (line 258) | def init_weights(self): method action_step (line 270) | def action_step(self): method prepare_for_train (line 276) | def prepare_for_train(self): method add_node_and_update (line 283) | def add_node_and_update(self, a=None): method add_edge_or_not (line 289) | def add_edge_or_not(self, a=None): method choose_dest_and_update (line 294) | def choose_dest_and_update(self, a=None): method get_log_prob (line 300) | def get_log_prob(self): method forward_train (line 307) | def forward_train(self, actions): method forward_inference (line 321) | def forward_inference(self): method forward (line 334) | def forward(self, actions=None): FILE: examples/pytorch/dgmg/utils.py function mkdir_p (line 17) | def mkdir_p(path): function date_filename (line 30) | def date_filename(base_dir="./"): function setup_log_dir (line 40) | def setup_log_dir(opts): function save_arg_dict (line 46) | def save_arg_dict(opts, filename="settings.txt"): function setup (line 62) | def setup(args): function weights_init (line 120) | def weights_init(m): function dgmg_message_weight_init (line 138) | def dgmg_message_weight_init(m): FILE: examples/pytorch/diffpool/data_utils.py function one_hotify (line 5) | def one_hotify(labels, pad=-1): function pre_process (line 21) | def pre_process(dataset, prog_args): FILE: examples/pytorch/diffpool/model/dgl_layers/aggregator.py class Aggregator (line 6) | class Aggregator(nn.Module): method __init__ (line 14) | def __init__(self): method forward (line 17) | def forward(self, node): method aggre (line 22) | def aggre(self, neighbour): class MeanAggregator (line 27) | class MeanAggregator(Aggregator): method __init__ (line 32) | def __init__(self): method aggre (line 35) | def aggre(self, neighbour): class MaxPoolAggregator (line 40) | class MaxPoolAggregator(Aggregator): method __init__ (line 45) | def __init__(self, in_feats, out_feats, activation, bias): method aggre (line 54) | def aggre(self, neighbour): class LSTMAggregator (line 62) | class LSTMAggregator(Aggregator): method __init__ (line 67) | def __init__(self, in_feats, hidden_feats): method init_hidden (line 77) | def init_hidden(self): method aggre (line 86) | def aggre(self, neighbours): method forward (line 99) | def forward(self, node): FILE: examples/pytorch/diffpool/model/dgl_layers/bundler.py class Bundler (line 6) | class Bundler(nn.Module): method __init__ (line 11) | def __init__(self, in_feats, out_feats, activation, dropout, bias=True): method concat (line 21) | def concat(self, h, aggre_result): method forward (line 26) | def forward(self, node): FILE: examples/pytorch/diffpool/model/dgl_layers/gnn.py class GraphSageLayer (line 15) | class GraphSageLayer(nn.Module): method __init__ (line 21) | def __init__( method forward (line 47) | def forward(self, g, h): class GraphSage (line 60) | class GraphSage(nn.Module): method __init__ (line 65) | def __init__( method forward (line 96) | def forward(self, g, features): class DiffPoolBatchedGraphLayer (line 103) | class DiffPoolBatchedGraphLayer(nn.Module): method __init__ (line 104) | def __init__( method forward (line 129) | def forward(self, g, h): FILE: examples/pytorch/diffpool/model/encoder.py class DiffPool (line 17) | class DiffPool(nn.Module): method __init__ (line 22) | def __init__( method gcn_forward (line 153) | def gcn_forward(self, g, h, gc_layers, cat=False): method gcn_forward_tensorized (line 169) | def gcn_forward_tensorized(self, h, adj, gc_layers, cat=False): method forward (line 180) | def forward(self, g): method loss (line 231) | def loss(self, pred, label): FILE: examples/pytorch/diffpool/model/loss.py class EntropyLoss (line 5) | class EntropyLoss(nn.Module): method forward (line 7) | def forward(self, adj, anext, s_l): class LinkPredLoss (line 17) | class LinkPredLoss(nn.Module): method forward (line 18) | def forward(self, adj, anext, s_l): FILE: examples/pytorch/diffpool/model/model_utils.py function batch2tensor (line 5) | def batch2tensor(batch_adj, batch_feat, node_per_pool_graph): function masked_softmax (line 25) | def masked_softmax( FILE: examples/pytorch/diffpool/model/tensorized_layers/assignment.py class DiffPoolAssignment (line 9) | class DiffPoolAssignment(nn.Module): method __init__ (line 10) | def __init__(self, nfeat, nnext): method forward (line 14) | def forward(self, x, adj, log=False): FILE: examples/pytorch/diffpool/model/tensorized_layers/diffpool.py class BatchedDiffPool (line 9) | class BatchedDiffPool(nn.Module): method __init__ (line 10) | def __init__(self, nfeat, nnext, nhid, link_pred=False, entropy=True): method forward (line 24) | def forward(self, x, adj, log=False): FILE: examples/pytorch/diffpool/model/tensorized_layers/graphsage.py class BatchedGraphSAGE (line 6) | class BatchedGraphSAGE(nn.Module): method __init__ (line 7) | def __init__( method forward (line 20) | def forward(self, x, adj): method __repr__ (line 39) | def __repr__(self): FILE: examples/pytorch/diffpool/train.py function arg_parse (line 23) | def arg_parse(): function prepare_data (line 138) | def prepare_data(dataset, prog_args, train=False, pre_process=None): function graph_classify_task (line 159) | def graph_classify_task(prog_args): function train (line 243) | def train(dataset, model, prog_args, same_feat=True, val_dataset=None): function evaluate (line 327) | def evaluate(dataloader, model, prog_args, logger=None): function main (line 360) | def main(): FILE: examples/pytorch/dimenet/convert_tf_ckpt_to_pytorch.py function main (line 28) | def main(model_cnf, convert_cnf): FILE: examples/pytorch/dimenet/main.py function split_dataset (line 23) | def split_dataset( function ema (line 68) | def ema(ema_model, model, decay): function edge_init (line 75) | def edge_init(edges): function _collate_fn (line 82) | def _collate_fn(batch): function train (line 89) | def train(device, model, opt, loss_fn, train_loader): function evaluate (line 110) | def evaluate(device, model, valid_loader): function main (line 137) | def main(model_cnf): FILE: examples/pytorch/dimenet/modules/activations.py function swish (line 4) | def swish(x): FILE: examples/pytorch/dimenet/modules/basis_utils.py function Jn (line 7) | def Jn(r, n): function Jn_zeros (line 23) | def Jn_zeros(n, k): function spherical_bessel_formulas (line 45) | def spherical_bessel_formulas(n): function bessel_basis (line 64) | def bessel_basis(n, k): function sph_harm_prefactor (line 99) | def sph_harm_prefactor(l, m): function associated_legendre_polynomials (line 116) | def associated_legendre_polynomials(l, zero_m_only=True): function real_sph_harm (line 155) | def real_sph_harm(l, zero_m_only=True, spherical_coordinates=True): FILE: examples/pytorch/dimenet/modules/bessel_basis_layer.py class BesselBasisLayer (line 7) | class BesselBasisLayer(nn.Module): method __init__ (line 8) | def __init__(self, num_radial, cutoff, envelope_exponent=5): method reset_params (line 16) | def reset_params(self): method forward (line 23) | def forward(self, g): FILE: examples/pytorch/dimenet/modules/dimenet.py class DimeNet (line 11) | class DimeNet(nn.Module): method __init__ (line 45) | def __init__( method edge_init (line 121) | def edge_init(self, edges): method forward (line 135) | def forward(self, g, l_g): FILE: examples/pytorch/dimenet/modules/dimenet_pp.py class DimeNetPP (line 11) | class DimeNetPP(nn.Module): method __init__ (line 51) | def __init__( method edge_init (line 133) | def edge_init(self, edges): method forward (line 148) | def forward(self, g, l_g): FILE: examples/pytorch/dimenet/modules/embedding_block.py class EmbeddingBlock (line 8) | class EmbeddingBlock(nn.Module): method __init__ (line 9) | def __init__( method reset_params (line 30) | def reset_params(self): method edge_init (line 35) | def edge_init(self, edges): method forward (line 57) | def forward(self, g): FILE: examples/pytorch/dimenet/modules/envelope.py class Envelope (line 4) | class Envelope(nn.Module): method __init__ (line 9) | def __init__(self, exponent): method forward (line 17) | def forward(self, x): FILE: examples/pytorch/dimenet/modules/initializers.py function GlorotOrthogonal (line 4) | def GlorotOrthogonal(tensor, scale=2.0): FILE: examples/pytorch/dimenet/modules/interaction_block.py class InteractionBlock (line 8) | class InteractionBlock(nn.Module): method __init__ (line 9) | def __init__( method reset_params (line 53) | def reset_params(self): method edge_transfer (line 60) | def edge_transfer(self, edges): method msg_func (line 73) | def msg_func(self, edges): method forward (line 82) | def forward(self, g, l_g): FILE: examples/pytorch/dimenet/modules/interaction_pp_block.py class InteractionPPBlock (line 8) | class InteractionPPBlock(nn.Module): method __init__ (line 9) | def __init__( method reset_params (line 54) | def reset_params(self): method edge_transfer (line 66) | def edge_transfer(self, edges): method msg_func (line 82) | def msg_func(self, edges): method forward (line 88) | def forward(self, g, l_g): FILE: examples/pytorch/dimenet/modules/output_block.py class OutputBlock (line 7) | class OutputBlock(nn.Module): method __init__ (line 8) | def __init__( method reset_params (line 28) | def reset_params(self): method forward (line 34) | def forward(self, g): FILE: examples/pytorch/dimenet/modules/output_pp_block.py class OutputPPBlock (line 7) | class OutputPPBlock(nn.Module): method __init__ (line 8) | def __init__( method reset_params (line 32) | def reset_params(self): method forward (line 39) | def forward(self, g): FILE: examples/pytorch/dimenet/modules/residual_layer.py class ResidualLayer (line 5) | class ResidualLayer(nn.Module): method __init__ (line 6) | def __init__(self, units, activation=None): method reset_params (line 15) | def reset_params(self): method forward (line 21) | def forward(self, inputs): FILE: examples/pytorch/dimenet/modules/spherical_basis_layer.py class SphericalBasisLayer (line 8) | class SphericalBasisLayer(nn.Module): method __init__ (line 9) | def __init__(self, num_spherical, num_radial, cutoff, envelope_exponen... method get_bessel_funcs (line 49) | def get_bessel_funcs(self): method get_sph_funcs (line 52) | def get_sph_funcs(self): FILE: examples/pytorch/dimenet/qm9.py class QM9 (line 15) | class QM9(QM9Dataset): method __init__ (line 100) | def __init__( method graph_path (line 134) | def graph_path(self): method line_graph_path (line 138) | def line_graph_path(self): method has_cache (line 141) | def has_cache(self): method process (line 147) | def process(self): method _load_graph (line 171) | def _load_graph(self): method save (line 206) | def save(self): method load (line 211) | def load(self): method __getitem__ (line 219) | def __getitem__(self, idx): FILE: examples/pytorch/dtgrnn/dataloading.py function download_file (line 12) | def download_file(dataset): class SnapShotDataset (line 24) | class SnapShotDataset(Dataset): method __init__ (line 25) | def __init__(self, path, npz_file): method __len__ (line 34) | def __len__(self): method __getitem__ (line 37) | def __getitem__(self, idx): function METR_LAGraphDataset (line 44) | def METR_LAGraphDataset(): class METR_LATrainDataset (line 53) | class METR_LATrainDataset(SnapShotDataset): method __init__ (line 54) | def __init__(self): class METR_LATestDataset (line 60) | class METR_LATestDataset(SnapShotDataset): method __init__ (line 61) | def __init__(self): class METR_LAValidDataset (line 65) | class METR_LAValidDataset(SnapShotDataset): method __init__ (line 66) | def __init__(self): function PEMS_BAYGraphDataset (line 70) | def PEMS_BAYGraphDataset(): class PEMS_BAYTrainDataset (line 79) | class PEMS_BAYTrainDataset(SnapShotDataset): method __init__ (line 80) | def __init__(self): class PEMS_BAYTestDataset (line 86) | class PEMS_BAYTestDataset(SnapShotDataset): method __init__ (line 87) | def __init__(self): class PEMS_BAYValidDataset (line 91) | class PEMS_BAYValidDataset(SnapShotDataset): method __init__ (line 92) | def __init__(self): FILE: examples/pytorch/dtgrnn/dcrnn.py class DiffConv (line 10) | class DiffConv(nn.Module): method __init__ (line 30) | def __init__( method attach_graph (line 49) | def attach_graph(g, k): method get_weight_matrix (line 73) | def get_weight_matrix(g): method diffuse (line 82) | def diffuse(progress_g, weighted_adj, degree): method forward (line 93) | def forward(self, g, x): FILE: examples/pytorch/dtgrnn/gaan.py class WeightedGATConv (line 11) | class WeightedGATConv(dglnn.GATConv): method forward (line 17) | def forward(self, graph, feat, get_attention=False): class GatedGAT (line 99) | class GatedGAT(nn.Module): method __init__ (line 118) | def __init__(self, in_feats, out_feats, map_feats, num_heads): method forward (line 135) | def forward(self, g, x): FILE: examples/pytorch/dtgrnn/model.py class GraphGRUCell (line 12) | class GraphGRUCell(nn.Module): method __init__ (line 27) | def __init__(self, in_feats, out_feats, net): method forward (line 41) | def forward(self, g, x, h): class StackedEncoder (line 52) | class StackedEncoder(nn.Module): method __init__ (line 71) | def __init__(self, in_feats, out_feats, num_layers, net): method forward (line 89) | def forward(self, g, x, hidden_states): class StackedDecoder (line 97) | class StackedDecoder(nn.Module): method __init__ (line 119) | def __init__(self, in_feats, hid_feats, out_feats, num_layers, net): method forward (line 136) | def forward(self, g, x, hidden_states): class GraphRNN (line 145) | class GraphRNN(nn.Module): method __init__ (line 170) | def __init__( method compute_thresh (line 195) | def compute_thresh(self, batch_cnt): method encode (line 200) | def encode(self, g, inputs, device): method decode (line 210) | def decode(self, g, teacher_states, hidden_states, batch_cnt, device): method forward (line 227) | def forward(self, g, inputs, teacher_states, batch_cnt, device): FILE: examples/pytorch/dtgrnn/train.py function train (line 28) | def train( function eval (line 94) | def eval(model, graph, dataloader, normalizer, loss_fn, device, args): FILE: examples/pytorch/dtgrnn/utils.py class NormalizationLayer (line 8) | class NormalizationLayer(nn.Module): method __init__ (line 9) | def __init__(self, mean, std): method normalize (line 14) | def normalize(self, x): method denormalize (line 17) | def denormalize(self, x): function masked_mae_loss (line 21) | def masked_mae_loss(y_pred, y_true): function get_learning_rate (line 31) | def get_learning_rate(optimizer): FILE: examples/pytorch/eeg-gcnn/EEGGraphDataset.py class EEGGraphDataset (line 12) | class EEGGraphDataset(DGLDataset): method __init__ (line 27) | def __init__(self, x, y, num_nodes, indices): method get_sensor_distances (line 70) | def get_sensor_distances(self): method get_geodesic_distance (line 84) | def get_geodesic_distance( method __len__ (line 113) | def __len__(self): method __getitem__ (line 117) | def __getitem__(self, idx): FILE: examples/pytorch/eeg-gcnn/deep_EEGGraphConvNet.py class EEGGraphConvNet (line 8) | class EEGGraphConvNet(nn.Module): method __init__ (line 15) | def __init__(self, num_feats): method forward (line 37) | def forward(self, g, return_graph_embedding=False): FILE: examples/pytorch/eeg-gcnn/main.py function _load_memory_mapped_array (line 17) | def _load_memory_mapped_array(file_name): FILE: examples/pytorch/eeg-gcnn/shallow_EEGGraphConvNet.py class EEGGraphConvNet (line 7) | class EEGGraphConvNet(nn.Module): method __init__ (line 14) | def __init__(self, num_feats): method forward (line 29) | def forward(self, g, return_graph_embedding=False): FILE: examples/pytorch/eges/main.py function train (line 11) | def train(args, train_g, sku_info, num_skus, num_brands, num_shops, num_... function eval (line 58) | def eval(model, test_graph, sku_info): FILE: examples/pytorch/eges/model.py class EGES (line 4) | class EGES(th.nn.Module): method __init__ (line 5) | def __init__(self, dim, num_nodes, num_brands, num_shops, num_cates): method forward (line 17) | def forward(self, srcs, dsts): method query_node_embed (line 24) | def query_node_embed(self, nodes): method loss (line 57) | def loss(self, srcs, dsts, labels): FILE: examples/pytorch/eges/sampler.py class Sampler (line 6) | class Sampler: method __init__ (line 7) | def __init__( method sample (line 17) | def sample(self, batch, sku_info): method filter_padding (line 40) | def filter_padding(self, traces): method generate_pos_pairs (line 44) | def generate_pos_pairs(self, nodes): method compute_node_sample_weight (line 69) | def compute_node_sample_weight(self): method generate_neg_pairs (line 75) | def generate_neg_pairs(self, pos_pairs): FILE: examples/pytorch/eges/utils.py function init_args (line 12) | def init_args(): function construct_graph (line 35) | def construct_graph(datapath, session_interval_gap_sec, valid_sku_raw_ids): function convert_to_dgl_graph (line 70) | def convert_to_dgl_graph(graph): function add_session (line 81) | def add_session(session, graph): function parse_actions (line 98) | def parse_actions(datapath, valid_sku_raw_ids): function encode_id (line 129) | def encode_id(encoder, decoder, raw_id, encoded_id): function get_valid_sku_set (line 140) | def get_valid_sku_set(datapath): function encode_sku_fields (line 151) | def encode_sku_fields(datapath, sku_encoder, sku_decoder): class TestEdge (line 197) | class TestEdge: method __init__ (line 198) | def __init__(self, src, dst, label): function split_train_test_graph (line 204) | def split_train_test_graph(graph): FILE: examples/pytorch/evolveGCN/dataset.py function process_raw_data (line 10) | def process_raw_data(raw_dir, processed_dir): class EllipticDataset (line 98) | class EllipticDataset: method __init__ (line 99) | def __init__( method process (line 107) | def process(self): method num_classes (line 178) | def num_classes(self): FILE: examples/pytorch/evolveGCN/model.py class MatGRUCell (line 9) | class MatGRUCell(torch.nn.Module): method __init__ (line 15) | def __init__(self, in_feats, out_feats): method forward (line 23) | def forward(self, prev_Q, z_topk=None): class MatGRUGate (line 38) | class MatGRUGate(torch.nn.Module): method __init__ (line 44) | def __init__(self, rows, cols, activation): method reset_parameters (line 52) | def reset_parameters(self): method forward (line 57) | def forward(self, x, hidden): class TopK (line 65) | class TopK(torch.nn.Module): method __init__ (line 72) | def __init__(self, feats, k): method reset_parameters (line 79) | def reset_parameters(self): method forward (line 82) | def forward(self, node_embs): class EvolveGCNH (line 94) | class EvolveGCNH(nn.Module): method __init__ (line 95) | def __init__( method reset_parameters (line 153) | def reset_parameters(self): method forward (line 157) | def forward(self, g_list): class EvolveGCNO (line 172) | class EvolveGCNO(nn.Module): method __init__ (line 173) | def __init__( method reset_parameters (line 235) | def reset_parameters(self): method forward (line 239) | def forward(self, g_list): FILE: examples/pytorch/evolveGCN/train.py function train (line 13) | def train(args, device): FILE: examples/pytorch/evolveGCN/utils.py function calculate_measure (line 1) | def calculate_measure(tp, fn, fp): class Measure (line 15) | class Measure(object): method __init__ (line 16) | def __init__(self, num_classes, target_class): method reset_info (line 32) | def reset_info(self): method append_measures (line 46) | def append_measures(self, predictions, labels): method get_each_timestamp_measure (line 61) | def get_each_timestamp_measure(self): method get_total_measure (line 76) | def get_total_measure(self): method update_best_f1 (line 84) | def update_best_f1(self, cur_f1, cur_epoch): FILE: examples/pytorch/gas/dataloader.py class GASDataset (line 12) | class GASDataset(DGLBuiltinDataset): method __init__ (line 15) | def __init__( method process (line 25) | def process(self): method graph_path (line 72) | def graph_path(self): method save (line 75) | def save(self): method has_cache (line 79) | def has_cache(self): method load (line 83) | def load(self): method num_classes (line 89) | def num_classes(self): method __getitem__ (line 93) | def __getitem__(self, idx): method __len__ (line 106) | def __len__(self): method _random_split (line 114) | def _random_split(self, graph, seed=717, train_size=0.7, val_size=0.1): FILE: examples/pytorch/gas/main.py function main (line 11) | def main(args): FILE: examples/pytorch/gas/main_sampling.py function evaluate (line 12) | def evaluate(model, loss_fn, dataloader, device="cpu"): function main (line 45) | def main(args): FILE: examples/pytorch/gas/model.py class MLP (line 7) | class MLP(nn.Module): method __init__ (line 8) | def __init__(self, in_dim, out_dim): method apply_edges (line 12) | def apply_edges(self, edges): method forward (line 19) | def forward(self, g, e_feat, u_feat, v_feat): class GASConv (line 28) | class GASConv(nn.Module): method __init__ (line 31) | def __init__( method forward (line 64) | def forward(self, g, e_feat, u_feat, v_feat): class GAS (line 158) | class GAS(nn.Module): method __init__ (line 159) | def __init__( method forward (line 215) | def forward(self, graph, e_feat, u_feat, v_feat): FILE: examples/pytorch/gas/model_sampling.py class MLP (line 7) | class MLP(nn.Module): method __init__ (line 8) | def __init__(self, in_dim, out_dim): method apply_edges (line 12) | def apply_edges(self, edges): method forward (line 19) | def forward(self, g, e_feat, u_feat, v_feat): class GASConv (line 28) | class GASConv(nn.Module): method __init__ (line 31) | def __init__( method forward (line 64) | def forward(self, g, f_feat, b_feat, u_feat, v_feat): class GAS (line 183) | class GAS(nn.Module): method __init__ (line 184) | def __init__( method forward (line 240) | def forward(self, subgraph, blocks, f_feat, b_feat, u_feat, v_feat): FILE: examples/pytorch/gat/train.py class GAT (line 13) | class GAT(nn.Module): method __init__ (line 14) | def __init__(self, in_size, hid_size, out_size, heads): method forward (line 39) | def forward(self, g, inputs): function evaluate (line 50) | def evaluate(g, features, labels, mask, model): function train (line 61) | def train(g, features, labels, masks, model): FILE: examples/pytorch/gat/train_ppi.py class GAT (line 11) | class GAT(nn.Module): method __init__ (line 12) | def __init__(self, in_size, hid_size, out_size, heads): method forward (line 38) | def forward(self, g, inputs): function evaluate (line 49) | def evaluate(g, features, labels, model): function evaluate_in_batches (line 58) | def evaluate_in_batches(dataloader, device, model): function train (line 69) | def train(train_dataloader, val_dataloader, device, model): FILE: examples/pytorch/gatv2/gatv2.py class GATv2 (line 15) | class GATv2(nn.Module): method __init__ (line 16) | def __init__( method forward (line 81) | def forward(self, g, inputs): FILE: examples/pytorch/gatv2/train.py class EarlyStopping (line 23) | class EarlyStopping: method __init__ (line 24) | def __init__(self, patience=10): method step (line 30) | def step(self, acc, model): method save_checkpoint (line 48) | def save_checkpoint(self, model): function accuracy (line 53) | def accuracy(logits, labels): function evaluate (line 59) | def evaluate(g, model, features, labels, mask): function main (line 68) | def main(args): FILE: examples/pytorch/gcmc/data.py class MovieLens (line 45) | class MovieLens(object): method __init__ (line 118) | def __init__( method _generate_pair_value (line 354) | def _generate_pair_value(self, rating_info): method _generate_enc_graph (line 374) | def _generate_enc_graph( method _generate_dec_graph (line 443) | def _generate_dec_graph(self, rating_pairs): method num_links (line 459) | def num_links(self): method num_user (line 463) | def num_user(self): method num_movie (line 467) | def num_movie(self): method _drop_unseen_nodes (line 470) | def _drop_unseen_nodes( method _load_raw_rates (line 496) | def _load_raw_rates(self, file_path, sep): method _load_raw_user_info (line 530) | def _load_raw_user_info(self): method _process_user_fea (line 586) | def _process_user_fea(self): method _load_raw_movie_info (line 636) | def _load_raw_movie_info(self): method _process_movie_fea (line 718) | def _process_movie_fea(self): FILE: examples/pytorch/gcmc/model.py class GCMCGraphConv (line 11) | class GCMCGraphConv(nn.Module): method __init__ (line 28) | def __init__( method reset_parameters (line 43) | def reset_parameters(self): method forward (line 48) | def forward(self, graph, feat, weight=None): class GCMCLayer (line 102) | class GCMCLayer(nn.Module): method __init__ (line 151) | def __init__( method partial_to (line 227) | def partial_to(self, device): method reset_parameters (line 242) | def reset_parameters(self): method forward (line 247) | def forward(self, graph, ufeat=None, ifeat=None): class BiDecoder (line 294) | class BiDecoder(nn.Module): method __init__ (line 321) | def __init__(self, in_units, num_classes, num_basis=2, dropout_rate=0.0): method reset_parameters (line 331) | def reset_parameters(self): method forward (line 336) | def forward(self, graph, ufeat, ifeat): class DenseBiDecoder (line 367) | class DenseBiDecoder(nn.Module): method __init__ (line 386) | def __init__(self, in_units, num_classes, num_basis=2, dropout_rate=0.0): method reset_parameters (line 394) | def reset_parameters(self): method forward (line 399) | def forward(self, ufeat, ifeat): function dot_or_identity (line 423) | def dot_or_identity(A, B, device=None): FILE: examples/pytorch/gcmc/train.py class Net (line 26) | class Net(nn.Module): method __init__ (line 27) | def __init__(self, args): method forward (line 48) | def forward(self, enc_graph, dec_graph, ufeat, ifeat): function evaluate (line 54) | def evaluate(args, net, dataset, segment="valid"): function train (line 85) | def train(args): function config (line 258) | def config(): FILE: examples/pytorch/gcmc/train_sampling.py class Net (line 34) | class Net(nn.Module): method __init__ (line 35) | def __init__(self, args, dev_id): method forward (line 64) | def forward( function load_subtensor (line 72) | def load_subtensor(input_nodes, pair_graph, blocks, dataset, parent_graph): function flatten_etypes (line 102) | def flatten_etypes(pair_graph, dataset, segment): function evaluate (line 134) | def evaluate(args, dev_id, net, dataset, dataloader, segment="valid"): function config (line 185) | def config(): function run (line 244) | def run(proc_id, n_gpus, args, devices, dataset): FILE: examples/pytorch/gcmc/utils.py class MetricLogger (line 11) | class MetricLogger(object): method __init__ (line 12) | def __init__(self, attr_names, parse_formats, save_path): method log (line 19) | def log(self, **kwargs): method close (line 28) | def close(self): function torch_total_param_num (line 32) | def torch_total_param_num(net): function torch_net_info (line 36) | def torch_net_info(net, save_path=None): function get_activation (line 50) | def get_activation(act): function get_optimizer (line 80) | def get_optimizer(opt): function to_etype_name (line 89) | def to_etype_name(rating): FILE: examples/pytorch/gcn/train.py class GCN (line 13) | class GCN(nn.Module): method __init__ (line 14) | def __init__(self, in_size, hid_size, out_size): method forward (line 24) | def forward(self, g, features): function evaluate (line 33) | def evaluate(g, features, labels, mask, model): function train (line 44) | def train(g, features, labels, masks, model): FILE: examples/pytorch/geniepath/model.py class GeniePathConv (line 8) | class GeniePathConv(nn.Module): method __init__ (line 9) | def __init__(self, in_dim, hid_dim, out_dim, num_heads=1, residual=Fal... method forward (line 16) | def forward(self, graph, x, h, c): class GeniePath (line 25) | class GeniePath(nn.Module): method __init__ (line 26) | def __init__( method forward (line 51) | def forward(self, graph, x): class GeniePathLazy (line 63) | class GeniePathLazy(nn.Module): method __init__ (line 64) | def __init__( method forward (line 87) | def forward(self, graph, x): FILE: examples/pytorch/geniepath/ppi.py function evaluate (line 13) | def evaluate(model, loss_fn, dataloader, device="cpu"): function main (line 32) | def main(args): FILE: examples/pytorch/geniepath/pubmed.py function main (line 11) | def main(args): FILE: examples/pytorch/ggnn/data_utils.py function get_babi_dataloaders (line 20) | def get_babi_dataloaders(batch_size, train_size=50, task_id=4, q_type=0): function _ns_dataloader (line 92) | def _ns_dataloader( function _convert_ns_dataset (line 155) | def _convert_ns_dataset(train_size, node_dict, edge_dict, path, q_type): function _gc_dataloader (line 205) | def _gc_dataloader( function _convert_gc_dataset (line 273) | def _convert_gc_dataset( function _path_finding_dataloader (line 327) | def _path_finding_dataloader( function _convert_path_finding (line 401) | def _convert_path_finding(train_size, node_dict, edge_dict, path): function _download_babi_data (line 450) | def _download_babi_data(): FILE: examples/pytorch/ggnn/ggnn_gc.py class GraphClsGGNN (line 10) | class GraphClsGGNN(nn.Module): method __init__ (line 11) | def __init__(self, annotation_size, out_feats, n_steps, n_etypes, num_... method forward (line 30) | def forward(self, graph, labels=None): FILE: examples/pytorch/ggnn/ggnn_ns.py class NodeSelectionGGNN (line 10) | class NodeSelectionGGNN(nn.Module): method __init__ (line 11) | def __init__(self, annotation_size, out_feats, n_steps, n_etypes): method forward (line 27) | def forward(self, graph, labels=None): FILE: examples/pytorch/ggnn/ggsnn.py class GGSNN (line 12) | class GGSNN(nn.Module): method __init__ (line 13) | def __init__( method forward (line 45) | def forward(self, graph, seq_lengths, ground_truth=None): function sequence_loss (line 79) | def sequence_loss(logits, ground_truth, seq_length=None): FILE: examples/pytorch/ggnn/train_gc.py function main (line 14) | def main(args): FILE: examples/pytorch/ggnn/train_ns.py function main (line 15) | def main(args): FILE: examples/pytorch/ggnn/train_path_finding.py function main (line 15) | def main(args): FILE: examples/pytorch/gin/train.py class MLP (line 17) | class MLP(nn.Module): method __init__ (line 20) | def __init__(self, input_dim, hidden_dim, output_dim): method forward (line 28) | def forward(self, x): class GIN (line 34) | class GIN(nn.Module): method __init__ (line 35) | def __init__(self, input_dim, hidden_dim, output_dim): method forward (line 62) | def forward(self, g, h): function split_fold10 (line 78) | def split_fold10(labels, fold_idx=0): function evaluate (line 87) | def evaluate(dataloader, device, model): function train (line 103) | def train(train_loader, val_loader, device, model): FILE: examples/pytorch/gnn_explainer/explain_main.py function main (line 19) | def main(args): FILE: examples/pytorch/gnn_explainer/models.py class Layer (line 7) | class Layer(nn.Module): method __init__ (line 8) | def __init__(self, in_dim, out_dim): method forward (line 12) | def forward(self, graph, feat, eweight=None): class Model (line 27) | class Model(nn.Module): method __init__ (line 28) | def __init__(self, in_dim, out_dim, hid_dim=40): method forward (line 34) | def forward(self, graph, feat, eweight=None): FILE: examples/pytorch/gnn_explainer/train_main.py function main (line 18) | def main(args): FILE: examples/pytorch/grace/aug.py function aug (line 8) | def aug(graph, x, feat_drop_rate, edge_mask_rate): function drop_feature (line 26) | def drop_feature(x, drop_prob): function mask_edge (line 37) | def mask_edge(graph, mask_prob): FILE: examples/pytorch/grace/dataset.py function load (line 4) | def load(name): FILE: examples/pytorch/grace/eval.py function repeat (line 16) | def repeat(n_times): function prob_to_one_hot (line 36) | def prob_to_one_hot(y_pred): function print_statistics (line 44) | def print_statistics(statistics, function_name): function label_classification (line 57) | def label_classification( FILE: examples/pytorch/grace/main.py function count_parameters (line 15) | def count_parameters(model): FILE: examples/pytorch/grace/model.py class GCN (line 9) | class GCN(nn.Module): method __init__ (line 10) | def __init__(self, in_dim, out_dim, act_fn, num_layers=2): method forward (line 24) | def forward(self, graph, feat): class MLP (line 32) | class MLP(nn.Module): method __init__ (line 33) | def __init__(self, in_dim, out_dim): method forward (line 38) | def forward(self, x): class Grace (line 43) | class Grace(nn.Module): method __init__ (line 62) | def __init__(self, in_dim, hid_dim, out_dim, num_layers, act_fn, temp): method sim (line 68) | def sim(self, z1, z2): method get_loss (line 76) | def get_loss(self, z1, z2): method get_embedding (line 89) | def get_embedding(self, graph, feat): method forward (line 95) | def forward(self, graph1, graph2, feat1, feat2): FILE: examples/pytorch/grand/main.py function argument (line 17) | def argument(): function consis_loss (line 95) | def consis_loss(logps, temp, lam): FILE: examples/pytorch/grand/model.py function drop_node (line 8) | def drop_node(feats, drop_rate, training): class MLP (line 22) | class MLP(nn.Module): method __init__ (line 23) | def __init__( method reset_parameters (line 37) | def reset_parameters(self): method forward (line 41) | def forward(self, x): function GRANDConv (line 55) | def GRANDConv(graph, feats, order): class GRAND (line 87) | class GRAND(nn.Module): method __init__ (line 113) | def __init__( method forward (line 139) | def forward(self, graph, feats, training=True): FILE: examples/pytorch/graph_matching/ged.py function validate_cost_functions (line 14) | def validate_cost_functions( function construct_cost_functions (line 83) | def construct_cost_functions( function get_edges_to_match (line 177) | def get_edges_to_match(G, node_id, matched_nodes): function subset_cost_matrix (line 222) | def subset_cost_matrix(cost_matrix, row_ids, col_ids, num_rows, num_cols): class search_tree_node (line 234) | class search_tree_node: method __init__ (line 235) | def __init__( method __lt__ (line 449) | def __lt__(self, other): function edit_cost_from_node_matching (line 477) | def edit_cost_from_node_matching( function contextual_cost_matrix_construction (line 587) | def contextual_cost_matrix_construction( function hausdorff_matching (line 764) | def hausdorff_matching( function a_star_search (line 994) | def a_star_search(G1, G2, cost_matrix_nodes, cost_matrix_edges, max_beam... function get_sorted_mapping (line 1136) | def get_sorted_mapping(mapping_tuple, len1, len2): function graph_edit_distance (line 1147) | def graph_edit_distance( FILE: examples/pytorch/graphsage/advanced/model.py class SAGE (line 11) | class SAGE(nn.Module): method __init__ (line 12) | def __init__( method init (line 18) | def init( method forward (line 35) | def forward(self, blocks, x): method inference (line 44) | def inference(self, g, x, device, batch_size, num_workers): function compute_acc_unsupervised (line 92) | def compute_acc_unsupervised(emb, labels, train_nids, val_nids, test_nids): FILE: examples/pytorch/graphsage/advanced/negative_sampler.py class NegativeSampler (line 5) | class NegativeSampler(object): method __init__ (line 6) | def __init__(self, g, k, neg_share=False, device=None): method __call__ (line 13) | def __call__(self, g, eids): FILE: examples/pytorch/graphsage/advanced/train_lightning_unsupervised.py class CrossEntropyLoss (line 27) | class CrossEntropyLoss(nn.Module): method forward (line 28) | def forward(self, block_outputs, pos_graph, neg_graph): class SAGELightning (line 46) | class SAGELightning(LightningModule): method __init__ (line 47) | def __init__( method training_step (line 58) | def training_step(self, batch, batch_idx): method validation_step (line 72) | def validation_step(self, batch, batch_idx): method configure_optimizers (line 80) | def configure_optimizers(self): class DataModule (line 85) | class DataModule(LightningDataModule): method __init__ (line 86) | def __init__( method train_dataloader (line 144) | def train_dataloader(self): method val_dataloader (line 164) | def val_dataloader(self): class UnsupervisedClassification (line 179) | class UnsupervisedClassification(Callback): method on_validation_epoch_start (line 180) | def on_validation_epoch_start(self, trainer, pl_module): method on_validation_batch_end (line 183) | def on_validation_batch_end( method on_validation_epoch_end (line 188) | def on_validation_epoch_end(self, trainer, pl_module): FILE: examples/pytorch/graphsage/lightning/node_classification.py class SAGE (line 19) | class SAGE(LightningModule): method __init__ (line 20) | def __init__(self, in_feats, n_hidden, n_classes): method forward (line 33) | def forward(self, blocks, x): method inference (line 42) | def inference(self, g, device, batch_size, num_workers, buffer_device=... method training_step (line 79) | def training_step(self, batch, batch_idx): method validation_step (line 95) | def validation_step(self, batch, batch_idx): method configure_optimizers (line 110) | def configure_optimizers(self): class DataModule (line 117) | class DataModule(LightningDataModule): method __init__ (line 118) | def __init__( method train_dataloader (line 134) | def train_dataloader(self): method val_dataloader (line 150) | def val_dataloader(self): FILE: examples/pytorch/graphsage/link_pred.py function to_bidirected_with_reverse_mapping (line 19) | def to_bidirected_with_reverse_mapping(g): class SAGE (line 46) | class SAGE(nn.Module): method __init__ (line 47) | def __init__(self, in_size, hid_size): method forward (line 63) | def forward(self, pair_graph, neg_pair_graph, blocks, x): method inference (line 75) | def inference(self, g, device, batch_size): function compute_mrr (line 111) | def compute_mrr( function evaluate (line 127) | def evaluate(device, graph, edge_split, model, batch_size): function train (line 145) | def train(args, device, g, reverse_eids, seed_edges, model): FILE: examples/pytorch/graphsage/load_graph.py function load_reddit (line 5) | def load_reddit(self_loop=True): function load_ogb (line 16) | def load_ogb(name, root="dataset"): function inductive_split (line 50) | def inductive_split(g): FILE: examples/pytorch/graphsage/node_classification.py class SAGE (line 19) | class SAGE(nn.Module): method __init__ (line 20) | def __init__(self, in_size, hid_size, out_size): method forward (line 31) | def forward(self, blocks, x): method inference (line 40) | def inference(self, g, device, batch_size): function evaluate (line 78) | def evaluate(model, graph, dataloader, num_classes): function layerwise_infer (line 95) | def layerwise_infer(device, graph, nid, model, num_classes, batch_size): function train (line 108) | def train(args, device, g, dataset, model, num_classes): FILE: examples/pytorch/graphsage/train_full.py class SAGE (line 13) | class SAGE(nn.Module): method __init__ (line 14) | def __init__(self, in_size, hid_size, out_size): method forward (line 22) | def forward(self, graph, x): function evaluate (line 32) | def evaluate(g, features, labels, mask, model): function train (line 43) | def train(g, features, labels, masks, model): FILE: examples/pytorch/graphsaint/modules.py class GCNLayer (line 7) | class GCNLayer(nn.Module): method __init__ (line 8) | def __init__( method reset_parameters (line 39) | def reset_parameters(self): method feat_trans (line 43) | def feat_trans( method forward (line 58) | def forward(self, graph, features): class GCNNet (line 90) | class GCNNet(nn.Module): method __init__ (line 91) | def __init__( method forward (line 156) | def forward(self, graph): FILE: examples/pytorch/graphsaint/sampler.py class SAINTSampler (line 17) | class SAINTSampler: method __init__ (line 72) | def __init__( method __len__ (line 165) | def __len__(self): method __getitem__ (line 174) | def __getitem__(self, idx): method __collate_fn__ (line 191) | def __collate_fn__(self, batch): method __clear__ (line 206) | def __clear__(self): method __generate_fn__ (line 212) | def __generate_fn__(self): method __compute_norm__ (line 215) | def __compute_norm__(self): method __compute_degree_norm (line 231) | def __compute_degree_norm(self): method __sample__ (line 239) | def __sample__(self): class SAINTNodeSampler (line 243) | class SAINTNodeSampler(SAINTSampler): method __init__ (line 255) | def __init__(self, node_budget, **kwargs): method __generate_fn__ (line 261) | def __generate_fn__(self): method __sample__ (line 274) | def __sample__(self): class SAINTEdgeSampler (line 284) | class SAINTEdgeSampler(SAINTSampler): method __init__ (line 296) | def __init__(self, edge_budget, **kwargs): method __generate_fn__ (line 304) | def __generate_fn__(self): method __sample__ (line 319) | def __sample__(self): class SAINTRandomWalkSampler (line 352) | class SAINTRandomWalkSampler(SAINTSampler): method __init__ (line 367) | def __init__(self, num_roots, length, **kwargs): method __generate_fn__ (line 373) | def __generate_fn__(self): method __sample__ (line 386) | def __sample__(self): FILE: examples/pytorch/graphsaint/train_sampling.py function main (line 15) | def main(args, task): FILE: examples/pytorch/graphsaint/utils.py class Logger (line 14) | class Logger(object): method __init__ (line 17) | def __init__(self, path): method write (line 27) | def write(self, s): function save_log_dir (line 34) | def save_log_dir(args): function calc_f1 (line 40) | def calc_f1(y_true, y_pred, multilabel): function evaluate (line 51) | def evaluate(model, g, labels, mask, multilabel=False): function load_data (line 64) | def load_data(args, multilabel): FILE: examples/pytorch/graphsim/dataloader.py function build_dense_graph (line 12) | def build_dense_graph(n_particles): class MultiBodyDataset (line 17) | class MultiBodyDataset(Dataset): method __init__ (line 18) | def __init__(self, path): method __len__ (line 25) | def __len__(self): method __getitem__ (line 28) | def __getitem__(self, idx): class MultiBodyTrainDataset (line 37) | class MultiBodyTrainDataset(MultiBodyDataset): method __init__ (line 38) | def __init__(self, data_path="./data/"): class MultiBodyValidDataset (line 47) | class MultiBodyValidDataset(MultiBodyDataset): method __init__ (line 48) | def __init__(self, data_path="./data/"): class MultiBodyTestDataset (line 54) | class MultiBodyTestDataset(MultiBodyDataset): method __init__ (line 55) | def __init__(self, data_path="./data/"): class MultiBodyGraphCollator (line 66) | class MultiBodyGraphCollator: method __init__ (line 67) | def __init__(self, n_particles): method __call__ (line 71) | def __call__(self, batch): FILE: examples/pytorch/graphsim/models.py class MLP (line 13) | class MLP(nn.Module): method __init__ (line 14) | def __init__(self, in_feats, out_feats, num_layers=2, hidden=128): method forward (line 32) | def forward(self, x): class PrepareLayer (line 40) | class PrepareLayer(nn.Module): method __init__ (line 53) | def __init__(self, node_feats, stat): method normalize_input (line 59) | def normalize_input(self, node_feature): method forward (line 64) | def forward(self, g, node_feature): class InteractionNet (line 73) | class InteractionNet(nn.Module): method __init__ (line 87) | def __init__(self, node_feats, stat): method denormalize_output (line 105) | def denormalize_output(self, out): method forward (line 111) | def forward(self, g, n_feat, e_feat, global_feats, relation_feats): class InteractionLayer (line 120) | class InteractionLayer(nn.Module): method __init__ (line 155) | def __init__( method update_edge_fn (line 189) | def update_edge_fn(self, edges): method update_node_fn (line 197) | def update_node_fn(self, nodes): method forward (line 202) | def forward(self, g, node_feats, edge_feats, global_feats, relation_fe... FILE: examples/pytorch/graphsim/n_body_sim.py function init (line 22) | def init(total_state, n_body, fea_num, orbit): function norm (line 63) | def norm(x): function get_f (line 67) | def get_f(reciever, sender): function compute_stats (line 76) | def compute_stats(train_curr): function calc (line 84) | def calc(cur_state, n_body): function gen (line 104) | def gen(n_body, num_steps, orbit): FILE: examples/pytorch/graphsim/train.py function train (line 21) | def train( function eval (line 59) | def eval(loss_fn, model, prep, dataloader, device): function eval_rollout (line 85) | def eval_rollout(model, prep, initial_frame, n_object, device): FILE: examples/pytorch/graphsim/utils.py function make_video (line 14) | def make_video(xy, filename): FILE: examples/pytorch/graphwriter/graphwriter.py class GraphWriter (line 9) | class GraphWriter(nn.Module): method __init__ (line 10) | def __init__(self, args): method enc_forward (line 40) | def enc_forward( method forward (line 59) | def forward(self, batch, beam_size=-1): FILE: examples/pytorch/graphwriter/modules.py class MSA (line 13) | class MSA(nn.Module): method __init__ (line 19) | def __init__(self, args, mode="normal"): method forward (line 41) | def forward(self, inp1, inp2, mask=None): class BiLSTM (line 74) | class BiLSTM(nn.Module): method __init__ (line 76) | def __init__(self, args, enc_type="title"): method forward (line 88) | def forward(self, inp, mask, ent_len=None): class GAT (line 107) | class GAT(nn.Module): method __init__ (line 109) | def __init__( method forward (line 138) | def forward(self, graph, feat): class GraphTrans (line 164) | class GraphTrans(nn.Module): method __init__ (line 165) | def __init__(self, args): method forward (line 198) | def forward(self, ent, ent_mask, ent_len, rel, rel_mask, graphs): FILE: examples/pytorch/graphwriter/opts.py function fill_config (line 6) | def fill_config(args): function vocab_config (line 14) | def vocab_config( function get_args (line 26) | def get_args(): FILE: examples/pytorch/graphwriter/train.py function train_one_epoch (line 20) | def train_one_epoch(model, dataloader, optimizer, args, epoch): function eval_it (line 62) | def eval_it(model, dataloader, args, epoch): function test (line 98) | def test(model, dataloader, args): function main (line 126) | def main(args): FILE: examples/pytorch/graphwriter/utlis.py function write_txt (line 13) | def write_txt(batch, seqs, w_file, args): function replace_ent (line 38) | def replace_ent(x, ent, V): function len2mask (line 49) | def len2mask(lens, device): function pad (line 60) | def pad(var_len_list, out_type="list", flatten=False): class Vocab (line 110) | class Vocab(object): method __init__ (line 111) | def __init__( method __len__ (line 122) | def __len__(self): method __str__ (line 125) | def __str__(self): method update (line 128) | def update(self, token): method build (line 135) | def build(self): method __call__ (line 147) | def __call__(self, x): method save (line 153) | def save(self, fname): method load (line 156) | def load(self, fname): function at_least (line 160) | def at_least(x): class Example (line 168) | class Example(object): method __init__ (line 169) | def __init__(self, title, ent_text, ent_type, rel, text): method __str__ (line 199) | def __str__(self): method __len__ (line 204) | def __len__(self): method from_json (line 208) | def from_json(json_data): method build_graph (line 217) | def build_graph(self): method get_tensor (line 255) | def get_tensor( method update_vocab (line 297) | def update_vocab( class BucketSampler (line 312) | class BucketSampler(torch.utils.data.Sampler): method __init__ (line 313) | def __init__(self, data_source, batch_size=32, bucket=3): method __iter__ (line 318) | def __iter__(self): method __len__ (line 353) | def __len__(self): class GWdataset (line 357) | class GWdataset(torch.utils.data.Dataset): method __init__ (line 358) | def __init__( method __iter__ (line 386) | def __iter__(self): method __getitem__ (line 389) | def __getitem__(self, index): method __len__ (line 392) | def __len__(self): method batch_fn (line 395) | def batch_fn(self, batch_ex): function get_datasets (line 445) | def get_datasets( FILE: examples/pytorch/gxn/data_preprocess.py function _load_check_mark (line 12) | def _load_check_mark(path: str): function _save_check_mark (line 20) | def _save_check_mark(path: str, marks: dict): function node_label_as_feature (line 25) | def node_label_as_feature(dataset: LegacyTUDataset, mode="concat", save=... function degree_as_feature (line 111) | def degree_as_feature(dataset: LegacyTUDataset, save=True): FILE: examples/pytorch/gxn/layers.py class GraphConvWithDropout (line 12) | class GraphConvWithDropout(GraphConv): method __init__ (line 17) | def __init__( method call (line 39) | def call(self, graph, feat, weight=None): class Discriminator (line 44) | class Discriminator(torch.nn.Module): method __init__ (line 58) | def __init__(self, feat_dim: int): method reset_parameters (line 63) | def reset_parameters(self): method forward (line 67) | def forward( class DenseLayer (line 110) | class DenseLayer(torch.nn.Module): method __init__ (line 117) | def __init__( method reset_parameters (line 125) | def reset_parameters(self): method forward (line 134) | def forward(self, x): class IndexSelect (line 139) | class IndexSelect(torch.nn.Module): method __init__ (line 159) | def __init__( method forward (line 173) | def forward( class GraphPool (line 227) | class GraphPool(torch.nn.Module): method __init__ (line 242) | def __init__(self, hidden_dim: int, use_gcn=False): method forward (line 249) | def forward( class GraphUnpool (line 299) | class GraphUnpool(torch.nn.Module): method __init__ (line 311) | def __init__(self, hidden_dim: int): method forward (line 315) | def forward(self, graph: DGLGraph, feat: Tensor, select_idx: Tensor): FILE: examples/pytorch/gxn/main.py function compute_loss (line 19) | def compute_loss( function train (line 47) | def train( function test (line 78) | def test(model: torch.nn.Module, loader, device): function main (line 96) | def main(args): FILE: examples/pytorch/gxn/main_early_stop.py function compute_loss (line 19) | def compute_loss( function train (line 47) | def train( function test (line 78) | def test(model: torch.nn.Module, loader, device): function validate (line 97) | def validate(model: torch.nn.Module, loader, device, curr_epoch, total_e... function main (line 120) | def main(args): FILE: examples/pytorch/gxn/networks.py class GraphCrossModule (line 11) | class GraphCrossModule(torch.nn.Module): method __init__ (line 39) | def __init__( method forward (line 96) | def forward(self, graph, feat): class GraphCrossNet (line 240) | class GraphCrossNet(torch.nn.Module): method __init__ (line 282) | def __init__( method init_weights (line 339) | def init_weights(self): method forward (line 347) | def forward( class GraphClassifier (line 380) | class GraphClassifier(torch.nn.Module): method __init__ (line 388) | def __init__(self, args): method forward (line 406) | def forward( FILE: examples/pytorch/gxn/utils.py function get_stats (line 13) | def get_stats( function parse_args (line 57) | def parse_args(): FILE: examples/pytorch/han/main.py function score (line 6) | def score(logits, labels): function evaluate (line 18) | def evaluate(model, g, features, labels, mask, loss_func): function main (line 28) | def main(args): FILE: examples/pytorch/han/model.py class SemanticAttention (line 8) | class SemanticAttention(nn.Module): method __init__ (line 9) | def __init__(self, in_size, hidden_size=128): method forward (line 18) | def forward(self, z): class HANLayer (line 26) | class HANLayer(nn.Module): method __init__ (line 51) | def __init__( method forward (line 74) | def forward(self, gs, h): class HAN (line 86) | class HAN(nn.Module): method __init__ (line 87) | def __init__( method forward (line 110) | def forward(self, g, h): FILE: examples/pytorch/han/model_hetero.py class SemanticAttention (line 17) | class SemanticAttention(nn.Module): method __init__ (line 18) | def __init__(self, in_size, hidden_size=128): method forward (line 27) | def forward(self, z): class HANLayer (line 35) | class HANLayer(nn.Module): method __init__ (line 60) | def __init__(self, meta_paths, in_size, out_size, layer_num_heads, dro... method forward (line 85) | def forward(self, g, h): class HAN (line 106) | class HAN(nn.Module): method __init__ (line 107) | def __init__( method forward (line 128) | def forward(self, g, h): FILE: examples/pytorch/han/train_sampling.py class HANLayer (line 23) | class HANLayer(torch.nn.Module): method __init__ (line 48) | def __init__( method forward (line 72) | def forward(self, block_list, h_list): class HAN (line 86) | class HAN(nn.Module): method __init__ (line 87) | def __init__( method forward (line 108) | def forward(self, g, h): class HANSampler (line 115) | class HANSampler(object): method __init__ (line 116) | def __init__(self, g, metapath_list, num_neighbors): method sample_blocks (line 132) | def sample_blocks(self, seeds): function score (line 145) | def score(logits, labels): function evaluate (line 157) | def evaluate( function load_subtensors (line 214) | def load_subtensors(blocks, features): function main (line 222) | def main(args): FILE: examples/pytorch/han/utils.py function set_random_seed (line 16) | def set_random_seed(seed=0): function mkdir_p (line 30) | def mkdir_p(path, log=True): function get_date_postfix (line 50) | def get_date_postfix(): function setup_log_dir (line 64) | def setup_log_dir(args, sampling=False): function setup (line 103) | def setup(args): function setup_for_sampling (line 112) | def setup_for_sampling(args): function get_binary_mask (line 121) | def get_binary_mask(total_size, indices): function load_acm (line 127) | def load_acm(remove_self_loop): function load_acm_raw (line 186) | def load_acm_raw(remove_self_loop): function load_data (line 260) | def load_data(dataset, remove_self_loop=False): class EarlyStopping (line 269) | class EarlyStopping(object): method __init__ (line 270) | def __init__(self, patience=10): method step (line 281) | def step(self, loss, acc, model): method save_checkpoint (line 301) | def save_checkpoint(self, model): method load_checkpoint (line 305) | def load_checkpoint(self, model): FILE: examples/pytorch/hardgat/hgao.py class HardGAO (line 22) | class HardGAO(nn.Module): method __init__ (line 23) | def __init__( method reset_parameters (line 68) | def reset_parameters(self): method forward (line 77) | def forward(self, graph, feat, get_attention=False): class HardGAT (line 133) | class HardGAT(nn.Module): method __init__ (line 134) | def __init__( method forward (line 198) | def forward(self, inputs): FILE: examples/pytorch/hardgat/train.py function accuracy (line 26) | def accuracy(logits, labels): function evaluate (line 32) | def evaluate(model, features, labels, mask): function main (line 41) | def main(args): FILE: examples/pytorch/hardgat/utils.py class EarlyStopping (line 13) | class EarlyStopping: method __init__ (line 14) | def __init__(self, patience=10): method step (line 20) | def step(self, acc, model): method save_checkpoint (line 38) | def save_checkpoint(self, model): FILE: examples/pytorch/hgp_sl/functions.py function _neighbor_sort (line 20) | def _neighbor_sort( function _threshold_and_support_graph (line 82) | def _threshold_and_support_graph( class EdgeSparsemaxFunction (line 119) | class EdgeSparsemaxFunction(Function): method forward (line 131) | def forward( method backward (line 158) | def backward(ctx, grad_out): function edge_sparsemax (line 178) | def edge_sparsemax(graph: dgl.DGLGraph, logits, eids=ALL, norm_by="dst"): class EdgeSparsemax (line 225) | class EdgeSparsemax(torch.nn.Module): method __init__ (line 259) | def __init__(self): method forward (line 262) | def forward(self, graph, logits, eids=ALL, norm_by="dst"): FILE: examples/pytorch/hgp_sl/layers.py class WeightedGraphConv (line 17) | class WeightedGraphConv(GraphConv): method forward (line 34) | def forward(self, graph: DGLGraph, n_feat, e_feat=None): class NodeInfoScoreLayer (line 58) | class NodeInfoScoreLayer(nn.Module): method __init__ (line 87) | def __init__(self, sym_norm: bool = True): method forward (line 91) | def forward(self, graph: dgl.DGLGraph, feat: Tensor, e_feat: Tensor): class HGPSLPool (line 127) | class HGPSLPool(nn.Module): method __init__ (line 168) | def __init__( method reset_parameters (line 195) | def reset_parameters(self): method forward (line 198) | def forward(self, graph: DGLGraph, feat: Tensor, e_feat=None): class ConvPoolReadout (line 357) | class ConvPoolReadout(torch.nn.Module): method __init__ (line 360) | def __init__( method forward (line 388) | def forward(self, graph, feature, e_feat=None): FILE: examples/pytorch/hgp_sl/main.py function parse_args (line 19) | def parse_args(): function train (line 112) | def train(model: torch.nn.Module, optimizer, trainloader, device): function test (line 132) | def test(model: torch.nn.Module, loader, device): function main (line 149) | def main(args): FILE: examples/pytorch/hgp_sl/networks.py class HGPSLModel (line 9) | class HGPSLModel(torch.nn.Module): method __init__ (line 42) | def __init__( method forward (line 86) | def forward(self, graph, n_feat): FILE: examples/pytorch/hgp_sl/utils.py function get_stats (line 8) | def get_stats( function get_batch_id (line 52) | def get_batch_id(num_nodes: torch.Tensor): function topk (line 70) | def topk( FILE: examples/pytorch/hgt/model.py class HGTLayer (line 12) | class HGTLayer(nn.Module): method __init__ (line 13) | def __init__( method forward (line 67) | def forward(self, G, h): class HGT (line 133) | class HGT(nn.Module): method __init__ (line 134) | def __init__( method forward (line 170) | def forward(self, G, out_key): class HeteroRGCNLayer (line 180) | class HeteroRGCNLayer(nn.Module): method __init__ (line 181) | def __init__(self, in_size, out_size, etypes): method forward (line 188) | def forward(self, G, feat_dict): class HeteroRGCN (line 209) | class HeteroRGCN(nn.Module): method __init__ (line 210) | def __init__(self, G, in_size, hidden_size, out_size): method forward (line 216) | def forward(self, G, out_key): FILE: examples/pytorch/hgt/train_acm.py function get_n_params (line 39) | def get_n_params(model): function train (line 49) | def train(model, G): FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/auxiliaries.py function args2exp_name (line 26) | def args2exp_name(args): function gimme_params (line 32) | def gimme_params(model): function gimme_save_string (line 47) | def gimme_save_string(opt): function f1_score (line 69) | def f1_score( function eval_metrics_one_dataset (line 170) | def eval_metrics_one_dataset(model, test_dataloader, device, k_vals, opt): function eval_metrics_query_and_gallery_dataset (line 262) | def eval_metrics_query_and_gallery_dataset( function recover_closest_one_dataset (line 389) | def recover_closest_one_dataset( function recover_closest_inshop (line 435) | def recover_closest_inshop( function set_checkpoint (line 499) | def set_checkpoint(model, opt, progress_saver, savepath): class CSV_Writer (line 526) | class CSV_Writer: method __init__ (line 533) | def __init__(self, save_path, columns): method log (line 548) | def log(self, inputs): function set_logging (line 563) | def set_logging(opt): class LOGGER (line 604) | class LOGGER: method __init__ (line 611) | def __init__(self, opt, metrics_to_log, name="Basic", start_new=True): method provide_progress_saver (line 642) | def provide_progress_saver(self, metrics_to_log): method log (line 655) | def log(self, main_keys, metric_keys, values): method update_info_plot (line 678) | def update_info_plot(self): function metrics_to_examine (line 752) | def metrics_to_examine(dataset, k_vals): function bool_flag (line 783) | def bool_flag(s): function vis (line 797) | def vis(model, test_dataloader, device, split, opt): FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/datasets.py function give_dataloaders (line 26) | def give_dataloaders(dataset, trainset, testset, opt, cluster_path=""): function give_inaturalist_datasets (line 87) | def give_inaturalist_datasets(opt): function give_inaturalist_datasets_for_features (line 160) | def give_inaturalist_datasets_for_features(opt): function give_inat_datasets_finetune_1head (line 279) | def give_inat_datasets_finetune_1head(testset, cluster_label_path, opt): class BaseTripletDataset (line 339) | class BaseTripletDataset(Dataset): method __init__ (line 346) | def __init__( method ensure_3dim (line 423) | def ensure_3dim(self, img): method __getitem__ (line 436) | def __getitem__(self, idx): method __len__ (line 537) | def __len__(self): class TrainDatasetsmoothap (line 546) | class TrainDatasetsmoothap(Dataset): method __init__ (line 552) | def __init__(self, image_dict, opt): method ensure_3dim (line 590) | def ensure_3dim(self, img): method reshuffle (line 595) | def reshuffle(self): method __getitem__ (line 627) | def __getitem__(self, idx): method __len__ (line 640) | def __len__(self): class TrainDatasetsmoothap1Head (line 644) | class TrainDatasetsmoothap1Head(Dataset): method __init__ (line 650) | def __init__(self, image_dict_L, image_dict_U, opt): method sample_same_size (line 695) | def sample_same_size(self): method ensure_3dim (line 716) | def ensure_3dim(self, img): method reshuffle (line 721) | def reshuffle(self): method __getitem__ (line 753) | def __getitem__(self, idx): method __len__ (line 765) | def __len__(self): FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/evaluate.py function evaluate (line 31) | def evaluate(dataset, LOG, **kwargs): class DistanceMeasure (line 55) | class DistanceMeasure: method __init__ (line 61) | def __init__(self, checkdata, opt, name="Train", update_epochs=1): method measure (line 88) | def measure(self, model, epoch): method update (line 156) | def update(self, mean_class_dist): method update_csv (line 168) | def update_csv(self, mean_class_dist): method update_plot (line 181) | def update_plot(self): class GradientMeasure (line 201) | class GradientMeasure: method __init__ (line 208) | def __init__(self, opt, name="class-it"): method include (line 225) | def include(self, params): method dump (line 243) | def dump(self, epoch): function evaluate_one_dataset (line 267) | def evaluate_one_dataset( function evaluate_query_and_gallery_dataset (line 346) | def evaluate_query_and_gallery_dataset( function evaluate_multiple_datasets (line 442) | def evaluate_multiple_datasets( FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/finetune_1head.py function same_model (line 370) | def same_model(model1, model2): function train_one_epoch_finetune (line 381) | def train_one_epoch_finetune( FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/get_features.py function same_model (line 364) | def same_model(model1, model2): FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/losses.py function loss_select (line 17) | def loss_select(loss, opt, to_optim): function sigmoid (line 45) | def sigmoid(tensor, temp=1.0): function compute_aff (line 56) | def compute_aff(x): class BinarizedF (line 61) | class BinarizedF(torch.autograd.Function): method forward (line 62) | def forward(self, inp): method backward (line 69) | def backward(self, output_grad): class BinarizedModule (line 78) | class BinarizedModule(torch.nn.Module): method __init__ (line 79) | def __init__(self): method forward (line 83) | def forward(self, inp): class SmoothAP (line 88) | class SmoothAP(torch.nn.Module): method __init__ (line 119) | def __init__(self, anneal, batch_size, num_id, feat_dims): method forward (line 141) | def forward(self, preds): FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/main.py function same_model (line 382) | def same_model(model1, model2): function train_one_epoch (line 393) | def train_one_epoch(train_dataloader, model, optimizer, criterion, opt, ... FILE: examples/pytorch/hilander/PSS/Smooth_AP/src/netlib.py function initialize_weights (line 15) | def initialize_weights(model): function rename_attr (line 42) | def rename_attr(model, attr, name): function networkselect (line 59) | def networkselect(opt): class ResNet50 (line 101) | class ResNet50(nn.Module): method __init__ (line 108) | def __init__(self, opt, list_style=False, no_norm=False): method forward (line 146) | def forward(self, x, feature=False, is_init_cluster_generation=False): FILE: examples/pytorch/hilander/PSS/train_subg_inat.py function setup_seed (line 58) | def setup_seed(seed): function set_train_sampler_loader (line 116) | def set_train_sampler_loader(g, k): FILE: examples/pytorch/hilander/models/focal_loss.py class FocalLoss (line 10) | class FocalLoss(nn.Module): method __init__ (line 30) | def __init__(self, class_num, alpha=None, gamma=2, size_average=True): method forward (line 43) | def forward(self, inputs, targets): FILE: examples/pytorch/hilander/models/graphconv.py class GraphConvLayer (line 12) | class GraphConvLayer(nn.Module): method __init__ (line 13) | def __init__(self, in_feats, out_feats, bias=True): method forward (line 17) | def forward(self, bipartite, feat): class GraphConv (line 35) | class GraphConv(nn.Module): method __init__ (line 36) | def __init__(self, in_dim, out_dim, dropout=0, use_GAT=False, K=1): method forward (line 53) | def forward(self, bipartite, features): FILE: examples/pytorch/hilander/models/lander.py class LANDER (line 14) | class LANDER(nn.Module): method __init__ (line 15) | def __init__( method pred_conn (line 65) | def pred_conn(self, edges): method pred_den_msg (line 71) | def pred_den_msg(self, edges): method forward (line 76) | def forward(self, bipartites): method compute_loss (line 138) | def compute_loss(self, bipartite): FILE: examples/pytorch/hilander/train.py function prepare_dataset_graphs (line 56) | def prepare_dataset_graphs(data_path, k_list, lvl_list): FILE: examples/pytorch/hilander/train_subg.py function set_train_sampler_loader (line 80) | def set_train_sampler_loader(g, k): FILE: examples/pytorch/hilander/utils/adjacency.py function row_normalize (line 12) | def row_normalize(mx): function sparse_mx_to_indices_values (line 24) | def sparse_mx_to_indices_values(sparse_mx): FILE: examples/pytorch/hilander/utils/deduce.py function _find_parent (line 19) | def _find_parent(parent, u): function edge_to_connected_graph (line 30) | def edge_to_connected_graph(edges, num): function peaks_to_edges (line 47) | def peaks_to_edges(peaks, dist2peak, tau): function peaks_to_labels (line 59) | def peaks_to_labels(peaks, dist2peak, tau, inst_num): function get_dists (line 65) | def get_dists(g, nbrs, use_gt): function get_edge_dist (line 93) | def get_edge_dist(g, threshold): function tree_generation (line 99) | def tree_generation(ng): function peak_propogation (line 119) | def peak_propogation(treeg): function decode (line 136) | def decode( function build_next_level (line 185) | def build_next_level( FILE: examples/pytorch/hilander/utils/density.py function density_estimation (line 20) | def density_estimation(dists, nbrs, labels, **kwargs): function density_to_peaks_vectorize (line 32) | def density_to_peaks_vectorize(dists, nbrs, density, max_conn=1, name=""): function density_to_peaks (line 72) | def density_to_peaks(dists, nbrs, density, max_conn=1, sort="dist"): FILE: examples/pytorch/hilander/utils/evaluate.py function _read_meta (line 12) | def _read_meta(fn): function evaluate (line 23) | def evaluate(gt_labels, pred_labels, metric="pairwise"): function evaluation (line 58) | def evaluation(pred_labels, labels, metrics): FILE: examples/pytorch/hilander/utils/faiss_gpu.py class faiss_index_wrapper (line 14) | class faiss_index_wrapper: method __init__ (line 15) | def __init__( method search (line 87) | def search(self, *args, **kargs): method __del__ (line 90) | def __del__(self): function batch_search (line 97) | def batch_search(index, query, k, bs, verbose=False): function faiss_search_approx_knn (line 110) | def faiss_search_approx_knn( FILE: examples/pytorch/hilander/utils/faiss_search.py function precise_dist (line 13) | def precise_dist(feat, nbrs, num_process=4, sort=True, verbose=False): function precise_dist_share_mem (line 34) | def precise_dist_share_mem( function bmm (line 65) | def bmm( function faiss_search_knn (line 95) | def faiss_search_knn( FILE: examples/pytorch/hilander/utils/knn.py function knns2ordered_nbrs (line 26) | def knns2ordered_nbrs(knns, sort=True): function fast_knns2spmat (line 40) | def fast_knns2spmat(knns, k, th_sim=0, use_sim=True, fill_value=None): function build_knns (line 83) | def build_knns(feats, k, knn_method, dump=True): class knn (line 99) | class knn: method __init__ (line 100) | def __init__(self, feats, k, index_path="", verbose=True): method filter_by_th (line 103) | def filter_by_th(self, i): method get_knns (line 116) | def get_knns(self, th=None): class knn_faiss (line 139) | class knn_faiss(knn): method __init__ (line 140) | def __init__( class knn_faiss_gpu (line 171) | class knn_faiss_gpu(knn): method __init__ (line 172) | def __init__( FILE: examples/pytorch/hilander/utils/metrics.py function _check (line 19) | def _check(gt_labels, pred_labels): function _get_lb2idxs (line 36) | def _get_lb2idxs(labels): function _compute_fscore (line 45) | def _compute_fscore(pre, rec): function fowlkes_mallows_score (line 49) | def fowlkes_mallows_score(gt_labels, pred_labels, sparse=True): function pairwise (line 68) | def pairwise(gt_labels, pred_labels, sparse=True): function bcubed (line 73) | def bcubed(gt_labels, pred_labels): function nmi (line 101) | def nmi(gt_labels, pred_labels): function precision (line 105) | def precision(gt_labels, pred_labels): function recall (line 109) | def recall(gt_labels, pred_labels): function accuracy (line 113) | def accuracy(gt_labels, pred_labels): FILE: examples/pytorch/hilander/utils/misc.py class TextColors (line 16) | class TextColors: class Timer (line 27) | class Timer: method __init__ (line 28) | def __init__(self, name="task", verbose=True): method __enter__ (line 32) | def __enter__(self): method __exit__ (line 36) | def __exit__(self, exc_type, exc_val, exc_tb): function set_random_seed (line 46) | def set_random_seed(seed, cuda=False): function l2norm (line 56) | def l2norm(vec): function is_l2norm (line 61) | def is_l2norm(features, size): function is_spmat_eq (line 67) | def is_spmat_eq(a, b): function aggregate (line 71) | def aggregate(features, adj, times): function mkdir_if_no_exists (line 78) | def mkdir_if_no_exists(path, subdirs=[""], is_folder=False): function stop_iterating (line 90) | def stop_iterating( FILE: examples/pytorch/infograph/evaluate_embedding.py class LogReg (line 13) | class LogReg(nn.Module): method __init__ (line 14) | def __init__(self, ft_in, nb_classes): method weights_init (line 18) | def weights_init(self, m): method forward (line 24) | def forward(self, seq): function logistic_classify (line 29) | def logistic_classify(x, y, device="cpu"): function svc_classify (line 69) | def svc_classify(x, y, search): function evaluate_embedding (line 88) | def evaluate_embedding(embeddings, labels, search=True, device="cpu"): FILE: examples/pytorch/infograph/model.py class FeedforwardNetwork (line 13) | class FeedforwardNetwork(nn.Module): method __init__ (line 31) | def __init__(self, in_dim, hid_dim): method forward (line 45) | def forward(self, feat): class GINEncoder (line 57) | class GINEncoder(nn.Module): method __init__ (line 77) | def __init__(self, in_dim, hid_dim, n_layer): method forward (line 104) | def forward(self, graph, feat): class InfoGraph (line 118) | class InfoGraph(nn.Module): method __init__ (line 138) | def __init__(self, in_dim, hid_dim, n_layer): method get_embedding (line 156) | def get_embedding(self, graph, feat): method forward (line 164) | def forward(self, graph, feat, graph_id): class NNConvEncoder (line 178) | class NNConvEncoder(nn.Module): method __init__ (line 199) | def __init__(self, in_dim, hid_dim): method forward (line 221) | def forward(self, graph, nfeat, efeat): class InfoGraphS (line 240) | class InfoGraphS(nn.Module): method __init__ (line 261) | def __init__(self, in_dim, hid_dim): method forward (line 278) | def forward(self, graph, nfeat, efeat): method unsup_forward (line 286) | def unsup_forward(self, graph, nfeat, efeat, graph_id): FILE: examples/pytorch/infograph/semisupervised.py function argument (line 14) | def argument(): class DenseQM9EdgeDataset (line 63) | class DenseQM9EdgeDataset(QM9EdgeDataset): method __getitem__ (line 64) | def __getitem__(self, idx): function collate (line 131) | def collate(samples): function evaluate (line 148) | def evaluate(model, loader, num, device): FILE: examples/pytorch/infograph/unsupervised.py function argument (line 12) | def argument(): function collate (line 59) | def collate(samples): FILE: examples/pytorch/infograph/utils.py function get_positive_expectation (line 9) | def get_positive_expectation(p_samples, average=True): function get_negative_expectation (line 26) | def get_negative_expectation(q_samples, average=True): function local_global_loss_ (line 43) | def local_global_loss_(l_enc, g_enc, graph_id): function global_global_loss_ (line 66) | def global_global_loss_(sup_enc, unsup_enc): FILE: examples/pytorch/jknet/main.py function main (line 17) | def main(args): FILE: examples/pytorch/jknet/model.py class JKNet (line 8) | class JKNet(nn.Module): method __init__ (line 9) | def __init__( method reset_params (line 32) | def reset_params(self): method forward (line 38) | def forward(self, g, feats): FILE: examples/pytorch/jtnn/jtnn/chemutils.py function set_atommap (line 12) | def set_atommap(mol, num=0): function get_mol (line 17) | def get_mol(smiles): function get_smiles (line 25) | def get_smiles(mol): function decode_stereo (line 29) | def decode_stereo(smiles2D): function sanitize (line 57) | def sanitize(mol): function copy_atom (line 66) | def copy_atom(atom): function copy_edit_mol (line 73) | def copy_edit_mol(mol): function get_clique_mol (line 86) | def get_clique_mol(mol, atoms): function tree_decomp (line 94) | def tree_decomp(mol): function atom_equal (line 177) | def atom_equal(a1, a2): function ring_bond_equal (line 185) | def ring_bond_equal(b1, b2, reverse=False): function attach_mols_nx (line 194) | def attach_mols_nx(ctr_mol, neighbors, prev_nodes, nei_amap): function local_attach_nx (line 220) | def local_attach_nx(ctr_mol, neighbors, prev_nodes, amap_list): function enum_attach_nx (line 232) | def enum_attach_nx(ctr_mol, nei_node, amap, singletons): function enum_assemble_nx (line 317) | def enum_assemble_nx(node, neighbors, prev_nodes=[], prev_amap=[]): function dfs_assemble_nx (line 372) | def dfs_assemble_nx( FILE: examples/pytorch/jtnn/jtnn/datautils.py function _unpack_field (line 21) | def _unpack_field(examples, field): function _set_node_id (line 25) | def _set_node_id(mol_tree, vocab): class JTNNDataset (line 34) | class JTNNDataset(Dataset): method __init__ (line 35) | def __init__(self, data, vocab, training=True): method __len__ (line 52) | def __len__(self): method __getitem__ (line 55) | def __getitem__(self, idx): class JTNNCollator (line 144) | class JTNNCollator(object): method __init__ (line 145) | def __init__(self, vocab, training): method _batch_and_set (line 150) | def _batch_and_set(graphs, atom_x, bond_x, flatten): method __call__ (line 163) | def __call__(self, examples): FILE: examples/pytorch/jtnn/jtnn/jtmpn.py function onek_encoding_unk (line 45) | def onek_encoding_unk(x, allowable_set): function atom_features (line 55) | def atom_features(atom): function bond_features (line 64) | def bond_features(bond): function mol2dgl_single (line 77) | def mol2dgl_single(cand_batch): class LoopyBPUpdate (line 147) | class LoopyBPUpdate(nn.Module): method __init__ (line 148) | def __init__(self, hidden_size): method forward (line 154) | def forward(self, node): class GatherUpdate (line 179) | class GatherUpdate(nn.Module): method __init__ (line 180) | def __init__(self, hidden_size): method forward (line 186) | def forward(self, node): class DGLJTMPN (line 197) | class DGLJTMPN(nn.Module): method __init__ (line 198) | def __init__(self, hidden_size, depth): method forward (line 214) | def forward(self, cand_batch, mol_tree_batch): method run (line 249) | def run( FILE: examples/pytorch/jtnn/jtnn/jtnn_dec.py function dfs_order (line 16) | def dfs_order(forest, roots): function dec_tree_node_update (line 30) | def dec_tree_node_update(nodes): function have_slots (line 34) | def have_slots(fa_slots, ch_slots): function can_assemble (line 61) | def can_assemble(mol_tree, u, v_node_dict): function create_node_dict (line 83) | def create_node_dict(smiles, clique=[]): class DGLJTNNDecoder (line 91) | class DGLJTNNDecoder(nn.Module): method __init__ (line 92) | def __init__(self, vocab, hidden_size, latent_size, embedding=None): method forward (line 111) | def forward(self, mol_trees, tree_vec): method run (line 124) | def run(self, mol_tree_batch, mol_tree_batch_lg, n_trees, tree_vec): method decode (line 261) | def decode(self, mol_vec): FILE: examples/pytorch/jtnn/jtnn/jtnn_enc.py function level_order (line 12) | def level_order(forest, roots): class EncoderGatherUpdate (line 24) | class EncoderGatherUpdate(nn.Module): method __init__ (line 25) | def __init__(self, hidden_size): method forward (line 31) | def forward(self, nodes): class DGLJTNNEncoder (line 39) | class DGLJTNNEncoder(nn.Module): method __init__ (line 40) | def __init__(self, vocab, hidden_size, embedding=None): method forward (line 54) | def forward(self, mol_trees): method run (line 64) | def run(self, mol_tree_batch, mol_tree_batch_lg): FILE: examples/pytorch/jtnn/jtnn/jtnn_vae.py class DGLJTNNVAE (line 24) | class DGLJTNNVAE(nn.Module): method __init__ (line 25) | def __init__(self, vocab, hidden_size, latent_size, depth): method move_to_cuda (line 51) | def move_to_cuda(mol_batch): method encode (line 65) | def encode(self, mol_batch): method sample (line 82) | def sample(self, tree_vec, mol_vec, e1=None, e2=None): method forward (line 98) | def forward(self, mol_batch, beta=0, e1=None, e2=None): method assm (line 129) | def assm(self, mol_batch, mol_tree_batch, mol_vec): method stereo (line 176) | def stereo(self, mol_batch, mol_vec): method decode (line 209) | def decode(self, tree_vec, mol_vec): method dfs_assemble (line 269) | def dfs_assemble( FILE: examples/pytorch/jtnn/jtnn/line_profiler_integration.py function profile (line 22) | def profile(f): FILE: examples/pytorch/jtnn/jtnn/mol_tree.py function get_slots (line 6) | def get_slots(smiles): class Vocab (line 14) | class Vocab(object): method __init__ (line 15) | def __init__(self, smiles_list): method get_index (line 20) | def get_index(self, smiles): method get_smiles (line 23) | def get_smiles(self, idx): method get_slots (line 26) | def get_slots(self, idx): method size (line 29) | def size(self): FILE: examples/pytorch/jtnn/jtnn/mol_tree_nx.py class DGLMolTree (line 16) | class DGLMolTree(object): method __init__ (line 17) | def __init__(self, smiles): method treesize (line 74) | def treesize(self): method _recover_node (line 77) | def _recover_node(self, i, original_mol): method _assemble_node (line 109) | def _assemble_node(self, i): method recover (line 141) | def recover(self): method assemble (line 145) | def assemble(self): FILE: examples/pytorch/jtnn/jtnn/mpn.py function onek_encoding_unk (line 42) | def onek_encoding_unk(x, allowable_set): function atom_features (line 48) | def atom_features(atom): function bond_features (line 58) | def bond_features(bond): function mol2dgl_single (line 72) | def mol2dgl_single(smiles): class LoopyBPUpdate (line 107) | class LoopyBPUpdate(nn.Module): method __init__ (line 108) | def __init__(self, hidden_size): method forward (line 114) | def forward(self, nodes): class GatherUpdate (line 121) | class GatherUpdate(nn.Module): method __init__ (line 122) | def __init__(self, hidden_size): method forward (line 128) | def forward(self, nodes): class DGLMPN (line 135) | class DGLMPN(nn.Module): method __init__ (line 136) | def __init__(self, hidden_size, depth): method forward (line 152) | def forward(self, mol_graph): method run (line 172) | def run(self, mol_graph, mol_line_graph): FILE: examples/pytorch/jtnn/jtnn/nnutils.py function cuda (line 9) | def cuda(x): class GRUUpdate (line 16) | class GRUUpdate(nn.Module): method __init__ (line 17) | def __init__(self, hidden_size): method update_zm (line 26) | def update_zm(self, node): method update_r (line 35) | def update_r(self, node, zm=None): method forward (line 43) | def forward(self, node): function tocpu (line 49) | def tocpu(g): FILE: examples/pytorch/jtnn/vaetrain_dgl.py function worker_init_fn (line 19) | def worker_init_fn(id_): function train (line 79) | def train(): function test (line 144) | def test(): FILE: examples/pytorch/label_propagation/main.py function main (line 10) | def main(): FILE: examples/pytorch/labor/ladies_sampler.py function find_indices_in (line 8) | def find_indices_in(a, b): function union (line 15) | def union(*arrays): function normalized_edata (line 19) | def normalized_edata(g, weight=None): class LadiesSampler (line 29) | class LadiesSampler(dgl.dataloading.BlockSampler): method __init__ (line 30) | def __init__( method compute_prob (line 45) | def compute_prob(self, g, seed_nodes, weight, num): method select_neighbors (line 65) | def select_neighbors(self, prob, num): method generate_block (line 84) | def generate_block(self, insg, neighbor_nodes_idx, seed_nodes, P_sg, W... method sample_blocks (line 124) | def sample_blocks(self, g, seed_nodes, exclude_eids=None): class PoissonLadiesSampler (line 148) | class PoissonLadiesSampler(LadiesSampler): method __init__ (line 149) | def __init__( method compute_prob (line 163) | def compute_prob(self, g, seed_nodes, weight, num): method select_neighbors (line 191) | def select_neighbors(self, prob, num): FILE: examples/pytorch/labor/load_graph.py function load_data (line 5) | def load_data(data): function load_dgl (line 12) | def load_dgl(name): function load_reddit (line 34) | def load_reddit(self_loop=True): function load_mag240m (line 42) | def load_mag240m(root="dataset"): function load_ogb (line 86) | def load_ogb(name, root="dataset"): function load_dataset (line 123) | def load_dataset(dataset_name): FILE: examples/pytorch/labor/model.py class SAGE (line 11) | class SAGE(nn.Module): method __init__ (line 12) | def __init__( method init (line 18) | def init( method forward (line 35) | def forward(self, blocks, x): method inference (line 50) | def inference(self, g, device, batch_size, use_uva, num_workers): FILE: examples/pytorch/labor/train_lightning.py class SAGELightning (line 44) | class SAGELightning(LightningModule): method __init__ (line 45) | def __init__( method num_sampled_nodes (line 76) | def num_sampled_nodes(self, i): method num_sampled_edges (line 85) | def num_sampled_edges(self, i): method training_step (line 94) | def training_step(self, batch, batch_idx): method on_train_batch_end (line 164) | def on_train_batch_end(self, outputs, batch, batch_idx): method validation_step (line 173) | def validation_step(self, batch, batch_idx, dataloader_idx=0): method configure_optimizers (line 199) | def configure_optimizers(self): class DataModule (line 204) | class DataModule(LightningDataModule): method __init__ (line 205) | def __init__( method train_dataloader (line 285) | def train_dataloader(self): method val_dataloader (line 299) | def val_dataloader(self): class BatchSizeCallback (line 314) | class BatchSizeCallback(Callback): method __init__ (line 315) | def __init__(self, limit, factor=3): method clear (line 321) | def clear(self): method push (line 326) | def push(self, x): method var (line 333) | def var(self): method std (line 337) | def std(self): method on_train_batch_start (line 340) | def on_train_batch_start(self, trainer, datamodule, batch, batch_idx): method on_train_batch_end (line 352) | def on_train_batch_end( method on_train_epoch_end (line 358) | def on_train_epoch_end(self, trainer, datamodule): FILE: examples/pytorch/lda/example_20newsgroups.py function plot_top_words (line 42) | def plot_top_words(model, feature_names, n_top_words, title): FILE: examples/pytorch/lda/lda_model.py class EdgeData (line 42) | class EdgeData: method __init__ (line 43) | def __init__(self, src_data, dst_data): method loglike (line 48) | def loglike(self): method phi (line 52) | def phi(self): method expectation (line 60) | def expectation(self): class _Dirichlet (line 66) | class _Dirichlet: method __init__ (line 67) | def __init__(self, prior, nphi, _chunksize=int(1e6)): method _posterior (line 79) | def _posterior(self, _ID=slice(None)): method posterior_sum (line 83) | def posterior_sum(self): method _Elog (line 86) | def _Elog(self, _ID=slice(None)): method loglike (line 92) | def loglike(self): method n (line 108) | def n(self): method cdf (line 112) | def cdf(self): method _expectation (line 118) | def _expectation(self, _ID=slice(None)): method Bayesian_gap (line 124) | def Bayesian_gap(self): method clear_cache (line 135) | def clear_cache(self): method update (line 142) | def update(self, new, _ID=slice(None), rho=1): class DocData (line 152) | class DocData(_Dirichlet): method prepare_graph (line 155) | def prepare_graph(self, G, key="Elog"): method update_from (line 158) | def update_from(self, G, mult): class _Distributed (line 163) | class _Distributed(collections.UserList): method __init__ (line 166) | def __init__(self, prior, nphi): method split_device (line 171) | def split_device(self, other, dim=0): class WordData (line 177) | class WordData(_Distributed): method prepare_graph (line 180) | def prepare_graph(self, G, key="Elog"): method update_from (line 189) | def update_from(self, G, mult, rho): class Gamma (line 203) | class Gamma(collections.namedtuple("Gamma", "concentration, rate")): method shape (line 207) | def shape(self): method scale (line 211) | def scale(self): method sample (line 214) | def sample(self, shape, device): class LatentDirichletAllocation (line 221) | class LatentDirichletAllocation: method __init__ (line 249) | def __init__( method _init_word_data (line 277) | def _init_word_data(self): method _init_doc_data (line 289) | def _init_doc_data(self, n_docs, device): method save (line 295) | def save(self, f): method _prepare_graph (line 309) | def _prepare_graph(self, G, doc_data, key="Elog"): method _e_step (line 313) | def _e_step(self, G, doc_data=None, mean_change_tol=1e-3, max_iters=100): method predict (line 341) | def predict(self, doc_data): method sample (line 357) | def sample(self, doc_data, num_samples): method _m_step (line 394) | def _m_step(self, G, doc_data): method partial_fit (line 415) | def partial_fit(self, G): method fit (line 420) | def fit(self, G, mean_change_tol=1e-3, max_epochs=10): method perplexity (line 430) | def perplexity(self, G, doc_data=None): function doc_subgraph (line 467) | def doc_subgraph(G, doc_ids): FILE: examples/pytorch/line_graph/gnn.py class GNNModule (line 13) | class GNNModule(nn.Module): method __init__ (line 14) | def __init__(self, in_feats, out_feats, radius): method aggregate (line 41) | def aggregate(self, g, z): method forward (line 54) | def forward(self, g, lg, x, y, deg_g, deg_lg, pm_pd): class GNN (line 91) | class GNN(nn.Module): method __init__ (line 92) | def __init__(self, feats, radius, n_classes): method forward (line 99) | def forward(self, g, lg, deg_g, deg_lg, pm_pd): FILE: examples/pytorch/line_graph/train.py function compute_overlap (line 67) | def compute_overlap(z_list): function from_np (line 77) | def from_np(f, *args): function step (line 88) | def step(i, j, g, lg, deg_g, deg_lg, pm_pd): function inference (line 116) | def inference(g, lg, deg_g, deg_lg, pm_pd): function test (line 128) | def test(): FILE: examples/pytorch/metapath2vec/download.py class PBar (line 8) | class PBar(object): method __enter__ (line 9) | def __enter__(self): method __call__ (line 13) | def __call__(self, blockno, readsize, totalsize): method __exit__ (line 18) | def __exit__(self, exc_type, exc_value, traceback): class AminerDataset (line 22) | class AminerDataset(object): method __init__ (line 27) | def __init__(self, path): method _download_and_extract (line 35) | def _download_and_extract(self, path, filename): class CustomDataset (line 51) | class CustomDataset(object): method __init__ (line 56) | def __init__(self, path): FILE: examples/pytorch/metapath2vec/metapath2vec.py class Metapath2VecTrainer (line 14) | class Metapath2VecTrainer: method __init__ (line 15) | def __init__(self, args): method train (line 43) | def train(self): FILE: examples/pytorch/metapath2vec/model.py class SkipGramModel (line 12) | class SkipGramModel(nn.Module): method __init__ (line 13) | def __init__(self, emb_size, emb_dimension): method forward (line 24) | def forward(self, pos_u, pos_v, neg_v): method save_embedding (line 39) | def save_embedding(self, id2word, file_name): FILE: examples/pytorch/metapath2vec/reading_data.py class DataReader (line 9) | class DataReader: method __init__ (line 12) | def __init__(self, dataset, min_count, care_type): method read_words (line 27) | def read_words(self, min_count): method initTableDiscards (line 57) | def initTableDiscards(self): method initTableNegatives (line 64) | def initTableNegatives(self): method getNegatives (line 77) | def getNegatives(self, target, size): # TODO check equality with target class Metapath2vecDataset (line 91) | class Metapath2vecDataset(Dataset): method __init__ (line 92) | def __init__(self, data, window_size): method __len__ (line 98) | def __len__(self): method __getitem__ (line 102) | def __getitem__(self, idx): method collate (line 140) | def collate(batches): FILE: examples/pytorch/metapath2vec/sampler.py function construct_graph (line 15) | def construct_graph(): function generate_metapath (line 91) | def generate_metapath(): FILE: examples/pytorch/mixhop/main.py class MixHopConv (line 18) | class MixHopConv(nn.Module): method __init__ (line 50) | def __init__( method forward (line 78) | def forward(self, graph, feats): class MixHop (line 109) | class MixHop(nn.Module): method __init__ (line 110) | def __init__( method forward (line 165) | def forward(self, graph, feats): function main (line 175) | def main(args): FILE: examples/pytorch/model_zoo/citation_network/models.py class GCN (line 17) | class GCN(nn.Module): method __init__ (line 18) | def __init__( method forward (line 35) | def forward(self, features): class GAT (line 44) | class GAT(nn.Module): method __init__ (line 45) | def __init__( method forward (line 106) | def forward(self, inputs): class GraphSAGE (line 115) | class GraphSAGE(nn.Module): method __init__ (line 116) | def __init__( method forward (line 163) | def forward(self, features): class APPNP (line 170) | class APPNP(nn.Module): method __init__ (line 171) | def __init__( method reset_parameters (line 202) | def reset_parameters(self): method forward (line 206) | def forward(self, features): class TAGCN (line 219) | class TAGCN(nn.Module): method __init__ (line 220) | def __init__( method forward (line 237) | def forward(self, features): class AGNN (line 246) | class AGNN(nn.Module): method __init__ (line 247) | def __init__( method forward (line 270) | def forward(self, features): class SGC (line 277) | class SGC(nn.Module): method __init__ (line 278) | def __init__(self, g, in_feats, n_classes, n_hidden, k, bias): method forward (line 283) | def forward(self, features): class GIN (line 287) | class GIN(nn.Module): method __init__ (line 288) | def __init__( method forward (line 331) | def forward(self, features): class ChebNet (line 338) | class ChebNet(nn.Module): method __init__ (line 339) | def __init__(self, g, in_feats, n_classes, n_hidden, n_layers, k, bias): method forward (line 349) | def forward(self, features): FILE: examples/pytorch/model_zoo/citation_network/run.py function get_model_and_config (line 16) | def get_model_and_config(name): function evaluate (line 38) | def evaluate(model, features, labels, mask): function main (line 49) | def main(args): FILE: examples/pytorch/model_zoo/geometric/coarsening.py function laplacian (line 9) | def laplacian(W, normalized=True): function rescale_L (line 31) | def rescale_L(L, lmax=2): function lmax_L (line 40) | def lmax_L(L): function coarsen (line 48) | def coarsen(A, levels): function HEM (line 74) | def HEM(W, levels, rid=None): function HEM_one_level (line 156) | def HEM_one_level(rr, cc, vv, rid, weights): function compute_perm (line 218) | def compute_perm(parents): function perm_adjacency (line 269) | def perm_adjacency(A, indices): function perm_data (line 297) | def perm_data(x, indices): FILE: examples/pytorch/model_zoo/geometric/coordinate.py function get_coordinates (line 7) | def get_coordinates(graphs, grid_side, coarsening_levels, perm): function z2polar (line 31) | def z2polar(edges): FILE: examples/pytorch/model_zoo/geometric/grid_graph.py function grid_graph (line 11) | def grid_graph(grid_side, number_edges, metric): function grid (line 20) | def grid(m, dtype=np.float32): function distance_sklearn_metrics (line 32) | def distance_sklearn_metrics(z, k=4, metric="euclidean"): function adjacency (line 43) | def adjacency(dist, idx): FILE: examples/pytorch/model_zoo/geometric/mnist.py function batcher (line 54) | def batcher(batch): class MoNet (line 95) | class MoNet(nn.Module): method __init__ (line 96) | def __init__(self, n_kernels, in_feats, hiddens, out_feats): method forward (line 115) | def forward(self, g_arr, feat): class ChebNet (line 126) | class ChebNet(nn.Module): method __init__ (line 127) | def __init__(self, k, in_feats, hiddens, out_feats): method forward (line 143) | def forward(self, g_arr, feat): FILE: examples/pytorch/monet/citation.py class MoNet (line 15) | class MoNet(nn.Module): method __init__ (line 16) | def __init__( method forward (line 46) | def forward(self, feat, pseudo): function evaluate (line 55) | def evaluate(model, features, pseudo, labels, mask): function main (line 66) | def main(args): FILE: examples/pytorch/multigpu/multi_gpu_graph_prediction.py class MLP (line 18) | class MLP(nn.Module): method __init__ (line 19) | def __init__(self, in_feats): method forward (line 29) | def forward(self, h): class GIN (line 33) | class GIN(nn.Module): method __init__ (line 34) | def __init__(self, n_hidden, n_output, n_layers=5): method forward (line 56) | def forward(self, g, x, x_e): function evaluate (line 75) | def evaluate(dataloader, device, model, evaluator): function train (line 94) | def train(rank, world_size, dataset_name, root): FILE: examples/pytorch/multigpu/multi_gpu_link_prediction.py class SAGE (line 29) | class SAGE(nn.Module): method __init__ (line 30) | def __init__(self, in_size, hid_size, out_size): method forward (line 40) | def forward(self, blocks, x): method inference (line 49) | def inference(self, g, device, batch_size, use_uva): class NegativeSampler (line 93) | class NegativeSampler(object): method __init__ (line 94) | def __init__(self, g, k, neg_share=False, device=None): method __call__ (line 101) | def __call__(self, g, eids): class CrossEntropyLoss (line 113) | class CrossEntropyLoss(nn.Module): method forward (line 114) | def forward(self, block_outputs, pos_graph, neg_graph): function compute_acc_unsupervised (line 132) | def compute_acc_unsupervised(emb, labels, train_nids, val_nids, test_nids): function evaluate (line 153) | def evaluate(proc_id, model, g, device, use_uva): function train (line 161) | def train( function run (line 237) | def run(proc_id, nprocs, devices, g, data, mode): FILE: examples/pytorch/multigpu/multi_gpu_node_classification.py class SAGE (line 24) | class SAGE(nn.Module): method __init__ (line 25) | def __init__(self, in_size, hid_size, out_size): method forward (line 36) | def forward(self, blocks, x): method inference (line 45) | def inference(self, g, device, batch_size, use_uva): function evaluate (line 89) | def evaluate(model, g, num_classes, dataloader): function layerwise_infer (line 106) | def layerwise_infer( function train (line 121) | def train( function run (line 178) | def run(proc_id, nprocs, devices, g, data, mode): FILE: examples/pytorch/mvgrl/graph/dataset.py function compute_ppr (line 17) | def compute_ppr(graph: nx.Graph, alpha=0.2, self_loop=True): function download (line 29) | def download(dataset, datadir): function process (line 41) | def process(dataset): function load (line 139) | def load(dataset): class TUDataset (line 203) | class TUDataset(DGLDataset): method __init__ (line 204) | def __init__(self, graphs, diff_graphs, labels): method process (line 210) | def process(self): method __len__ (line 213) | def __len__(self): method __getitem__ (line 216) | def __getitem__(self, idx): FILE: examples/pytorch/mvgrl/graph/main.py function collate (line 50) | def collate(samples): FILE: examples/pytorch/mvgrl/graph/model.py class MLP (line 9) | class MLP(nn.Module): method __init__ (line 10) | def __init__(self, in_dim, out_dim): method forward (line 22) | def forward(self, x): class GCN (line 26) | class GCN(nn.Module): method __init__ (line 27) | def __init__(self, in_dim, out_dim, num_layers, norm): method forward (line 51) | def forward(self, graph, feat, edge_weight=None): class MVGRL (line 62) | class MVGRL(nn.Module): method __init__ (line 86) | def __init__(self, in_dim, out_dim, num_layers): method get_embedding (line 93) | def get_embedding(self, graph1, graph2, feat, edge_weight): method forward (line 104) | def forward(self, graph1, graph2, feat, edge_weight, graph_id): FILE: examples/pytorch/mvgrl/graph/utils.py function linearsvc (line 12) | def linearsvc(embeds, labels): function get_positive_expectation (line 29) | def get_positive_expectation(p_samples, average=True): function get_negative_expectation (line 46) | def get_negative_expectation(q_samples, average=True): function local_global_loss_ (line 63) | def local_global_loss_(l_enc, g_enc, graph_id): FILE: examples/pytorch/mvgrl/node/dataset.py function preprocess_features (line 13) | def preprocess_features(features): function sparse_to_tuple (line 26) | def sparse_to_tuple(sparse_mx): function compute_ppr (line 46) | def compute_ppr(graph: nx.Graph, alpha=0.2, self_loop=True): function process_dataset (line 58) | def process_dataset(name, epsilon): function process_dataset_appnp (line 108) | def process_dataset_appnp(epsilon): FILE: examples/pytorch/mvgrl/node/model.py class LogReg (line 8) | class LogReg(nn.Module): method __init__ (line 9) | def __init__(self, hid_dim, n_classes): method forward (line 14) | def forward(self, x): class Discriminator (line 19) | class Discriminator(nn.Module): method __init__ (line 20) | def __init__(self, dim): method forward (line 24) | def forward(self, h1, h2, h3, h4, c1, c2): class MVGRL (line 41) | class MVGRL(nn.Module): method __init__ (line 42) | def __init__(self, in_dim, out_dim): method get_embedding (line 56) | def get_embedding(self, graph, diff_graph, feat, edge_weight): method forward (line 62) | def forward(self, graph, diff_graph, feat, shuf_feat, edge_weight): FILE: examples/pytorch/node2vec/main.py function time_randomwalk (line 9) | def time_randomwalk(graph, args): function train_node2vec (line 30) | def train_node2vec(graph, eval_set, args): FILE: examples/pytorch/node2vec/model.py class Node2vec (line 9) | class Node2vec(nn.Module): method __init__ (line 43) | def __init__( method reset_parameters (line 76) | def reset_parameters(self): method sample (line 79) | def sample(self, batch): method forward (line 107) | def forward(self, nodes=None): method loss (line 127) | def loss(self, pos_trace, neg_trace): method loader (line 162) | def loader(self, batch_size): method evaluate (line 184) | def evaluate(self, x_train, y_train, x_val, y_val): class Node2vecModel (line 200) | class Node2vecModel(object): method __init__ (line 242) | def __init__( method _train_step (line 280) | def _train_step(self, model, loader, optimizer, device): method _evaluate_step (line 295) | def _evaluate_step(self): method train (line 302) | def train(self, epochs, batch_size, learning_rate=0.01): method embedding (line 337) | def embedding(self, nodes=None): FILE: examples/pytorch/node2vec/utils.py function load_graph (line 9) | def load_graph(name): function parse_arguments (line 44) | def parse_arguments(): FILE: examples/pytorch/ogb/cluster-gat/main.py class GAT (line 19) | class GAT(nn.Module): method __init__ (line 20) | def __init__( method forward (line 71) | def forward(self, g, x): method inference (line 80) | def inference(self, g, x, batch_size, device): function compute_acc (line 131) | def compute_acc(pred, labels): function evaluate (line 138) | def evaluate(model, g, nfeat, labels, val_nid, test_nid, batch_size, dev... function model_param_summary (line 160) | def model_param_summary(model): function run (line 167) | def run(args, device, data, nfeat): FILE: examples/pytorch/ogb/cluster-gat/partition_utils.py function get_partition_list (line 10) | def get_partition_list(g, psize): FILE: examples/pytorch/ogb/cluster-gat/sampler.py class ClusterIter (line 7) | class ClusterIter(object): method __init__ (line 12) | def __init__(self, dn, g, psize, batch_size): method __len__ (line 46) | def __len__(self): method __getitem__ (line 49) | def __getitem__(self, idx): function subgraph_collate_fn (line 53) | def subgraph_collate_fn(g, batch): FILE: examples/pytorch/ogb/cluster-sage/main.py class SAGE (line 25) | class SAGE(nn.Module): method __init__ (line 26) | def __init__( method forward (line 41) | def forward(self, g, x): method inference (line 50) | def inference(self, g, x, batch_size, device): function compute_acc (line 72) | def compute_acc(pred, labels): function evaluate (line 79) | def evaluate(model, g, labels, val_nid, test_nid, batch_size, device): function load_subtensor (line 102) | def load_subtensor(g, labels, seeds, input_nodes, device): function run (line 112) | def run(args, device, data): FILE: examples/pytorch/ogb/cluster-sage/partition_utils.py function get_partition_list (line 10) | def get_partition_list(g, psize): FILE: examples/pytorch/ogb/cluster-sage/sampler.py class ClusterIter (line 11) | class ClusterIter(object): method __init__ (line 16) | def __init__(self, dn, g, psize, batch_size, seed_nid): method get_norm (line 53) | def get_norm(self, g): method __len__ (line 59) | def __len__(self): method __getitem__ (line 62) | def __getitem__(self, idx): function subgraph_collate_fn (line 66) | def subgraph_collate_fn(g, batch): FILE: examples/pytorch/ogb/deepwalk/deepwalk.py class DeepwalkTrainer (line 17) | class DeepwalkTrainer: method __init__ (line 18) | def __init__(self, args): method init_device_emb (line 37) | def init_device_emb(self): method train (line 84) | def train(self): method fast_train_mp (line 91) | def fast_train_mp(self): method fast_train_sp (line 126) | def fast_train_sp(self, rank, gpu_id): method fast_train (line 196) | def fast_train(self): FILE: examples/pytorch/ogb/deepwalk/load_dataset.py function load_from_ogbl_with_name (line 9) | def load_from_ogbl_with_name(name): FILE: examples/pytorch/ogb/deepwalk/model.py function init_emb2pos_index (line 12) | def init_emb2pos_index(walk_length, window_size, batch_size): function init_emb2neg_index (line 45) | def init_emb2neg_index(walk_length, window_size, negative, batch_size): function init_weight (line 82) | def init_weight(walk_length, window_size, batch_size): function init_empty_grad (line 98) | def init_empty_grad(emb_dimension, walk_length, batch_size): function adam (line 106) | def adam(grad, state_sum, nodes, lr, device, only_gpu): function async_update (line 119) | def async_update(num_threads, model, queue): class SkipGramModel (line 135) | class SkipGramModel(nn.Module): method __init__ (line 138) | def __init__( method create_async_update (line 249) | def create_async_update(self): method finish_async_update (line 257) | def finish_async_update(self): method share_memory (line 262) | def share_memory(self): method set_device (line 269) | def set_device(self, gpu_id): method all_to_device (line 285) | def all_to_device(self, gpu_id): method fast_sigmoid (line 294) | def fast_sigmoid(self, score): method fast_logsigmoid (line 299) | def fast_logsigmoid(self, score): method fast_learn (line 304) | def fast_learn(self, batch_walks, neg_nodes=None): method forward (line 496) | def forward(self, pos_u, pos_v, neg_v): method save_embedding (line 513) | def save_embedding(self, dataset, file_name): method save_embedding_pt (line 528) | def save_embedding_pt(self, dataset, file_name): method save_embedding_pt_dgl_graph (line 554) | def save_embedding_pt_dgl_graph(self, dataset, file_name): method save_embedding_txt (line 570) | def save_embedding_txt(self, dataset, file_name): FILE: examples/pytorch/ogb/deepwalk/reading_data.py function ReadTxtNet (line 21) | def ReadTxtNet(file_path="", undirected=True): function net2graph (line 112) | def net2graph(net_sm): function make_undirected (line 127) | def make_undirected(G): function find_connected_nodes (line 132) | def find_connected_nodes(G): class DeepwalkDataset (line 137) | class DeepwalkDataset: method __init__ (line 138) | def __init__( method create_sampler (line 225) | def create_sampler(self, i): method save_mapping (line 229) | def save_mapping(self, map_file): class DeepwalkSampler (line 235) | class DeepwalkSampler(object): method __init__ (line 236) | def __init__(self, G, seeds, walk_length): method sample (line 249) | def sample(self, seeds): FILE: examples/pytorch/ogb/deepwalk/utils.py function shuffle_walks (line 4) | def shuffle_walks(walks): function sum_up_params (line 9) | def sum_up_params(model): FILE: examples/pytorch/ogb/directional_GSN/main.py function aggregate_mean (line 19) | def aggregate_mean(h, vector_field, h_in): function aggregate_max (line 23) | def aggregate_max(h, vector_field, h_in): function aggregate_sum (line 27) | def aggregate_sum(h, vector_field, h_in): function aggregate_dir_dx (line 31) | def aggregate_dir_dx(h, vector_field, h_in, eig_idx=1): class FCLayer (line 45) | class FCLayer(nn.Module): method __init__ (line 46) | def __init__(self, in_size, out_size): method reset_parameters (line 55) | def reset_parameters(self): method forward (line 59) | def forward(self, x): class MLP (line 64) | class MLP(nn.Module): method __init__ (line 65) | def __init__(self, in_size, out_size): method forward (line 72) | def forward(self, x): class DGNLayer (line 77) | class DGNLayer(nn.Module): method __init__ (line 78) | def __init__(self, in_dim, out_dim, dropout, aggregators): method pretrans_edges (line 91) | def pretrans_edges(self, edges): method message_func (line 96) | def message_func(self, edges): method reduce_func (line 102) | def reduce_func(self, nodes): method forward (line 118) | def forward(self, g, h, snorm_n): class MLPReadout (line 141) | class MLPReadout(nn.Module): method __init__ (line 142) | def __init__(self, input_dim, output_dim, L=2): # L=nb_hidden_layers method forward (line 154) | def forward(self, x): class DGNNet (line 163) | class DGNNet(nn.Module): method __init__ (line 164) | def __init__(self, hidden_dim=420, out_dim=420, dropout=0.2, n_layers=4): method forward (line 198) | def forward(self, g, h, snorm_n): method loss (line 211) | def loss(self, scores, labels): function train_epoch (line 219) | def train_epoch(model, optimizer, device, data_loader): function evaluate_network (line 253) | def evaluate_network(model, device, data_loader): function train (line 285) | def train(dataset, params): class Subset (line 381) | class Subset(object): method __init__ (line 382) | def __init__(self, dataset, labels, indices): method __getitem__ (line 392) | def __getitem__(self, item): method __len__ (line 395) | def __len__(self): class PCBADataset (line 399) | class PCBADataset(Dataset): method __init__ (line 400) | def __init__(self, name): method collate (line 430) | def collate(self, samples): FILE: examples/pytorch/ogb/directional_GSN/preprocessing.py function to_undirected (line 14) | def to_undirected(edge_index): function induced_edge_automorphism_orbits (line 22) | def induced_edge_automorphism_orbits(edge_list): function subgraph_isomorphism_edge_counts (line 99) | def subgraph_isomorphism_edge_counts(edge_index, subgraph_dict): function prepare_dataset (line 148) | def prepare_dataset(name): function generate_dataset (line 172) | def generate_dataset(path, name): function _prepare (line 215) | def _prepare(g, subgraph_dicts): FILE: examples/pytorch/ogb/line/line.py class LineTrainer (line 17) | class LineTrainer: method __init__ (line 18) | def __init__(self, args): method init_device_emb (line 36) | def init_device_emb(self): method train (line 80) | def train(self): method fast_train_mp (line 87) | def fast_train_mp(self): method fast_train_sp (line 117) | def fast_train_sp(self, rank, gpu_id): method fast_train (line 208) | def fast_train(self): FILE: examples/pytorch/ogb/line/load_dataset.py function load_from_ogbl_with_name (line 11) | def load_from_ogbl_with_name(name): function load_from_ogbn_with_name (line 18) | def load_from_ogbn_with_name(name): FILE: examples/pytorch/ogb/line/model.py function init_emb2neg_index (line 12) | def init_emb2neg_index(negative, batch_size): function adam (line 36) | def adam(grad, state_sum, nodes, lr, device, only_gpu): function async_update (line 49) | def async_update(num_threads, model, queue): class SkipGramModel (line 82) | class SkipGramModel(nn.Module): method __init__ (line 85) | def __init__( method create_async_update (line 199) | def create_async_update(self): method finish_async_update (line 207) | def finish_async_update(self): method share_memory (line 212) | def share_memory(self): method set_device (line 223) | def set_device(self, gpu_id): method all_to_device (line 233) | def all_to_device(self, gpu_id): method fast_sigmoid (line 246) | def fast_sigmoid(self, score): method fast_logsigmoid (line 251) | def fast_logsigmoid(self, score): method fast_pos_bp (line 256) | def fast_pos_bp(self, emb_pos_u, emb_pos_v, first_flag): method fast_neg_bp (line 286) | def fast_neg_bp(self, emb_neg_u, emb_neg_v, first_flag): method fast_learn (line 311) | def fast_learn(self, batch_edges, neg_nodes=None): method get_embedding (line 563) | def get_embedding(self): method save_embedding (line 588) | def save_embedding(self, dataset, file_name): method save_embedding_pt (line 599) | def save_embedding_pt(self, dataset, file_name): FILE: examples/pytorch/ogb/line/reading_data.py function ReadTxtNet (line 20) | def ReadTxtNet(file_path="", undirected=True): function net2graph (line 111) | def net2graph(net_sm): function make_undirected (line 126) | def make_undirected(G): function find_connected_nodes (line 131) | def find_connected_nodes(G): class LineDataset (line 136) | class LineDataset: method __init__ (line 137) | def __init__( method create_sampler (line 221) | def create_sampler(self, i): method save_mapping (line 225) | def save_mapping(self, map_file): class EdgeSampler (line 230) | class EdgeSampler(object): method __init__ (line 231) | def __init__(self, G, seeds): method sample (line 238) | def sample(self, seeds): FILE: examples/pytorch/ogb/line/utils.py function check_args (line 4) | def check_args(args): function sum_up_params (line 15) | def sum_up_params(model): FILE: examples/pytorch/ogb/ngnn/main.py class Logger (line 15) | class Logger(object): method __init__ (line 16) | def __init__(self, runs, info=None): method add_result (line 20) | def add_result(self, run, result): method print_statistics (line 25) | def print_statistics(self, run=None): class NGNN_GCNConv (line 58) | class NGNN_GCNConv(torch.nn.Module): method __init__ (line 59) | def __init__( method reset_parameters (line 71) | def reset_parameters(self): method forward (line 80) | def forward(self, g, x): class GCN (line 92) | class GCN(torch.nn.Module): method __init__ (line 93) | def __init__( method reset_parameters (line 139) | def reset_parameters(self): method forward (line 143) | def forward(self, g, x): class NGNN_SAGEConv (line 152) | class NGNN_SAGEConv(torch.nn.Module): method __init__ (line 153) | def __init__( method reset_parameters (line 171) | def reset_parameters(self): method forward (line 180) | def forward(self, g, x): class SAGE (line 192) | class SAGE(torch.nn.Module): method __init__ (line 193) | def __init__( method reset_parameters (line 244) | def reset_parameters(self): method forward (line 248) | def forward(self, g, x): class LinkPredictor (line 257) | class LinkPredictor(torch.nn.Module): method __init__ (line 258) | def __init__( method reset_parameters (line 272) | def reset_parameters(self): method forward (line 276) | def forward(self, x_i, x_j): function train (line 286) | def train(model, predictor, g, x, split_edge, optimizer, batch_size): function test (line 328) | def test(model, predictor, g, x, split_edge, evaluator, batch_size): function main (line 381) | def main(): FILE: examples/pytorch/ogb/ngnn_seal/main.py class SEALOGBLDataset (line 20) | class SEALOGBLDataset(Dataset): method __init__ (line 21) | def __init__( method __len__ (line 61) | def __len__(self): method __getitem__ (line 64) | def __getitem__(self, idx): method cached_name (line 105) | def cached_name(self): method process (line 108) | def process(self): method load_cached (line 123) | def load_cached(self): function ogbl_collate_fn (line 136) | def ogbl_collate_fn(batch): function train (line 153) | def train(): function test (line 173) | def test(dataloader, hits_K=["hits@100"]): function evaluate_hits (line 200) | def evaluate_hits(y_pred_pos, y_pred_neg, hits_K): function evaluate_mrr (line 219) | def evaluate_mrr(y_pred_pos, y_pred_neg): function evaluate_rocauc (line 238) | def evaluate_rocauc(y_pred_pos, y_pred_neg): function print_log (line 252) | def print_log(*x, sep="\n", end="\n", mode="a"): FILE: examples/pytorch/ogb/ngnn_seal/models.py class NGNN_GCNConv (line 9) | class NGNN_GCNConv(torch.nn.Module): method __init__ (line 10) | def __init__( method reset_parameters (line 19) | def reset_parameters(self): method forward (line 28) | def forward(self, g, x, edge_weight=None): class DGCNN (line 39) | class DGCNN(torch.nn.Module): method __init__ (line 40) | def __init__( method forward (line 114) | def forward(self, g, z, x=None, edge_weight=None): FILE: examples/pytorch/ogb/ngnn_seal/utils.py function k_hop_subgraph (line 10) | def k_hop_subgraph(src, dst, num_hops, g, sample_ratio=1.0, directed=Fal... function drnl_node_labeling (line 38) | def drnl_node_labeling(adj, src, dst): function get_pos_neg_edges (line 76) | def get_pos_neg_edges(split, split_edge, g, percent=100): class Logger (line 108) | class Logger(object): method __init__ (line 109) | def __init__(self, runs, info=None): method add_result (line 116) | def add_result(self, run, result, split="valid"): method print_statistics (line 121) | def print_statistics(self, run=None, f=sys.stdout): FILE: examples/pytorch/ogb/ogbn-arxiv/correct_and_smooth.py function load_data (line 17) | def load_data(dataset): function preprocess (line 37) | def preprocess(graph): function general_outcome_correlation (line 54) | def general_outcome_correlation( function evaluate (line 86) | def evaluate(labels, pred, train_idx, val_idx, test_idx, evaluator): function run (line 94) | def run(args, graph, labels, pred, train_idx, val_idx, test_idx, evaluat... function main (line 140) | def main(): FILE: examples/pytorch/ogb/ogbn-arxiv/gat.py function seed (line 29) | def seed(seed=0): function load_data (line 40) | def load_data(dataset): function preprocess (line 60) | def preprocess(graph): function gen_model (line 78) | def gen_model(args): function custom_loss_function (line 102) | def custom_loss_function(x, labels): function add_labels (line 108) | def add_labels(feat, labels, idx): function adjust_learning_rate (line 114) | def adjust_learning_rate(optimizer, lr, epoch): function train (line 120) | def train( function evaluate (line 168) | def evaluate( function run (line 203) | def run( function count_parameters (line 353) | def count_parameters(args): function main (line 358) | def main(): FILE: examples/pytorch/ogb/ogbn-arxiv/gcn.py function gen_model (line 22) | def gen_model(args): function cross_entropy (line 46) | def cross_entropy(x, labels): function compute_acc (line 52) | def compute_acc(pred, labels, evaluator): function add_labels (line 58) | def add_labels(feat, labels, idx): function adjust_learning_rate (line 64) | def adjust_learning_rate(optimizer, lr, epoch): function train (line 70) | def train(model, graph, labels, train_idx, optimizer, use_labels): function evaluate (line 99) | def evaluate( function run (line 124) | def run( function count_parameters (line 266) | def count_parameters(args): function main (line 273) | def main(): FILE: examples/pytorch/ogb/ogbn-arxiv/models.py class ElementWiseLinear (line 9) | class ElementWiseLinear(nn.Module): method __init__ (line 10) | def __init__(self, size, weight=True, bias=True, inplace=False): method reset_parameters (line 24) | def reset_parameters(self): method forward (line 30) | def forward(self, x): class GCN (line 44) | class GCN(nn.Module): method __init__ (line 45) | def __init__( method forward (line 83) | def forward(self, graph, feat): class GATConv (line 104) | class GATConv(nn.Module): method __init__ (line 105) | def __init__( method reset_parameters (line 159) | def reset_parameters(self): method set_allow_zero_in_degree (line 172) | def set_allow_zero_in_degree(self, set_value): method forward (line 175) | def forward(self, graph, feat): class GAT (line 270) | class GAT(nn.Module): method __init__ (line 271) | def __init__( method forward (line 326) | def forward(self, graph, feat): FILE: examples/pytorch/ogb/ogbn-mag/hetero_rgcn.py function prepare_data (line 21) | def prepare_data(args, device): function extract_embed (line 51) | def extract_embed(node_embed, input_nodes): function rel_graph_embed (line 58) | def rel_graph_embed(graph, embed_size): class RelGraphConvLayer (line 68) | class RelGraphConvLayer(nn.Module): method __init__ (line 69) | def __init__( method reset_parameters (line 106) | def reset_parameters(self): method forward (line 113) | def forward(self, g, inputs): class EntityClassify (line 148) | class EntityClassify(nn.Module): method __init__ (line 149) | def __init__(self, g, in_dim, out_dim): method reset_parameters (line 182) | def reset_parameters(self): method forward (line 186) | def forward(self, h, blocks): class Logger (line 192) | class Logger(object): method __init__ (line 200) | def __init__(self, runs): method add_result (line 203) | def add_result(self, run, result): method print_statistics (line 208) | def print_statistics(self, run=None): function train (line 241) | def train( function test (line 310) | def test(g, model, node_embed, y_true, device, split_idx): function is_support_affinity (line 378) | def is_support_affinity(v_t): function main (line 383) | def main(args): FILE: examples/pytorch/ogb/ogbn-products/gat/gat.py function seed (line 36) | def seed(seed=0): function load_data (line 47) | def load_data(dataset): function preprocess (line 63) | def preprocess(graph, labels, train_idx): function gen_model (line 84) | def gen_model(args): function custom_loss_function (line 111) | def custom_loss_function(x, labels): function add_soft_labels (line 117) | def add_soft_labels(graph, soft_labels): function update_hard_labels (line 122) | def update_hard_labels(graph, idx=None): function train (line 133) | def train( function evaluate (line 195) | def evaluate( function run (line 251) | def run( function count_parameters (line 480) | def count_parameters(args): function main (line 487) | def main(): FILE: examples/pytorch/ogb/ogbn-products/gat/main.py class GAT (line 16) | class GAT(nn.Module): method __init__ (line 17) | def __init__( method forward (line 51) | def forward(self, blocks, x): method inference (line 68) | def inference(self, g, x, num_heads, device): function compute_acc (line 126) | def compute_acc(pred, labels): function evaluate (line 133) | def evaluate(model, g, nfeat, labels, val_nid, test_nid, num_heads, devi... function load_subtensor (line 154) | def load_subtensor(nfeat, labels, seeds, input_nodes): function run (line 164) | def run(args, device, data): FILE: examples/pytorch/ogb/ogbn-products/gat/models.py class GATConv (line 11) | class GATConv(nn.Module): method __init__ (line 12) | def __init__( method reset_parameters (line 65) | def reset_parameters(self): method set_allow_zero_in_degree (line 80) | def set_allow_zero_in_degree(self, set_value): method forward (line 83) | def forward(self, graph, feat_src, feat_edge=None): class GAT (line 178) | class GAT(nn.Module): method __init__ (line 179) | def __init__( method forward (line 239) | def forward(self, g, inference=False): class MLP (line 277) | class MLP(nn.Module): method __init__ (line 278) | def __init__( method forward (line 311) | def forward(self, h): FILE: examples/pytorch/ogb/ogbn-products/graphsage/main.py class SAGE (line 16) | class SAGE(nn.Module): method __init__ (line 17) | def __init__( method forward (line 32) | def forward(self, blocks, x): method inference (line 48) | def inference(self, g, x, device): function compute_acc (line 94) | def compute_acc(pred, labels): function evaluate (line 101) | def evaluate(model, g, nfeat, labels, val_nid, test_nid, device): function load_subtensor (line 121) | def load_subtensor(nfeat, labels, seeds, input_nodes): function run (line 131) | def run(args, device, data): FILE: examples/pytorch/ogb/ogbn-products/mlp/mlp.py function seed (line 35) | def seed(seed=0): function load_data (line 45) | def load_data(dataset): function preprocess (line 61) | def preprocess(graph, labels): function gen_model (line 72) | def gen_model(args): function custom_loss_function (line 87) | def custom_loss_function(x, labels): function train (line 93) | def train( function evaluate (line 129) | def evaluate( function run (line 170) | def run( function count_parameters (line 390) | def count_parameters(args): function main (line 397) | def main(): FILE: examples/pytorch/ogb/ogbn-products/mlp/models.py class MLP (line 6) | class MLP(nn.Module): method __init__ (line 7) | def __init__( method forward (line 40) | def forward(self, h): FILE: examples/pytorch/ogb/ogbn-proteins/configure.py function get_exp_configure (line 36) | def get_exp_configure(args): FILE: examples/pytorch/ogb/ogbn-proteins/gat.py function seed (line 33) | def seed(seed=0): function load_data (line 44) | def load_data(dataset): function preprocess (line 60) | def preprocess(graph, labels, train_idx): function gen_model (line 81) | def gen_model(args): function add_labels (line 106) | def add_labels(graph, idx): function train (line 113) | def train( function evaluate (line 160) | def evaluate( function run (line 208) | def run( function count_parameters (line 403) | def count_parameters(args): function main (line 410) | def main(): FILE: examples/pytorch/ogb/ogbn-proteins/main_proteins_full_dgl.py function normalize_edge_weights (line 18) | def normalize_edge_weights(graph, device, num_ew_channels): function run_a_train_epoch (line 32) | def run_a_train_epoch(graph, node_idx, model, criterion, optimizer, eval... function run_an_eval_epoch (line 48) | def run_an_eval_epoch(graph, splitted_idx, model, evaluator): function main (line 77) | def main(args): FILE: examples/pytorch/ogb/ogbn-proteins/models.py class MWEConv (line 19) | class MWEConv(nn.Module): method __init__ (line 20) | def __init__( method reset_parameters (line 51) | def reset_parameters(self): method forward (line 58) | def forward(self, g, node_state_prev): class MWE_GCN (line 98) | class MWE_GCN(nn.Module): method __init__ (line 99) | def __init__( method forward (line 133) | def forward(self, g, node_state=None): class MWE_DGCN (line 147) | class MWE_DGCN(nn.Module): method __init__ (line 148) | def __init__( method forward (line 193) | def forward(self, g, node_state=None): class GATConv (line 221) | class GATConv(nn.Module): method __init__ (line 222) | def __init__( method reset_parameters (line 275) | def reset_parameters(self): method set_allow_zero_in_degree (line 290) | def set_allow_zero_in_degree(self, set_value): method forward (line 293) | def forward(self, graph, feat_src, feat_edge=None): class GAT (line 391) | class GAT(nn.Module): method __init__ (line 392) | def __init__( method forward (line 450) | def forward(self, g): FILE: examples/pytorch/ogb/ogbn-proteins/utils.py function set_random_seed (line 9) | def set_random_seed(seed): function load_model (line 18) | def load_model(args): class Logger (line 48) | class Logger(object): method __init__ (line 49) | def __init__(self, runs, info=None): method add_result (line 53) | def add_result(self, run, result): method print_statistics (line 58) | def print_statistics(self, run=None): FILE: examples/pytorch/ogb/seal_ogbl/main.py class Logger (line 29) | class Logger(object): method __init__ (line 30) | def __init__(self, runs, info=None): method add_result (line 34) | def add_result(self, run, result): method print_statistics (line 40) | def print_statistics(self, run=None, f=sys.stdout): class SealSampler (line 66) | class SealSampler(Sampler): method __init__ (line 67) | def __init__( method _double_radius_node_labeling (line 84) | def _double_radius_node_labeling(self, adj): method sample (line 116) | def sample(self, aug_g, seed_edges): class DGCNN (line 170) | class DGCNN(torch.nn.Module): method __init__ (line 171) | def __init__( method forward (line 203) | def forward(self, g, z, x=None, edge_weight=None): function get_pos_neg_edges (line 232) | def get_pos_neg_edges(split, split_edge, g, percent=100): function train (line 264) | def train(): function test (line 288) | def test(): function evaluate_hits (line 331) | def evaluate_hits(pos_val_pred, neg_val_pred, pos_test_pred, neg_test_pr... function evaluate_mrr (line 353) | def evaluate_mrr(pos_val_pred, neg_val_pred, pos_test_pred, neg_test_pred): FILE: examples/pytorch/ogb/sign/dataset.py function get_ogb_evaluator (line 8) | def get_ogb_evaluator(dataset): function convert_mag_to_homograph (line 21) | def convert_mag_to_homograph(g, device): function load_dataset (line 56) | def load_dataset(name, device): FILE: examples/pytorch/ogb/sign/sign.py class FeedForwardNet (line 13) | class FeedForwardNet(nn.Module): method __init__ (line 14) | def __init__(self, in_feats, hidden, out_feats, n_layers, dropout): method reset_parameters (line 30) | def reset_parameters(self): method forward (line 36) | def forward(self, x): class SIGN (line 44) | class SIGN(nn.Module): method __init__ (line 45) | def __init__( method forward (line 68) | def forward(self, feats): method reset_parameters (line 76) | def reset_parameters(self): function get_n_params (line 82) | def get_n_params(model): function neighbor_average_features (line 92) | def neighbor_average_features(g, args): function prepare_data (line 123) | def prepare_data(device, args): function train (line 148) | def train(model, feats, labels, loss_fcn, optimizer, train_loader): function test (line 159) | def test( function run (line 176) | def run(args, data, device): function main (line 253) | def main(args): FILE: examples/pytorch/ogb_lsc/MAG240M/train.py class RGAT (line 20) | class RGAT(nn.Module): method __init__ (line 21) | def __init__( method forward (line 82) | def forward(self, mfgs, x): class ExternalNodeCollator (line 103) | class ExternalNodeCollator(dgl.dataloading.NodeCollator): method __init__ (line 104) | def __init__(self, g, idx, sampler, offset, feats, label): method collate (line 110) | def collate(self, items): function train (line 120) | def train(args, dataset, g, feats, paper_offset): function test (line 208) | def test(args, dataset, g, feats, paper_offset): FILE: examples/pytorch/ogb_lsc/MAG240M/train_multi_gpus.py class RGAT (line 21) | class RGAT(nn.Module): method __init__ (line 22) | def __init__( method forward (line 83) | def forward(self, mfgs, x): class ExternalNodeCollator (line 104) | class ExternalNodeCollator(dgl.dataloading.NodeCollator): method __init__ (line 105) | def __init__(self, g, idx, sampler, offset, feats, label): method collate (line 111) | def collate(self, items): function train (line 121) | def train(proc_id, n_gpus, args, dataset, g, feats, paper_offset): function test (line 263) | def test(args, dataset, g, feats, paper_offset): FILE: examples/pytorch/ogb_lsc/PCQM4M/conv.py class GINConv (line 11) | class GINConv(nn.Module): method __init__ (line 12) | def __init__(self, emb_dim): method forward (line 29) | def forward(self, g, x, edge_attr): class GCNConv (line 42) | class GCNConv(nn.Module): method __init__ (line 43) | def __init__(self, emb_dim): method forward (line 54) | def forward(self, g, x, edge_attr): class GNN_node (line 80) | class GNN_node(nn.Module): method __init__ (line 86) | def __init__( method forward (line 126) | def forward(self, g, x, edge_attr): class GNN_node_Virtualnode (line 158) | class GNN_node_Virtualnode(nn.Module): method __init__ (line 164) | def __init__( method forward (line 225) | def forward(self, g, x, edge_attr): FILE: examples/pytorch/ogb_lsc/PCQM4M/gnn.py class GNN (line 14) | class GNN(nn.Module): method __init__ (line 15) | def __init__( method forward (line 90) | def forward(self, g, x, edge_attr): FILE: examples/pytorch/ogb_lsc/PCQM4M/main.py function collate_dgl (line 20) | def collate_dgl(samples): function train (line 28) | def train(model, device, loader, optimizer): function eval (line 51) | def eval(model, device, loader, evaluator): function test (line 78) | def test(model, device, loader): function main (line 99) | def main(): FILE: examples/pytorch/ogb_lsc/PCQM4M/test_inference.py function collate_dgl (line 16) | def collate_dgl(graphs): function test (line 22) | def test(model, device, loader): class OnTheFlyPCQMDataset (line 43) | class OnTheFlyPCQMDataset(object): method __init__ (line 44) | def __init__(self, smiles_list, smiles2graph=smiles2graph): method __getitem__ (line 49) | def __getitem__(self, idx): method __len__ (line 67) | def __len__(self): function main (line 77) | def main(): FILE: examples/pytorch/ogc/ogc.py class OGC (line 8) | class OGC(nn.Module): method __init__ (line 9) | def __init__(self, graph): method forward (line 25) | def forward(self, x): method update_embeds (line 28) | def update_embeds(self, embeds, lazy_adj, args): FILE: examples/pytorch/ogc/train.py function train (line 15) | def train(model, embeds, lazy_adj, args): FILE: examples/pytorch/ogc/utils.py class LinearNeuralNetwork (line 7) | class LinearNeuralNetwork(nn.Module): method __init__ (line 8) | def __init__(self, nfeat, nclass, bias=True): method forward (line 12) | def forward(self, x): function symmetric_normalize_adjacency (line 16) | def symmetric_normalize_adjacency(graph): function model_test (line 25) | def model_test(model, embeds): FILE: examples/pytorch/pagerank.py function compute_pagerank (line 14) | def compute_pagerank(g): FILE: examples/pytorch/pinsage/builder.py function _series_to_tensor (line 15) | def _series_to_tensor(series): class PandasGraphBuilder (line 22) | class PandasGraphBuilder(object): method __init__ (line 72) | def __init__(self): method add_entities (line 91) | def add_entities(self, entity_table, primary_key, name): method add_binary_relations (line 108) | def add_binary_relations( method build (line 146) | def build(self): FILE: examples/pytorch/pinsage/data_utils.py function train_test_split_by_time (line 13) | def train_test_split_by_time(df, timestamp, user): function build_train_graph (line 54) | def build_train_graph(g, train_indices, utype, itype, etype, etype_rev): function build_val_test_matrix (line 72) | def build_val_test_matrix(g, val_indices, test_indices, utype, itype, et... function linear_normalize (line 91) | def linear_normalize(values): FILE: examples/pytorch/pinsage/evaluation.py function prec (line 10) | def prec(recommendations, ground_truth): class LatestNNRecommender (line 20) | class LatestNNRecommender(object): method __init__ (line 21) | def __init__( method recommend (line 30) | def recommend(self, full_graph, K, h_user, h_item): function evaluate_nn (line 64) | def evaluate_nn(dataset, h_item, k, batch_size): FILE: examples/pytorch/pinsage/layers.py function disable_grad (line 9) | def disable_grad(module): function _init_input_modules (line 14) | def _init_input_modules(g, ntype, textset, hidden_dims): class BagOfWords (line 47) | class BagOfWords(nn.Module): method __init__ (line 48) | def __init__(self, vocab, hidden_dims): method forward (line 58) | def forward(self, x, length): class LinearProjector (line 62) | class LinearProjector(nn.Module): method __init__ (line 67) | def __init__(self, full_graph, ntype, textset, hidden_dims): method forward (line 75) | def forward(self, ndata): class WeightedSAGEConv (line 95) | class WeightedSAGEConv(nn.Module): method __init__ (line 96) | def __init__(self, input_dims, hidden_dims, output_dims, act=F.relu): method reset_parameters (line 105) | def reset_parameters(self): method forward (line 112) | def forward(self, g, h, weights): class SAGENet (line 135) | class SAGENet(nn.Module): method __init__ (line 136) | def __init__(self, hidden_dims, n_layers): method forward (line 152) | def forward(self, blocks, h): class ItemToItemScorer (line 159) | class ItemToItemScorer(nn.Module): method __init__ (line 160) | def __init__(self, full_graph, ntype): method _add_bias (line 166) | def _add_bias(self, edges): method forward (line 171) | def forward(self, item_item_graph, h): FILE: examples/pytorch/pinsage/model.py class PinSAGEModel (line 20) | class PinSAGEModel(nn.Module): method __init__ (line 21) | def __init__(self, full_graph, ntype, textsets, hidden_dims, n_layers): method forward (line 30) | def forward(self, pos_graph, neg_graph, blocks): method get_repr (line 36) | def get_repr(self, blocks): function train (line 42) | def train(dataset, args): FILE: examples/pytorch/pinsage/model_sparse.py class PinSAGEModel (line 20) | class PinSAGEModel(nn.Module): method __init__ (line 21) | def __init__(self, full_graph, ntype, textsets, hidden_dims, n_layers): method forward (line 30) | def forward(self, pos_graph, neg_graph, blocks, item_emb): method get_repr (line 36) | def get_repr(self, blocks, item_emb): function train (line 50) | def train(dataset, args): FILE: examples/pytorch/pinsage/sampler.py function padding (line 8) | def padding(array, yy, val): function compact_and_copy (line 24) | def compact_and_copy(frontier, seeds): class ItemToItemBatchSampler (line 33) | class ItemToItemBatchSampler(IterableDataset): method __init__ (line 34) | def __init__(self, g, user_type, item_type, batch_size): method __iter__ (line 42) | def __iter__(self): class NeighborSampler (line 60) | class NeighborSampler(object): method __init__ (line 61) | def __init__( method sample_blocks (line 90) | def sample_blocks(self, seeds, heads=None, tails=None, neg_tails=None): method sample_from_item_pairs (line 112) | def sample_from_item_pairs(self, heads, tails, neg_tails): function assign_simple_node_features (line 128) | def assign_simple_node_features(ndata, g, ntype, assign_id=False): function assign_textual_node_features (line 139) | def assign_textual_node_features(ndata, textset, ntype): function assign_features_to_blocks (line 188) | def assign_features_to_blocks(blocks, g, textset, ntype): class PinSAGECollator (line 197) | class PinSAGECollator(object): method __init__ (line 198) | def __init__(self, sampler, g, ntype, textset): method collate_train (line 204) | def collate_train(self, batches): method collate_test (line 214) | def collate_test(self, samples): FILE: examples/pytorch/pointcloud/bipointnet/ModelNetDataLoader.py function pc_normalize (line 10) | def pc_normalize(pc): function farthest_point_sample (line 18) | def farthest_point_sample(point, npoint): class ModelNetDataLoader (line 47) | class ModelNetDataLoader(Dataset): method __init__ (line 48) | def __init__( method __len__ (line 101) | def __len__(self): method _get_item (line 104) | def _get_item(self, index): FILE: examples/pytorch/pointcloud/bipointnet/basic.py class BinaryQuantize (line 10) | class BinaryQuantize(Function): method forward (line 12) | def forward(ctx, input): method backward (line 18) | def backward(ctx, grad_output): class BiLinearLSR (line 26) | class BiLinearLSR(torch.nn.Linear): method __init__ (line 27) | def __init__(self, in_features, out_features, bias=False, binary_act=T... method reset_scale (line 41) | def reset_scale(self, input): method forward (line 59) | def forward(self, input): class BiLinear (line 75) | class BiLinear(torch.nn.Linear): method __init__ (line 76) | def __init__(self, in_features, out_features, bias=True, binary_act=Tr... method forward (line 81) | def forward(self, input): class BiConv2d (line 92) | class BiConv2d(torch.nn.Conv2d): method __init__ (line 93) | def __init__( method forward (line 117) | def forward(self, input): function square_distance (line 149) | def square_distance(src, dst): function index_points (line 161) | def index_points(points, idx): class FixedRadiusNearNeighbors (line 181) | class FixedRadiusNearNeighbors(nn.Module): method __init__ (line 186) | def __init__(self, radius, n_neighbor): method forward (line 191) | def forward(self, pos, centroids): class FixedRadiusNNGraph (line 216) | class FixedRadiusNNGraph(nn.Module): method __init__ (line 221) | def __init__(self, radius, n_neighbor): method forward (line 227) | def forward(self, pos, centroids, feat=None): class RelativePositionMessage (line 253) | class RelativePositionMessage(nn.Module): method __init__ (line 258) | def __init__(self, n_neighbor): method forward (line 262) | def forward(self, edges): FILE: examples/pytorch/pointcloud/bipointnet/bipointnet2.py class BiPointNetConv (line 13) | class BiPointNetConv(nn.Module): method __init__ (line 18) | def __init__(self, sizes, batch_size): method forward (line 27) | def forward(self, nodes): method group_all (line 43) | def group_all(self, pos, feat): class BiSAModule (line 63) | class BiSAModule(nn.Module): method __init__ (line 68) | def __init__( method forward (line 86) | def forward(self, pos, feat): class BiPointNet2SSGCls (line 102) | class BiPointNet2SSGCls(nn.Module): method __init__ (line 103) | def __init__( method forward (line 129) | def forward(self, x): FILE: examples/pytorch/pointcloud/bipointnet/bipointnet_cls.py class Conv1d (line 11) | class Conv1d(nn.Module): method __init__ (line 12) | def __init__(self, inplane, outplane, Linear): method forward (line 16) | def forward(self, x): class EmaMaxPool (line 23) | class EmaMaxPool(nn.Module): method __init__ (line 24) | def __init__(self, kernel_size, affine=True, Linear=BiLinear, use_bn=T... method forward (line 30) | def forward(self, x): class BiPointNetCls (line 39) | class BiPointNetCls(nn.Module): method __init__ (line 40) | def __init__( method forward (line 89) | def forward(self, x): class TransformNet (line 127) | class TransformNet(nn.Module): method __init__ (line 128) | def __init__(self, input_dims=3, conv1_dim=64, Linear=BiLinear): method forward (line 155) | def forward(self, h): FILE: examples/pytorch/pointcloud/bipointnet/train_cls.py function train (line 62) | def train(net, opt, scheduler, train_loader, dev): function evaluate (line 106) | def evaluate(net, test_loader, dev): FILE: examples/pytorch/pointcloud/edgeconv/main.py function train (line 47) | def train(model, opt, scheduler, train_loader, dev): function evaluate (line 85) | def evaluate(model, test_loader, dev): FILE: examples/pytorch/pointcloud/edgeconv/model.py class Model (line 8) | class Model(nn.Module): method __init__ (line 9) | def __init__( method forward (line 53) | def forward(self, x): function compute_loss (line 82) | def compute_loss(logits, y, eps=0.2): FILE: examples/pytorch/pointcloud/edgeconv/modelnet.py class ModelNet (line 5) | class ModelNet(object): method __init__ (line 6) | def __init__(self, path, num_points): method train (line 17) | def train(self): method valid (line 20) | def valid(self): method test (line 23) | def test(self): class ModelNetDataset (line 27) | class ModelNetDataset(Dataset): method __init__ (line 28) | def __init__(self, modelnet, mode): method translate (line 43) | def translate(self, x, scale=(2 / 3, 3 / 2), shift=(-0.2, 0.2)): method __len__ (line 49) | def __len__(self): method __getitem__ (line 52) | def __getitem__(self, i): FILE: examples/pytorch/pointcloud/pct/ModelNetDataLoader.py function pc_normalize (line 10) | def pc_normalize(pc): function farthest_point_sample (line 18) | def farthest_point_sample(point, npoint): class ModelNetDataLoader (line 47) | class ModelNetDataLoader(Dataset): method __init__ (line 48) | def __init__( method __len__ (line 101) | def __len__(self): method _get_item (line 104) | def _get_item(self, index): method __getitem__ (line 127) | def __getitem__(self, index): FILE: examples/pytorch/pointcloud/pct/ShapeNet.py class ShapeNet (line 14) | class ShapeNet(object): method __init__ (line 15) | def __init__(self, num_points=2048, normal_channel=True): method train (line 83) | def train(self): method valid (line 88) | def valid(self): method trainval (line 93) | def trainval(self): method test (line 98) | def test(self): class ShapeNetDataset (line 104) | class ShapeNetDataset(Dataset): method __init__ (line 105) | def __init__(self, shapenet, mode, num_points, normal_channel=True): method translate (line 141) | def translate(self, x, scale=(2 / 3, 3 / 2), shift=(-0.2, 0.2), size=3): method __len__ (line 147) | def __len__(self): method __getitem__ (line 150) | def __getitem__(self, i): FILE: examples/pytorch/pointcloud/pct/helper.py function square_distance (line 13) | def square_distance(src, dst): function index_points (line 25) | def index_points(points, idx): class KNearNeighbors (line 45) | class KNearNeighbors(nn.Module): method __init__ (line 50) | def __init__(self, n_neighbor): method forward (line 54) | def forward(self, pos, centroids): class KNNGraphBuilder (line 64) | class KNNGraphBuilder(nn.Module): method __init__ (line 69) | def __init__(self, n_neighbor): method forward (line 74) | def forward(self, pos, centroids, feat=None): class KNNMessage (line 107) | class KNNMessage(nn.Module): method __init__ (line 112) | def __init__(self, n_neighbor): method forward (line 116) | def forward(self, edges): class KNNConv (line 125) | class KNNConv(nn.Module): method __init__ (line 130) | def __init__(self, sizes): method forward (line 138) | def forward(self, nodes): class TransitionDown (line 155) | class TransitionDown(nn.Module): method __init__ (line 160) | def __init__(self, in_channels, out_channels, n_neighbor=64): method forward (line 166) | def forward(self, pos, feat, n_point): FILE: examples/pytorch/pointcloud/pct/pct.py class PCTPositionEmbedding (line 11) | class PCTPositionEmbedding(nn.Module): method __init__ (line 12) | def __init__(self, channels=256): method forward (line 26) | def forward(self, x, xyz): class SALayerCLS (line 43) | class SALayerCLS(nn.Module): method __init__ (line 44) | def __init__(self, channels): method forward (line 55) | def forward(self, x, xyz): class SALayerSeg (line 69) | class SALayerSeg(nn.Module): method __init__ (line 70) | def __init__(self, channels): method forward (line 81) | def forward(self, x): class PointTransformerCLS (line 94) | class PointTransformerCLS(nn.Module): method __init__ (line 95) | def __init__(self, output_channels=40): method forward (line 125) | def forward(self, x): class PointTransformerSeg (line 153) | class PointTransformerSeg(nn.Module): method __init__ (line 154) | def __init__(self, part_num=50): method forward (line 189) | def forward(self, x, cls_label): class PartSegLoss (line 216) | class PartSegLoss(nn.Module): method __init__ (line 217) | def __init__(self, eps=0.2): method forward (line 222) | def forward(self, logits, y): FILE: examples/pytorch/pointcloud/pct/provider.py function normalize_data (line 7) | def normalize_data(batch_data): function shuffle_data (line 26) | def shuffle_data(data, labels): function shuffle_points (line 39) | def shuffle_points(batch_data): function rotate_point_cloud (line 52) | def rotate_point_cloud(batch_data): function rotate_point_cloud_z (line 75) | def rotate_point_cloud_z(batch_data): function rotate_point_cloud_with_normal (line 98) | def rotate_point_cloud_with_normal(batch_xyz_normal): function rotate_perturbation_point_cloud_with_normal (line 123) | def rotate_perturbation_point_cloud_with_normal( function rotate_point_cloud_by_angle (line 166) | def rotate_point_cloud_by_angle(batch_data, rotation_angle): function rotate_point_cloud_by_angle_with_normal (line 188) | def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle): function rotate_perturbation_point_cloud (line 215) | def rotate_perturbation_point_cloud( function jitter_point_cloud (line 256) | def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): function shift_point_cloud (line 270) | def shift_point_cloud(batch_data, shift_range=0.1): function random_scale_point_cloud (line 284) | def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): function random_point_dropout (line 298) | def random_point_dropout(batch_pc, max_dropout_ratio=0.875): FILE: examples/pytorch/pointcloud/pct/train_cls.py function train (line 56) | def train(net, opt, scheduler, train_loader, dev): function evaluate (line 108) | def evaluate(net, test_loader, dev): FILE: examples/pytorch/pointcloud/pct/train_partseg.py function collate (line 30) | def collate(samples): function train (line 44) | def train(net, opt, scheduler, train_loader, dev): function mIoU (line 96) | def mIoU(preds, label, cat, cat_miou, seg_classes): function evaluate (line 116) | def evaluate(net, test_loader, dev, per_cat_verbose=False): function paint (line 268) | def paint(batched_points): FILE: examples/pytorch/pointcloud/point_transformer/ModelNetDataLoader.py function pc_normalize (line 10) | def pc_normalize(pc): function farthest_point_sample (line 18) | def farthest_point_sample(point, npoint): class ModelNetDataLoader (line 47) | class ModelNetDataLoader(Dataset): method __init__ (line 48) | def __init__( method __len__ (line 101) | def __len__(self): method _get_item (line 104) | def _get_item(self, index): method __getitem__ (line 127) | def __getitem__(self, index): FILE: examples/pytorch/pointcloud/point_transformer/ShapeNet.py class ShapeNet (line 14) | class ShapeNet(object): method __init__ (line 15) | def __init__(self, num_points=2048, normal_channel=True): method train (line 83) | def train(self): method valid (line 88) | def valid(self): method trainval (line 93) | def trainval(self): method test (line 98) | def test(self): class ShapeNetDataset (line 104) | class ShapeNetDataset(Dataset): method __init__ (line 105) | def __init__(self, shapenet, mode, num_points, normal_channel=True): method translate (line 141) | def translate(self, x, scale=(2 / 3, 3 / 2), shift=(-0.2, 0.2), size=3): method __len__ (line 147) | def __len__(self): method __getitem__ (line 150) | def __getitem__(self, i): FILE: examples/pytorch/pointcloud/point_transformer/helper.py function square_distance (line 13) | def square_distance(src, dst): function index_points (line 25) | def index_points(points, idx): class KNearNeighbors (line 45) | class KNearNeighbors(nn.Module): method __init__ (line 50) | def __init__(self, n_neighbor): method forward (line 54) | def forward(self, pos, centroids): class KNNGraphBuilder (line 64) | class KNNGraphBuilder(nn.Module): method __init__ (line 69) | def __init__(self, n_neighbor): method forward (line 74) | def forward(self, pos, centroids, feat=None): class RelativePositionMessage (line 107) | class RelativePositionMessage(nn.Module): method __init__ (line 112) | def __init__(self, n_neighbor): method forward (line 116) | def forward(self, edges): class KNNConv (line 125) | class KNNConv(nn.Module): method __init__ (line 130) | def __init__(self, sizes, batch_size): method forward (line 139) | def forward(self, nodes): method group_all (line 155) | def group_all(self, pos, feat): class TransitionDown (line 175) | class TransitionDown(nn.Module): method __init__ (line 180) | def __init__(self, n_points, batch_size, mlp_sizes, n_neighbors=64): method forward (line 188) | def forward(self, pos, feat): class FeaturePropagation (line 201) | class FeaturePropagation(nn.Module): method __init__ (line 206) | def __init__(self, input_dims, sizes): method forward (line 216) | def forward(self, x1, x2, feat1, feat2): class SwapAxes (line 256) | class SwapAxes(nn.Module): method __init__ (line 257) | def __init__(self, dim1=1, dim2=2): method forward (line 262) | def forward(self, x): class TransitionUp (line 266) | class TransitionUp(nn.Module): method __init__ (line 271) | def __init__(self, dim1, dim2, dim_out): method forward (line 289) | def forward(self, pos1, feat1, pos2, feat2): FILE: examples/pytorch/pointcloud/point_transformer/point_transformer.py class PointTransformerBlock (line 12) | class PointTransformerBlock(nn.Module): method __init__ (line 13) | def __init__(self, input_dim, n_neighbors, transformer_dim=None): method forward (line 34) | def forward(self, x, pos): class PointTransformer (line 58) | class PointTransformer(nn.Module): method __init__ (line 59) | def __init__( method forward (line 102) | def forward(self, x): class PointTransformerCLS (line 121) | class PointTransformerCLS(nn.Module): method __init__ (line 122) | def __init__( method forward (line 153) | def forward(self, x): class PointTransformerSeg (line 159) | class PointTransformerSeg(nn.Module): method __init__ (line 160) | def __init__( method forward (line 219) | def forward(self, x, cat_vec=None): class PartSegLoss (line 233) | class PartSegLoss(nn.Module): method __init__ (line 234) | def __init__(self, eps=0.2): method forward (line 239) | def forward(self, logits, y): FILE: examples/pytorch/pointcloud/point_transformer/provider.py function normalize_data (line 7) | def normalize_data(batch_data): function shuffle_data (line 26) | def shuffle_data(data, labels): function shuffle_points (line 39) | def shuffle_points(batch_data): function rotate_point_cloud (line 52) | def rotate_point_cloud(batch_data): function rotate_point_cloud_z (line 75) | def rotate_point_cloud_z(batch_data): function rotate_point_cloud_with_normal (line 98) | def rotate_point_cloud_with_normal(batch_xyz_normal): function rotate_perturbation_point_cloud_with_normal (line 123) | def rotate_perturbation_point_cloud_with_normal( function rotate_point_cloud_by_angle (line 166) | def rotate_point_cloud_by_angle(batch_data, rotation_angle): function rotate_point_cloud_by_angle_with_normal (line 188) | def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle): function rotate_perturbation_point_cloud (line 215) | def rotate_perturbation_point_cloud( function jitter_point_cloud (line 256) | def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): function shift_point_cloud (line 270) | def shift_point_cloud(batch_data, shift_range=0.1): function random_scale_point_cloud (line 284) | def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): function random_point_dropout (line 298) | def random_point_dropout(batch_pc, max_dropout_ratio=0.875): FILE: examples/pytorch/pointcloud/point_transformer/train_cls.py function train (line 57) | def train(net, opt, scheduler, train_loader, dev): function evaluate (line 109) | def evaluate(net, test_loader, dev): FILE: examples/pytorch/pointcloud/point_transformer/train_partseg.py function collate (line 30) | def collate(samples): function train (line 44) | def train(net, opt, scheduler, train_loader, dev): function mIoU (line 98) | def mIoU(preds, label, cat, cat_miou, seg_classes): function evaluate (line 118) | def evaluate(net, test_loader, dev, per_cat_verbose=False): function paint (line 284) | def paint(batched_points): FILE: examples/pytorch/pointcloud/pointnet/ModelNetDataLoader.py function pc_normalize (line 10) | def pc_normalize(pc): function farthest_point_sample (line 18) | def farthest_point_sample(point, npoint): class ModelNetDataLoader (line 47) | class ModelNetDataLoader(Dataset): method __init__ (line 48) | def __init__( method __len__ (line 101) | def __len__(self): method _get_item (line 104) | def _get_item(self, index): method __getitem__ (line 127) | def __getitem__(self, index): FILE: examples/pytorch/pointcloud/pointnet/ShapeNet.py class ShapeNet (line 14) | class ShapeNet(object): method __init__ (line 15) | def __init__(self, num_points=2048, normal_channel=True): method train (line 83) | def train(self): method valid (line 88) | def valid(self): method trainval (line 93) | def trainval(self): method test (line 98) | def test(self): class ShapeNetDataset (line 104) | class ShapeNetDataset(Dataset): method __init__ (line 105) | def __init__(self, shapenet, mode, num_points, normal_channel=True): method translate (line 141) | def translate(self, x, scale=(2 / 3, 3 / 2), shift=(-0.2, 0.2), size=3): method __len__ (line 147) | def __len__(self): method __getitem__ (line 150) | def __getitem__(self, i): FILE: examples/pytorch/pointcloud/pointnet/pointnet2.py function square_distance (line 18) | def square_distance(src, dst): function index_points (line 30) | def index_points(points, idx): class FixedRadiusNearNeighbors (line 50) | class FixedRadiusNearNeighbors(nn.Module): method __init__ (line 55) | def __init__(self, radius, n_neighbor): method forward (line 60) | def forward(self, pos, centroids): class FixedRadiusNNGraph (line 85) | class FixedRadiusNNGraph(nn.Module): method __init__ (line 90) | def __init__(self, radius, n_neighbor): method forward (line 96) | def forward(self, pos, centroids, feat=None): class RelativePositionMessage (line 122) | class RelativePositionMessage(nn.Module): method __init__ (line 127) | def __init__(self, n_neighbor): method forward (line 131) | def forward(self, edges): class PointNetConv (line 140) | class PointNetConv(nn.Module): method __init__ (line 145) | def __init__(self, sizes, batch_size): method forward (line 154) | def forward(self, nodes): method group_all (line 170) | def group_all(self, pos, feat): class SAModule (line 190) | class SAModule(nn.Module): method __init__ (line 195) | def __init__( method forward (line 213) | def forward(self, pos, feat): class SAMSGModule (line 229) | class SAMSGModule(nn.Module): method __init__ (line 234) | def __init__( method forward (line 254) | def forward(self, pos, feat): class PointNet2FP (line 277) | class PointNet2FP(nn.Module): method __init__ (line 282) | def __init__(self, input_dims, sizes): method forward (line 292) | def forward(self, x1, x2, feat1, feat2): class PointNet2SSGCls (line 332) | class PointNet2SSGCls(nn.Module): method __init__ (line 333) | def __init__( method forward (line 359) | def forward(self, x): class PointNet2MSGCls (line 383) | class PointNet2MSGCls(nn.Module): method __init__ (line 384) | def __init__( method forward (line 426) | def forward(self, x): FILE: examples/pytorch/pointcloud/pointnet/pointnet2_partseg.py class PointNet2SSGPartSeg (line 9) | class PointNet2SSGPartSeg(nn.Module): method __init__ (line 10) | def __init__(self, output_classes, batch_size, input_dims=6): method forward (line 35) | def forward(self, x, cat_vec=None): class PointNet2MSGPartSeg (line 60) | class PointNet2MSGPartSeg(nn.Module): method __init__ (line 61) | def __init__(self, output_classes, batch_size, input_dims=6): method forward (line 99) | def forward(self, x, cat_vec=None): FILE: examples/pytorch/pointcloud/pointnet/pointnet_cls.py class PointNetCls (line 8) | class PointNetCls(nn.Module): method __init__ (line 9) | def __init__( method forward (line 58) | def forward(self, x): class TransformNet (line 96) | class TransformNet(nn.Module): method __init__ (line 97) | def __init__(self, input_dims=3, conv1_dim=64): method forward (line 123) | def forward(self, h): FILE: examples/pytorch/pointcloud/pointnet/pointnet_partseg.py class PointNetPartSeg (line 8) | class PointNetPartSeg(nn.Module): method __init__ (line 9) | def __init__( method forward (line 56) | def forward(self, x, cat_vec=None): class TransformNet (line 104) | class TransformNet(nn.Module): method __init__ (line 105) | def __init__(self, input_dims=3, num_points=2048): method forward (line 131) | def forward(self, h): class PartSegLoss (line 161) | class PartSegLoss(nn.Module): method __init__ (line 162) | def __init__(self, eps=0.2): method forward (line 167) | def forward(self, logits, y): FILE: examples/pytorch/pointcloud/pointnet/provider.py function normalize_data (line 7) | def normalize_data(batch_data): function shuffle_data (line 26) | def shuffle_data(data, labels): function shuffle_points (line 39) | def shuffle_points(batch_data): function rotate_point_cloud (line 52) | def rotate_point_cloud(batch_data): function rotate_point_cloud_z (line 75) | def rotate_point_cloud_z(batch_data): function rotate_point_cloud_with_normal (line 98) | def rotate_point_cloud_with_normal(batch_xyz_normal): function rotate_perturbation_point_cloud_with_normal (line 123) | def rotate_perturbation_point_cloud_with_normal( function rotate_point_cloud_by_angle (line 166) | def rotate_point_cloud_by_angle(batch_data, rotation_angle): function rotate_point_cloud_by_angle_with_normal (line 188) | def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle): function rotate_perturbation_point_cloud (line 215) | def rotate_perturbation_point_cloud( function jitter_point_cloud (line 256) | def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): function shift_point_cloud (line 270) | def shift_point_cloud(batch_data, shift_range=0.1): function random_scale_point_cloud (line 284) | def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): function random_point_dropout (line 298) | def random_point_dropout(batch_pc, max_dropout_ratio=0.875): FILE: examples/pytorch/pointcloud/pointnet/train_cls.py function train (line 64) | def train(net, opt, scheduler, train_loader, dev): function evaluate (line 108) | def evaluate(net, test_loader, dev): FILE: examples/pytorch/pointcloud/pointnet/train_partseg.py function collate (line 36) | def collate(samples): function train (line 50) | def train(net, opt, scheduler, train_loader, dev): function mIoU (line 99) | def mIoU(preds, label, cat, cat_miou, seg_classes): function evaluate (line 119) | def evaluate(net, test_loader, dev, per_cat_verbose=False): function paint (line 268) | def paint(batched_points): FILE: examples/pytorch/rect/classify.py class LogisticRegressionClassifier (line 8) | class LogisticRegressionClassifier(nn.Module): method __init__ (line 11) | def __init__(self, nfeat, nclass): method forward (line 15) | def forward(self, x): function _evaluate (line 20) | def _evaluate(model, features, labels, test_mask): function _train_test_with_lrc (line 31) | def _train_test_with_lrc(model, features, labels, train_mask, test_mask): function evaluate_embeds (line 46) | def evaluate_embeds( FILE: examples/pytorch/rect/label_utils.py function remove_unseen_classes_from_training (line 7) | def remove_unseen_classes_from_training(train_mask, labels, removed_class): function get_class_set (line 19) | def get_class_set(labels): function get_label_attributes (line 31) | def get_label_attributes(train_mask_zs, nodeids, labellist, features): function get_labeled_nodes_label_attribute (line 51) | def get_labeled_nodes_label_attribute(train_mask_zs, labels, features, c... FILE: examples/pytorch/rect/main.py function main (line 12) | def main(args): FILE: examples/pytorch/rect/model.py class GCN (line 7) | class GCN(nn.Module): method __init__ (line 8) | def __init__(self, g, in_feats, n_hidden, n_classes, activation, dropo... method forward (line 15) | def forward(self, features): method embed (line 21) | def embed(self, inputs): class RECT_L (line 26) | class RECT_L(nn.Module): method __init__ (line 27) | def __init__(self, g, in_feats, n_hidden, activation, dropout=0.0): method forward (line 35) | def forward(self, inputs): method embed (line 42) | def embed(self, inputs): FILE: examples/pytorch/rect/utils.py function load_data (line 6) | def load_data(args): function svd_feature (line 27) | def svd_feature(features, d=200): function process_classids (line 36) | def process_classids(labels_temp): FILE: examples/pytorch/rgat/train.py class HeteroGAT (line 14) | class HeteroGAT(nn.Module): method __init__ (line 15) | def __init__(self, etypes, in_size, hid_size, out_size, n_heads=4): method forward (line 45) | def forward(self, blocks, x): function evaluate (line 60) | def evaluate(num_classes, model, dataloader, desc): function train (line 80) | def train(train_loader, val_loader, test_loader, num_classes, model): FILE: examples/pytorch/rgcn-hetero/entity_classify.py function main (line 17) | def main(args): FILE: examples/pytorch/rgcn-hetero/entity_classify_heteroAPI.py function main (line 17) | def main(args): FILE: examples/pytorch/rgcn-hetero/entity_classify_mb.py function extract_embed (line 18) | def extract_embed(node_embed, input_nodes): function evaluate (line 26) | def evaluate(model, loader, node_embed, labels, category, device): function main (line 47) | def main(args): FILE: examples/pytorch/rgcn-hetero/model.py class RelGraphConvLayer (line 14) | class RelGraphConvLayer(nn.Module): method __init__ (line 39) | def __init__( method forward (line 99) | def forward(self, g, inputs): class RelGraphConvLayerHeteroAPI (line 146) | class RelGraphConvLayerHeteroAPI(nn.Module): method __init__ (line 171) | def __init__( method forward (line 222) | def forward(self, g, inputs): class RelGraphEmbed (line 278) | class RelGraphEmbed(nn.Module): method __init__ (line 281) | def __init__( method forward (line 298) | def forward(self, block=None): class EntityClassify (line 316) | class EntityClassify(nn.Module): method __init__ (line 317) | def __init__( method forward (line 381) | def forward(self, h=None, blocks=None): method inference (line 395) | def inference(self, g, batch_size, device, num_workers, x=None): class EntityClassify_HeteroAPI (line 443) | class EntityClassify_HeteroAPI(nn.Module): method __init__ (line 444) | def __init__( method forward (line 508) | def forward(self, h=None, blocks=None): method inference (line 522) | def inference(self, g, batch_size, device, num_workers, x=None): FILE: examples/pytorch/rgcn-hetero/test_classify.py function main (line 13) | def main(args): FILE: examples/pytorch/rgcn/entity.py class RGCN (line 12) | class RGCN(nn.Module): method __init__ (line 13) | def __init__(self, num_nodes, h_dim, out_dim, num_rels): method forward (line 34) | def forward(self, g): function evaluate (line 41) | def evaluate(g, target_idx, labels, num_classes, test_mask, model): function train (line 55) | def train(g, target_idx, labels, num_classes, train_mask, model): FILE: examples/pytorch/rgcn/entity_sample.py class RGCN (line 13) | class RGCN(nn.Module): method __init__ (line 14) | def __init__(self, num_nodes, h_dim, out_dim, num_rels): method forward (line 35) | def forward(self, g): function evaluate (line 44) | def evaluate(model, labels, num_classes, dataloader, inv_target): function train (line 66) | def train(device, g, target_idx, labels, train_mask, num_classes, model): FILE: examples/pytorch/rgcn/entity_sample_multi_gpu.py class RGCN (line 17) | class RGCN(nn.Module): method __init__ (line 18) | def __init__(self, num_nodes, h_dim, out_dim, num_rels): method forward (line 39) | def forward(self, g): function evaluate (line 48) | def evaluate(model, labels, num_classes, dataloader, inv_target): function train (line 72) | def train( function run (line 135) | def run(proc_id, nprocs, devices, g, data): FILE: examples/pytorch/rgcn/entity_utils.py function load_data (line 7) | def load_data(data_name, get_norm=False, inv_target=False): FILE: examples/pytorch/rgcn/experimental/entity_classify_dist.py class RelGraphConvLayer (line 38) | class RelGraphConvLayer(nn.Module): method __init__ (line 62) | def __init__( method forward (line 122) | def forward(self, g, inputs): class EntityClassify (line 167) | class EntityClassify(nn.Module): method __init__ (line 191) | def __init__( method forward (line 251) | def forward(self, blocks, feats, norm=None): function init_emb (line 262) | def init_emb(shape, dtype): class DistEmbedLayer (line 268) | class DistEmbedLayer(nn.Module): method __init__ (line 288) | def __init__( method forward (line 352) | def forward(self, node_ids): function compute_acc (line 378) | def compute_acc(results, labels): function evaluate (line 386) | def evaluate( function run (line 448) | def run(args, device, data): function main (line 707) | def main(args): FILE: examples/pytorch/rgcn/experimental/partition_graph.py function load_ogb (line 11) | def load_ogb(dataset): FILE: examples/pytorch/rgcn/link.py function get_subset_g (line 13) | def get_subset_g(g, mask, num_rels, bidirected=False): class GlobalUniform (line 30) | class GlobalUniform: method __init__ (line 31) | def __init__(self, g, sample_size): method sample (line 35) | def sample(self): class NegativeSampler (line 39) | class NegativeSampler: method __init__ (line 40) | def __init__(self, k=10): # negative sampling rate = 10 method sample (line 43) | def sample(self, pos_samples, num_nodes): class SubgraphIterator (line 63) | class SubgraphIterator: method __init__ (line 64) | def __init__(self, g, num_rels, sample_size=30000, num_epochs=6000): method __len__ (line 72) | def __len__(self): method __getitem__ (line 75) | def __getitem__(self, i): class RGCN (line 109) | class RGCN(nn.Module): method __init__ (line 110) | def __init__(self, num_nodes, h_dim, num_rels): method forward (line 132) | def forward(self, g, nids): class LinkPredict (line 140) | class LinkPredict(nn.Module): method __init__ (line 141) | def __init__(self, num_nodes, num_rels, h_dim=500, reg_param=0.01): method calc_score (line 150) | def calc_score(self, embedding, triplets): method forward (line 157) | def forward(self, g, nids): method regularization_loss (line 160) | def regularization_loss(self, embedding): method get_loss (line 163) | def get_loss(self, embed, triplets, labels): function filter (line 171) | def filter( function perturb_and_get_filtered_rank (line 191) | def perturb_and_get_filtered_rank( function calc_mrr (line 226) | def calc_mrr(emb, w, mask, triplets_to_filter, batch_size=100, filter=Tr... function train (line 246) | def train( FILE: examples/pytorch/rgcn/model.py class RGCN (line 10) | class RGCN(nn.Module): method __init__ (line 11) | def __init__( method forward (line 42) | def forward(self, g, nids=None): FILE: examples/pytorch/rrn/rrn.py class RRNLayer (line 15) | class RRNLayer(nn.Module): method __init__ (line 16) | def __init__(self, msg_layer, node_update_func, edge_drop): method forward (line 22) | def forward(self, g): method get_msg (line 30) | def get_msg(self, edges): method node_update (line 35) | def node_update(self, nodes): class RRN (line 39) | class RRN(nn.Module): method __init__ (line 40) | def __init__(self, msg_layer, node_update_func, num_steps, edge_drop): method forward (line 45) | def forward(self, g, get_all_outputs=True): FILE: examples/pytorch/rrn/sudoku.py class SudokuNN (line 10) | class SudokuNN(nn.Module): method __init__ (line 11) | def __init__(self, num_steps, embed_size=16, hidden_dim=96, edge_drop=... method forward (line 47) | def forward(self, g, is_training=True): method node_update_func (line 72) | def node_update_func(self, nodes): FILE: examples/pytorch/rrn/sudoku_data.py function _basic_sudoku_graph (line 15) | def _basic_sudoku_graph(): class ListDataset (line 48) | class ListDataset(Dataset): method __init__ (line 49) | def __init__(self, *lists_of_data): method __getitem__ (line 53) | def __getitem__(self, index): method __len__ (line 56) | def __len__(self): function _get_sudoku_dataset (line 60) | def _get_sudoku_dataset(segment="train"): function sudoku_dataloader (line 94) | def sudoku_dataloader(batch_size, segment="train"): FILE: examples/pytorch/rrn/sudoku_solver.py function solve_sudoku (line 10) | def solve_sudoku(puzzle): FILE: examples/pytorch/rrn/train_sudoku.py function main (line 11) | def main(args): FILE: examples/pytorch/sagpool/grid_search.py function load_config (line 9) | def load_config(path="./grid_search_config.json"): function run_experiments (line 14) | def run_experiments(args): function grid_search (line 25) | def grid_search(config: dict): FILE: examples/pytorch/sagpool/layer.py class SAGPool (line 8) | class SAGPool(torch.nn.Module): method __init__ (line 22) | def __init__( method forward (line 35) | def forward(self, graph: dgl.DGLGraph, feature: torch.Tensor): class ConvPoolBlock (line 56) | class ConvPoolBlock(torch.nn.Module): method __init__ (line 61) | def __init__(self, in_dim: int, out_dim: int, pool_ratio=0.8): method forward (line 68) | def forward(self, graph, feature): FILE: examples/pytorch/sagpool/main.py function parse_args (line 19) | def parse_args(): function train (line 108) | def train(model: torch.nn.Module, optimizer, trainloader, device): function test (line 128) | def test(model: torch.nn.Module, loader, device): function main (line 145) | def main(args): FILE: examples/pytorch/sagpool/network.py class SAGNetworkHierarchical (line 9) | class SAGNetworkHierarchical(torch.nn.Module): method __init__ (line 24) | def __init__( method forward (line 51) | def forward(self, graph: dgl.DGLGraph): class SAGNetworkGlobal (line 70) | class SAGNetworkGlobal(torch.nn.Module): method __init__ (line 85) | def __init__( method forward (line 114) | def forward(self, graph: dgl.DGLGraph): function get_sag_network (line 137) | def get_sag_network(net_type: str = "hierarchical"): FILE: examples/pytorch/sagpool/utils.py function get_stats (line 8) | def get_stats( function get_batch_id (line 52) | def get_batch_id(num_nodes: torch.Tensor): function topk (line 70) | def topk( FILE: examples/pytorch/seal/logger.py function _transform_log_level (line 6) | def _transform_log_level(str_level): class LightLogging (line 21) | class LightLogging(object): method __init__ (line 22) | def __init__(self, log_path=None, log_name="lightlog", log_level="debu... method debug (line 72) | def debug(self, msg): method info (line 75) | def info(self, msg): method critical (line 78) | def critical(self, msg): method warning (line 81) | def warning(self, msg): method error (line 84) | def error(self, msg): FILE: examples/pytorch/seal/main.py function train (line 24) | def train( function evaluate (line 50) | def evaluate(model, dataloader, device): function main (line 67) | def main(args, print_fn=print): FILE: examples/pytorch/seal/model.py class GCN (line 8) | class GCN(nn.Module): method __init__ (line 29) | def __init__( method reset_parameters (line 102) | def reset_parameters(self): method forward (line 106) | def forward(self, g, z, node_id=None, edge_id=None): class DGCNN (line 149) | class DGCNN(nn.Module): method __init__ (line 169) | def __init__( method forward (line 256) | def forward(self, g, z, node_id=None, edge_id=None): FILE: examples/pytorch/seal/sampler.py class GraphDataSet (line 14) | class GraphDataSet(Dataset): method __init__ (line 19) | def __init__(self, graph_list, tensor): method __len__ (line 23) | def __len__(self): method __getitem__ (line 26) | def __getitem__(self, index): class PosNegEdgesGenerator (line 30) | class PosNegEdgesGenerator(object): method __init__ (line 41) | def __init__( method __call__ (line 50) | def __call__(self, split_type): method subsample (line 80) | def subsample(self, edges, subsample_ratio): class EdgeDataSet (line 99) | class EdgeDataSet(Dataset): method __init__ (line 104) | def __init__(self, edges, labels, transform): method __len__ (line 109) | def __len__(self): method __getitem__ (line 112) | def __getitem__(self, index): class SEALSampler (line 117) | class SEALSampler(object): method __init__ (line 128) | def __init__(self, graph, hop=1, num_workers=32, print_fn=print): method sample_subgraph (line 134) | def sample_subgraph(self, target_nodes): method _collate (line 178) | def _collate(self, batch): method __call__ (line 185) | def __call__(self, edges, labels): class SEALData (line 212) | class SEALData(object): method __init__ (line 226) | def __init__( method __call__ (line 272) | def __call__(self, split_type): FILE: examples/pytorch/seal/utils.py function parse_arguments (line 12) | def parse_arguments(): function load_ogb_dataset (line 42) | def load_ogb_dataset(dataset): function drnl_node_labeling (line 60) | def drnl_node_labeling(subgraph, src, dst): function evaluate_hits (line 108) | def evaluate_hits(name, pos_pred, neg_pred, K): FILE: examples/pytorch/sgc/sgc.py function evaluate (line 28) | def evaluate(model, g, features, labels, mask): function main (line 38) | def main(args): FILE: examples/pytorch/sgc/sgc_reddit.py function normalize (line 23) | def normalize(h): function evaluate (line 27) | def evaluate(model, features, graph, labels, mask): function main (line 37) | def main(args): FILE: examples/pytorch/sign/dataset.py function load_dataset (line 6) | def load_dataset(name): FILE: examples/pytorch/sign/sign.py class FeedForwardNet (line 14) | class FeedForwardNet(nn.Module): method __init__ (line 15) | def __init__(self, in_feats, hidden, out_feats, n_layers, dropout): method reset_parameters (line 31) | def reset_parameters(self): method forward (line 37) | def forward(self, x): class Model (line 45) | class Model(nn.Module): method __init__ (line 46) | def __init__(self, in_feats, hidden, out_feats, R, n_layers, dropout): method forward (line 60) | def forward(self, feats): function calc_weight (line 68) | def calc_weight(g): function preprocess (line 83) | def preprocess(g, features, args): function prepare_data (line 101) | def prepare_data(device, args): function evaluate (line 127) | def evaluate(epoch, args, model, feats, labels, train, val, test): function main (line 151) | def main(args): FILE: examples/pytorch/stgcn_wave/load_data.py function load_data (line 6) | def load_data(file_path, len_train, len_val): function data_transform (line 14) | def data_transform(data, n_his, n_pred, device): FILE: examples/pytorch/stgcn_wave/model.py class TemporalConvLayer (line 12) | class TemporalConvLayer(nn.Module): method __init__ (line 25) | def __init__(self, c_in, c_out, dia=1): method forward (line 33) | def forward(self, x): class SpatioConvLayer (line 37) | class SpatioConvLayer(nn.Module): method __init__ (line 38) | def __init__(self, c, Lk): # c : hidden dimension Lk: graph matrix method init (line 44) | def init(self): method forward (line 48) | def forward(self, x): class FullyConvLayer (line 57) | class FullyConvLayer(nn.Module): method __init__ (line 58) | def __init__(self, c): method forward (line 62) | def forward(self, x): class OutputLayer (line 66) | class OutputLayer(nn.Module): method __init__ (line 67) | def __init__(self, c, T, n): method forward (line 74) | def forward(self, x): class STGCN_WAVE (line 81) | class STGCN_WAVE(nn.Module): method __init__ (line 82) | def __init__( method forward (line 107) | def forward(self, x): FILE: examples/pytorch/stgcn_wave/sensors2graph.py function get_adjacency_matrix (line 4) | def get_adjacency_matrix(distance_df, sensor_ids, normalized_k=0.1): FILE: examples/pytorch/stgcn_wave/utils.py function evaluate_model (line 5) | def evaluate_model(model, loss, data_iter): function evaluate_metric (line 17) | def evaluate_metric(model, data_iter, scaler): FILE: examples/pytorch/tagcn/tagcn.py class TAGCN (line 13) | class TAGCN(nn.Module): method __init__ (line 14) | def __init__( method forward (line 31) | def forward(self, features): FILE: examples/pytorch/tagcn/train.py function evaluate (line 15) | def evaluate(model, features, labels, mask): function main (line 26) | def main(args): FILE: examples/pytorch/tree_lstm/train.py function batcher (line 21) | def batcher(device): function main (line 34) | def main(args): FILE: examples/pytorch/tree_lstm/tree_lstm.py class TreeLSTMCell (line 16) | class TreeLSTMCell(nn.Module): method __init__ (line 17) | def __init__(self, x_size, h_size): method message_func (line 24) | def message_func(self, edges): method reduce_func (line 27) | def reduce_func(self, nodes): method apply_node_func (line 33) | def apply_node_func(self, nodes): class ChildSumTreeLSTMCell (line 42) | class ChildSumTreeLSTMCell(nn.Module): method __init__ (line 43) | def __init__(self, x_size, h_size): method message_func (line 50) | def message_func(self, edges): method reduce_func (line 53) | def reduce_func(self, nodes): method apply_node_func (line 59) | def apply_node_func(self, nodes): class TreeLSTM (line 68) | class TreeLSTM(nn.Module): method __init__ (line 69) | def __init__( method forward (line 91) | def forward(self, batch, g, h, c): FILE: examples/pytorch/vgae/input_data.py function parse_index_file (line 16) | def parse_index_file(filename): function load_data (line 23) | def load_data(dataset): FILE: examples/pytorch/vgae/model.py class VGAEModel (line 9) | class VGAEModel(nn.Module): method __init__ (line 10) | def __init__(self, in_dim, hidden1_dim, hidden2_dim): method encoder (line 38) | def encoder(self, g, features): method decoder (line 50) | def decoder(self, z): method forward (line 54) | def forward(self, g, features): FILE: examples/pytorch/vgae/preprocess.py function mask_test_edges (line 6) | def mask_test_edges(adj): function mask_test_edges_dgl (line 101) | def mask_test_edges_dgl(graph, adj): function sparse_to_tuple (line 175) | def sparse_to_tuple(sparse_mx): function preprocess_graph (line 184) | def preprocess_graph(adj): FILE: examples/pytorch/vgae/train.py function compute_loss_para (line 69) | def compute_loss_para(adj): function get_acc (line 82) | def get_acc(adj_rec, adj_label): function get_scores (line 89) | def get_scores(edges_pos, edges_neg, adj_rec): function dgl_main (line 111) | def dgl_main(): function web_main (line 225) | def web_main(): FILE: examples/pytorch/vrgcn/train_cv.py class SAGEConvWithCV (line 18) | class SAGEConvWithCV(nn.Module): method __init__ (line 19) | def __init__(self, in_feats, out_feats, activation): method reset_parameters (line 25) | def reset_parameters(self): method forward (line 30) | def forward(self, block, H, HBar=None): class SAGE (line 59) | class SAGE(nn.Module): method __init__ (line 60) | def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation): method forward (line 71) | def forward(self, blocks): method inference (line 88) | def inference(self, g, x, batch_size, device): class NeighborSampler (line 130) | class NeighborSampler(object): method __init__ (line 131) | def __init__(self, g, fanouts): method sample_blocks (line 135) | def sample_blocks(self, seeds): function compute_acc (line 154) | def compute_acc(pred, labels): function evaluate (line 161) | def evaluate(model, g, labels, val_mask, batch_size, device): function load_subtensor (line 179) | def load_subtensor( function init_history (line 212) | def init_history(g, model, dev_id): function update_history (line 221) | def update_history(g, blocks): function run (line 231) | def run(args, dev_id, data): FILE: examples/pytorch/vrgcn/train_cv_multi_gpu.py class SAGEConvWithCV (line 22) | class SAGEConvWithCV(nn.Module): method __init__ (line 23) | def __init__(self, in_feats, out_feats, activation): method reset_parameters (line 29) | def reset_parameters(self): method forward (line 34) | def forward(self, block, H, HBar=None): class SAGE (line 63) | class SAGE(nn.Module): method __init__ (line 64) | def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation): method forward (line 75) | def forward(self, blocks): method inference (line 92) | def inference(self, g, x, batch_size, device): class NeighborSampler (line 129) | class NeighborSampler(object): method __init__ (line 130) | def __init__(self, g, fanouts): method sample_blocks (line 134) | def sample_blocks(self, seeds): function compute_acc (line 154) | def compute_acc(pred, labels): function evaluate (line 161) | def evaluate(model, g, labels, val_mask, batch_size, device): function load_subtensor (line 181) | def load_subtensor( function create_history_storage (line 215) | def create_history_storage(g, args, n_classes): function init_history (line 224) | def init_history(g, model, dev_id, batch_size): function update_history (line 231) | def update_history(g, blocks): function run (line 241) | def run(proc_id, n_gpus, args, devices, data): FILE: examples/sparse/appnp.py class APPNP (line 14) | class APPNP(nn.Module): method __init__ (line 15) | def __init__( method forward (line 37) | def forward(self, A_hat, X): function evaluate (line 45) | def evaluate(g, pred): function train (line 56) | def train(model, g, A_hat, X): FILE: examples/sparse/c_and_s.py function label_propagation (line 17) | def label_propagation(A_hat, label, num_layers=20, alpha=0.9): function correct (line 25) | def correct(A_hat, label, soft_label, mask): function smooth (line 44) | def smooth(A_hat, label, soft_label, mask): function evaluate (line 49) | def evaluate(g, pred): function train (line 60) | def train(base_model, g, X): FILE: examples/sparse/gat.py class GATConv (line 14) | class GATConv(nn.Module): method __init__ (line 15) | def __init__(self, in_size, out_size, num_heads, dropout): method reset_parameters (line 27) | def reset_parameters(self): method forward (line 37) | def forward(self, A_hat, Z): class GAT (line 53) | class GAT(nn.Module): method __init__ (line 54) | def __init__( method forward (line 66) | def forward(self, A_hat, X): function evaluate (line 74) | def evaluate(g, pred): function train (line 85) | def train(model, g, A_hat, X): FILE: examples/sparse/gcn.py class GCN (line 14) | class GCN(nn.Module): method __init__ (line 15) | def __init__(self, in_size, out_size, hidden_size=16): method forward (line 26) | def forward(self, A_norm, X): function evaluate (line 33) | def evaluate(g, pred): function train (line 44) | def train(model, g, A_norm, X): FILE: examples/sparse/gcnii.py class GCNIIConvolution (line 17) | class GCNIIConvolution(nn.Module): method __init__ (line 18) | def __init__(self, in_size, out_size): method forward (line 27) | def forward(self, A_norm, H, H0, lamda, alpha, l): class GCNII (line 37) | class GCNII(nn.Module): method __init__ (line 38) | def __init__( method forward (line 64) | def forward(self, A_norm, feature): function evaluate (line 83) | def evaluate(model, A_norm, H, label, val_mask, test_mask): function train (line 94) | def train(model, g, A_norm, H): FILE: examples/sparse/graph_transformer.py class SparseMHA (line 21) | class SparseMHA(nn.Module): method __init__ (line 24) | def __init__(self, hidden_size=80, num_heads=8): method forward (line 36) | def forward(self, A, h): class GTLayer (line 53) | class GTLayer(nn.Module): method __init__ (line 56) | def __init__(self, hidden_size=80, num_heads=8): method forward (line 64) | def forward(self, A, h): class GTModel (line 76) | class GTModel(nn.Module): method __init__ (line 77) | def __init__( method forward (line 100) | def forward(self, g, X, pos_enc): function evaluate (line 113) | def evaluate(model, dataloader, evaluator, device): function train (line 128) | def train(model, dataset, evaluator, device): FILE: examples/sparse/han.py class GATConv (line 17) | class GATConv(nn.Module): method __init__ (line 18) | def __init__(self, in_size, out_size, num_heads, dropout): method reset_parameters (line 30) | def reset_parameters(self): method forward (line 40) | def forward(self, A_hat, Z): class SemanticAttention (line 56) | class SemanticAttention(nn.Module): method __init__ (line 57) | def __init__(self, in_size, hidden_size=128): method forward (line 66) | def forward(self, z): class HAN (line 74) | class HAN(nn.Module): method __init__ (line 75) | def __init__( method forward (line 96) | def forward(self, A_list, X): function evaluate (line 110) | def evaluate(label, val_idx, test_idx, pred): function train (line 117) | def train(model, data, A_list, X, label): FILE: examples/sparse/hetero-rgcn.py class RelGraphEmbed (line 40) | class RelGraphEmbed(nn.Module): method __init__ (line 43) | def __init__( method forward (line 59) | def forward(self): class HeteroRelationalGraphConv (line 63) | class HeteroRelationalGraphConv(nn.Module): method __init__ (line 76) | def __init__( method forward (line 100) | def forward(self, A, inputs): class EntityClassify (line 140) | class EntityClassify(nn.Module): method __init__ (line 141) | def __init__( method forward (line 174) | def forward(self, A): function main (line 181) | def main(args): FILE: examples/sparse/hgnn.py class HGNN (line 13) | class HGNN(nn.Module): method __init__ (line 14) | def __init__(self, H, in_size, out_size, hidden_dims=16): method forward (line 32) | def forward(self, X): function train (line 39) | def train(model, optimizer, X, Y, train_mask): function evaluate (line 48) | def evaluate(model, X, Y, val_mask, test_mask, num_classes): function load_data (line 63) | def load_data(): function main (line 86) | def main(): FILE: examples/sparse/hypergraphatt.py function hypergraph_laplacian (line 17) | def hypergraph_laplacian(H): class HypergraphAttention (line 30) | class HypergraphAttention(nn.Module): method __init__ (line 36) | def __init__(self, in_size, out_size): method forward (line 42) | def forward(self, H, X, X_edges): class Net (line 53) | class Net(nn.Module): method __init__ (line 54) | def __init__(self, in_size, out_size, hidden_size=16): method forward (line 60) | def forward(self, H, X): function train (line 67) | def train(model, optimizer, H, X, Y, train_mask): function evaluate (line 77) | def evaluate(model, H, X, Y, val_mask, test_mask, num_classes): function load_data (line 92) | def load_data(): function main (line 115) | def main(args): FILE: examples/sparse/pagerank.py function pagerank (line 10) | def pagerank(A): FILE: examples/sparse/sampling/graphsage.py class SAGEConv (line 38) | class SAGEConv(nn.Module): method __init__ (line 43) | def __init__( method reset_parameters (line 56) | def reset_parameters(self): method forward (line 61) | def forward(self, A, feat): class SAGE (line 77) | class SAGE(nn.Module): method __init__ (line 78) | def __init__(self, in_size, hid_size, out_size): method forward (line 89) | def forward(self, sampled_matrices, x): function multilayer_sample (line 101) | def multilayer_sample(A, fanouts, seeds, ndata): function evaluate (line 125) | def evaluate(model, A, dataloader, ndata, num_classes): function validate (line 144) | def validate(device, A, ndata, dataset, model, batch_size): function train (line 151) | def train(device, A, ndata, dataset, model): FILE: examples/sparse/sampling/ladies.py class SAGEConv (line 39) | class SAGEConv(nn.Module): method __init__ (line 44) | def __init__( method reset_parameters (line 57) | def reset_parameters(self): method forward (line 62) | def forward(self, A, feat): class LADIES (line 78) | class LADIES(nn.Module): method __init__ (line 79) | def __init__(self, in_size, hid_size, out_size): method forward (line 91) | def forward(self, sampled_matrices, x): function multilayer_sample (line 103) | def multilayer_sample(A, fanouts, seeds, ndata): function evaluate (line 141) | def evaluate(model, A, dataloader, ndata, num_classes): function validate (line 160) | def validate(device, A, ndata, dataset, model, batch_size): function train (line 167) | def train(device, A, ndata, dataset, model): FILE: examples/sparse/sgc.py function pre_compute (line 18) | def pre_compute(A, X, k): function evaluate (line 24) | def evaluate(g, pred): function train (line 35) | def train(model, g, X_sgc): FILE: examples/sparse/sign.py function sign_diffusion (line 21) | def sign_diffusion(A, X, r): class SIGN (line 30) | class SIGN(nn.Module): method __init__ (line 31) | def __init__(self, in_size, out_size, r, hidden_size=256): method forward (line 41) | def forward(self, X_sign): function evaluate (line 49) | def evaluate(g, pred): function train (line 60) | def train(model, g, X_sign): FILE: examples/sparse/twirls.py class MLP (line 22) | class MLP(nn.Module): method __init__ (line 23) | def __init__(self, in_size, hidden_size): method forward (line 29) | def forward(self, X): class TWIRLS (line 41) | class TWIRLS(nn.Module): method __init__ (line 42) | def __init__( method forward (line 58) | def forward(self, A, X): class TWIRLSWithAttention (line 80) | class TWIRLSWithAttention(nn.Module): method __init__ (line 81) | def __init__( method forward (line 97) | def forward(self, A, X): function evaluate (line 130) | def evaluate(g, pred): function train (line 142) | def train(g, model, A, X): FILE: examples/tensorflow/dgi/dgi.py class Encoder (line 18) | class Encoder(layers.Layer): method __init__ (line 19) | def __init__(self, g, in_feats, n_hidden, n_layers, activation, dropout): method call (line 26) | def call(self, features, corrupt=False): class Discriminator (line 34) | class Discriminator(layers.Layer): method __init__ (line 35) | def __init__(self, n_hidden): method call (line 45) | def call(self, features, summary): class DGI (line 52) | class DGI(tf.keras.Model): method __init__ (line 53) | def __init__(self, g, in_feats, n_hidden, n_layers, activation, dropout): method call (line 61) | def call(self, features): class Classifier (line 75) | class Classifier(layers.Layer): method __init__ (line 76) | def __init__(self, n_hidden, n_classes): method call (line 80) | def call(self, features): FILE: examples/tensorflow/dgi/gcn.py class GCN (line 10) | class GCN(layers.Layer): method __init__ (line 11) | def __init__( method call (line 28) | def call(self, features): FILE: examples/tensorflow/dgi/train.py function evaluate (line 19) | def evaluate(model, features, labels, mask): function main (line 28) | def main(args): FILE: examples/tensorflow/gat/gat.py class GAT (line 16) | class GAT(tf.keras.Model): method __init__ (line 17) | def __init__( method call (line 78) | def call(self, inputs): FILE: examples/tensorflow/gat/train.py function accuracy (line 31) | def accuracy(logits, labels): function evaluate (line 37) | def evaluate(model, features, labels, mask): function main (line 44) | def main(args): FILE: examples/tensorflow/gat/utils.py class EarlyStopping (line 4) | class EarlyStopping: method __init__ (line 5) | def __init__(self, patience=10): method step (line 11) | def step(self, acc, model): method save_checkpoint (line 29) | def save_checkpoint(self, model): FILE: examples/tensorflow/gcn/gcn.py class GCN (line 14) | class GCN(tf.keras.Model): method __init__ (line 15) | def __init__( method call (line 34) | def call(self, features): FILE: examples/tensorflow/gcn/gcn_builtin.py class GCNLayer (line 19) | class GCNLayer(layers.Layer): method __init__ (line 20) | def __init__(self, g, in_feats, out_feats, activation, dropout, bias=T... method call (line 45) | def call(self, h): class GCN (line 59) | class GCN(layers.Layer): method __init__ (line 60) | def __init__( method call (line 76) | def call(self, features): function evaluate (line 83) | def evaluate(model, features, labels, mask): function main (line 92) | def main(args): FILE: examples/tensorflow/gcn/gcn_mp.py function gcn_msg (line 19) | def gcn_msg(edge): function gcn_reduce (line 24) | def gcn_reduce(node): class GCNLayer (line 29) | class GCNLayer(layers.Layer): method __init__ (line 30) | def __init__(self, g, in_feats, out_feats, activation, dropout, bias=T... method call (line 53) | def call(self, h): class GCN (line 66) | class GCN(layers.Layer): method __init__ (line 67) | def __init__( method call (line 83) | def call(self, features): function evaluate (line 90) | def evaluate(model, features, labels, mask): function main (line 99) | def main(args): FILE: examples/tensorflow/gcn/train.py function evaluate (line 12) | def evaluate(model, features, labels, mask): function main (line 21) | def main(args): FILE: examples/tensorflow/rgcn/entity_classify.py class EntityClassify (line 25) | class EntityClassify(BaseRGCN): method create_features (line 26) | def create_features(self): method build_input_layer (line 30) | def build_input_layer(self): method build_hidden_layer (line 42) | def build_hidden_layer(self, idx): method build_output_layer (line 54) | def build_output_layer(self): function acc (line 66) | def acc(logits, labels, mask): function main (line 74) | def main(args): FILE: examples/tensorflow/rgcn/model.py class BaseRGCN (line 5) | class BaseRGCN(layers.Layer): method __init__ (line 6) | def __init__( method build_model (line 32) | def build_model(self): method build_input_layer (line 47) | def build_input_layer(self): method build_hidden_layer (line 50) | def build_hidden_layer(self, idx): method build_output_layer (line 53) | def build_output_layer(self): method call (line 56) | def call(self, g, h, r, norm): FILE: examples/tensorflow/rgcn/utils.py function get_adj_and_degrees (line 19) | def get_adj_and_degrees(num_nodes, triplets): function sample_edge_neighborhood (line 31) | def sample_edge_neighborhood(adj_list, degrees, n_triplets, sample_size): function sample_edge_uniform (line 77) | def sample_edge_uniform(adj_list, degrees, n_triplets, sample_size): function generate_sampled_graph_and_labels (line 83) | def generate_sampled_graph_and_labels( function comp_deg_norm (line 140) | def comp_deg_norm(g): function build_graph_from_triplets (line 148) | def build_graph_from_triplets(num_nodes, num_rels, triplets): function build_test_graph (line 167) | def build_test_graph(num_nodes, num_rels, edges): function negative_sampling (line 173) | def negative_sampling(pos_samples, num_entity, negative_rate): FILE: examples/tensorflow/sgc/sgc.py function load_data (line 26) | def load_data(dataset): function _sum_boolean_tensor (line 30) | def _sum_boolean_tensor(x): function describe_data (line 34) | def describe_data(data): class SGC (line 57) | class SGC(tf.keras.Model): method __init__ (line 58) | def __init__(self, g, num_classes, bias=False): method call (line 70) | def call(self, inputs): method in_feats (line 74) | def in_feats(self): method num_nodes (line 78) | def num_nodes(self): method ensure_self_loop (line 82) | def ensure_self_loop(g): method train_step (line 87) | def train_step(self, data): method test_step (line 101) | def test_step(self, data): method compile (line 109) | def compile(self, *args, **kwargs): method fit (line 112) | def fit(self, *args, **kwargs): method predict (line 117) | def predict(self, *args, **kwargs): function main (line 122) | def main(dataset, lr, bias, n_epochs, weight_decay): function _parse_args (line 145) | def _parse_args(): FILE: graphbolt/include/graphbolt/async.h function namespace (line 47) | namespace graphbolt { type c10 (line 152) | struct c10 function parallel_for_interop (line 326) | void parallel_for_interop( function parallel_for_each_interop (line 337) | void parallel_for_each_interop( FILE: graphbolt/include/graphbolt/continuous_seed.h function namespace (line 38) | namespace graphbolt { function get_seed (line 59) | uint64_t get_seed(int i) const { return s[i != 0]; } function __device__ (line 62) | __device__ inline float uniform(const uint64_t t) const { function uniform (line 78) | inline float uniform(const uint64_t t) const { function class (line 97) | class single_seed { FILE: graphbolt/include/graphbolt/cuda_ops.h function namespace (line 27) | namespace graphbolt { FILE: graphbolt/include/graphbolt/cuda_sampling_ops.h function namespace (line 26) | namespace graphbolt { FILE: graphbolt/include/graphbolt/fused_csc_sampling_graph.h function namespace (line 18) | namespace graphbolt { FILE: graphbolt/include/graphbolt/fused_sampled_subgraph.h function namespace (line 13) | namespace graphbolt { FILE: graphbolt/include/graphbolt/isin.h function namespace (line 13) | namespace graphbolt { FILE: graphbolt/include/graphbolt/serialize.h function namespace (line 20) | namespace torch { function namespace (line 57) | namespace graphbolt { FILE: graphbolt/include/graphbolt/shared_memory.h function namespace (line 17) | namespace graphbolt { FILE: graphbolt/include/graphbolt/unique_and_compact.h function namespace (line 13) | namespace graphbolt { FILE: graphbolt/src/cache_policy.cc type graphbolt (line 24) | namespace graphbolt { type storage (line 25) | namespace storage { FILE: graphbolt/src/cache_policy.h function namespace (line 33) | namespace graphbolt { function InUse (line 111) | bool InUse() const { function class (line 143) | class BaseCachePolicy { function class (line 256) | class S3FifoCachePolicy : public BaseCachePolicy { function class (line 389) | class SieveCachePolicy : public BaseCachePolicy { function class (line 485) | class LruCachePolicy : public BaseCachePolicy { function class (line 579) | class ClockCachePolicy : public BaseCachePolicy { FILE: graphbolt/src/circular_queue.h function namespace (line 25) | namespace graphbolt { FILE: graphbolt/src/cnumpy.cc type graphbolt (line 32) | namespace graphbolt { type storage (line 33) | namespace storage { type stat (line 54) | struct stat class ReadRequest (line 134) | class ReadRequest { method AlignedOffset (line 142) | auto AlignedOffset() const { return offset_ & ~(block_size_ - 1); } method ReadBuffer (line 144) | auto ReadBuffer() const { method AlignedReadSize (line 148) | auto AlignedReadSize() const { method MinimumReadSize (line 155) | auto MinimumReadSize() const { return offset_ + read_len_ - Aligne... type io_uring_sqe (line 243) | struct io_uring_sqe type io_uring_cqe (line 255) | struct io_uring_cqe type io_uring_sqe (line 283) | struct io_uring_sqe FILE: graphbolt/src/cnumpy.h function namespace (line 27) | namespace graphbolt { function class (line 49) | class OnDiskNpyArray : public torch::CustomClassHolder { FILE: graphbolt/src/concurrent_id_hash_map.cc function GetMapSize (line 23) | inline size_t GetMapSize(size_t num) { type graphbolt (line 29) | namespace graphbolt { type sampling (line 30) | namespace sampling { function IdType (line 115) | constexpr IdType getKeyIndex(IdType pos) { function IdType (line 120) | constexpr IdType getValueIndex(IdType pos) { function IdType (line 133) | inline IdType ConcurrentIdHashMap::MapId(IdType id) const { class ConcurrentIdHashMap (line 215) | class ConcurrentIdHashMap class ConcurrentIdHashMap (line 216) | class ConcurrentIdHashMap FILE: graphbolt/src/concurrent_id_hash_map.h function namespace (line 16) | namespace graphbolt { FILE: graphbolt/src/cuda/common.h function namespace (line 34) | namespace graphbolt { FILE: graphbolt/src/cuda/cooperative_minibatching_utils.h function namespace (line 28) | namespace graphbolt { FILE: graphbolt/src/cuda/extension/gpu_cache.h function namespace (line 31) | namespace graphbolt { FILE: graphbolt/src/cuda/extension/gpu_graph_cache.h function namespace (line 30) | namespace graphbolt { FILE: graphbolt/src/cuda/extension/unique_and_compact.h function namespace (line 28) | namespace graphbolt { FILE: graphbolt/src/cuda/max_uva_threads.cc type graphbolt (line 22) | namespace graphbolt { type cuda (line 23) | namespace cuda { function set_max_uva_threads (line 25) | void set_max_uva_threads(int64_t count) { max_uva_threads = count; } FILE: graphbolt/src/cuda/max_uva_threads.h function namespace (line 26) | namespace graphbolt { FILE: graphbolt/src/cuda/utils.h function namespace (line 16) | namespace graphbolt { FILE: graphbolt/src/expand_indptr.cc type graphbolt (line 13) | namespace graphbolt { type ops (line 14) | namespace ops { function ExpandIndptr (line 16) | torch::Tensor ExpandIndptr( function IndptrEdgeIds (line 33) | torch::Tensor IndptrEdgeIds( function TORCH_LIBRARY_IMPL (line 46) | TORCH_LIBRARY_IMPL(graphbolt, CPU, m) { function TORCH_LIBRARY_IMPL (line 51) | TORCH_LIBRARY_IMPL(graphbolt, CUDA, m) { function TORCH_LIBRARY_IMPL (line 56) | TORCH_LIBRARY_IMPL(graphbolt, Autograd, m) { function TORCH_LIBRARY_IMPL (line 60) | TORCH_LIBRARY_IMPL(graphbolt, CPU, m) { function TORCH_LIBRARY_IMPL (line 65) | TORCH_LIBRARY_IMPL(graphbolt, CUDA, m) { function TORCH_LIBRARY_IMPL (line 70) | TORCH_LIBRARY_IMPL(graphbolt, Autograd, m) { FILE: graphbolt/src/expand_indptr.h function namespace (line 12) | namespace graphbolt { FILE: graphbolt/src/feature_cache.cc type graphbolt (line 25) | namespace graphbolt { type storage (line 26) | namespace storage { FILE: graphbolt/src/feature_cache.h function namespace (line 29) | namespace graphbolt { FILE: graphbolt/src/fused_csc_sampling_graph.cc function TensorizeDict (line 29) | torch::optional> TensorizeDict( function DetensorizeDict (line 41) | torch::optional> DetensorizeDict( type graphbolt (line 54) | namespace graphbolt { type sampling (line 55) | namespace sampling { function GetNumPickFn (line 337) | auto GetNumPickFn( function GetTemporalNumPickFn (line 361) | auto GetTemporalNumPickFn( function GetPickFn (line 413) | auto GetPickFn( function GetTemporalPickFn (line 446) | auto GetTemporalPickFn( function BuildGraphFromSharedMemoryHelper (line 1012) | static c10::intrusive_ptr function NumPick (line 1062) | void NumPick( function TemporalMask (line 1084) | torch::Tensor TemporalMask( function FastTemporalPick (line 1119) | std::pair> FastTemporalPick( function TemporalNumPick (line 1180) | int64_t TemporalNumPick( function NumPickByEtype (line 1212) | void NumPickByEtype( function TemporalNumPickByEtype (line 1257) | int64_t TemporalNumPickByEtype( function UniformPick (line 1314) | inline int64_t UniformPick( function NonUniformPickOp (line 1413) | static torch::Tensor NonUniformPickOp( function NonUniformPick (line 1545) | inline int64_t NonUniformPick( function Pick (line 1563) | int64_t Pick( function TemporalPick (line 1580) | int64_t TemporalPick( function PickByEtype (line 1635) | int64_t PickByEtype( function TemporalPickByEtype (line 1693) | int64_t TemporalPickByEtype( function Pick (line 1736) | std::enable_if_t Pick( function safe_divide (line 1778) | inline void safe_divide(T& a, U b) { type labor (line 1782) | namespace labor { function T (line 1785) | inline T invcdf(T u, int64_t n, T rem) { function T (line 1791) | inline T jth_sorted_uniform_random( function LaborPick (line 1828) | inline std::enable_if_t LaborPick( FILE: graphbolt/src/index_select.cc type graphbolt (line 17) | namespace graphbolt { type ops (line 18) | namespace ops { function IndexSelect (line 22) | torch::Tensor IndexSelect(torch::Tensor input, torch::Tensor index) { function IndexSelectAsync (line 64) | c10::intrusive_ptr> IndexSelectAsync( function ScatterAsync (line 70) | c10::intrusive_ptr> ScatterAsync( function IndexSelectCSC (line 102) | std::tuple IndexSelectCSC( function IndexSelectCSCBatched (line 118) | std::tuple> IndexSelectCSC... FILE: graphbolt/src/index_select.h function namespace (line 12) | namespace graphbolt { FILE: graphbolt/src/io_uring.cc type io_uring_probe_destroyer (line 34) | struct io_uring_probe_destroyer { type io_uring_probe (line 35) | struct io_uring_probe type graphbolt (line 41) | namespace graphbolt { type io_uring (line 42) | namespace io_uring { function IsAvailable (line 44) | bool IsAvailable() { function SetNumThreads (line 79) | void SetNumThreads(int64_t count) { num_threads = count; } FILE: graphbolt/src/io_uring.h function namespace (line 26) | namespace graphbolt { FILE: graphbolt/src/isin.cc type graphbolt (line 18) | namespace graphbolt { type sampling (line 19) | namespace sampling { function IsInCPU (line 21) | torch::Tensor IsInCPU( function IsIn (line 49) | torch::Tensor IsIn( function IsNotInIndex (line 60) | torch::Tensor IsNotInIndex( function IsNotInIndexAsync (line 71) | c10::intrusive_ptr> IsNotInIndexAsync( FILE: graphbolt/src/macro.h function namespace (line 12) | namespace graphbolt { FILE: graphbolt/src/partitioned_cache_policy.cc type graphbolt (line 28) | namespace graphbolt { type storage (line 29) | namespace storage { function AddOffset (line 33) | torch::Tensor AddOffset(torch::Tensor keys, int64_t offset) { FILE: graphbolt/src/partitioned_cache_policy.h function namespace (line 35) | namespace graphbolt { FILE: graphbolt/src/python_binding.cc type graphbolt (line 29) | namespace graphbolt { type sampling (line 30) | namespace sampling { function TORCH_LIBRARY (line 32) | TORCH_LIBRARY(graphbolt, m) { FILE: graphbolt/src/random.cc type graphbolt (line 11) | namespace graphbolt { function GetThreadId (line 16) | inline uint32_t GetThreadId() { function RandomEngine (line 51) | RandomEngine* RandomEngine::ThreadLocal() { FILE: graphbolt/src/random.h function namespace (line 17) | namespace graphbolt { FILE: graphbolt/src/serialize.cc type torch (line 10) | namespace torch { FILE: graphbolt/src/shared_memory.cc type graphbolt (line 18) | namespace graphbolt { type sampling (line 19) | namespace sampling { function DecorateName (line 28) | inline std::string DecorateName(const std::string& name) { type stat (line 139) | struct stat FILE: graphbolt/src/shared_memory_helper.cc type graphbolt (line 18) | namespace graphbolt { type sampling (line 19) | namespace sampling { function GetSharedMemoryMetadataName (line 21) | static std::string GetSharedMemoryMetadataName(const std::string& na... function GetSharedMemoryDataName (line 25) | static std::string GetSharedMemoryDataName(const std::string& name) { function GetRoundedSize (line 31) | inline static int64_t GetRoundedSize(int64_t size) { FILE: graphbolt/src/shared_memory_helper.h function namespace (line 19) | namespace graphbolt { FILE: graphbolt/src/unique_and_compact.cc type graphbolt (line 15) | namespace graphbolt { type sampling (line 16) | namespace sampling { function UniqueAndCompact (line 17) | std::tuple function UniqueAndCompactBatched (line 47) | std::vector< FILE: graphbolt/src/utils.cc type graphbolt (line 24) | namespace graphbolt { type utils (line 25) | namespace utils { function GetWorkerId (line 31) | std::optional GetWorkerId() { return worker_id; } function SetWorkerId (line 33) | void SetWorkerId(int64_t worker_id_value) { worker_id = worker_id_va... FILE: graphbolt/src/utils.h function namespace (line 14) | namespace graphbolt { FILE: include/dgl/array_iterator.h function namespace (line 19) | namespace dgl { function CUB_INLINE (line 159) | CUB_INLINE PairRef operator*() const { return PairRef(a, b... function CUB_INLINE (line 160) | CUB_INLINE PairRef operator*() { return PairRef(a, b); } function CUB_INLINE (line 161) | CUB_INLINE PairRef operator[](size_t offset) const { function CUB_INLINE (line 164) | CUB_INLINE PairRef operator[](size_t offset) { FILE: include/dgl/aten/array_ops.h function IsNullArray (line 37) | inline bool IsNullArray(NDArray array) { return array->shape[0] == 0; } function IsValidIdArray (line 240) | inline bool IsValidIdArray(const dgl::runtime::NDArray& arr) { function DGLContext (line 375) | inline DGLContext GetContextOf(const std::vector& arrays) { FILE: include/dgl/aten/coo.h function namespace (line 23) | namespace dgl { function IsEmpty (line 127) | inline bool IsEmpty() const { function COOMatrix (line 151) | inline COOMatrix PinMemory() { function PinMemory_ (line 177) | inline void PinMemory_() { function UnpinMemory_ (line 198) | inline void UnpinMemory_() { function RecordStream (line 216) | inline void RecordStream(DGLStreamHandle stream) const { function COOHasData (line 246) | inline bool COOHasData(COOMatrix csr) { return !IsNullArray(csr.data); } function runtime (line 280) | inline runtime::NDArray COOGetAllData(COOMatrix mat, int64_t row, int64_... function namespace (line 874) | namespace dmlc { FILE: include/dgl/aten/csr.h function namespace (line 22) | namespace dgl { function IsEmpty (line 121) | inline bool IsEmpty() const { function CSRMatrix (line 144) | inline CSRMatrix PinMemory() { function PinMemory_ (line 169) | inline void PinMemory_() { function UnpinMemory_ (line 190) | inline void UnpinMemory_() { function RecordStream (line 209) | inline void RecordStream(DGLStreamHandle stream) const { function CSRHasData (line 241) | inline bool CSRHasData(CSRMatrix csr) { return !IsNullArray(csr.data); } function runtime (line 270) | inline runtime::NDArray CSRGetAllData(CSRMatrix mat, int64_t row, int64_... FILE: include/dgl/aten/spmat.h type class (line 20) | enum class function SparseFormat (line 36) | inline SparseFormat ParseSparseFormat(const std::string& name) { function std (line 49) | inline std::string ToStringSparseFormat(SparseFormat sparse_format) { function std (line 58) | inline std::vector CodeToSparseFormats(dgl_format_code_t c... function dgl_format_code_t (line 66) | inline dgl_format_code_t SparseFormatsToCode( FILE: include/dgl/aten/types.h function namespace (line 13) | namespace dgl { FILE: include/dgl/base_heterograph.h function namespace (line 22) | namespace dgl { type HeteroSubgraph (line 536) | struct HeteroSubgraph type FlattenedHeteroGraph (line 562) | struct FlattenedHeteroGraph function VisitAttrs (line 604) | void VisitAttrs(runtime::AttrVisitor* v) final { type HeteroPickleStates (line 834) | struct HeteroPickleStates FILE: include/dgl/bcast.h function namespace (line 15) | namespace dgl { FILE: include/dgl/env_variable.h function namespace (line 11) | namespace dgl { FILE: include/dgl/graph.h function namespace (line 18) | namespace dgl { FILE: include/dgl/graph_interface.h function namespace (line 18) | namespace dgl { function class (line 51) | class DGLIdIters32 { function const (line 62) | int32_t operator[](int32_t i) const { return *(this->begin_ + i); } type EdgeArray (line 70) | typedef struct { type Subgraph (line 76) | struct Subgraph type std (line 79) | typedef std::shared_ptr GraphPtr; function class (line 92) | class GraphInterface : public runtime::Object { type Subgraph (line 422) | struct Subgraph function Subgraph (line 443) | struct NegSubgraph : public Subgraph { function Subgraph (line 455) | struct HaloSubgraph : public Subgraph { FILE: include/dgl/graph_op.h function namespace (line 14) | namespace dgl { FILE: include/dgl/graph_serializer.h function namespace (line 11) | namespace dgl { FILE: include/dgl/graph_traversal.h function namespace (line 12) | namespace dgl { FILE: include/dgl/immutable_graph.h function namespace (line 22) | namespace dgl { function DGLIdIters (line 165) | DGLIdIters PredVec(dgl_id_t vid) const override { function DGLIdIters (line 171) | DGLIdIters InEdgeVec(dgl_id_t vid) const override { function override (line 178) | const override { function CSRPtr (line 187) | CSRPtr Transpose() const; function SortCSR (line 234) | void SortCSR() override { function class (line 253) | class COO : public GraphInterface { function IdArray (line 318) | IdArray EdgeId(dgl_id_t src, dgl_id_t dst) const override { function EdgeArray (line 324) | EdgeArray EdgeIds(IdArray src, IdArray dst) const override { function EdgeArray (line 334) | EdgeArray InEdges(dgl_id_t vid) const override { function EdgeArray (line 340) | EdgeArray InEdges(IdArray vids) const override { function EdgeArray (line 346) | EdgeArray OutEdges(dgl_id_t vid) const override { function EdgeArray (line 352) | EdgeArray OutEdges(IdArray vids) const override { function InDegree (line 360) | uint64_t InDegree(dgl_id_t vid) const override { function DegreeArray (line 366) | DegreeArray InDegrees(IdArray vids) const override { function OutDegree (line 372) | uint64_t OutDegree(dgl_id_t vid) const override { function DegreeArray (line 378) | DegreeArray OutDegrees(IdArray vids) const override { function Subgraph (line 384) | Subgraph VertexSubgraph(IdArray vids) const override { function DGLIdIters (line 393) | DGLIdIters SuccVec(dgl_id_t vid) const override { function DGLIdIters (line 399) | DGLIdIters OutEdgeVec(dgl_id_t vid) const override { function DGLIdIters (line 405) | DGLIdIters PredVec(dgl_id_t vid) const override { function DGLIdIters (line 411) | DGLIdIters InEdgeVec(dgl_id_t vid) const override { function override (line 418) | const override { function class (line 486) | class ImmutableGraph : public GraphInterface { function explicit (line 510) | explicit ImmutableGraph(CSRPtr csr) : out_csr_(csr) {} function AddVertices (line 535) | void AddVertices(uint64_t num_vertices) override { function AddEdge (line 539) | void AddEdge(dgl_id_t src, dgl_id_t dst) override { function AddEdges (line 543) | void AddEdges(IdArray src_ids, IdArray dst_ids) override { function Clear (line 547) | void Clear() override { function HasVertex (line 587) | bool HasVertex(dgl_id_t vid) const override { return vid < NumVertices(); } function HasEdgeBetween (line 592) | bool HasEdgeBetween(dgl_id_t src, dgl_id_t dst) const override { function BoolArray (line 600) | BoolArray HasEdgesBetween(IdArray src, IdArray dst) const override { function IdArray (line 638) | IdArray EdgeId(dgl_id_t src, dgl_id_t dst) const override { function EdgeArray (line 655) | EdgeArray EdgeIds(IdArray src, IdArray dst) const override { function override (line 670) | const override { function EdgeArray (line 680) | EdgeArray FindEdges(IdArray eids) const override { function EdgeArray (line 690) | EdgeArray InEdges(dgl_id_t vid) const override { function EdgeArray (line 700) | EdgeArray InEdges(IdArray vids) const override { function EdgeArray (line 711) | EdgeArray OutEdges(dgl_id_t vid) const override { function EdgeArray (line 720) | EdgeArray OutEdges(IdArray vids) const override { function InDegree (line 739) | uint64_t InDegree(dgl_id_t vid) const override { function DegreeArray (line 748) | DegreeArray InDegrees(IdArray vids) const override { function OutDegree (line 757) | uint64_t OutDegree(dgl_id_t vid) const override { function DegreeArray (line 766) | DegreeArray OutDegrees(IdArray vids) const override { function DGLIdIters (line 814) | DGLIdIters SuccVec(dgl_id_t vid) const override { function DGLIdIters (line 823) | DGLIdIters OutEdgeVec(dgl_id_t vid) const override { function DGLIdIters (line 832) | DGLIdIters PredVec(dgl_id_t vid) const override { function DGLIdIters (line 841) | DGLIdIters InEdgeVec(dgl_id_t vid) const override { function CSRPtr (line 858) | CSRPtr GetInCSR() const; function namespace (line 1016) | namespace dmlc { FILE: include/dgl/kernel.h function namespace (line 17) | namespace dgl { FILE: include/dgl/lazy.h function namespace (line 11) | namespace dgl { FILE: include/dgl/nodeflow.h function namespace (line 16) | namespace dgl { FILE: include/dgl/packed_func_ext.h function namespace (line 19) | namespace dgl { function PrintName (line 89) | static inline void PrintName(std::ostringstream& os) { // NOLINT(*) function const (line 164) | inline void DGLArgsSetter::operator()( FILE: include/dgl/random.h function GetThreadId (line 25) | inline uint32_t GetThreadId() { function class (line 43) | class RandomEngine { function DGLDataType (line 155) | const DGLDataType dtype{kDGLInt, sizeof(IdxType) * 8, 1}; function DGLDataType (line 247) | const DGLDataType dtype{kDGLInt, sizeof(IdxType) * 8, 1}; FILE: include/dgl/runtime/bfloat16.h function class (line 11) | class BFloat16 { function BFloat16 (line 33) | static constexpr BFloat16 Min() { function BFloat16 (line 39) | static constexpr BFloat16 Max() { FILE: include/dgl/runtime/c_backend_api.h type DGLParallelGroupEnv (line 75) | typedef struct { FILE: include/dgl/runtime/c_runtime_api.h type dgl_index_t (line 45) | typedef int64_t dgl_index_t; type DGLDeviceType (line 53) | typedef enum { type DGLObjectTypeCode (line 66) | typedef enum { type DGLDataTypeCode (line 96) | typedef enum { type DGLDataType (line 118) | typedef struct { type DGLContext (line 136) | typedef struct { type DGLArray (line 150) | typedef struct { type DGLArray (line 199) | typedef DGLArray* DGLArrayHandle; type DGLValue (line 205) | typedef union { type DGLByteArray (line 218) | typedef struct { FILE: include/dgl/runtime/config.h function namespace (line 10) | namespace dgl { FILE: include/dgl/runtime/container.h function namespace (line 18) | namespace dgl { FILE: include/dgl/runtime/device_api.h function namespace (line 14) | namespace dgl { FILE: include/dgl/runtime/dlpack_convert.h type DLManagedTensor (line 12) | struct DLManagedTensor function namespace (line 14) | namespace dgl { FILE: include/dgl/runtime/module.h function namespace (line 20) | namespace dgl { FILE: include/dgl/runtime/ndarray.h function DGLDataType (line 44) | static constexpr DGLDataType dtype{0, 0, 0}; // dummy type DLPackConvert (line 75) | struct DLPackConvert function class (line 81) | class NDArray { function swap (line 111) | void swap(NDArray& other) { // NOLINT(*) function same_as (line 137) | bool same_as(const NDArray& other) const { return data_ == other.data_; } function use_count (line 144) | inline int use_count() const; function IsPinned (line 215) | inline bool IsPinned() const; FILE: include/dgl/runtime/object.h function namespace (line 17) | namespace runtime { function class (line 55) | class Object { function namespace (line 318) | namespace std { FILE: include/dgl/runtime/packed_func.h function namespace (line 29) | namespace dgl { function class (line 264) | class DGLArgs { function class (line 306) | class ExtTypeVTable { function class (line 336) | class DGLPODValue_ { function class (line 425) | class DGLArgValue : public DGLPODValue_ { function IsObjectType (line 494) | bool IsObjectType() const; function operator (line 536) | operator std::string() const { function else (line 539) | else if (type_code_ == kBytes) { function MoveToCHost (line 664) | void MoveToCHost(DGLValue* ret_value, int* ret_type_code) { function TObjectRef (line 685) | TObjectRef AsObjectRef() const; function SwitchToPOD (line 737) | void SwitchToPOD(int type_code) { function Clear (line 753) | void Clear() { function DGLArgValue (line 786) | inline DGLArgValue DGLArgs::operator[](int i) const { function CallPacked (line 795) | inline void PackedFunc::CallPacked(DGLArgs args, DGLRetValue* rv) const { function namespace (line 802) | namespace detail { function class (line 827) | class DGLArgsSetter { function const (line 839) | void operator()(size_t i, uint64_t value) const { function const (line 844) | void operator()(size_t i, double value) const { function const (line 848) | void operator()(size_t i, std::nullptr_t value) const { function const (line 852) | void operator()(size_t i, const DGLArgValue& value) const { function const (line 856) | void operator()(size_t i, void* value) const { function const (line 860) | void operator()(size_t i, DGLArray* value) const { function const (line 864) | void operator()(size_t i, DGLContext value) const { function const (line 868) | void operator()(size_t i, DGLDataType value) const { function const (line 872) | void operator()(size_t i, const char* value) const { function const (line 879) | void operator()(size_t i, const std::string& value) const { // NOLINT(*) function const (line 883) | void operator()(size_t i, const DGLByteArray& value) const { // NOLINT(*) function const (line 887) | void operator()(size_t i, const PackedFunc& value) const { // NOLINT(*) function const (line 896) | void operator()(size_t i, const Module& value) const { // NOLINT(*) function const (line 900) | void operator()(size_t i, const NDArray& value) const { // NOLINT(*) function const (line 904) | void operator()(size_t i, const DGLRetValue& value) const { // NOLINT(*) function DGLRetValue (line 930) | DGLRetValue PackedFunc::operator()(Args&&... args) const { function namespace (line 942) | namespace detail { function run (line 968) | void run( function unpack_call (line 976) | void unpack_call(const F& f, const DGLArgs& args, DGLRetValue* rv) { function R (line 981) | R call_packed(const PackedFunc& pf, Args&&... args) { function R (line 988) | inline R run(const PackedFunc& pf, Args&&... args) { function void (line 994) | struct typed_packed_call_dispatcher { function packed_ (line 1004) | packed_(packed) {} function namespace (line 1021) | namespace detail { function const (line 1046) | void DGLArgsSetter::operator()(size_t i, const T& value) const { function destroy (line 1056) | static void destroy(void* handle) { delete static_cast(handle); } function PackedFunc (line 1074) | inline PackedFunc Module::GetFunction( FILE: include/dgl/runtime/parallel_for.h function divup (line 21) | int64_t divup(int64_t x, int64_t y) { return (x + y - 1) / y; } function namespace (line 24) | namespace dgl { function compute_num_threads (line 46) | inline size_t compute_num_threads(size_t begin, size_t end, size_t grain... function end_tid (line 88) | auto end_tid = std::min(end, static_cast(chunk_size + begin_tid)); function end_tid (line 164) | auto end_tid = std::min(end, static_cast(chunk_size + begin_tid)); FILE: include/dgl/runtime/registry.h function namespace (line 33) | namespace dgl { FILE: include/dgl/runtime/serializer.h function namespace (line 16) | namespace dmlc { FILE: include/dgl/runtime/shared_mem.h function namespace (line 14) | namespace dgl { FILE: include/dgl/runtime/smart_ptr_serializer.h function namespace (line 16) | namespace dmlc { FILE: include/dgl/runtime/tensordispatch.h function namespace (line 47) | namespace dgl { FILE: include/dgl/runtime/threading_backend.h function namespace (line 13) | namespace dgl { FILE: include/dgl/runtime/util.h function namespace (line 11) | namespace dgl { function namespace (line 28) | namespace dgl { FILE: include/dgl/sampler.h function namespace (line 17) | namespace dgl { FILE: include/dgl/sampling/negative.h function namespace (line 14) | namespace dgl { FILE: include/dgl/sampling/neighbor.h function namespace (line 15) | namespace dgl { FILE: include/dgl/sampling/randomwalks.h function namespace (line 16) | namespace dgl { FILE: include/dgl/scheduler.h function namespace (line 13) | namespace dgl { FILE: include/dgl/transform.h function namespace (line 17) | namespace dgl { FILE: include/dgl/zerocopy_serializer.h function namespace (line 24) | namespace dgl { function send_to_remote_ (line 142) | strm_(new dmlc::MemoryFixedSizeStream(p_buffer, size)), function virtual (line 150) | virtual size_t Read(void* ptr, size_t size) { return strm_->Read(ptr, si... function virtual (line 151) | virtual void Write(const void* ptr, size_t size) { strm_->Write(ptr, siz... function virtual (line 152) | virtual void Seek(size_t pos) { strm_->Seek(pos); } function virtual (line 153) | virtual size_t Tell(void) { return strm_->Tell(); } function send_to_remote (line 176) | bool send_to_remote() { return send_to_remote_; } FILE: python/dgl/_ffi/_ctypes/function.py function _ctypes_free_resource (line 31) | def _ctypes_free_resource(rhandle): function convert_to_dgl_func (line 42) | def convert_to_dgl_func(pyfunc): function _make_dgl_args (line 103) | def _make_dgl_args(args, temp_args): class FunctionBase (line 179) | class FunctionBase(object): method __init__ (line 184) | def __init__(self, handle, is_global): method __del__ (line 198) | def __del__(self): method __call__ (line 202) | def __call__(self, *args): function __init_handle_by_constructor__ (line 227) | def __init_handle_by_constructor__(fconstructor, args): function _return_module (line 250) | def _return_module(x): function _handle_return_func (line 258) | def _handle_return_func(x): function _set_class_module (line 290) | def _set_class_module(module_class): function _set_class_function (line 296) | def _set_class_function(func_class): FILE: python/dgl/_ffi/_ctypes/ndarray.py function _from_dlpack (line 28) | def _from_dlpack(dltensor): function _dlpack_deleter (line 48) | def _dlpack_deleter(pycapsule): class NDArrayBase (line 65) | class NDArrayBase(object): method __init__ (line 70) | def __init__(self, handle, is_view=False): method __del__ (line 81) | def __del__(self): method _dgl_handle (line 86) | def _dgl_handle(self): method to_dlpack (line 89) | def to_dlpack(self, alignment=0): function _make_array (line 113) | def _make_array(handle, is_view): function _reg_extension (line 121) | def _reg_extension(cls, fcreate): function _set_class_ndarray (line 135) | def _set_class_ndarray(cls): FILE: python/dgl/_ffi/_ctypes/object.py function _register_object (line 23) | def _register_object(index, cls): function _return_object (line 28) | def _return_object(x): class ObjectBase (line 49) | class ObjectBase(object): method __del__ (line 55) | def __del__(self): method __getattr__ (line 59) | def __getattr__(self, name): method __init_handle_by_constructor__ (line 82) | def __init_handle_by_constructor__(self, fconstructor, *args): FILE: python/dgl/_ffi/_ctypes/types.py class DGLValue (line 11) | class DGLValue(ctypes.Union): function _return_handle (line 37) | def _return_handle(x): function _return_bytes (line 45) | def _return_bytes(x): function _wrap_arg_func (line 59) | def _wrap_arg_func(return_f, type_code): FILE: python/dgl/_ffi/base.py class DGLError (line 30) | class DGLError(Exception): function _load_lib (line 36) | def _load_lib(): function check_call (line 58) | def check_call(ret): function c_str (line 73) | def c_str(string): function c_array (line 88) | def c_array(ctype, values): function decorate (line 107) | def decorate(func, fwrapped): function load_tensor_adapter (line 126) | def load_tensor_adapter(backend, version): function is_tensor_adaptor_enabled (line 153) | def is_tensor_adaptor_enabled() -> bool: FILE: python/dgl/_ffi/function.py class Function (line 42) | class Function(_FunctionBase): class ModuleBase (line 69) | class ModuleBase(object): method __init__ (line 74) | def __init__(self, handle): method __del__ (line 79) | def __del__(self): method entry_func (line 83) | def entry_func(self): method get_function (line 96) | def get_function(self, name, query_imports=False): method import_module (line 125) | def import_module(self, module): method __getitem__ (line 135) | def __getitem__(self, name): method __call__ (line 140) | def __call__(self, *args): function register_func (line 147) | def register_func(func_name, f=None, override=False): function get_global_func (line 209) | def get_global_func(name, allow_missing=False): function list_global_func_names (line 236) | def list_global_func_names(): function extract_ext_funcs (line 256) | def extract_ext_funcs(finit): function _get_api (line 283) | def _get_api(f): function _init_api (line 289) | def _init_api(namespace, target_module_name=None): function _init_api_prefix (line 305) | def _init_api_prefix(module_name, prefix): function _init_internal_api (line 333) | def _init_internal_api(): FILE: python/dgl/_ffi/libinfo.py function find_lib_path (line 9) | def find_lib_path(name=None, search_path=None, optional=False): FILE: python/dgl/_ffi/ndarray.py function context (line 53) | def context(dev_type, dev_id=0): function numpyasarray (line 88) | def numpyasarray(np_data): function empty (line 104) | def empty(shape, dtype="float32", ctx=context(1, 0)): function empty_shared_mem (line 142) | def empty_shared_mem(name, is_create, shape, dtype="float32"): function from_dlpack (line 184) | def from_dlpack(dltensor): class NDArrayBase (line 203) | class NDArrayBase(_NDArrayBase): method shape (line 207) | def shape(self): method dtype (line 215) | def dtype(self): method ctx (line 220) | def ctx(self): method context (line 225) | def context(self): method __hash__ (line 229) | def __hash__(self): method __eq__ (line 232) | def __eq__(self, other): method __ne__ (line 235) | def __ne__(self, other): method same_as (line 238) | def same_as(self, other): method __setitem__ (line 255) | def __setitem__(self, in_slice, value): method copyfrom (line 271) | def copyfrom(self, source_array): method __repr__ (line 318) | def __repr__(self): method __str__ (line 322) | def __str__(self): method asnumpy (line 325) | def asnumpy(self): method copyto (line 346) | def copyto(self, target): method pin_memory_ (line 362) | def pin_memory_(self): method unpin_memory_ (line 366) | def unpin_memory_(self): method record_stream (line 370) | def record_stream(self, stream): function free_extension_handle (line 385) | def free_extension_handle(handle, type_code): function register_extension (line 399) | def register_extension(cls, fcreate=None): FILE: python/dgl/_ffi/object.py function _new_object (line 27) | def _new_object(cls): class ObjectBase (line 32) | class ObjectBase(_ObjectBase): method __dir__ (line 41) | def __dir__(self): method __hash__ (line 54) | def __hash__(self): method __eq__ (line 57) | def __eq__(self, other): method __ne__ (line 60) | def __ne__(self, other): method __reduce__ (line 63) | def __reduce__(self): method __getstate__ (line 67) | def __getstate__(self): method __setstate__ (line 74) | def __setstate__(self, state): method same_as (line 82) | def same_as(self, other): function register_object (line 89) | def register_object(type_key=None): FILE: python/dgl/_ffi/object_generic.py function _set_class_object_base (line 14) | def _set_class_object_base(cls): class ObjectGeneric (line 19) | class ObjectGeneric(object): method asobject (line 22) | def asobject(self): function convert_to_object (line 27) | def convert_to_object(value): FILE: python/dgl/_ffi/runtime_ctypes.py class TypeCode (line 16) | class TypeCode(object): class DGLByteArray (line 36) | class DGLByteArray(ctypes.Structure): class DGLDataType (line 45) | class DGLDataType(ctypes.Structure): method __new__ (line 56) | def __new__(cls, type_str): method __init__ (line 90) | def __init__(self, type_str): method __repr__ (line 93) | def __repr__(self): method __eq__ (line 99) | def __eq__(self, other): method __ne__ (line 106) | def __ne__(self, other): class DGLContext (line 113) | class DGLContext(ctypes.Structure): method __new__ (line 151) | def __new__(cls, device_type, device_id): method __init__ (line 163) | def __init__(self, device_type, device_id): method exist (line 167) | def exist(self): method max_threads_per_block (line 175) | def max_threads_per_block(self): method warp_size (line 180) | def warp_size(self): method max_shared_memory_per_block (line 185) | def max_shared_memory_per_block(self): method compute_version (line 190) | def compute_version(self): method device_name (line 203) | def device_name(self): method max_clock_rate (line 208) | def max_clock_rate(self): method multi_processor_count (line 213) | def multi_processor_count(self): method max_thread_dimensions (line 218) | def max_thread_dimensions(self): method sync (line 230) | def sync(self): method __eq__ (line 234) | def __eq__(self, other): method __ne__ (line 241) | def __ne__(self, other): method __repr__ (line 244) | def __repr__(self): method __hash__ (line 258) | def __hash__(self): class DGLArray (line 262) | class DGLArray(ctypes.Structure): FILE: python/dgl/_ffi/streams.py function to_dgl_stream_handle (line 13) | def to_dgl_stream_handle(cuda_stream): function _dgl_get_stream (line 28) | def _dgl_get_stream(ctx): FILE: python/dgl/_sparse_ops.py function infer_broadcast_shape (line 10) | def infer_broadcast_shape(op, shp1, shp2): function to_dgl_nd (line 63) | def to_dgl_nd(x): function to_dgl_nd_for_write (line 68) | def to_dgl_nd_for_write(x): function get_typeid_by_target (line 77) | def get_typeid_by_target(gidx, etid, target): function _edge_softmax_backward (line 90) | def _edge_softmax_backward(gidx, out, sds): function _edge_softmax_forward (line 123) | def _edge_softmax_forward(gidx, e, op): function _gspmm (line 156) | def _gspmm(gidx, op, reduce_op, u, e): function _gspmm_hetero (line 268) | def _gspmm_hetero(gidx, op, reduce_op, u_len, u_and_e_tuple): function _segment_mm (line 436) | def _segment_mm(A, B, out, seglen_A, b_trans=False): function _segment_mm_backward_B (line 449) | def _segment_mm_backward_B(A, dC, dB, seglen): function _gather_mm (line 457) | def _gather_mm(A, B, out, idx_a=None, idx_b=None): function _gather_mm_scatter (line 469) | def _gather_mm_scatter(A, B, out, idx_a=None, idx_b=None, idx_c=None): function _gsddmm (line 482) | def _gsddmm(gidx, op, lhs, rhs, lhs_target="u", rhs_target="v"): function _gsddmm_hetero (line 568) | def _gsddmm_hetero( function _segment_reduce (line 641) | def _segment_reduce(op, feat, offsets): function _scatter_add (line 694) | def _scatter_add(x, idx, m): function _update_grad_minmax_hetero (line 723) | def _update_grad_minmax_hetero( function _bwd_segment_cmp (line 772) | def _bwd_segment_cmp(feat, arg, m): function _csrmm (line 802) | def _csrmm(A, A_weights, B, B_weights, num_vtypes): function _csrsum (line 835) | def _csrsum(As, A_weights): function _csrmask (line 860) | def _csrmask(A, A_weights, B): function libra_vertex_cut (line 891) | def libra_vertex_cut( function libra2dgl_build_dict (line 923) | def libra2dgl_build_dict( function libra2dgl_build_adjlist (line 961) | def libra2dgl_build_adjlist( function libra2dgl_set_lr (line 1021) | def libra2dgl_set_lr(gdt_key, gdt_value, lrtensor, nc, Nn): FILE: python/dgl/backend/__init__.py function _gen_missing_api (line 17) | def _gen_missing_api(api, mod_name): function load_backend (line 28) | def load_backend(mod_name): function get_preferred_backend (line 95) | def get_preferred_backend(): function is_enabled (line 125) | def is_enabled(api): function to_dgl_nd (line 141) | def to_dgl_nd(data): function from_dgl_nd (line 145) | def from_dgl_nd(data): FILE: python/dgl/backend/backend.py function data_type_dict (line 20) | def data_type_dict(): function cpu (line 58) | def cpu(): function tensor (line 63) | def tensor(data, dtype=None): function as_scalar (line 89) | def as_scalar(data): function get_preferred_sparse_format (line 105) | def get_preferred_sparse_format(): function sparse_matrix (line 119) | def sparse_matrix(data, index, shape, force_format=False): function sparse_matrix_indices (line 157) | def sparse_matrix_indices(spmat): function is_tensor (line 181) | def is_tensor(obj): function shape (line 186) | def shape(input): function dtype (line 202) | def dtype(input): function ndim (line 218) | def ndim(input): function context (line 234) | def context(input): function device_type (line 250) | def device_type(ctx): function device_id (line 265) | def device_id(ctx): function to_backend_ctx (line 284) | def to_backend_ctx(dglctx): function astype (line 299) | def astype(input, ty): function asnumpy (line 317) | def asnumpy(input): function copy_to (line 335) | def copy_to(input, ctx, **kwargs): function is_pinned (line 353) | def is_pinned(input): function sum (line 376) | def sum(input, dim, keepdims=False): function floor_div (line 396) | def floor_div(in1, in2): function reduce_sum (line 413) | def reduce_sum(input): function cumsum (line 429) | def cumsum(input, dim): function mean (line 447) | def mean(input, dim): function reduce_mean (line 465) | def reduce_mean(input): function max (line 481) | def max(input, dim): function reduce_max (line 499) | def reduce_max(input): function min (line 515) | def min(input, dim): function reduce_min (line 533) | def reduce_min(input): function argsort (line 549) | def argsort(input, dim, descending): function topk (line 568) | def topk(input, k, dim, descending=True): function argtopk (line 587) | def argtopk(input, k, dim, descending=True): function exp (line 607) | def exp(input): function inverse (line 623) | def inverse(input): function sqrt (line 639) | def sqrt(input): function softmax (line 655) | def softmax(input, dim=-1): function cat (line 673) | def cat(seq, dim): function stack (line 691) | def stack(seq, dim): function split (line 709) | def split(input, sizes_or_sections, dim): function repeat (line 735) | def repeat(input, repeats, dim): function gather_row (line 755) | def gather_row(data, row_index): function slice_axis (line 773) | def slice_axis(data, axis, begin, end): function take (line 795) | def take(data, indices, dim): function narrow_row (line 810) | def narrow_row(x, start, stop): function scatter_row (line 834) | def scatter_row(data, row_index, value): function index_add_inplace (line 856) | def index_add_inplace(data, row_idx, value): function scatter_row_inplace (line 882) | def scatter_row_inplace(data, row_index, value): function squeeze (line 899) | def squeeze(input, dim): function unsqueeze (line 917) | def unsqueeze(input, dim): function reshape (line 935) | def reshape(input, shape): function swapaxes (line 953) | def swapaxes(input, axis1, axis2): function empty (line 971) | def empty(shape, dtype, ctx): function zeros (line 991) | def zeros(shape, dtype, ctx): function zeros_like (line 1011) | def zeros_like(input): function ones (line 1028) | def ones(shape, dtype, ctx): function uniform (line 1048) | def uniform(shape, dtype, ctx, low, high): function randint (line 1069) | def randint(shape, dtype, ctx, low, high): function pad_packed_tensor (line 1090) | def pad_packed_tensor(input, lengths, value, l_min=None): function pack_padded_tensor (line 1115) | def pack_padded_tensor(input, lengths): function boolean_mask (line 1136) | def boolean_mask(input, mask): function equal (line 1155) | def equal(x, y): function allclose (line 1171) | def allclose(x, y, rtol=1e-4, atol=1e-4): function logical_not (line 1187) | def logical_not(input): function logical_and (line 1203) | def logical_and(input1, input2): function clone (line 1207) | def clone(input): function clamp (line 1223) | def clamp(data, min_val, max_val): function replace_inf_with_zero (line 1244) | def replace_inf_with_zero(x): function count_nonzero (line 1260) | def count_nonzero(input): function unique (line 1285) | def unique(input, return_inverse=False, return_counts=False): function full_1d (line 1312) | def full_1d(length, fill_value, dtype, ctx): function nonzero_1d (line 1334) | def nonzero_1d(input): function sort_1d (line 1350) | def sort_1d(input): function arange (line 1368) | def arange(start, stop, dtype, ctx): function rand_shuffle (line 1390) | def rand_shuffle(arr): function zerocopy_to_dlpack (line 1408) | def zerocopy_to_dlpack(input): function zerocopy_from_dlpack (line 1424) | def zerocopy_from_dlpack(dlpack_tensor): function zerocopy_to_numpy (line 1440) | def zerocopy_to_numpy(input): function zerocopy_from_numpy (line 1456) | def zerocopy_from_numpy(np_array): function zerocopy_to_dgl_ndarray (line 1472) | def zerocopy_to_dgl_ndarray(input): function zerocopy_to_dgl_ndarray_for_write (line 1486) | def zerocopy_to_dgl_ndarray_for_write(input): function zerocopy_from_dgl_ndarray (line 1501) | def zerocopy_from_dgl_ndarray(input): function binary_reduce (line 1523) | def binary_reduce( function copy_reduce (line 1573) | def copy_reduce(reducer, graph, target, in_data, out_size, in_map, out_m... function gspmm (line 1602) | def gspmm(gidx, op, reduce_op, lhs_data, rhs_data): function gspmm_hetero (line 1640) | def gspmm_hetero(g, op, reduce_op, lhs_len, *lhs_and_rhs_tuple): function gsddmm (line 1682) | def gsddmm(gidx, op, lhs_data, rhs_data, lhs_target="u", rhs_target="v"): function gsddmm_hetero (line 1718) | def gsddmm_hetero( function edge_softmax (line 1761) | def edge_softmax(gidx, logits, eids, norm_by): function edge_softmax_hetero (line 1799) | def edge_softmax_hetero(gidx, eids, norm_by, *logits): function segment_reduce (line 1837) | def segment_reduce(op, x, offsets): function scatter_add (line 1866) | def scatter_add(x, idx, m): function csrmm (line 1889) | def csrmm(A, A_weights, B, B_weights, num_vtypes): function csrsum (line 1922) | def csrsum(gidxs, weights): function csrmask (line 1949) | def csrmask(A, A_weights, B): function gather_mm (line 1977) | def gather_mm(A, B, idx_a, idx_b): function segment_mm (line 2001) | def segment_mm(A, B, seglen_A): function sync (line 2031) | def sync(): function attach_grad (line 2041) | def attach_grad(tensor): function backward (line 2046) | def backward(x, head_gradient=None): function grad (line 2051) | def grad(x): function is_no_grad (line 2056) | def is_no_grad(x): function is_recording (line 2061) | def is_recording(): class record_grad (line 2066) | class record_grad(object): method __init__ (line 2069) | def __init__(self): method __enter__ (line 2072) | def __enter__(self): method __exit__ (line 2075) | def __exit__(self, exc_type, exc_value, exc_traceback): class no_grad (line 2079) | class no_grad(object): method __init__ (line 2082) | def __init__(self): method __enter__ (line 2085) | def __enter__(self): method __exit__ (line 2088) | def __exit__(self, exc_type, exc_value, exc_traceback): class NodeEmbedding (line 2092) | class NodeEmbedding(object): method __init__ (line 2095) | def __init__(self): method __enter__ (line 2098) | def __enter__(self): method __exit__ (line 2101) | def __exit__(self, exc_type, exc_value, exc_traceback): FILE: python/dgl/backend/mxnet/sparse.py function _scatter_nd (line 38) | def _scatter_nd(index, src, n_rows): function _gather_nd (line 71) | def _gather_nd(index, src): function _reduce_grad (line 97) | def _reduce_grad(grad, shape): function _need_reduce_last_dim (line 130) | def _need_reduce_last_dim(ufeat, efeat): function _muldiv (line 139) | def _muldiv(op, x): function _addsub (line 143) | def _addsub(op, x): function _expand (line 147) | def _expand(x, shape): class GSpMM (line 151) | class GSpMM(mx.autograd.Function): method __init__ (line 152) | def __init__(self, gidx, op, reduce_op): method forward (line 158) | def forward(self, X, Y): method backward (line 163) | def backward(self, dZ): function gspmm (line 217) | def gspmm(gidx, op, reduce_op, lhs_data, rhs_data): class GSDDMM (line 234) | class GSDDMM(mx.autograd.Function): method __init__ (line 235) | def __init__(self, gidx, op, lhs_target, rhs_target): method forward (line 242) | def forward(self, X, Y): method backward (line 249) | def backward(self, dZ): function gsddmm (line 310) | def gsddmm(gidx, op, lhs_data, rhs_data, lhs_target="u", rhs_target="v"): class EdgeSoftmax (line 320) | class EdgeSoftmax(mx.autograd.Function): method __init__ (line 321) | def __init__(self, gidx, eids, norm_by): method forward (line 329) | def forward(self, score): method backward (line 351) | def backward(self, grad_out): function edge_softmax (line 374) | def edge_softmax(gidx, logits, eids=ALL, norm_by="dst"): class SegmentReduce (line 379) | class SegmentReduce(mx.autograd.Function): method __init__ (line 380) | def __init__(self, op, offsets): method forward (line 385) | def forward(self, x): method backward (line 390) | def backward(self, dy): function segment_reduce (line 406) | def segment_reduce(op, x, offsets): class ScatterAdd (line 411) | class ScatterAdd(mx.autograd.Function): method __init__ (line 412) | def __init__(self, idx, m): method forward (line 417) | def forward(self, x): method backward (line 421) | def backward(self, dy): function scatter_add (line 425) | def scatter_add(x, idx, m): class CSRMM (line 430) | class CSRMM(mx.autograd.Function): method __init__ (line 431) | def __init__(self, gidxA, gidxB, num_vtypes): method forward (line 437) | def forward(self, A_weights, B_weights): method backward (line 456) | def backward( function csrmm (line 481) | def csrmm(gidxA, A_weights, gidxB, B_weights, num_vtypes): class CSRSum (line 498) | class CSRSum(mx.autograd.Function): method __init__ (line 499) | def __init__(self, gidxs): method forward (line 503) | def forward(self, *weights): method backward (line 519) | def backward( function csrsum (line 527) | def csrsum(gidxs, weights): class CSRMask (line 543) | class CSRMask(mx.autograd.Function): method __init__ (line 544) | def __init__(self, gidxA, gidxB): method forward (line 549) | def forward(self, A_weights): method backward (line 552) | def backward(self, dB_weights): function csrmask (line 556) | def csrmask(gidxA, A_weights, gidxB): FILE: python/dgl/backend/mxnet/tensor.py function data_type_dict (line 23) | def data_type_dict(): function cpu (line 37) | def cpu(): function tensor (line 41) | def tensor(data, dtype=None): function as_scalar (line 67) | def as_scalar(data): function get_preferred_sparse_format (line 75) | def get_preferred_sparse_format(): function sparse_matrix (line 84) | def sparse_matrix(data, index, shape, force_format=False): function sparse_matrix_indices (line 122) | def sparse_matrix_indices(spmat): function is_tensor (line 126) | def is_tensor(obj): function shape (line 130) | def shape(input): function dtype (line 135) | def dtype(input): function ndim (line 140) | def ndim(input): function context (line 144) | def context(input): function device_type (line 148) | def device_type(ctx): function device_id (line 152) | def device_id(ctx): function to_backend_ctx (line 156) | def to_backend_ctx(dglctx): function astype (line 166) | def astype(input, ty): function asnumpy (line 172) | def asnumpy(input): function copy_to (line 176) | def copy_to(input, ctx, **kwargs): function is_pinned (line 180) | def is_pinned(input): function sum (line 184) | def sum(input, dim, keepdims=False): function floor_div (line 190) | def floor_div(in1, in2): function reduce_sum (line 194) | def reduce_sum(input): function cumsum (line 198) | def cumsum(input, dim): function mean (line 202) | def mean(input, dim): function reduce_mean (line 206) | def reduce_mean(input): function max (line 210) | def max(input, dim): function reduce_max (line 214) | def reduce_max(input): function min (line 218) | def min(input, dim): function reduce_min (line 222) | def reduce_min(input): function topk (line 226) | def topk(input, k, dim, descending=True): function argtopk (line 232) | def argtopk(input, k, dim, descending=True): function argsort (line 237) | def argsort(input, dim, descending): function exp (line 243) | def exp(input): function inverse (line 247) | def inverse(input): function sqrt (line 251) | def sqrt(input): function softmax (line 255) | def softmax(input, dim=-1): function cat (line 259) | def cat(seq, dim): function stack (line 263) | def stack(seq, dim): function split (line 267) | def split(x, sizes_or_sections, dim): function repeat (line 277) | def repeat(input, repeats, dim): function gather_row (line 288) | def gather_row(data, row_index): function slice_axis (line 304) | def slice_axis(data, axis, begin, end): function take (line 313) | def take(data, indices, dim): function narrow_row (line 317) | def narrow_row(data, start, stop): function index_add_inplace (line 321) | def index_add_inplace(data, row_idx, value): function scatter_row (line 325) | def scatter_row(data, row_index, value): function scatter_row_inplace (line 329) | def scatter_row_inplace(data, row_index, value): function squeeze (line 333) | def squeeze(input, dim): function unsqueeze (line 337) | def unsqueeze(input, dim): function reshape (line 341) | def reshape(input, shape): function swapaxes (line 346) | def swapaxes(input, axis1, axis2): function empty (line 350) | def empty(shape, dtype, ctx): function zeros (line 354) | def zeros(shape, dtype, ctx): function zeros_like (line 358) | def zeros_like(input): function ones (line 362) | def ones(shape, dtype, ctx): function uniform (line 366) | def uniform(shape, dtype, ctx, low, high): function randint (line 370) | def randint(shape, dtype, ctx, low, high): function pad_packed_tensor (line 374) | def pad_packed_tensor(input, lengths, value, l_min=None): function pack_padded_tensor (line 398) | def pack_padded_tensor(input, lengths): function boolean_mask (line 408) | def boolean_mask(input, mask): function equal (line 412) | def equal(x, y): function allclose (line 416) | def allclose(x, y, rtol=1e-4, atol=1e-4): function logical_not (line 420) | def logical_not(input): function logical_and (line 424) | def logical_and(input1, input2): function clone (line 428) | def clone(input): function clamp (line 432) | def clamp(data, min_val, max_val): function replace_inf_with_zero (line 436) | def replace_inf_with_zero(x): function count_nonzero (line 440) | def count_nonzero(input): function unique (line 446) | def unique(input, return_inverse=False, return_counts=False): function full_1d (line 469) | def full_1d(length, fill_value, dtype, ctx): function nonzero_1d (line 473) | def nonzero_1d(input): function sort_1d (line 481) | def sort_1d(input): function arange (line 489) | def arange(start, stop, dtype=np.int64, ctx=None): function rand_shuffle (line 496) | def rand_shuffle(arr): function zerocopy_to_dlpack (line 500) | def zerocopy_to_dlpack(arr): function zerocopy_from_dlpack (line 504) | def zerocopy_from_dlpack(dlpack_arr): function zerocopy_to_numpy (line 508) | def zerocopy_to_numpy(arr): function zerocopy_from_numpy (line 513) | def zerocopy_from_numpy(np_data): function zerocopy_to_dgl_ndarray (line 518) | def zerocopy_to_dgl_ndarray(arr): function zerocopy_to_dgl_ndarray_for_write (line 523) | def zerocopy_to_dgl_ndarray_for_write(arr): function zerocopy_from_dgl_ndarray (line 527) | def zerocopy_from_dgl_ndarray(arr): function sync (line 531) | def sync(): function attach_grad (line 541) | def attach_grad(tensor): function backward (line 546) | def backward(x, head_gradient=None): function grad (line 550) | def grad(x): function is_no_grad (line 554) | def is_no_grad(x): function is_recording (line 558) | def is_recording(): class no_grad (line 565) | class no_grad(object): method __init__ (line 566) | def __init__(self): method __enter__ (line 569) | def __enter__(self): method __exit__ (line 572) | def __exit__(self, exc_type, exc_value, exc_traceback): FILE: python/dgl/backend/pytorch/sparse.py function _reduce_grad (line 43) | def _reduce_grad(grad, shape): function _need_reduce_last_dim (line 77) | def _need_reduce_last_dim(ufeat, efeat): function _expand (line 88) | def _expand(x, shape): function spmm_cache_X (line 92) | def spmm_cache_X(binary_op, reduce_op, req_grad_X, req_grad_Y): function spmm_cache_Y (line 103) | def spmm_cache_Y(binary_op, reduce_op, req_grad_X, req_grad_Y): function spmm_cache_argX (line 115) | def spmm_cache_argX(binary_op, reduce_op, req_grad_X, req_grad_Y): function spmm_cache_argY (line 123) | def spmm_cache_argY(binary_op, reduce_op, req_grad_X, req_grad_Y): class empty_context (line 131) | class empty_context: method __init__ (line 134) | def __init__(self, *args, **kargs): method __enter__ (line 137) | def __enter__(self, *args, **kargs): method __exit__ (line 140) | def __exit__(self, *args, **kargs): function _disable_autocast_if_enabled (line 146) | def _disable_autocast_if_enabled(): function _cast_if_autocast_enabled (line 153) | def _cast_if_autocast_enabled(*args): class GSpMM (line 162) | class GSpMM(th.autograd.Function): method forward (line 164) | def forward(ctx, gidx, op, reduce_op, X, Y): method backward (line 195) | def backward(ctx, dZ): class GSpMM_hetero (line 251) | class GSpMM_hetero(th.autograd.Function): method forward (line 253) | def forward( method backward (line 309) | def backward(ctx, *dZ): function sddmm_cache_X (line 429) | def sddmm_cache_X(op, req_grad_X, req_grad_Y): function sddmm_cache_Y (line 436) | def sddmm_cache_Y(op, req_grad_X, req_grad_Y): class GSDDMM (line 443) | class GSDDMM(th.autograd.Function): method forward (line 445) | def forward(ctx, gidx, op, X, Y, lhs_target, rhs_target): method backward (line 460) | def backward(ctx, dZ): class GSDDMM_hetero (line 506) | class GSDDMM_hetero(th.autograd.Function): method forward (line 508) | def forward( method backward (line 545) | def backward(ctx, *dZ): class EdgeSoftmax (line 685) | class EdgeSoftmax(th.autograd.Function): method forward (line 687) | def forward(ctx, gidx, score, eids, norm_by): method backward (line 721) | def backward(ctx, grad_out): class EdgeSoftmax_hetero (line 750) | class EdgeSoftmax_hetero(th.autograd.Function): method forward (line 752) | def forward(ctx, gidx, eids, norm_by, *score): method backward (line 797) | def backward(ctx, *grad_out): class SegmentReduce (line 828) | class SegmentReduce(th.autograd.Function): method forward (line 830) | def forward(ctx, op, x, offsets): method backward (line 837) | def backward(ctx, dy): class ScatterAdd (line 856) | class ScatterAdd(th.autograd.Function): method forward (line 858) | def forward(ctx, x, idx, m): method backward (line 864) | def backward(ctx, dy): class CSRMM (line 869) | class CSRMM(th.autograd.Function): method forward (line 871) | def forward(ctx, gidxA, A_weights, gidxB, B_weights, num_vtypes): method backward (line 896) | def backward( class CSRSum (line 921) | class CSRSum(th.autograd.Function): method forward (line 923) | def forward(ctx, gidxs, *weights): method backward (line 946) | def backward( class CSRMask (line 956) | class CSRMask(th.autograd.Function): method forward (line 958) | def forward(ctx, gidxA, A_weights, gidxB): method backward (line 963) | def backward(ctx, dB_weights): class SEGMENTMM (line 968) | class SEGMENTMM(th.autograd.Function): method forward (line 970) | def forward(ctx, A, B, seglen_A): method backward (line 979) | def backward(ctx, dZ): class GATHERMM (line 993) | class GATHERMM(th.autograd.Function): method forward (line 995) | def forward(ctx, A, B, idx_a, idx_b): method backward (line 1007) | def backward(ctx, dZ): function gspmm (line 1023) | def gspmm(gidx, op, reduce_op, lhs_data, rhs_data): function gsddmm (line 1035) | def gsddmm(gidx, op, lhs_data, rhs_data, lhs_target="u", rhs_target="v"): function gspmm_hetero (line 1049) | def gspmm_hetero(g, op, reduce_op, lhs_len, *lhs_and_rhs_tuple): function gsddmm_hetero (line 1080) | def gsddmm_hetero( function edge_softmax (line 1113) | def edge_softmax(gidx, logits, eids=ALL, norm_by="dst"): function edge_softmax_hetero (line 1119) | def edge_softmax_hetero(gidx, eids=ALL, norm_by="dst", *logits): function segment_reduce (line 1125) | def segment_reduce(op, x, offsets): function scatter_add (line 1131) | def scatter_add(x, idx, m): function csrmm (line 1137) | def csrmm(gidxA, A_weights, gidxB, B_weights, num_vtypes): function csrsum (line 1153) | def csrsum(gidxs, weights): function csrmask (line 1169) | def csrmask(gidxA, A_weights, gidxB): function segment_mm (line 1173) | def segment_mm(A, B, seglen_A): function gather_mm (line 1187) | def gather_mm(A, B, idx_A=None, idx_B=None): FILE: python/dgl/backend/pytorch/tensor.py function data_type_dict (line 19) | def data_type_dict(): function cpu (line 34) | def cpu(): function tensor (line 38) | def tensor(data, dtype=None): function as_scalar (line 56) | def as_scalar(data): function get_preferred_sparse_format (line 60) | def get_preferred_sparse_format(): function sparse_matrix (line 69) | def sparse_matrix(data, index, shape, force_format=False): function sparse_matrix_indices (line 79) | def sparse_matrix_indices(spmat): function is_tensor (line 83) | def is_tensor(obj): function shape (line 87) | def shape(input): function dtype (line 91) | def dtype(input): function ndim (line 95) | def ndim(input): function context (line 99) | def context(input): function device_type (line 103) | def device_type(ctx): function device_id (line 107) | def device_id(ctx): function to_backend_ctx (line 115) | def to_backend_ctx(dglctx): function astype (line 125) | def astype(input, ty): function asnumpy (line 129) | def asnumpy(input): function copy_to (line 136) | def copy_to(input, ctx, **kwargs): function is_pinned (line 148) | def is_pinned(input): function sum (line 152) | def sum(input, dim, keepdims=False): function floor_div (line 156) | def floor_div(in1, in2): function reduce_sum (line 160) | def reduce_sum(input): function cumsum (line 164) | def cumsum(input, dim): function mean (line 168) | def mean(input, dim): function reduce_mean (line 172) | def reduce_mean(input): function max (line 176) | def max(input, dim): function reduce_max (line 181) | def reduce_max(input): function min (line 185) | def min(input, dim): function reduce_min (line 190) | def reduce_min(input): function argsort (line 194) | def argsort(input, dim, descending): function topk (line 198) | def topk(input, k, dim, descending=True): function argtopk (line 202) | def argtopk(input, k, dim, descending=True): function exp (line 206) | def exp(input): function inverse (line 210) | def inverse(input): function sqrt (line 214) | def sqrt(input): function softmax (line 218) | def softmax(input, dim=-1): function cat (line 222) | def cat(seq, dim): function stack (line 226) | def stack(seq, dim): function split (line 230) | def split(input, sizes_or_sections, dim): function repeat (line 234) | def repeat(input, repeats, dim): function gather_row (line 238) | def gather_row(data, row_index): function slice_axis (line 242) | def slice_axis(data, axis, begin, end): function take (line 246) | def take(data, indices, dim): function narrow_row (line 251) | def narrow_row(x, start, stop): function index_add_inplace (line 255) | def index_add_inplace(data, row_idx, value): function scatter_row (line 259) | def scatter_row(data, row_index, value): function scatter_row_inplace (line 263) | def scatter_row_inplace(data, row_index, value): function squeeze (line 267) | def squeeze(input, dim): function unsqueeze (line 271) | def unsqueeze(input, dim): function reshape (line 275) | def reshape(input, shape): function swapaxes (line 279) | def swapaxes(input, axis1, axis2): function empty (line 283) | def empty(shape, dtype, ctx): function zeros (line 287) | def zeros(shape, dtype, ctx): function zeros_like (line 291) | def zeros_like(input): function ones (line 295) | def ones(shape, dtype, ctx): function uniform (line 299) | def uniform(shape, dtype, ctx, low, high): function randint (line 303) | def randint(shape, dtype, ctx, low, high): function pad_packed_tensor (line 307) | def pad_packed_tensor(input, lengths, value, l_min=None): function pack_padded_tensor (line 330) | def pack_padded_tensor(input, lengths): function boolean_mask (line 346) | def boolean_mask(input, mask): function equal (line 352) | def equal(x, y): function allclose (line 356) | def allclose(x, y, rtol=1e-4, atol=1e-4): function logical_not (line 360) | def logical_not(input): function logical_and (line 364) | def logical_and(input1, input2): function clone (line 368) | def clone(input): function clamp (line 372) | def clamp(data, min_val, max_val): function replace_inf_with_zero (line 376) | def replace_inf_with_zero(x): function count_nonzero (line 380) | def count_nonzero(input): function unique (line 385) | def unique(input, return_inverse=False, return_counts=False): function full_1d (line 393) | def full_1d(length, fill_value, dtype, ctx): function nonzero_1d (line 397) | def nonzero_1d(input): function sort_1d (line 402) | def sort_1d(input): function arange (line 406) | def arange(start, stop, dtype=th.int64, ctx=None): function rand_shuffle (line 410) | def rand_shuffle(arr): function zerocopy_to_dlpack (line 415) | def zerocopy_to_dlpack(input): function zerocopy_from_dlpack (line 419) | def zerocopy_from_dlpack(dlpack_tensor): function zerocopy_to_numpy (line 423) | def zerocopy_to_numpy(input): function zerocopy_from_numpy (line 428) | def zerocopy_from_numpy(np_array): function zerocopy_to_dgl_ndarray (line 432) | def zerocopy_to_dgl_ndarray(data): function check_is_view (line 441) | def check_is_view(input): function check_is_view (line 448) | def check_is_view(input): function zerocopy_to_dgl_ndarray_for_write (line 454) | def zerocopy_to_dgl_ndarray_for_write(input): function zerocopy_from_dgl_ndarray (line 465) | def zerocopy_from_dgl_ndarray(data): function sync (line 487) | def sync(): function attach_grad (line 492) | def attach_grad(x): function backward (line 500) | def backward(x, head_gradient=None): function grad (line 511) | def grad(x): function is_no_grad (line 516) | def is_no_grad(x): function is_recording (line 520) | def is_recording(): class record_grad (line 524) | class record_grad(object): method __init__ (line 525) | def __init__(self): method __enter__ (line 528) | def __enter__(self): method __exit__ (line 531) | def __exit__(self, exc_type, exc_value, exc_traceback): FILE: python/dgl/backend/set_default_backend.py function set_default_backend (line 6) | def set_default_backend(default_dir, backend_name): FILE: python/dgl/backend/tensorflow/sparse.py function _scatter_nd (line 31) | def _scatter_nd(index, src, n_rows): function _gather_nd (line 59) | def _gather_nd(index, src): function _reduce_grad (line 84) | def _reduce_grad(grad, shape): function _need_reduce_last_dim (line 118) | def _need_reduce_last_dim(ufeat, efeat): function _muldiv (line 127) | def _muldiv(op, x): function _addsub (line 131) | def _addsub(op, x): function _expand (line 135) | def _expand(x, shape): function gspmm_real (line 139) | def gspmm_real(gidx, op, reduce_op, X, Y): function gspmm (line 196) | def gspmm(gidx, op, reduce_op, X, Y): function gsddmm_real (line 208) | def gsddmm_real(gidx, op, X, Y, lhs_target, rhs_target): function gsddmm (line 269) | def gsddmm(gidx, op, X, Y, lhs_target="u", rhs_target="v"): function edge_softmax_real (line 281) | def edge_softmax_real(gidx, score, eids=ALL, norm_by="dst"): function edge_softmax (line 300) | def edge_softmax(gidx, logits, eids=ALL, norm_by="dst"): function segment_reduce_real (line 308) | def segment_reduce_real(op, x, offsets): function segment_reduce (line 327) | def segment_reduce(op, x, offsets): function scatter_add_real (line 335) | def scatter_add_real(x, idx, m): function scatter_add (line 344) | def scatter_add(x, idx, m): function csrmm_real (line 352) | def csrmm_real(gidxA, A_weights, gidxB, B_weights, num_vtypes): function csrmm (line 388) | def csrmm(gidxA, A_weights, gidxB, B_weights, num_vtypes): function csrsum_real (line 408) | def csrsum_real(gidxs, weights): function csrsum (line 428) | def csrsum(gidxs, weights): function csrmask_real (line 447) | def csrmask_real(gidxA, A_weights, gidxB): function csrmask (line 456) | def csrmask(gidxA, A_weights, gidxB): FILE: python/dgl/backend/tensorflow/tensor.py function zerocopy_to_dlpack (line 20) | def zerocopy_to_dlpack(data): function zerocopy_from_dlpack (line 24) | def zerocopy_from_dlpack(dlpack_tensor): function data_type_dict (line 31) | def data_type_dict(): function cpu (line 46) | def cpu(): function tensor (line 50) | def tensor(data, dtype=None): function initialize_context (line 62) | def initialize_context(): function as_scalar (line 66) | def as_scalar(data): function get_preferred_sparse_format (line 71) | def get_preferred_sparse_format(): function sparse_matrix (line 80) | def sparse_matrix(data, index, shape, force_format=False): function sparse_matrix_indices (line 96) | def sparse_matrix_indices(spmat): function is_tensor (line 100) | def is_tensor(obj): function shape (line 104) | def shape(input): function dtype (line 108) | def dtype(input): function ndim (line 112) | def ndim(input): function context (line 116) | def context(input): function device_type (line 121) | def device_type(ctx): function device_id (line 125) | def device_id(ctx): function to_backend_ctx (line 129) | def to_backend_ctx(dglctx): function astype (line 139) | def astype(input, ty): function asnumpy (line 144) | def asnumpy(input): function copy_to (line 152) | def copy_to(input, ctx, **kwargs): function is_pinned (line 158) | def is_pinned(input): function sum (line 162) | def sum(input, dim, keepdims=False): function floor_div (line 168) | def floor_div(in1, in2): function reduce_sum (line 172) | def reduce_sum(input): function cumsum (line 178) | def cumsum(input, dim): function mean (line 184) | def mean(input, dim): function reduce_mean (line 188) | def reduce_mean(input): function max (line 192) | def max(input, dim): function reduce_max (line 196) | def reduce_max(input): function min (line 200) | def min(input, dim): function reduce_min (line 204) | def reduce_min(input): function argsort (line 208) | def argsort(input, dim, descending): function topk (line 219) | def topk(input, k, dim, descending=True): function argtopk (line 232) | def argtopk(input, k, dim, descending=True): function exp (line 245) | def exp(input): function inverse (line 249) | def inverse(input): function sqrt (line 253) | def sqrt(input): function softmax (line 257) | def softmax(input, dim=-1): function cat (line 261) | def cat(seq, dim): function stack (line 265) | def stack(seq, dim): function split (line 269) | def split(input, sizes_or_sections, dim): function repeat (line 276) | def repeat(input, repeats, dim): function gather_row (line 280) | def gather_row(data, row_index): function slice_axis (line 284) | def slice_axis(data, axis, begin, end): function take (line 294) | def take(data, indices, dim): function narrow_row (line 298) | def narrow_row(x, start, stop): function scatter_row (line 302) | def scatter_row(data, row_index, value): function index_add_inplace (line 313) | def index_add_inplace(data, row_idx, value): function scatter_row_inplace (line 317) | def scatter_row_inplace(data, row_index, value): function squeeze (line 321) | def squeeze(input, dim): function unsqueeze (line 325) | def unsqueeze(input, dim): function reshape (line 329) | def reshape(input, shape): function swapaxes (line 333) | def swapaxes(input, axis1, axis2): function empty (line 340) | def empty(shape, dtype, ctx): function zeros (line 345) | def zeros(shape, dtype, ctx): function zeros_like (line 351) | def zeros_like(input): function ones (line 355) | def ones(shape, dtype, ctx): function uniform (line 361) | def uniform(shape, dtype, ctx, low, high): function randint (line 367) | def randint(shape, dtype, ctx, low, high): function pad_packed_tensor (line 373) | def pad_packed_tensor(input, lengths, value, l_min=None): function pack_padded_tensor (line 399) | def pack_padded_tensor(input, lengths): function boolean_mask (line 408) | def boolean_mask(input, mask): function equal (line 412) | def equal(x, y): function allclose (line 416) | def allclose(x, y, rtol=1e-4, atol=1e-4): function logical_not (line 425) | def logical_not(input): function logical_and (line 429) | def logical_and(input1, input2): function clone (line 433) | def clone(input): function clamp (line 438) | def clamp(data, min_val, max_val): function replace_inf_with_zero (line 442) | def replace_inf_with_zero(x): function count_nonzero (line 446) | def count_nonzero(input): function unique (line 450) | def unique(input, return_inverse=False, return_counts=False): function full_1d (line 462) | def full_1d(length, fill_value, dtype, ctx): function nonzero_1d (line 469) | def nonzero_1d(input): function sort_1d (line 474) | def sort_1d(input): function arange (line 478) | def arange(start, stop, dtype=tf.int64, ctx=None): function rand_shuffle (line 486) | def rand_shuffle(arr): function zerocopy_to_numpy (line 490) | def zerocopy_to_numpy(input): function zerocopy_from_numpy (line 494) | def zerocopy_from_numpy(np_array): function zerocopy_to_dgl_ndarray (line 502) | def zerocopy_to_dgl_ndarray(data): function zerocopy_to_dgl_ndarray_for_write (line 515) | def zerocopy_to_dgl_ndarray_for_write(input): function zerocopy_from_dgl_ndarray (line 519) | def zerocopy_from_dgl_ndarray(input): function sync (line 523) | def sync(): class GradContext (line 528) | class GradContext: method __init__ (line 529) | def __init__(self): method set_tape (line 534) | def set_tape(self, tape): method add_tensor (line 537) | def add_tensor(self, x): method backward (line 548) | def backward(self, x, head_gradient=None): method is_no_grad (line 553) | def is_no_grad(self, x): method grad (line 563) | def grad(self, x): function get_cgrad (line 576) | def get_cgrad(): class record_grad (line 580) | class record_grad: method __init__ (line 581) | def __init__(self): method __enter__ (line 584) | def __enter__(self): method __exit__ (line 590) | def __exit__(self, exc_type, exc_value, exc_traceback): function attach_grad (line 596) | def attach_grad(x): function backward (line 601) | def backward(x, head_gradient=None): function grad (line 605) | def grad(x): function is_no_grad (line 609) | def is_no_grad(x): function is_recording (line 613) | def is_recording(): FILE: python/dgl/base.py function is_internal_column (line 22) | def is_internal_column(name): function is_all (line 27) | def is_all(arg): class DGLWarning (line 36) | class DGLWarning(UserWarning): function dgl_warning_format (line 41) | def dgl_warning_format(message, category, filename, lineno, line=None): function dgl_warning (line 51) | def dgl_warning(message, category=DGLWarning, stacklevel=2): FILE: python/dgl/batch.py function batch (line 13) | def batch(graphs, ndata=ALL, edata=ALL): function _batch_feat_dicts (line 225) | def _batch_feat_dicts(frames, keys, feat_dict_name): function unbatch (line 256) | def unbatch(g, node_split=None, edge_split=None): function slice_batch (line 446) | def slice_batch(g, gid, store_ids=False): FILE: python/dgl/container.py class List (line 12) | class List(ObjectBase): method __getitem__ (line 21) | def __getitem__(self, i): method __len__ (line 45) | def __len__(self): class Map (line 50) | class Map(ObjectBase): method __getitem__ (line 58) | def __getitem__(self, k): method __contains__ (line 61) | def __contains__(self, k): method items (line 64) | def items(self): method __len__ (line 69) | def __len__(self): class StrMap (line 74) | class StrMap(Map): method items (line 80) | def items(self): class Value (line 87) | class Value(ObjectBase): method data (line 91) | def data(self): function convert_to_strmap (line 96) | def convert_to_strmap(value): FILE: python/dgl/convert.py function graph (line 32) | def graph( function hetero_from_shared_memory (line 187) | def hetero_from_shared_memory(name): function heterograph (line 208) | def heterograph(data_dict, num_nodes_dict=None, idtype=None, device=None): function create_block (line 389) | def create_block( function block_to_graph (line 626) | def block_to_graph(block): function to_heterogeneous (line 672) | def to_heterogeneous( function to_homogeneous (line 892) | def to_homogeneous( function from_scipy (line 1058) | def from_scipy(sp_mat, eweight_name=None, idtype=None, device=None): function bipartite_from_scipy (line 1149) | def bipartite_from_scipy( function _batcher (line 1246) | def _batcher(lst): function from_networkx (line 1256) | def from_networkx( function bipartite_from_networkx (line 1435) | def bipartite_from_networkx( function _to_networkx_homogeneous (line 1656) | def _to_networkx_homogeneous(g, node_attrs, edge_attrs): function _to_networkx_heterogeneous (line 1685) | def _to_networkx_heterogeneous( function to_networkx (line 1749) | def to_networkx( function to_cugraph (line 1874) | def to_cugraph(g): function from_cugraph (line 1937) | def from_cugraph(cugraph_graph): function create_from_edges (line 1990) | def create_from_edges( FILE: python/dgl/core.py function is_builtin (line 11) | def is_builtin(func): function invoke_node_udf (line 16) | def invoke_node_udf(graph, nid, ntype, func, *, ndata=None, orig_nid=None): function invoke_edge_udf (line 52) | def invoke_edge_udf(graph, eid, etype, func, *, orig_eid=None): function invoke_udf_reduce (line 99) | def invoke_udf_reduce(graph, func, msgdata, *, orig_nid=None): function _bucketing (line 177) | def _bucketing(val): function data_dict_to_list (line 207) | def data_dict_to_list(graph, data_dict, func, target): function invoke_gsddmm (line 273) | def invoke_gsddmm(graph, func): function invoke_gspmm (line 311) | def invoke_gspmm( function message_passing (line 372) | def message_passing(g, mfunc, rfunc, afunc): FILE: python/dgl/cuda/gpu_cache.py class GPUCache (line 24) | class GPUCache(object): method __init__ (line 27) | def __init__(self, num_items, num_feats, idtype=F.int64): method query (line 36) | def query(self, keys): method replace (line 63) | def replace(self, keys, values): method miss_rate (line 81) | def miss_rate(self): FILE: python/dgl/cuda/nccl.py function sparse_all_to_all_push (line 7) | def sparse_all_to_all_push(idx, value, partition): function sparse_all_to_all_pull (line 98) | def sparse_all_to_all_pull(req_idx, value, partition): FILE: python/dgl/data/__init__.py function register_data_args (line 93) | def register_data_args(parser): function load_data (line 102) | def load_data(args): FILE: python/dgl/data/actor.py class ActorDataset (line 13) | class ActorDataset(DGLBuiltinDataset): method __init__ (line 58) | def __init__( method process (line 70) | def process(self): method has_cache (line 120) | def has_cache(self): method load (line 123) | def load(self): method __getitem__ (line 126) | def __getitem__(self, idx): method __len__ (line 133) | def __len__(self): method num_classes (line 137) | def num_classes(self): FILE: python/dgl/data/adapter.py class AsNodePredDataset (line 18) | class AsNodePredDataset(DGLDataset): method __init__ (line 80) | def __init__(self, dataset, split_ratio=None, target_ntype=None, **kwa... method process (line 90) | def process(self): method has_cache (line 149) | def has_cache(self): method load (line 154) | def load(self): method save (line 176) | def save(self): method __getitem__ (line 193) | def __getitem__(self, idx): method __len__ (line 196) | def __len__(self): method _set_split_index (line 199) | def _set_split_index(self): function negative_sample (line 207) | def negative_sample(g, num_samples): class AsLinkPredDataset (line 227) | class AsLinkPredDataset(DGLDataset): method __init__ (line 273) | def __init__(self, dataset, split_ratio=None, neg_ratio=3, **kwargs): method process (line 285) | def process(self): method has_cache (line 387) | def has_cache(self): method load (line 392) | def load(self): method save (line 414) | def save(self): method feat_size (line 439) | def feat_size(self): method train_graph (line 443) | def train_graph(self): method val_edges (line 447) | def val_edges(self): method test_edges (line 451) | def test_edges(self): method __getitem__ (line 454) | def __getitem__(self, idx): method __len__ (line 457) | def __len__(self): class AsGraphPredDataset (line 461) | class AsGraphPredDataset(DGLDataset): method __init__ (line 517) | def __init__(self, dataset, split_ratio=None, **kwargs): method process (line 526) | def process(self): method has_cache (line 572) | def has_cache(self): method load (line 577) | def load(self): method save (line 598) | def save(self): method __getitem__ (line 619) | def __getitem__(self, idx): method __len__ (line 622) | def __len__(self): method node_feat_size (line 626) | def node_feat_size(self): method edge_feat_size (line 631) | def edge_feat_size(self): FILE: python/dgl/data/bitcoinotc.py class BitcoinOTCDataset (line 15) | class BitcoinOTCDataset(DGLBuiltinDataset): method __init__ (line 75) | def __init__( method download (line 87) | def download(self): method process (line 100) | def process(self): method graph_path (line 123) | def graph_path(self): method has_cache (line 126) | def has_cache(self): method save (line 129) | def save(self): method load (line 132) | def load(self): method graphs (line 136) | def graphs(self): method __len__ (line 139) | def __len__(self): method __getitem__ (line 148) | def __getitem__(self, item): method is_temporal (line 170) | def is_temporal(self): method _extract_gz (line 179) | def _extract_gz(self, file, target_dir, overwrite=False): FILE: python/dgl/data/citation_graph.py function _pickle_load (line 39) | def _pickle_load(pkl_file): class CitationGraphDataset (line 48) | class CitationGraphDataset(DGLBuiltinDataset): method __init__ (line 79) | def __init__( method process (line 109) | def process(self): method graph_path (line 221) | def graph_path(self): method info_path (line 225) | def info_path(self): method has_cache (line 228) | def has_cache(self): method save (line 234) | def save(self): method load (line 239) | def load(self): method __getitem__ (line 287) | def __getitem__(self, idx): method __len__ (line 294) | def __len__(self): method save_name (line 298) | def save_name(self): method num_labels (line 302) | def num_labels(self): method num_classes (line 307) | def num_classes(self): method reverse_edge (line 315) | def reverse_edge(self): function _preprocess_features (line 319) | def _preprocess_features(features): function _parse_index_file (line 325) | def _parse_index_file(filename): function _sample_mask (line 333) | def _sample_mask(idx, l): class CoraGraphDataset (line 340) | class CoraGraphDataset(CitationGraphDataset): method __init__ (line 407) | def __init__( method __getitem__ (line 428) | def __getitem__(self, idx): method __len__ (line 450) | def __len__(self): class CiteseerGraphDataset (line 455) | class CiteseerGraphDataset(CitationGraphDataset): method __init__ (line 525) | def __init__( method __getitem__ (line 546) | def __getitem__(self, idx): method __len__ (line 568) | def __len__(self): class PubmedGraphDataset (line 573) | class PubmedGraphDataset(CitationGraphDataset): method __init__ (line 640) | def __init__( method __getitem__ (line 661) | def __getitem__(self, idx): method __len__ (line 683) | def __len__(self): function load_cora (line 688) | def load_cora( function load_citeseer (line 723) | def load_citeseer( function load_pubmed (line 758) | def load_pubmed( class CoraBinary (line 793) | class CoraBinary(DGLBuiltinDataset): method __init__ (line 817) | def __init__( method process (line 831) | def process(self): method graph_path (line 866) | def graph_path(self): method has_cache (line 869) | def has_cache(self): method save (line 875) | def save(self): method load (line 884) | def load(self): method __len__ (line 899) | def __len__(self): method __getitem__ (line 902) | def __getitem__(self, i): method save_name (line 922) | def save_name(self): method collate_fn (line 926) | def collate_fn(cur): function _normalize (line 934) | def _normalize(mx): function _encode_onehot (line 945) | def _encode_onehot(labels): FILE: python/dgl/data/cluster.py class CLUSTERDataset (line 8) | class CLUSTERDataset(DGLBuiltinDataset): method __init__ (line 68) | def __init__( method process (line 88) | def process(self): method has_cache (line 91) | def has_cache(self): method load (line 97) | def load(self): method num_classes (line 104) | def num_classes(self): method __len__ (line 108) | def __len__(self): method __getitem__ (line 112) | def __getitem__(self, idx): FILE: python/dgl/data/csv_dataset.py class CSVDataset (line 11) | class CSVDataset(DGLDataset): method __init__ (line 73) | def __init__( method process (line 111) | def process(self): method has_cache (line 177) | def has_cache(self): method save (line 184) | def save(self): method load (line 190) | def load(self): method __getitem__ (line 196) | def __getitem__(self, i): method __len__ (line 213) | def __len__(self): FILE: python/dgl/data/csv_dataset_base.py class MetaNode (line 15) | class MetaNode(dt.BaseModel): class MetaEdge (line 24) | class MetaEdge(dt.BaseModel): class MetaGraph (line 34) | class MetaGraph(dt.BaseModel): class MetaYaml (line 41) | class MetaYaml(dt.BaseModel): function load_yaml_with_sanity_check (line 52) | def load_yaml_with_sanity_check(yaml_file): function _validate_data_length (line 86) | def _validate_data_length(data_dict): function _tensor (line 98) | def _tensor(data, dtype=None): class BaseData (line 108) | class BaseData: method read_csv (line 112) | def read_csv(file_name, base_dir, separator): method pop_from_dataframe (line 119) | def pop_from_dataframe(df: pd.DataFrame, item: str): class NodeData (line 128) | class NodeData(BaseData): method __init__ (line 131) | def __init__(self, node_id, data, type=None, graph_id=None): method load_from_csv (line 145) | def load_from_csv( method to_dict (line 162) | def to_dict(node_data: List["NodeData"]) -> dict: class EdgeData (line 194) | class EdgeData(BaseData): method __init__ (line 197) | def __init__(self, src_id, dst_id, data, type=None, graph_id=None): method load_from_csv (line 215) | def load_from_csv( method to_dict (line 239) | def to_dict(edge_data: List["EdgeData"], node_dict: dict) -> dict: class GraphData (line 271) | class GraphData(BaseData): method __init__ (line 274) | def __init__(self, graph_id, data): method load_from_csv (line 280) | def load_from_csv( method to_dict (line 295) | def to_dict(graph_data: "GraphData", graphs_dict: dict) -> dict: class DGLGraphConstructor (line 321) | class DGLGraphConstructor: method construct_graphs (line 325) | def construct_graphs(node_data, edge_data, graph_data=None): method _construct_graphs (line 339) | def _construct_graphs(node_dict, edge_dict): class DefaultDataParser (line 367) | class DefaultDataParser: method __call__ (line 375) | def __call__(self, df: pd.DataFrame): FILE: python/dgl/data/dgl_dataset.py class DGLDataset (line 15) | class DGLDataset(object): method __init__ (line 82) | def __init__( method download (line 114) | def download(self): method save (line 123) | def save(self): method load (line 134) | def load(self): method process (line 146) | def process(self): method has_cache (line 150) | def has_cache(self): method _download (line 159) | def _download(self): method _load (line 172) | def _load(self): method _get_hash (line 208) | def _get_hash(self): method _get_hash_url_suffix (line 223) | def _get_hash_url_suffix(self): method url (line 233) | def url(self): method name (line 238) | def name(self): method raw_dir (line 243) | def raw_dir(self): method raw_path (line 248) | def raw_path(self): method save_dir (line 257) | def save_dir(self): method save_path (line 262) | def save_path(self): method verbose (line 269) | def verbose(self): method hash (line 274) | def hash(self): method __getitem__ (line 279) | def __getitem__(self, idx): method __len__ (line 284) | def __len__(self): method __repr__ (line 288) | def __repr__(self): class DGLBuiltinDataset (line 295) | class DGLBuiltinDataset(DGLDataset): method __init__ (line 323) | def __init__( method download (line 344) | def download(self): FILE: python/dgl/data/fakenews.py class FakeNewsDataset (line 12) | class FakeNewsDataset(DGLBuiltinDataset): method __init__ (line 120) | def __init__(self, name, feature_name, raw_dir=None, transform=None): method process (line 138) | def process(self): method save (line 180) | def save(self): method graph_path (line 195) | def graph_path(self): method info_path (line 199) | def info_path(self): method has_cache (line 202) | def has_cache(self): method load (line 208) | def load(self): method num_classes (line 221) | def num_classes(self): method num_graphs (line 226) | def num_graphs(self): method __getitem__ (line 230) | def __getitem__(self, i): method __len__ (line 248) | def __len__(self): FILE: python/dgl/data/flickr.py class FlickrDataset (line 15) | class FlickrDataset(DGLBuiltinDataset): method __init__ (line 70) | def __init__( method process (line 89) | def process(self): method has_cache (line 132) | def has_cache(self): method save (line 136) | def save(self): method load (line 140) | def load(self): method num_classes (line 146) | def num_classes(self): method __len__ (line 149) | def __len__(self): method __getitem__ (line 153) | def __getitem__(self, idx): FILE: python/dgl/data/fraud.py class FraudDataset (line 14) | class FraudDataset(DGLBuiltinDataset): method __init__ (line 91) | def __init__( method process (line 117) | def process(self): method __getitem__ (line 147) | def __getitem__(self, idx): method __len__ (line 172) | def __len__(self): method num_classes (line 177) | def num_classes(self): method save (line 186) | def save(self): method load (line 193) | def load(self): method has_cache (line 202) | def has_cache(self): method _random_split (line 209) | def _random_split(self, x, seed=717, train_size=0.7, val_size=0.1): class FraudYelpDataset (line 241) | class FraudYelpDataset(FraudDataset): method __init__ (line 305) | def __init__( class FraudAmazonDataset (line 327) | class FraudAmazonDataset(FraudDataset): method __init__ (line 396) | def __init__( FILE: python/dgl/data/gdelt.py class GDELTDataset (line 12) | class GDELTDataset(DGLBuiltinDataset): method __init__ (line 74) | def __init__( method process (line 96) | def process(self): method info_path (line 107) | def info_path(self): method has_cache (line 110) | def has_cache(self): method save (line 113) | def save(self): method load (line 124) | def load(self): method start_time (line 134) | def start_time(self): method end_time (line 144) | def end_time(self): method __getitem__ (line 153) | def __getitem__(self, t): method __len__ (line 183) | def __len__(self): method is_temporal (line 193) | def is_temporal(self): FILE: python/dgl/data/geom_gcn.py class GeomGCNDataset (line 11) | class GeomGCNDataset(DGLBuiltinDataset): method __init__ (line 32) | def __init__(self, name, raw_dir, force_reload, verbose, transform): method process (line 43) | def process(self): method has_cache (line 85) | def has_cache(self): method load (line 88) | def load(self): method __getitem__ (line 91) | def __getitem__(self, idx): method __len__ (line 98) | def __len__(self): method num_classes (line 102) | def num_classes(self): class ChameleonDataset (line 106) | class ChameleonDataset(GeomGCNDataset): method __init__ (line 170) | def __init__( class SquirrelDataset (line 182) | class SquirrelDataset(GeomGCNDataset): method __init__ (line 246) | def __init__( class CornellDataset (line 258) | class CornellDataset(GeomGCNDataset): method __init__ (line 322) | def __init__( class TexasDataset (line 334) | class TexasDataset(GeomGCNDataset): method __init__ (line 398) | def __init__( class WisconsinDataset (line 410) | class WisconsinDataset(GeomGCNDataset): method __init__ (line 474) | def __init__( FILE: python/dgl/data/gindt.py class GINDataset (line 27) | class GINDataset(DGLBuiltinDataset): method __init__ (line 92) | def __init__( method raw_path (line 143) | def raw_path(self): method download (line 146) | def download(self): method __len__ (line 152) | def __len__(self): method __getitem__ (line 156) | def __getitem__(self, idx): method _file_path (line 175) | def _file_path(self): method process (line 184) | def process(self): method save (line 360) | def save(self): method load (line 380) | def load(self): method graph_path (line 402) | def graph_path(self): method info_path (line 408) | def info_path(self): method has_cache (line 413) | def has_cache(self): method num_classes (line 419) | def num_classes(self): FILE: python/dgl/data/gnn_benchmark.py function eliminate_self_loops (line 31) | def eliminate_self_loops(A): class GNNBenchmarkDataset (line 40) | class GNNBenchmarkDataset(DGLBuiltinDataset): method __init__ (line 46) | def __init__( method process (line 64) | def process(self): method has_cache (line 77) | def has_cache(self): method save (line 83) | def save(self): method load (line 87) | def load(self): method _print_info (line 94) | def _print_info(self): method _load_npz (line 101) | def _load_npz(self, file_name): method num_classes (line 152) | def num_classes(self): method __getitem__ (line 156) | def __getitem__(self, idx): method __len__ (line 179) | def __len__(self): class CoraFullDataset (line 184) | class CoraFullDataset(GNNBenchmarkDataset): method __init__ (line 227) | def __init__( method num_classes (line 239) | def num_classes(self): class CoauthorCSDataset (line 249) | class CoauthorCSDataset(GNNBenchmarkDataset): method __init__ (line 295) | def __init__( method num_classes (line 307) | def num_classes(self): class CoauthorPhysicsDataset (line 317) | class CoauthorPhysicsDataset(GNNBenchmarkDataset): method __init__ (line 363) | def __init__( method num_classes (line 375) | def num_classes(self): class AmazonCoBuyComputerDataset (line 385) | class AmazonCoBuyComputerDataset(GNNBenchmarkDataset): method __init__ (line 430) | def __init__( method num_classes (line 442) | def num_classes(self): class AmazonCoBuyPhotoDataset (line 452) | class AmazonCoBuyPhotoDataset(GNNBenchmarkDataset): method __init__ (line 497) | def __init__( method num_classes (line 509) | def num_classes(self): class CoraFull (line 519) | class CoraFull(CoraFullDataset): method __init__ (line 520) | def __init__(self, **kwargs): function AmazonCoBuy (line 525) | def AmazonCoBuy(name): function Coauthor (line 536) | def Coauthor(name): FILE: python/dgl/data/graph_serialize.py class StorageMetaData (line 19) | class StorageMetaData(ObjectBase): function is_local_path (line 30) | def is_local_path(filepath): function check_local_file_exists (line 38) | def check_local_file_exists(filename): class GraphData (line 44) | class GraphData(ObjectBase): method create (line 48) | def create(g): method get_graph (line 69) | def get_graph(self): function save_graphs (line 83) | def save_graphs(filename, g_list, labels=None, formats=None): function load_graphs (line 149) | def load_graphs(filename, idx_list=None): function load_graph_v2 (line 200) | def load_graph_v2(filename, idx_list=None): function load_graph_v1 (line 210) | def load_graph_v1(filename, idx_list=None): function load_labels (line 222) | def load_labels(filename): function load_labels_v2 (line 257) | def load_labels_v2(filename): function load_labels_v1 (line 266) | def load_labels_v1(filename): FILE: python/dgl/data/heterograph_serialize.py function tensor_dict_to_ndarray_dict (line 14) | def tensor_dict_to_ndarray_dict(tensor_dict): function save_heterographs (line 22) | def save_heterographs(filename, g_list, labels, formats): class HeteroGraphData (line 42) | class HeteroGraphData(ObjectBase): method create (line 46) | def create(g): method get_graph (line 57) | def get_graph(self): FILE: python/dgl/data/heterophilous_graphs.py class HeterophilousGraphDataset (line 15) | class HeterophilousGraphDataset(DGLBuiltinDataset): method __init__ (line 36) | def __init__( method download (line 55) | def download(self): method process (line 60) | def process(self): method has_cache (line 89) | def has_cache(self): method load (line 92) | def load(self): method __getitem__ (line 95) | def __getitem__(self, idx): method __len__ (line 102) | def __len__(self): method num_classes (line 106) | def num_classes(self): class RomanEmpireDataset (line 110) | class RomanEmpireDataset(HeterophilousGraphDataset): method __init__ (line 171) | def __init__( class AmazonRatingsDataset (line 183) | class AmazonRatingsDataset(HeterophilousGraphDataset): method __init__ (line 240) | def __init__( class MinesweeperDataset (line 252) | class MinesweeperDataset(HeterophilousGraphDataset): method __init__ (line 310) | def __init__( class TolokersDataset (line 322) | class TolokersDataset(HeterophilousGraphDataset): method __init__ (line 378) | def __init__( class QuestionsDataset (line 390) | class QuestionsDataset(HeterophilousGraphDataset): method __init__ (line 447) | def __init__( FILE: python/dgl/data/icews18.py class ICEWS18Dataset (line 12) | class ICEWS18Dataset(DGLBuiltinDataset): method __init__ (line 70) | def __init__( method process (line 91) | def process(self): method has_cache (line 113) | def has_cache(self): method save (line 119) | def save(self): method load (line 125) | def load(self): method __getitem__ (line 131) | def __getitem__(self, idx): method __len__ (line 152) | def __len__(self): method is_temporal (line 162) | def is_temporal(self): FILE: python/dgl/data/karate.py class KarateClubDataset (line 14) | class KarateClubDataset(DGLDataset): method __init__ (line 50) | def __init__(self, transform=None): method process (line 55) | def process(self): method num_classes (line 67) | def num_classes(self): method __getitem__ (line 71) | def __getitem__(self, idx): method __len__ (line 93) | def __len__(self): FILE: python/dgl/data/knowledge_graph.py class KnowledgeGraphDataset (line 32) | class KnowledgeGraphDataset(DGLBuiltinDataset): method __init__ (line 59) | def __init__( method download (line 80) | def download(self): method process (line 86) | def process(self): method graph_path (line 149) | def graph_path(self): method info_path (line 153) | def info_path(self): method has_cache (line 156) | def has_cache(self): method __getitem__ (line 162) | def __getitem__(self, idx): method __len__ (line 169) | def __len__(self): method save (line 172) | def save(self): method load (line 180) | def load(self): method num_nodes (line 238) | def num_nodes(self): method num_rels (line 242) | def num_rels(self): method save_name (line 246) | def save_name(self): function _read_dictionary (line 250) | def _read_dictionary(filename): function _read_triplets (line 259) | def _read_triplets(filename): function _read_triplets_as_list (line 266) | def _read_triplets_as_list(filename, entity_dict, relation_dict): function build_knowledge_graph (line 276) | def build_knowledge_graph( class FB15k237Dataset (line 413) | class FB15k237Dataset(KnowledgeGraphDataset): method __init__ (line 484) | def __init__( method __getitem__ (line 497) | def __getitem__(self, idx): method __len__ (line 522) | def __len__(self): class FB15kDataset (line 527) | class FB15kDataset(KnowledgeGraphDataset): method __init__ (line 601) | def __init__( method __getitem__ (line 614) | def __getitem__(self, idx): method __len__ (line 639) | def __len__(self): class WN18Dataset (line 644) | class WN18Dataset(KnowledgeGraphDataset): method __init__ (line 717) | def __init__( method __getitem__ (line 730) | def __getitem__(self, idx): method __len__ (line 755) | def __len__(self): function load_data (line 760) | def load_data(dataset): FILE: python/dgl/data/lrgb.py class PeptidesStructuralDataset (line 23) | class PeptidesStructuralDataset(DGLDataset): method __init__ (line 113) | def __init__( method raw_data_path (line 143) | def raw_data_path(self): method split_data_path (line 148) | def split_data_path(self): method graph_path (line 155) | def graph_path(self): method num_atom_types (line 160) | def num_atom_types(self): method num_bond_types (line 165) | def num_bond_types(self): method _md5sum (line 169) | def _md5sum(self, path): method download (line 176) | def download(self): method process (line 191) | def process(self): method load (line 237) | def load(self): method save (line 241) | def save(self): method has_cache (line 246) | def has_cache(self): method get_idx_split (line 249) | def get_idx_split(self): method __len__ (line 261) | def __len__(self): method __getitem__ (line 264) | def __getitem__(self, idx): class PeptidesFunctionalDataset (line 295) | class PeptidesFunctionalDataset(DGLDataset): method __init__ (line 376) | def __init__( method raw_data_path (line 406) | def raw_data_path(self): method split_data_path (line 411) | def split_data_path(self): method graph_path (line 418) | def graph_path(self): method num_atom_types (line 423) | def num_atom_types(self): method num_bond_types (line 428) | def num_bond_types(self): method num_classes (line 433) | def num_classes(self): method _md5sum (line 437) | def _md5sum(self, path): method download (line 444) | def download(self): method process (line 459) | def process(self): method load (line 485) | def load(self): method save (line 489) | def save(self): method has_cache (line 494) | def has_cache(self): method get_idx_split (line 497) | def get_idx_split(self): method __len__ (line 509) | def __len__(self): method __getitem__ (line 512) | def __getitem__(self, idx): class VOCSuperpixelsDataset (line 543) | class VOCSuperpixelsDataset(DGLDataset): method __init__ (line 655) | def __init__( method save_path (line 688) | def save_path(self): method raw_data_path (line 697) | def raw_data_path(self): method graph_path (line 702) | def graph_path(self): method num_classes (line 707) | def num_classes(self): method __len__ (line 711) | def __len__(self): method download (line 715) | def download(self): method process (line 730) | def process(self): method load (line 755) | def load(self): method save (line 760) | def save(self): method has_cache (line 764) | def has_cache(self): method __getitem__ (line 767) | def __getitem__(self, idx): class COCOSuperpixelsDataset (line 802) | class COCOSuperpixelsDataset(DGLDataset): method __init__ (line 911) | def __init__( method save_path (line 944) | def save_path(self): method raw_data_path (line 953) | def raw_data_path(self): method graph_path (line 958) | def graph_path(self): method num_classes (line 963) | def num_classes(self): method __len__ (line 967) | def __len__(self): method download (line 971) | def download(self): method label_remap (line 986) | def label_remap(self): method process (line 1004) | def process(self): method load (line 1039) | def load(self): method save (line 1044) | def save(self): method has_cache (line 1048) | def has_cache(self): method __getitem__ (line 1051) | def __getitem__(self, idx): FILE: python/dgl/data/minigc.py class MiniGCDataset (line 17) | class MiniGCDataset(DGLDataset): method __init__ (line 84) | def __init__( method process (line 109) | def process(self): method __len__ (line 114) | def __len__(self): method __getitem__ (line 118) | def __getitem__(self, idx): method has_cache (line 137) | def has_cache(self): method save (line 146) | def save(self): method load (line 154) | def load(self): method num_classes (line 162) | def num_classes(self): method _generate (line 166) | def _generate(self, seed): method _gen_cycle (line 183) | def _gen_cycle(self, n): method _gen_star (line 190) | def _gen_star(self, n): method _gen_wheel (line 198) | def _gen_wheel(self, n): method _gen_lollipop (line 205) | def _gen_lollipop(self, n): method _gen_hypercube (line 213) | def _gen_hypercube(self, n): method _gen_grid (line 221) | def _gen_grid(self, n): method _gen_clique (line 236) | def _gen_clique(self, n): method _gen_circular_ladder (line 243) | def _gen_circular_ladder(self, n): FILE: python/dgl/data/movielens.py function check_pytorch (line 56) | def check_pytorch(): class MovieLensDataset (line 64) | class MovieLensDataset(DGLDataset): method __init__ (line 168) | def __init__( method check_version (line 229) | def check_version(self): method download (line 252) | def download(self): method process (line 257) | def process(self): method construct_g (line 352) | def construct_g(self, rate_pairs, rate_values, user_feat, movie_feat): method add_masks (line 365) | def add_masks( method has_cache (line 422) | def has_cache(self): method save (line 431) | def save(self): method load (line 437) | def load(self): method __getitem__ (line 457) | def __getitem__(self, idx): method __len__ (line 466) | def __len__(self): method raw_path (line 470) | def raw_path(self): method graph_path (line 474) | def graph_path(self): method version_path (line 478) | def version_path(self): method _process_user_feat (line 481) | def _process_user_feat(self, user_data): method _load_raw_user_data (line 513) | def _load_raw_user_data(self): method _load_raw_movie_data (line 552) | def _load_raw_movie_data(self): method _load_raw_rates (line 600) | def _load_raw_rates(self, file_path, sep): method _drop_unseen_nodes (line 617) | def _drop_unseen_nodes(self, data_df, col_name, reserved_ids_set): method _generate_pair_value (line 622) | def _generate_pair_value(self, rating_data): method __repr__ (line 642) | def __repr__(self): FILE: python/dgl/data/pattern.py class PATTERNDataset (line 8) | class PATTERNDataset(DGLBuiltinDataset): method __init__ (line 65) | def __init__( method process (line 86) | def process(self): method graph_path (line 90) | def graph_path(self): method has_cache (line 95) | def has_cache(self): method load (line 98) | def load(self): method num_classes (line 102) | def num_classes(self): method __len__ (line 106) | def __len__(self): method __getitem__ (line 110) | def __getitem__(self, idx): FILE: python/dgl/data/ppi.py class PPIDataset (line 15) | class PPIDataset(DGLBuiltinDataset): method __init__ (line 70) | def __init__( method process (line 90) | def process(self): method graph_list_path (line 135) | def graph_list_path(self): method g_path (line 141) | def g_path(self): method info_path (line 147) | def info_path(self): method has_cache (line 150) | def has_cache(self): method save (line 157) | def save(self): method load (line 164) | def load(self): method num_labels (line 173) | def num_labels(self): method num_classes (line 177) | def num_classes(self): method __len__ (line 180) | def __len__(self): method __getitem__ (line 184) | def __getitem__(self, item): class LegacyPPIDataset (line 206) | class LegacyPPIDataset(PPIDataset): method __getitem__ (line 209) | def __getitem__(self, item): FILE: python/dgl/data/qm7b.py class QM7bDataset (line 13) | class QM7bDataset(DGLDataset): method __init__ (line 76) | def __init__( method process (line 88) | def process(self): method _load_graph (line 92) | def _load_graph(self, filename): method save (line 108) | def save(self): method has_cache (line 113) | def has_cache(self): method load (line 117) | def load(self): method download (line 124) | def download(self): method num_tasks (line 135) | def num_tasks(self): method num_labels (line 140) | def num_labels(self): method num_classes (line 145) | def num_classes(self): method __getitem__ (line 149) | def __getitem__(self, idx): method __len__ (line 167) | def __len__(self): FILE: python/dgl/data/qm9.py class QM9Dataset (line 15) | class QM9Dataset(DGLDataset): method __init__ (line 109) | def __init__( method process (line 131) | def process(self): method download (line 145) | def download(self): method num_labels (line 151) | def num_labels(self): method num_classes (line 161) | def num_classes(self): method num_tasks (line 171) | def num_tasks(self): method __getitem__ (line 180) | def __getitem__(self, idx): method __len__ (line 221) | def __len__(self): FILE: python/dgl/data/qm9_edge.py class QM9EdgeDataset (line 14) | class QM9EdgeDataset(DGLDataset): method __init__ (line 160) | def __init__( method download (line 186) | def download(self): method process (line 190) | def process(self): method npz_path (line 194) | def npz_path(self): method has_cache (line 197) | def has_cache(self): method save (line 200) | def save(self): method load (line 213) | def load(self): method __getitem__ (line 229) | def __getitem__(self, idx): method __len__ (line 276) | def __len__(self): method num_tasks (line 286) | def num_tasks(self): FILE: python/dgl/data/rdf.py class Entity (line 39) | class Entity: method __init__ (line 49) | def __init__(self, e_id, cls): method __str__ (line 53) | def __str__(self): class Relation (line 57) | class Relation: method __init__ (line 65) | def __init__(self, cls): method __str__ (line 68) | def __str__(self): class RDFGraphDataset (line 72) | class RDFGraphDataset(DGLBuiltinDataset): method __init__ (line 118) | def __init__( method process (line 143) | def process(self): method load_raw_tuples (line 147) | def load_raw_tuples(self, root_path): method process_raw_tuples (line 176) | def process_raw_tuples(self, raw_tuples, root_path): method build_graph (line 285) | def build_graph(self, mg, src, dst, ntid, etid, ntypes, etypes): method load_data_split (line 337) | def load_data_split(self, ent2id, root_path): method parse_idx_file (line 375) | def parse_idx_file(self, filename, ent2id, label_dict, labels): method has_cache (line 414) | def has_cache(self): method save (line 423) | def save(self): method load (line 436) | def load(self): method __getitem__ (line 452) | def __getitem__(self, idx): method __len__ (line 459) | def __len__(self): method save_name (line 464) | def save_name(self): method predict_category (line 468) | def predict_category(self): method num_classes (line 472) | def num_classes(self): method parse_entity (line 476) | def parse_entity(self, term): method parse_relation (line 492) | def parse_relation(self, term): method process_tuple (line 508) | def process_tuple(self, raw_tuple, sbj, rel, obj): method process_idx_file_line (line 531) | def process_idx_file_line(self, line): function _get_id (line 545) | def _get_id(dict, key): class AIFBDataset (line 553) | class AIFBDataset(RDFGraphDataset): method __init__ (line 611) | def __init__( method __getitem__ (line 643) | def __getitem__(self, idx): method __len__ (line 663) | def __len__(self): method parse_entity (line 672) | def parse_entity(self, term): method parse_relation (line 686) | def parse_relation(self, term): method process_tuple (line 696) | def process_tuple(self, raw_tuple, sbj, rel, obj): method process_idx_file_line (line 701) | def process_idx_file_line(self, line): class MUTAGDataset (line 706) | class MUTAGDataset(RDFGraphDataset): method __init__ (line 765) | def __init__( method __getitem__ (line 804) | def __getitem__(self, idx): method __len__ (line 824) | def __len__(self): method parse_entity (line 833) | def parse_entity(self, term): method parse_relation (line 853) | def parse_relation(self, term): method process_tuple (line 864) | def process_tuple(self, raw_tuple, sbj, rel, obj): method process_idx_file_line (line 879) | def process_idx_file_line(self, line): class BGSDataset (line 884) | class BGSDataset(RDFGraphDataset): method __init__ (line 945) | def __init__( method __getitem__ (line 974) | def __getitem__(self, idx): method __len__ (line 994) | def __len__(self): method parse_entity (line 1003) | def parse_entity(self, term): method parse_relation (line 1024) | def parse_relation(self, term): method process_tuple (line 1039) | def process_tuple(self, raw_tuple, sbj, rel, obj): method process_idx_file_line (line 1044) | def process_idx_file_line(self, line): class AMDataset (line 1049) | class AMDataset(RDFGraphDataset): method __init__ (line 1111) | def __init__( method __getitem__ (line 1143) | def __getitem__(self, idx): method __len__ (line 1163) | def __len__(self): method parse_entity (line 1172) | def parse_entity(self, term): method parse_relation (line 1194) | def parse_relation(self, term): method process_tuple (line 1207) | def process_tuple(self, raw_tuple, sbj, rel, obj): method process_idx_file_line (line 1212) | def process_idx_file_line(self, line): FILE: python/dgl/data/reddit.py class RedditDataset (line 24) | class RedditDataset(DGLBuiltinDataset): method __init__ (line 86) | def __init__( method process (line 108) | def process(self): method has_cache (line 143) | def has_cache(self): method save (line 149) | def save(self): method load (line 153) | def load(self): method _print_info (line 168) | def _print_info(self): method num_classes (line 192) | def num_classes(self): method __getitem__ (line 196) | def __getitem__(self, idx): method __len__ (line 221) | def __len__(self): FILE: python/dgl/data/sbm.py function sbm (line 16) | def sbm(n_blocks, block_size, p, q, rng=None): class SBMMixtureDataset (line 66) | class SBMMixtureDataset(DGLDataset): method __init__ (line 106) | def __init__( method process (line 130) | def process(self): method graph_path (line 152) | def graph_path(self): method line_graph_path (line 156) | def line_graph_path(self): method info_path (line 162) | def info_path(self): method has_cache (line 165) | def has_cache(self): method save (line 172) | def save(self): method load (line 184) | def load(self): method __len__ (line 192) | def __len__(self): method __getitem__ (line 196) | def __getitem__(self, idx): method _appendix_c (line 225) | def _appendix_c(self): method collate_fn (line 233) | def collate_fn(self, x): FILE: python/dgl/data/superpixel.py function sigma (line 15) | def sigma(dists, kth=8): function compute_adjacency_matrix_images (line 30) | def compute_adjacency_matrix_images(coord, feat, use_feat=True): function compute_edges_list (line 51) | def compute_edges_list(A, kth=9): class SuperPixelDataset (line 78) | class SuperPixelDataset(DGLDataset): method __init__ (line 79) | def __init__( method img_size (line 110) | def img_size(self): method save_path (line 117) | def save_path(self): method raw_data_path (line 122) | def raw_data_path(self): method graph_path (line 127) | def graph_path(self): method download (line 138) | def download(self): method process (line 142) | def process(self): method load (line 224) | def load(self): method save (line 228) | def save(self): method has_cache (line 233) | def has_cache(self): method __len__ (line 236) | def __len__(self): method __getitem__ (line 239) | def __getitem__(self, idx): class MNISTSuperPixelDataset (line 270) | class MNISTSuperPixelDataset(SuperPixelDataset): method __init__ (line 334) | def __init__( class CIFAR10SuperPixelDataset (line 354) | class CIFAR10SuperPixelDataset(SuperPixelDataset): method __init__ (line 418) | def __init__( FILE: python/dgl/data/synthetic.py class BAShapeDataset (line 18) | class BAShapeDataset(DGLBuiltinDataset): method __init__ (line 76) | def __init__( method process (line 102) | def process(self): method graph_path (line 169) | def graph_path(self): method save (line 174) | def save(self): method has_cache (line 177) | def has_cache(self): method load (line 180) | def load(self): method __getitem__ (line 184) | def __getitem__(self, idx): method __len__ (line 191) | def __len__(self): method num_classes (line 195) | def num_classes(self): class BACommunityDataset (line 199) | class BACommunityDataset(DGLBuiltinDataset): method __init__ (line 262) | def __init__( method process (line 290) | def process(self): method graph_path (line 351) | def graph_path(self): method save (line 356) | def save(self): method has_cache (line 359) | def has_cache(self): method load (line 362) | def load(self): method __getitem__ (line 366) | def __getitem__(self, idx): method __len__ (line 373) | def __len__(self): method num_classes (line 377) | def num_classes(self): class TreeCycleDataset (line 381) | class TreeCycleDataset(DGLBuiltinDataset): method __init__ (line 437) | def __init__( method process (line 463) | def process(self): method graph_path (line 529) | def graph_path(self): method save (line 534) | def save(self): method has_cache (line 537) | def has_cache(self): method load (line 540) | def load(self): method __getitem__ (line 544) | def __getitem__(self, idx): method __len__ (line 551) | def __len__(self): method num_classes (line 555) | def num_classes(self): class TreeGridDataset (line 559) | class TreeGridDataset(DGLBuiltinDataset): method __init__ (line 615) | def __init__( method process (line 641) | def process(self): method graph_path (line 704) | def graph_path(self): method save (line 709) | def save(self): method has_cache (line 712) | def has_cache(self): method load (line 715) | def load(self): method __getitem__ (line 719) | def __getitem__(self, idx): method __len__ (line 726) | def __len__(self): method num_classes (line 730) | def num_classes(self): class BA2MotifDataset (line 734) | class BA2MotifDataset(DGLBuiltinDataset): method __init__ (line 776) | def __init__( method download (line 788) | def download(self): method process (line 793) | def process(self): method graph_path (line 807) | def graph_path(self): method save (line 812) | def save(self): method has_cache (line 816) | def has_cache(self): method load (line 819) | def load(self): method __getitem__ (line 823) | def __getitem__(self, idx): method __len__ (line 829) | def __len__(self): method num_classes (line 833) | def num_classes(self): FILE: python/dgl/data/tensor_serialize.py function save_tensors (line 13) | def save_tensors(filename, tensor_dict): function load_tensors (line 46) | def load_tensors(filename, return_dgl_ndarray=False): FILE: python/dgl/data/tree.py class SSTDataset (line 31) | class SSTDataset(DGLBuiltinDataset): method __init__ (line 116) | def __init__( method process (line 140) | def process(self): method _build_tree (line 201) | def _build_tree(self, root): method graph_path (line 225) | def graph_path(self): method vocab_path (line 229) | def vocab_path(self): method has_cache (line 232) | def has_cache(self): method save (line 237) | def save(self): method load (line 244) | def load(self): method vocab (line 254) | def vocab(self): method pretrained_emb (line 264) | def pretrained_emb(self): method __getitem__ (line 268) | def __getitem__(self, idx): method __len__ (line 290) | def __len__(self): method vocab_size (line 295) | def vocab_size(self): method num_classes (line 300) | def num_classes(self): FILE: python/dgl/data/tu.py class LegacyTUDataset (line 14) | class LegacyTUDataset(DGLBuiltinDataset): method __init__ (line 84) | def __init__( method process (line 109) | def process(self): method save (line 216) | def save(self): method load (line 225) | def load(self): method graph_path (line 235) | def graph_path(self): method info_path (line 241) | def info_path(self): method has_cache (line 246) | def has_cache(self): method __getitem__ (line 251) | def __getitem__(self, idx): method __len__ (line 270) | def __len__(self): method _file_path (line 274) | def _file_path(self, category): method _idx_from_zero (line 280) | def _idx_from_zero(idx_tensor): method _to_onehot (line 284) | def _to_onehot(label_tensor): method statistics (line 291) | def statistics(self): method num_classes (line 299) | def num_classes(self): class TUDataset (line 303) | class TUDataset(DGLBuiltinDataset): method __init__ (line 375) | def __init__( method process (line 393) | def process(self): method graph_path (line 453) | def graph_path(self): method info_path (line 457) | def info_path(self): method save (line 460) | def save(self): method load (line 469) | def load(self): method has_cache (line 478) | def has_cache(self): method __getitem__ (line 483) | def __getitem__(self, idx): method __len__ (line 502) | def __len__(self): method _file_path (line 506) | def _file_path(self, category): method _idx_from_zero (line 512) | def _idx_from_zero(idx_tensor): method _idx_reset (line 516) | def _idx_reset(idx_tensor): method statistics (line 523) | def statistics(self): method num_classes (line 531) | def num_classes(self): FILE: python/dgl/data/utils.py function loadtxt (line 40) | def loadtxt(path, delimiter, dtype=None): function _get_dgl_url (line 54) | def _get_dgl_url(file_url): function split_dataset (line 63) | def split_dataset(dataset, frac_list=None, shuffle=False, random_state=N... function download (line 110) | def download( function check_sha1 (line 219) | def check_sha1(filename, sha1_hash): function extract_archive (line 247) | def extract_archive(file, target_dir, overwrite=True): function get_download_dir (line 305) | def get_download_dir(): function makedirs (line 320) | def makedirs(path): function save_info (line 328) | def save_info(path, info): function load_info (line 342) | def load_info(path): function deprecate_property (line 360) | def deprecate_property(old, new): function deprecate_function (line 368) | def deprecate_function(old, new): function deprecate_class (line 376) | def deprecate_class(old, new): function idx2mask (line 382) | def idx2mask(idx, len): function generate_mask_tensor (line 389) | def generate_mask_tensor(mask): class Subset (line 407) | class Subset(object): method __init__ (line 420) | def __init__(self, dataset, indices): method __getitem__ (line 424) | def __getitem__(self, item): method __len__ (line 434) | def __len__(self): function add_nodepred_split (line 445) | def add_nodepred_split(dataset, ratio, ntype=None): function mask_nodes_by_property (line 498) | def mask_nodes_by_property(property_values, part_ratios, random_seed=None): function add_node_property_split (line 574) | def add_node_property_split( function _compute_popularity_property (line 651) | def _compute_popularity_property(graph_nx, ascending=True): function _compute_locality_property (line 657) | def _compute_locality_property(graph_nx, ascending=True): function _compute_density_property (line 671) | def _compute_density_property(graph_nx, ascending=True): FILE: python/dgl/data/wikics.py class WikiCSDataset (line 15) | class WikiCSDataset(DGLBuiltinDataset): method __init__ (line 78) | def __init__( method process (line 91) | def process(self): method has_cache (line 126) | def has_cache(self): method save (line 130) | def save(self): method load (line 134) | def load(self): method num_classes (line 140) | def num_classes(self): method __len__ (line 143) | def __len__(self): method __getitem__ (line 147) | def __getitem__(self, idx): FILE: python/dgl/data/yelp.py class YelpDataset (line 15) | class YelpDataset(DGLBuiltinDataset): method __init__ (line 69) | def __init__( method process (line 88) | def process(self): method has_cache (line 131) | def has_cache(self): method save (line 135) | def save(self): method load (line 139) | def load(self): method num_classes (line 145) | def num_classes(self): method __len__ (line 148) | def __len__(self): method __getitem__ (line 152) | def __getitem__(self, idx): FILE: python/dgl/data/zinc.py class ZINCDataset (line 7) | class ZINCDataset(DGLBuiltinDataset): method __init__ (line 70) | def __init__( method process (line 90) | def process(self): method graph_path (line 94) | def graph_path(self): method has_cache (line 97) | def has_cache(self): method load (line 100) | def load(self): method num_atom_types (line 104) | def num_atom_types(self): method num_bond_types (line 108) | def num_bond_types(self): method __len__ (line 111) | def __len__(self): method __getitem__ (line 114) | def __getitem__(self, idx): FILE: python/dgl/dataloading/base.py function _set_lazy_features (line 13) | def _set_lazy_features(x, xdata, feature_names): function set_node_lazy_features (line 23) | def set_node_lazy_features(g, feature_names): function set_edge_lazy_features (line 58) | def set_edge_lazy_features(g, feature_names): function set_src_lazy_features (line 94) | def set_src_lazy_features(g, feature_names): function set_dst_lazy_features (line 129) | def set_dst_lazy_features(g, feature_names): class Sampler (line 164) | class Sampler(object): method sample (line 182) | def sample(self, g, indices): class BlockSampler (line 195) | class BlockSampler(Sampler): method __init__ (line 224) | def __init__( method sample_blocks (line 237) | def sample_blocks(self, g, seed_nodes, exclude_eids=None): method assign_lazy_features (line 247) | def assign_lazy_features(self, result): method sample (line 256) | def sample( function _find_exclude_eids_with_reverse_id (line 264) | def _find_exclude_eids_with_reverse_id(g, eids, reverse_eid_map): function _find_exclude_eids_with_reverse_types (line 276) | def _find_exclude_eids_with_reverse_types(g, eids, reverse_etype_map): function _find_exclude_eids (line 293) | def _find_exclude_eids(g, exclude_mode, eids, **kwargs): function find_exclude_eids (line 317) | def find_exclude_eids( class EdgePredictionSampler (line 386) | class EdgePredictionSampler(Sampler): method __init__ (line 395) | def __init__( method _build_neg_graph (line 420) | def _build_neg_graph(self, g, seed_edges): method assign_lazy_features (line 446) | def assign_lazy_features(self, result): method sample (line 453) | def sample(self, g, seed_edges): # pylint: disable=arguments-differ function as_edge_prediction_sampler (line 500) | def as_edge_prediction_sampler( FILE: python/dgl/dataloading/capped_neighbor_sampler.py class CappedNeighborSampler (line 11) | class CappedNeighborSampler(Sampler): method __init__ (line 44) | def __init__( method sample (line 65) | def sample( FILE: python/dgl/dataloading/cluster_gcn.py class ClusterGCNSampler (line 13) | class ClusterGCNSampler(Sampler): method __init__ (line 65) | def __init__( method sample (line 126) | def sample(self, g, partition_ids): # pylint: disable=arguments-differ FILE: python/dgl/dataloading/dataloader.py function _set_python_exit_flag (line 44) | def _set_python_exit_flag(): class _TensorizedDatasetIter (line 54) | class _TensorizedDatasetIter(object): method __init__ (line 55) | def __init__(self, dataset, batch_size, drop_last, mapping_keys, shuff... method __iter__ (line 64) | def __iter__(self): method _next_indices (line 67) | def _next_indices(self): method __next__ (line 81) | def __next__(self): function _get_id_tensor_from_mapping (line 108) | def _get_id_tensor_from_mapping(indices, device, keys): function _split_to_local_id_tensor_from_mapping (line 126) | def _split_to_local_id_tensor_from_mapping( function _split_to_local_id_tensor (line 158) | def _split_to_local_id_tensor(indices, local_lower_bound, local_upper_bo... function _divide_by_worker (line 172) | def _divide_by_worker(dataset, batch_size, drop_last): class TensorizedDataset (line 191) | class TensorizedDataset(torch.utils.data.IterableDataset): method __init__ (line 196) | def __init__( method shuffle (line 221) | def shuffle(self): method __iter__ (line 225) | def __iter__(self): method __len__ (line 238) | def __len__(self): function _decompose_one_dimension (line 245) | def _decompose_one_dimension(length, world_size, rank, drop_last): class DDPTensorizedDataset (line 255) | class DDPTensorizedDataset(torch.utils.data.IterableDataset): method __init__ (line 263) | def __init__(self, indices, batch_size, drop_last, ddp_seed, shuffle): method shuffle (line 314) | def shuffle(self): method __iter__ (line 323) | def __iter__(self): method __len__ (line 336) | def __len__(self): function _numel_of_shape (line 342) | def _numel_of_shape(shape): function _init_gpu_caches (line 346) | def _init_gpu_caches(graph, gpu_caches): function _prefetch_update_feats (line 372) | def _prefetch_update_feats( class _PrefetchedGraphFeatures (line 418) | class _PrefetchedGraphFeatures(object): method __init__ (line 421) | def __init__(self, node_feats, edge_feats): function _prefetch_for_subgraph (line 426) | def _prefetch_for_subgraph(subg, dataloader): function _prefetch_for (line 451) | def _prefetch_for(item, dataloader): function _await_or_return (line 462) | def _await_or_return(x): function _record_stream (line 473) | def _record_stream(x, stream): function _prefetch (line 487) | def _prefetch(batch, dataloader, stream): function _assign_for (line 516) | def _assign_for(item, feat): function _put_if_event_not_set (line 532) | def _put_if_event_not_set(queue, result, event): function _prefetcher_entry (line 541) | def _prefetcher_entry( function remove_parent_storage_columns (line 576) | def remove_parent_storage_columns(item, g): function restore_parent_storage_columns (line 600) | def restore_parent_storage_columns(item, g): class _PrefetchingIter (line 623) | class _PrefetchingIter(object): method __init__ (line 624) | def __init__(self, dataloader, dataloader_it, num_threads=None): method __iter__ (line 655) | def __iter__(self): method _shutdown (line 658) | def _shutdown(self): method __del__ (line 679) | def __del__(self): method _next_non_threaded (line 683) | def _next_non_threaded(self): method _next_threaded (line 693) | def _next_threaded(self): method __next__ (line 709) | def __next__(self): class CollateWrapper (line 722) | class CollateWrapper(object): method __init__ (line 727) | def __init__(self, sample_func, g, use_uva, device): method __call__ (line 733) | def __call__(self, items): class WorkerInitWrapper (line 743) | class WorkerInitWrapper(object): method __init__ (line 748) | def __init__(self, func): method __call__ (line 751) | def __call__(self, worker_id): function create_tensorized_dataset (line 757) | def create_tensorized_dataset( function _get_device (line 781) | def _get_device(device): class DataLoader (line 788) | class DataLoader(torch.utils.data.DataLoader): method __init__ (line 925) | def __init__( method __iter__ (line 1137) | def __iter__(self): method enable_cpu_affinity (line 1159) | def enable_cpu_affinity( method attach_data (line 1258) | def attach_data(self, name, data): function _create_dist_sampler (line 1268) | def _create_dist_sampler(dataset, dataloader_kwargs, ddp_seed): class GraphCollator (line 1279) | class GraphCollator(object): method __init__ (line 1298) | def __init__(self): method collate (line 1306) | def collate(self, items): class GraphDataLoader (line 1376) | class GraphDataLoader(torch.utils.data.DataLoader): method __init__ (line 1433) | def __init__( method set_epoch (line 1458) | def set_epoch(self, epoch): class NodeCollator (line 1477) | class NodeCollator: method __new__ (line 1480) | def __new__(cls, *args, **kwargs): class EdgeCollator (line 1491) | class EdgeCollator: method __new__ (line 1494) | def __new__(cls, *args, **kwargs): function _remove_kwargs_dist (line 1505) | def _remove_kwargs_dist(kwargs): class DistDataLoader (line 1515) | class DistDataLoader: method __new__ (line 1518) | def __new__(cls, *args, **kwargs): class DistNodeDataLoader (line 1529) | class DistNodeDataLoader: method __new__ (line 1534) | def __new__(cls, *args, **kwargs): class DistEdgeDataLoader (line 1546) | class DistEdgeDataLoader: method __new__ (line 1551) | def __new__(cls, *args, **kwargs): FILE: python/dgl/dataloading/graphsaint.py class SAINTSampler (line 13) | class SAINTSampler(Sampler): method __init__ (line 70) | def __init__( method node_sampler (line 98) | def node_sampler(self, g): method edge_sampler (line 115) | def edge_sampler(self, g): method walk_sampler (line 134) | def walk_sampler(self, g): method sample (line 142) | def sample(self, g, indices): FILE: python/dgl/dataloading/labor_sampler.py class LaborSampler (line 29) | class LaborSampler(BlockSampler): method __init__ (line 141) | def __init__( method set_seed (line 173) | def set_seed(self, random_seed=None): method sample_blocks (line 218) | def sample_blocks(self, g, seed_nodes, exclude_eids=None): FILE: python/dgl/dataloading/negative_sampler.py class _BaseNegativeSampler (line 7) | class _BaseNegativeSampler(object): method _generate (line 8) | def _generate(self, g, eids, canonical_etype): method __call__ (line 11) | def __call__(self, g, eids): class PerSourceUniform (line 38) | class PerSourceUniform(_BaseNegativeSampler): method __init__ (line 60) | def __init__(self, k): method _generate (line 63) | def _generate(self, g, eids, canonical_etype): class GlobalUniform (line 79) | class GlobalUniform(_BaseNegativeSampler): method __init__ (line 115) | def __init__(self, k, exclude_self_loops=True, replace=False): method _generate (line 120) | def _generate(self, g, eids, canonical_etype): FILE: python/dgl/dataloading/neighbor_sampler.py class NeighborSampler (line 11) | class NeighborSampler(BlockSampler): method __init__ (line 119) | def __init__( method sample_blocks (line 152) | def sample_blocks(self, g, seed_nodes, exclude_eids=None): class MultiLayerFullNeighborSampler (line 212) | class MultiLayerFullNeighborSampler(NeighborSampler): method __init__ (line 245) | def __init__(self, num_layers, **kwargs): FILE: python/dgl/dataloading/shadow.py class ShaDowKHopSampler (line 8) | class ShaDowKHopSampler(Sampler): method __init__ (line 73) | def __init__( method sample (line 90) | def sample( FILE: python/dgl/dataloading/spot_target.py class SpotTarget (line 7) | class SpotTarget(object): method __init__ (line 60) | def __init__( method __call__ (line 74) | def __call__(self, seed_edges): FILE: python/dgl/distgnn/partition/libra_partition.py function libra_partition (line 38) | def libra_partition(num_community, G, resultdir): function partition_graph (line 279) | def partition_graph(num_community, G, resultdir): FILE: python/dgl/distgnn/tools/tools.py function rep_per_node (line 20) | def rep_per_node(prefix, num_community): function download_proteins (line 57) | def download_proteins(): function proteins_mtx2dgl (line 77) | def proteins_mtx2dgl(): function save (line 127) | def save(g, dataset): function load_proteins (line 145) | def load_proteins(dataset): FILE: python/dgl/distributed/dist_context.py function set_initialized (line 27) | def set_initialized(value=True): function get_sampler_pool (line 33) | def get_sampler_pool(): function _init_rpc (line 38) | def _init_rpc( class MpCommand (line 59) | class MpCommand(Enum): function init_process (line 71) | def init_process(rpc_config, mp_contexts): class CustomPool (line 114) | class CustomPool: method __init__ (line 117) | def __init__(self, num_workers, rpc_config): method set_collate_fn (line 147) | def set_collate_fn(self, func, dataloader_name): method submit_task (line 155) | def submit_task(self, dataloader_name, args): method submit_task_to_all_workers (line 163) | def submit_task_to_all_workers(self, func, args): method get_result (line 170) | def get_result(self, dataloader_name, timeout=1800): method delete_collate_fn (line 181) | def delete_collate_fn(self, dataloader_name): method call_barrier (line 189) | def call_barrier(self): method close (line 194) | def close(self): method join (line 202) | def join(self): function initialize (line 208) | def initialize( function finalize_client (line 322) | def finalize_client(): function _exit (line 329) | def _exit(): function finalize_worker (line 334) | def finalize_worker(): function join_finalize_worker (line 343) | def join_finalize_worker(): function is_initialized (line 351) | def is_initialized(): function _shutdown_servers (line 356) | def _shutdown_servers(): function exit_client (line 365) | def exit_client(): FILE: python/dgl/distributed/dist_dataloader.py class DistDataLoader (line 23) | class DistDataLoader: method __init__ (line 76) | def __init__( method __del__ (line 114) | def __del__(self): method __next__ (line 121) | def __next__(self): method _get_data_from_result_queue (line 137) | def _get_data_from_result_queue(self, timeout=1800): method __iter__ (line 144) | def __iter__(self): method _request_next_batch (line 152) | def _request_next_batch(self): method _next_data (line 163) | def _next_data(self): function _find_exclude_eids_with_reverse_id (line 192) | def _find_exclude_eids_with_reverse_id(g, eids, reverse_eid_map): function _find_exclude_eids_with_reverse_types (line 204) | def _find_exclude_eids_with_reverse_types(g, eids, reverse_etype_map): function _find_exclude_eids (line 216) | def _find_exclude_eids(g, exclude_mode, eids, **kwargs): class Collator (line 276) | class Collator(ABC): method dataset (line 292) | def dataset(self): method collate (line 297) | def collate(self, items): method add_edge_attribute_to_graph (line 314) | def add_edge_attribute_to_graph(g, data_name, gb_padding): class NodeCollator (line 337) | class NodeCollator(Collator): method __init__ (line 376) | def __init__(self, g, nids, graph_sampler, gb_padding=1): method dataset (line 394) | def dataset(self): method collate (line 397) | def collate(self, items): class EdgeCollator (line 434) | class EdgeCollator(Collator): method __init__ (line 615) | def __init__( method dataset (line 660) | def dataset(self): method _collate (line 663) | def _collate(self, items): method _collate_with_negative_sampling (line 686) | def _collate_with_negative_sampling(self, items): method collate (line 741) | def collate(self, items): function _remove_kwargs_dist (line 783) | def _remove_kwargs_dist(kwargs): class DistNodeDataLoader (line 792) | class DistNodeDataLoader(DistDataLoader): method __init__ (line 816) | def __init__(self, g, nids, graph_sampler, device=None, **kwargs): class DistEdgeDataLoader (line 843) | class DistEdgeDataLoader(DistDataLoader): method __init__ (line 868) | def __init__(self, g, eids, graph_sampler, device=None, **kwargs): FILE: python/dgl/distributed/dist_graph.py class InitGraphRequest (line 59) | class InitGraphRequest(rpc.Request): method __init__ (line 67) | def __init__(self, graph_name): method __getstate__ (line 70) | def __getstate__(self): method __setstate__ (line 73) | def __setstate__(self, state): method process_request (line 76) | def process_request(self, server_state): class InitGraphResponse (line 84) | class InitGraphResponse(rpc.Response): method __init__ (line 87) | def __init__(self, graph_name): method __getstate__ (line 90) | def __getstate__(self): method __setstate__ (line 93) | def __setstate__(self, state): class QueryIfUseGraphBoltRequest (line 97) | class QueryIfUseGraphBoltRequest(rpc.Request): method __getstate__ (line 100) | def __getstate__(self): method __setstate__ (line 103) | def __setstate__(self, state): method process_request (line 106) | def process_request(self, server_state): class QueryIfUseGraphBoltResponse (line 110) | class QueryIfUseGraphBoltResponse(rpc.Response): method __init__ (line 113) | def __init__(self, use_graphbolt): method __getstate__ (line 116) | def __getstate__(self): method __setstate__ (line 119) | def __setstate__(self, state): function _copy_data_to_shared_mem (line 123) | def _copy_data_to_shared_mem(data, name): function _copy_data_from_shared_mem (line 135) | def _copy_data_from_shared_mem(name, shape): class AddEdgeAttributeFromKVRequest (line 143) | class AddEdgeAttributeFromKVRequest(rpc.Request): method __init__ (line 146) | def __init__(self, name, kv_names, padding): method __getstate__ (line 151) | def __getstate__(self): method __setstate__ (line 154) | def __setstate__(self, state): method process_request (line 157) | def process_request(self, server_state): class AddEdgeAttributeFromKVResponse (line 201) | class AddEdgeAttributeFromKVResponse(rpc.Response): method __init__ (line 204) | def __init__(self, name): method __getstate__ (line 207) | def __getstate__(self): method __setstate__ (line 210) | def __setstate__(self, state): class AddEdgeAttributeFromSharedMemRequest (line 214) | class AddEdgeAttributeFromSharedMemRequest(rpc.Request): method __init__ (line 217) | def __init__(self, name): method __getstate__ (line 220) | def __getstate__(self): method __setstate__ (line 223) | def __setstate__(self, state): method process_request (line 226) | def process_request(self, server_state): class AddEdgeAttributeFromSharedMemResponse (line 237) | class AddEdgeAttributeFromSharedMemResponse(rpc.Response): method __init__ (line 240) | def __init__(self, name): method __getstate__ (line 243) | def __getstate__(self): method __setstate__ (line 246) | def __setstate__(self, state): function _copy_graph_to_shared_mem (line 250) | def _copy_graph_to_shared_mem(g, graph_name, graph_format, use_graphbolt): function _get_shared_mem_ndata (line 279) | def _get_shared_mem_ndata(g, graph_name, name): function _get_shared_mem_edata (line 295) | def _get_shared_mem_edata(g, graph_name, name): function _exist_shared_mem_array (line 311) | def _exist_shared_mem_array(graph_name, name): function _get_graph_from_shared_mem (line 315) | def _get_graph_from_shared_mem(graph_name, use_graphbolt): class HeteroNodeView (line 347) | class HeteroNodeView(object): method __init__ (line 352) | def __init__(self, graph): method __getitem__ (line 355) | def __getitem__(self, key): class HeteroEdgeView (line 360) | class HeteroEdgeView(object): method __init__ (line 365) | def __init__(self, graph): method __getitem__ (line 368) | def __getitem__(self, key): class NodeDataView (line 375) | class NodeDataView(MutableMapping): method __init__ (line 380) | def __init__(self, g, ntype=None): method _get_names (line 389) | def _get_names(self): method __getitem__ (line 392) | def __getitem__(self, key): method __setitem__ (line 395) | def __setitem__(self, key, val): method __delitem__ (line 398) | def __delitem__(self, key): method __len__ (line 401) | def __len__(self): method __iter__ (line 406) | def __iter__(self): method __repr__ (line 409) | def __repr__(self): class EdgeDataView (line 420) | class EdgeDataView(MutableMapping): method __init__ (line 425) | def __init__(self, g, etype=None): method _get_names (line 433) | def _get_names(self): method __getitem__ (line 436) | def __getitem__(self, key): method __setitem__ (line 439) | def __setitem__(self, key, val): method __delitem__ (line 442) | def __delitem__(self, key): method __len__ (line 445) | def __len__(self): method __iter__ (line 450) | def __iter__(self): method __repr__ (line 453) | def __repr__(self): function _format_partition (line 464) | def _format_partition(graph, graph_format): class DistGraphServer (line 488) | class DistGraphServer(KVServer): method __init__ (line 524) | def __init__( method start (line 625) | def start(self): class DistGraph (line 648) | class DistGraph: method __init__ (line 722) | def __init__(self, graph_name, gpb=None, part_config=None): method _init (line 781) | def _init(self, gpb): method _init_ndata_store (line 794) | def _init_ndata_store(self): method _init_edata_store (line 819) | def _init_edata_store(self): method _init_metadata (line 844) | def _init_metadata(self): method __getstate__ (line 857) | def __getstate__(self): method __setstate__ (line 865) | def __setstate__(self, state): method local_partition (line 888) | def local_partition(self): method nodes (line 904) | def nodes(self): method edges (line 909) | def edges(self): method ndata (line 914) | def ndata(self): method edata (line 928) | def edata(self): method idtype (line 942) | def idtype(self): method device (line 964) | def device(self): method is_pinned (line 987) | def is_pinned(self): method ntypes (line 999) | def ntypes(self): method etypes (line 1016) | def etypes(self): method canonical_etypes (line 1033) | def canonical_etypes(self): method to_canonical_etype (line 1068) | def to_canonical_etype(self, etype): method get_ntype_id (line 1113) | def get_ntype_id(self, ntype): method get_etype_id (line 1137) | def get_etype_id(self, etype): method number_of_nodes (line 1162) | def number_of_nodes(self, ntype=None): method number_of_edges (line 1166) | def number_of_edges(self, etype=None): method num_nodes (line 1170) | def num_nodes(self, ntype=None): method num_edges (line 1199) | def num_edges(self, etype=None): method out_degrees (line 1234) | def out_degrees(self, u=ALL): method in_degrees (line 1283) | def in_degrees(self, v=ALL): method node_attr_schemes (line 1332) | def node_attr_schemes(self): method edge_attr_schemes (line 1359) | def edge_attr_schemes(self): method rank (line 1386) | def rank(self): method find_edges (line 1399) | def find_edges(self, edges, etype=None): method edge_subgraph (line 1440) | def edge_subgraph(self, edges, relabel_nodes=True, store_ids=True): method get_partition_book (line 1497) | def get_partition_book(self): method get_node_partition_policy (line 1507) | def get_node_partition_policy(self, ntype): method get_edge_partition_policy (line 1528) | def get_edge_partition_policy(self, etype): method barrier (line 1550) | def barrier(self): method sample_neighbors (line 1558) | def sample_neighbors( method _get_ndata_names (line 1602) | def _get_ndata_names(self, ntype=None): method _get_edata_names (line 1615) | def _get_edata_names(self, etype=None): method add_edge_attribute (line 1630) | def add_edge_attribute(self, name, padding): function _get_overlap (line 1683) | def _get_overlap(mask_arr, ids): function _split_local (line 1710) | def _split_local(partition_book, rank, elements, local_eles): function _even_offset (line 1736) | def _even_offset(n, k): function _split_even_to_part (line 1746) | def _split_even_to_part(partition_book, elements): function _split_random_within_part (line 1788) | def _split_random_within_part(partition_book, rank, part_eles): function _split_by_trainer_id (line 1819) | def _split_by_trainer_id( function node_split (line 1874) | def node_split( function edge_split (line 1973) | def edge_split( FILE: python/dgl/distributed/dist_tensor.py function _default_init_data (line 13) | def _default_init_data(shape, dtype): class DistTensor (line 21) | class DistTensor: method __init__ (line 111) | def __init__( method __del__ (line 194) | def __del__(self): method __getitem__ (line 202) | def __getitem__(self, idx): method __setitem__ (line 207) | def __setitem__(self, idx, val): method kvstore_key (line 214) | def kvstore_key(self): method local_partition (line 219) | def local_partition(self): method __or__ (line 223) | def __or__(self, other): method __len__ (line 236) | def __len__(self): method part_policy (line 240) | def part_policy(self): method shape (line 251) | def shape(self): method dtype (line 262) | def dtype(self): method name (line 273) | def name(self): method tensor_name (line 284) | def tensor_name(self): method count_nonzero (line 294) | def count_nonzero(self): method _attach_group_id (line 304) | def _attach_group_id(self, name): method _detach_group_id (line 316) | def _detach_group_id(self, name): FILE: python/dgl/distributed/graph_partition_book.py function _etype_tuple_to_str (line 25) | def _etype_tuple_to_str(c_etype): function _etype_str_to_tuple (line 43) | def _etype_str_to_tuple(c_etype): function _move_metadata_to_shared_mem (line 62) | def _move_metadata_to_shared_mem( function _get_shared_mem_metadata (line 124) | def _get_shared_mem_metadata(graph_name): function get_shared_mem_partition_book (line 182) | def get_shared_mem_partition_book(graph_name): function get_node_partition_from_book (line 233) | def get_node_partition_from_book(book, device): class GraphPartitionBook (line 267) | class GraphPartitionBook(ABC): method shared_memory (line 293) | def shared_memory(self, graph_name): method num_partitions (line 303) | def num_partitions(self): method metadata (line 312) | def metadata(self): method nid2partid (line 333) | def nid2partid(self, nids, ntype): method eid2partid (line 349) | def eid2partid(self, eids, etype): method partid2nids (line 365) | def partid2nids(self, partid, ntype): method partid2eids (line 381) | def partid2eids(self, partid, etype): method nid2localnid (line 397) | def nid2localnid(self, nids, partid, ntype): method eid2localeid (line 415) | def eid2localeid(self, eids, partid, etype): method partid (line 434) | def partid(self): method ntypes (line 444) | def ntypes(self): method etypes (line 448) | def etypes(self): method canonical_etypes (line 452) | def canonical_etypes(self): method to_canonical_etype (line 461) | def to_canonical_etype(self, etype): method is_homogeneous (line 476) | def is_homogeneous(self): method map_to_per_ntype (line 480) | def map_to_per_ntype(self, ids): method map_to_per_etype (line 494) | def map_to_per_etype(self, ids): method map_to_homo_nid (line 508) | def map_to_homo_nid(self, ids, ntype): method map_to_homo_eid (line 524) | def map_to_homo_eid(self, ids, etype): class RangePartitionBook (line 541) | class RangePartitionBook(GraphPartitionBook): method __init__ (line 573) | def __init__(self, part_id, num_parts, node_map, edge_map, ntypes, ety... method shared_memory (line 700) | def shared_memory(self, graph_name): method num_partitions (line 725) | def num_partitions(self): method _num_nodes (line 729) | def _num_nodes(self, ntype=DEFAULT_NTYPE): method _num_edges (line 736) | def _num_edges(self, etype=DEFAULT_ETYPE): method metadata (line 744) | def metadata(self): method map_to_per_ntype (line 748) | def map_to_per_ntype(self, ids): method map_to_per_etype (line 755) | def map_to_per_etype(self, ids): method map_to_homo_nid (line 762) | def map_to_homo_nid(self, ids, ntype): method map_to_homo_eid (line 773) | def map_to_homo_eid(self, ids, etype): method nid2partid (line 787) | def nid2partid(self, nids, ntype=DEFAULT_NTYPE): method eid2partid (line 801) | def eid2partid(self, eids, etype=DEFAULT_ETYPE): method partid2nids (line 816) | def partid2nids(self, partid, ntype=DEFAULT_NTYPE): method partid2eids (line 830) | def partid2eids(self, partid, etype=DEFAULT_ETYPE): method nid2localnid (line 847) | def nid2localnid(self, nids, partid, ntype=DEFAULT_NTYPE): method eid2localeid (line 865) | def eid2localeid(self, eids, partid, etype=DEFAULT_ETYPE): method partid (line 887) | def partid(self): method ntypes (line 892) | def ntypes(self): method etypes (line 897) | def etypes(self): method canonical_etypes (line 902) | def canonical_etypes(self): method local_ntype_offset (line 915) | def local_ntype_offset(self): method local_etype_offset (line 923) | def local_etype_offset(self): method to_canonical_etype (line 930) | def to_canonical_etype(self, etype): method global_nid_dtype (line 958) | def global_nid_dtype(self): method global_eid_dtype (line 963) | def global_eid_dtype(self): class PartitionPolicy (line 973) | class PartitionPolicy(object): method __init__ (line 991) | def __init__(self, policy_str, partition_book): method policy_str (line 1011) | def policy_str(self): method type_name (line 1022) | def type_name(self): method part_id (line 1033) | def part_id(self): method partition_book (line 1044) | def partition_book(self): method is_node (line 1055) | def is_node(self): method get_data_name (line 1065) | def get_data_name(self, name): method to_local (line 1069) | def to_local(self, id_tensor): method to_partid (line 1091) | def to_partid(self, id_tensor): method get_part_size (line 1109) | def get_part_size(self): method get_size (line 1126) | def get_size(self): class NodePartitionPolicy (line 1140) | class NodePartitionPolicy(PartitionPolicy): method __init__ (line 1143) | def __init__(self, partition_book, ntype=DEFAULT_NTYPE): class EdgePartitionPolicy (line 1149) | class EdgePartitionPolicy(PartitionPolicy): method __init__ (line 1152) | def __init__(self, partition_book, etype=DEFAULT_ETYPE): class HeteroDataName (line 1162) | class HeteroDataName(object): method __init__ (line 1180) | def __init__(self, is_node, entity_type, data_name): method policy_str (line 1191) | def policy_str(self): method is_node (line 1198) | def is_node(self): method is_edge (line 1202) | def is_edge(self): method get_type (line 1206) | def get_type(self): method get_name (line 1214) | def get_name(self): method __str__ (line 1218) | def __str__(self): function parse_hetero_data_name (line 1226) | def parse_hetero_data_name(name): FILE: python/dgl/distributed/graph_services.py class SubgraphResponse (line 43) | class SubgraphResponse(Response): method __init__ (line 46) | def __init__( method __setstate__ (line 54) | def __setstate__(self, state): method __getstate__ (line 62) | def __getstate__(self): class FindEdgeResponse (line 71) | class FindEdgeResponse(Response): method __init__ (line 74) | def __init__(self, global_src, global_dst, order_id): method __setstate__ (line 79) | def __setstate__(self, state): method __getstate__ (line 82) | def __getstate__(self): function _sample_neighbors_graphbolt (line 86) | def _sample_neighbors_graphbolt( function _sample_neighbors_dgl (line 185) | def _sample_neighbors_dgl( function _sample_neighbors (line 224) | def _sample_neighbors(use_graphbolt, *args, **kwargs): function _sample_etype_neighbors_dgl (line 255) | def _sample_etype_neighbors_dgl( function _sample_etype_neighbors (line 300) | def _sample_etype_neighbors(use_graphbolt, *args, **kwargs): function _find_edges (line 337) | def _find_edges(local_g, partition_book, seed_edges): function _in_degrees (line 368) | def _in_degrees(local_g, partition_book, n): function _out_degrees (line 375) | def _out_degrees(local_g, partition_book, n): function _in_subgraph (line 382) | def _in_subgraph(local_g, partition_book, seed_nodes): class SamplingRequest (line 427) | class SamplingRequest(Request): method __init__ (line 430) | def __init__( method __setstate__ (line 448) | def __setstate__(self, state): method __getstate__ (line 459) | def __getstate__(self): method process_request (line 470) | def process_request(self, server_state): class SamplingRequestEtype (line 497) | class SamplingRequestEtype(Request): method __init__ (line 500) | def __init__( method __setstate__ (line 520) | def __setstate__(self, state): method __getstate__ (line 532) | def __getstate__(self): method process_request (line 544) | def process_request(self, server_state): class EdgesRequest (line 578) | class EdgesRequest(Request): method __init__ (line 581) | def __init__(self, edge_ids, order_id): method __setstate__ (line 585) | def __setstate__(self, state): method __getstate__ (line 588) | def __getstate__(self): method process_request (line 591) | def process_request(self, server_state): class InDegreeRequest (line 601) | class InDegreeRequest(Request): method __init__ (line 604) | def __init__(self, n, order_id): method __setstate__ (line 608) | def __setstate__(self, state): method __getstate__ (line 611) | def __getstate__(self): method process_request (line 614) | def process_request(self, server_state): class InDegreeResponse (line 622) | class InDegreeResponse(Response): method __init__ (line 625) | def __init__(self, deg, order_id): method __setstate__ (line 629) | def __setstate__(self, state): method __getstate__ (line 632) | def __getstate__(self): class OutDegreeRequest (line 636) | class OutDegreeRequest(Request): method __init__ (line 639) | def __init__(self, n, order_id): method __setstate__ (line 643) | def __setstate__(self, state): method __getstate__ (line 646) | def __getstate__(self): method process_request (line 649) | def process_request(self, server_state): class OutDegreeResponse (line 657) | class OutDegreeResponse(Response): method __init__ (line 660) | def __init__(self, deg, order_id): method __setstate__ (line 664) | def __setstate__(self, state): method __getstate__ (line 667) | def __getstate__(self): class InSubgraphRequest (line 671) | class InSubgraphRequest(Request): method __init__ (line 674) | def __init__(self, nodes): method __setstate__ (line 677) | def __setstate__(self, state): method __getstate__ (line 680) | def __getstate__(self): method process_request (line 683) | def process_request(self, server_state): function merge_graphs (line 692) | def merge_graphs(res_list, num_nodes, exclude_edges=None): function _distributed_access (line 737) | def _distributed_access( function _frontier_to_heterogeneous_graph (line 807) | def _frontier_to_heterogeneous_graph(g, frontier, gpb): function sample_etype_neighbors (line 870) | def sample_etype_neighbors( function sample_neighbors (line 1037) | def sample_neighbors( function _distributed_edge_access (line 1165) | def _distributed_edge_access(g, edges, issue_remote_req, local_access): function find_edges (line 1236) | def find_edges(g, edge_ids): function in_subgraph (line 1266) | def in_subgraph(g, nodes): function _distributed_get_node_property (line 1308) | def _distributed_get_node_property(g, n, issue_remote_req, local_access): function in_degrees (line 1355) | def in_degrees(g, v): function out_degrees (line 1367) | def out_degrees(g, u): FILE: python/dgl/distributed/id_map.py class IdMap (line 14) | class IdMap: method __init__ (line 107) | def __init__(self, id_ranges): method __call__ (line 139) | def __call__(self, ids): method torch_dtype (line 172) | def torch_dtype(self): FILE: python/dgl/distributed/kvstore.py class PullResponse (line 19) | class PullResponse(rpc.Response): method __init__ (line 30) | def __init__(self, server_id, data_tensor): method __getstate__ (line 34) | def __getstate__(self): method __setstate__ (line 37) | def __setstate__(self, state): class PullRequest (line 41) | class PullRequest(rpc.Request): method __init__ (line 52) | def __init__(self, name, id_tensor): method __getstate__ (line 56) | def __getstate__(self): method __setstate__ (line 59) | def __setstate__(self, state): method process_request (line 62) | def process_request(self, server_state): class PushRequest (line 84) | class PushRequest(rpc.Request): method __init__ (line 99) | def __init__(self, name, id_tensor, data_tensor): method __getstate__ (line 104) | def __getstate__(self): method __setstate__ (line 107) | def __setstate__(self, state): method process_request (line 110) | def process_request(self, server_state): class InitDataResponse (line 131) | class InitDataResponse(rpc.Response): method __init__ (line 141) | def __init__(self, msg): method __getstate__ (line 144) | def __getstate__(self): method __setstate__ (line 147) | def __setstate__(self, state): class InitDataRequest (line 151) | class InitDataRequest(rpc.Request): method __init__ (line 169) | def __init__(self, name, shape, dtype, policy_str, init_func): method __getstate__ (line 176) | def __getstate__(self): method __setstate__ (line 185) | def __setstate__(self, state): method process_request (line 194) | def process_request(self, server_state): class BarrierResponse (line 229) | class BarrierResponse(rpc.Response): method __init__ (line 239) | def __init__(self, msg): method __getstate__ (line 242) | def __getstate__(self): method __setstate__ (line 245) | def __setstate__(self, state): class BarrierRequest (line 249) | class BarrierRequest(rpc.Request): method __init__ (line 258) | def __init__(self, role): method __getstate__ (line 262) | def __getstate__(self): method __setstate__ (line 265) | def __setstate__(self, state): method process_request (line 268) | def process_request(self, server_state): class RegisterPullHandlerResponse (line 288) | class RegisterPullHandlerResponse(rpc.Response): method __init__ (line 298) | def __init__(self, msg): method __getstate__ (line 301) | def __getstate__(self): method __setstate__ (line 304) | def __setstate__(self, state): class RegisterPullHandlerRequest (line 308) | class RegisterPullHandlerRequest(rpc.Request): method __init__ (line 317) | def __init__(self, name, pull_func): method __getstate__ (line 321) | def __getstate__(self): method __setstate__ (line 324) | def __setstate__(self, state): method process_request (line 327) | def process_request(self, server_state): class RegisterPushHandlerResponse (line 338) | class RegisterPushHandlerResponse(rpc.Response): method __init__ (line 348) | def __init__(self, msg): method __getstate__ (line 351) | def __getstate__(self): method __setstate__ (line 354) | def __setstate__(self, state): class RegisterPushHandlerRequest (line 358) | class RegisterPushHandlerRequest(rpc.Request): method __init__ (line 367) | def __init__(self, name, push_func): method __getstate__ (line 371) | def __getstate__(self): method __setstate__ (line 374) | def __setstate__(self, state): method process_request (line 377) | def process_request(self, server_state): class GetSharedDataResponse (line 388) | class GetSharedDataResponse(rpc.Response): method __init__ (line 400) | def __init__(self, meta): method __getstate__ (line 403) | def __getstate__(self): method __setstate__ (line 406) | def __setstate__(self, state): class GetSharedDataRequest (line 410) | class GetSharedDataRequest(rpc.Request): method __init__ (line 420) | def __init__(self, msg): method __getstate__ (line 423) | def __getstate__(self): method __setstate__ (line 426) | def __setstate__(self, state): method process_request (line 429) | def process_request(self, server_state): class GetPartShapeResponse (line 446) | class GetPartShapeResponse(rpc.Response): method __init__ (line 455) | def __init__(self, shape): method __getstate__ (line 458) | def __getstate__(self): method __setstate__ (line 461) | def __setstate__(self, state): class GetPartShapeRequest (line 469) | class GetPartShapeRequest(rpc.Request): method __init__ (line 478) | def __init__(self, name): method __getstate__ (line 481) | def __getstate__(self): method __setstate__ (line 484) | def __setstate__(self, state): method process_request (line 487) | def process_request(self, server_state): class SendMetaToBackupResponse (line 502) | class SendMetaToBackupResponse(rpc.Response): method __init__ (line 507) | def __init__(self, msg): method __getstate__ (line 510) | def __getstate__(self): method __setstate__ (line 513) | def __setstate__(self, state): class SendMetaToBackupRequest (line 517) | class SendMetaToBackupRequest(rpc.Request): method __init__ (line 537) | def __init__( method __getstate__ (line 547) | def __getstate__(self): method __setstate__ (line 557) | def __setstate__(self, state): method process_request (line 567) | def process_request(self, server_state): class DeleteDataResponse (line 602) | class DeleteDataResponse(rpc.Response): method __init__ (line 607) | def __init__(self, msg): method __getstate__ (line 610) | def __getstate__(self): method __setstate__ (line 613) | def __setstate__(self, state): class DeleteDataRequest (line 617) | class DeleteDataRequest(rpc.Request): method __init__ (line 626) | def __init__(self, name): method __getstate__ (line 629) | def __getstate__(self): method __setstate__ (line 632) | def __setstate__(self, state): method process_request (line 635) | def process_request(self, server_state): class CountLocalNonzeroResponse (line 649) | class CountLocalNonzeroResponse(rpc.Response): method __init__ (line 652) | def __init__(self, num_local_nonzero): method __getstate__ (line 655) | def __getstate__(self): method __setstate__ (line 658) | def __setstate__(self, state): class CountLocalNonzeroRequest (line 662) | class CountLocalNonzeroRequest(rpc.Request): method __init__ (line 670) | def __init__(self, name): method __getstate__ (line 673) | def __getstate__(self): method __setstate__ (line 676) | def __setstate__(self, state): method process_request (line 679) | def process_request(self, server_state): function default_push_handler (line 689) | def default_push_handler(target, name, id_tensor, data_tensor): function default_pull_handler (line 709) | def default_pull_handler(target, name, id_tensor): class KVServer (line 732) | class KVServer(object): method __init__ (line 755) | def __init__(self, server_id, ip_config, num_servers, num_clients): method server_id (line 824) | def server_id(self): method barrier_count (line 829) | def barrier_count(self): method barrier_count (line 834) | def barrier_count(self, count): method num_clients (line 839) | def num_clients(self): method data_store (line 844) | def data_store(self): method orig_data (line 849) | def orig_data(self): method part_policy (line 854) | def part_policy(self): method part_id (line 859) | def part_id(self): method push_handlers (line 864) | def push_handlers(self): method pull_handlers (line 869) | def pull_handlers(self): method is_backup_server (line 873) | def is_backup_server(self): method add_part_policy (line 879) | def add_part_policy(self, policy): method init_data (line 889) | def init_data(self, name, policy_str, data_tensor=None): method find_policy (line 925) | def find_policy(self, policy_str): method count_local_nonzero (line 940) | def count_local_nonzero(self, name): class KVClient (line 962) | class KVClient(object): method __init__ (line 982) | def __init__(self, ip_config, num_servers, role="default"): method all_possible_part_policy (line 1051) | def all_possible_part_policy(self): method client_id (line 1056) | def client_id(self): method role (line 1061) | def role(self): method machine_id (line 1066) | def machine_id(self): method num_servers (line 1071) | def num_servers(self): method group_count (line 1076) | def group_count(self): method barrier (line 1080) | def barrier(self): method register_push_handler (line 1090) | def register_push_handler(self, name, func): method register_pull_handler (line 1123) | def register_pull_handler(self, name, func): method init_data (line 1155) | def init_data( method delete_data (line 1249) | def delete_data(self, name): method map_shared_data (line 1283) | def map_shared_data(self, partition_book): method gdata_name_list (line 1359) | def gdata_name_list(self): method data_name_list (line 1363) | def data_name_list(self): method get_data_meta (line 1367) | def get_data_meta(self, name): method get_partid (line 1375) | def get_partid(self, name, id_tensor): method push (line 1393) | def push(self, name, id_tensor, data_tensor): method pull (line 1445) | def pull(self, name, id_tensor): method union (line 1528) | def union(self, operand1_name, operand2_name, output_name): method _take_id (line 1535) | def _take_id(self, elem): method count_nonzero (line 1539) | def count_nonzero(self, name): method data_store (line 1572) | def data_store(self): function init_kvstore (line 1586) | def init_kvstore(ip_config, num_servers, role): function close_kvstore (line 1596) | def close_kvstore(): function get_kvstore (line 1602) | def get_kvstore(): FILE: python/dgl/distributed/nn/__init__.py function _load_backend (line 10) | def _load_backend(mod_name): FILE: python/dgl/distributed/nn/pytorch/sparse_emb.py class DistEmbedding (line 9) | class DistEmbedding: method __init__ (line 69) | def __init__( method __call__ (line 100) | def __call__(self, idx, device=th.device("cpu")): method reset_trace (line 119) | def reset_trace(self): method part_policy (line 124) | def part_policy(self): method name (line 135) | def name(self): method data_name (line 146) | def data_name(self): method kvstore (line 157) | def kvstore(self): method num_embeddings (line 168) | def num_embeddings(self): method embedding_dim (line 179) | def embedding_dim(self): method optm_state (line 190) | def optm_state(self): method weight (line 201) | def weight(self): FILE: python/dgl/distributed/optim/__init__.py function _load_backend (line 10) | def _load_backend(mod_name): FILE: python/dgl/distributed/optim/pytorch/sparse_optim.py class DistSparseGradOptimizer (line 24) | class DistSparseGradOptimizer(abc.ABC): method __init__ (line 37) | def __init__(self, params, lr): method local_state_dict (line 56) | def local_state_dict(self): method load_local_state_dict (line 120) | def load_local_state_dict(self, local_state_dict): method save (line 166) | def save(self, f): method load (line 195) | def load(self, f): method _load_state_from (line 232) | def _load_state_from(self, f): method _get_local_ids (line 238) | def _get_local_ids(self, part_policy): method step (line 252) | def step(self): method update (line 431) | def update(self, idx, grad, emb): method zero_grad (line 446) | def zero_grad(self): function initializer (line 451) | def initializer(shape, dtype): class SparseAdagrad (line 465) | class SparseAdagrad(DistSparseGradOptimizer): method __init__ (line 490) | def __init__(self, params, lr, eps=1e-10): method update (line 514) | def update(self, idx, grad, emb): class SparseAdam (line 579) | class SparseAdam(DistSparseGradOptimizer): method __init__ (line 611) | def __init__(self, params, lr, betas=(0.9, 0.999), eps=1e-08): method update (line 658) | def update(self, idx, grad, emb): FILE: python/dgl/distributed/optim/pytorch/utils.py function alltoall_cpu (line 7) | def alltoall_cpu(rank, world_size, output_tensor_list, input_tensor_list): function alltoallv_cpu (line 31) | def alltoallv_cpu(rank, world_size, output_tensor_list, input_tensor_list): function alltoall (line 65) | def alltoall(rank, world_size, output_tensor_list, input_tensor_list): function alltoallv (line 91) | def alltoallv(rank, world_size, output_tensor_list, input_tensor_list): FILE: python/dgl/distributed/partition.py function _format_part_metadata (line 51) | def _format_part_metadata(part_metadata, formatter): function _load_part_config (line 67) | def _load_part_config(part_config): function _dump_part_config (line 84) | def _dump_part_config(part_config, part_metadata): function process_partitions (line 91) | def process_partitions(g, formats=None, sort_etypes=False): function _save_dgl_graphs (line 110) | def _save_dgl_graphs(filename, g_list, formats=None): function _get_inner_node_mask (line 114) | def _get_inner_node_mask(graph, ntype_id, gpb=None): function _get_inner_edge_mask (line 133) | def _get_inner_edge_mask( function _get_part_ranges (line 155) | def _get_part_ranges(id_ranges): function _verify_dgl_partition (line 173) | def _verify_dgl_partition(graph, part_id, gpb, ntypes, etypes): function _verify_graphbolt_partition (line 218) | def _verify_graphbolt_partition(graph, part_id, gpb, ntypes, etypes): function load_partition (line 286) | def load_partition(part_config, part_id, load_feats=True, use_graphbolt=... function load_partition_feats (line 408) | def load_partition_feats( function load_partition_book (line 488) | def load_partition_book(part_config, part_id, part_metadata=None): function _get_orig_ids (line 611) | def _get_orig_ids(g, sim_g, orig_nids, orig_eids): function _set_trainer_ids (line 660) | def _set_trainer_ids(g, sim_g, node_parts): function _partition_to_graphbolt (line 696) | def _partition_to_graphbolt( function _update_node_edge_map (line 728) | def _update_node_edge_map(node_map_val, edge_map_val, g, num_parts): function partition_graph (line 817) | def partition_graph( function _cast_to_minimum_dtype (line 1571) | def _cast_to_minimum_dtype(predicate, data, field=None): function is_homogeneous (line 1588) | def is_homogeneous(ntypes, etypes): function init_type_per_edge (line 1593) | def init_type_per_edge(graph, gpb): function _load_part (line 1599) | def _load_part(part_config, part_id, parts=None): function _save_graph_gb (line 1610) | def _save_graph_gb(part_config, part_id, csc_graph): function cast_various_to_minimum_dtype_gb (line 1623) | def cast_various_to_minimum_dtype_gb( function _create_attributes_gb (line 1680) | def _create_attributes_gb( function _convert_dgl_partition_to_gb (line 1726) | def _convert_dgl_partition_to_gb( function gb_convert_single_dgl_partition (line 1844) | def gb_convert_single_dgl_partition( function _convert_partition_to_graphbolt_wrapper (line 1900) | def _convert_partition_to_graphbolt_wrapper( function dgl_partition_to_graphbolt (line 1965) | def dgl_partition_to_graphbolt( FILE: python/dgl/distributed/role.py class RegisterRoleResponse (line 17) | class RegisterRoleResponse(rpc.Response): method __init__ (line 22) | def __init__(self, msg): method __getstate__ (line 25) | def __getstate__(self): method __setstate__ (line 28) | def __setstate__(self, state): class RegisterRoleRequest (line 32) | class RegisterRoleRequest(rpc.Request): method __init__ (line 43) | def __init__(self, client_id, machine_id, role): method __getstate__ (line 49) | def __getstate__(self): method __setstate__ (line 52) | def __setstate__(self, state): method process_request (line 55) | def process_request(self, server_state): class GetRoleResponse (line 82) | class GetRoleResponse(rpc.Response): method __init__ (line 85) | def __init__(self, role): method __getstate__ (line 89) | def __getstate__(self): method __setstate__ (line 92) | def __setstate__(self, state): class GetRoleRequest (line 96) | class GetRoleRequest(rpc.Request): method __init__ (line 99) | def __init__(self): method __getstate__ (line 103) | def __getstate__(self): method __setstate__ (line 106) | def __setstate__(self, state): method process_request (line 109) | def process_request(self, server_state): function init_role (line 126) | def init_role(role): function get_global_rank (line 203) | def get_global_rank(): function get_rank (line 215) | def get_rank(role): function get_trainer_rank (line 223) | def get_trainer_rank(): function get_role (line 236) | def get_role(): function get_num_trainers (line 241) | def get_num_trainers(): FILE: python/dgl/distributed/rpc.py function read_ip_config (line 56) | def read_ip_config(filename, num_servers): function reset (line 136) | def reset(): function create_sender (line 141) | def create_sender(max_queue_size): function create_receiver (line 153) | def create_receiver(max_queue_size): function finalize_sender (line 165) | def finalize_sender(): function finalize_receiver (line 170) | def finalize_receiver(): function wait_for_senders (line 175) | def wait_for_senders(ip_addr, port, num_senders): function connect_receiver (line 192) | def connect_receiver(ip_addr, port, recv_id, group_id=-1): function connect_receiver_finalize (line 212) | def connect_receiver_finalize(max_try_times): function set_rank (line 227) | def set_rank(rank): function get_rank (line 241) | def get_rank(): function set_machine_id (line 255) | def set_machine_id(machine_id): function get_machine_id (line 266) | def get_machine_id(): function set_num_machines (line 277) | def set_num_machines(num_machines): function get_num_machines (line 288) | def get_num_machines(): function set_num_server (line 299) | def set_num_server(num_server): function get_num_server (line 304) | def get_num_server(): function set_num_client (line 309) | def set_num_client(num_client): function get_num_client (line 314) | def get_num_client(): function set_num_server_per_machine (line 319) | def set_num_server_per_machine(num_server): function get_num_server_per_machine (line 324) | def get_num_server_per_machine(): function incr_msg_seq (line 329) | def incr_msg_seq(): function get_msg_seq (line 340) | def get_msg_seq(): function set_msg_seq (line 351) | def set_msg_seq(msg_seq): function register_service (line 362) | def register_service(service_id, req_cls, res_cls=None): function get_service_property (line 380) | def get_service_property(service_id): class Request (line 396) | class Request: method __getstate__ (line 400) | def __getstate__(self): method __setstate__ (line 409) | def __setstate__(self, state): method process_request (line 416) | def process_request(self, server_state): method service_id (line 433) | def service_id(self): class Response (line 446) | class Response: method __getstate__ (line 450) | def __getstate__(self): method __setstate__ (line 459) | def __setstate__(self, state): method service_id (line 466) | def service_id(self): function serialize_to_payload (line 479) | def serialize_to_payload(serializable): class PlaceHolder (line 512) | class PlaceHolder: function deserialize_from_payload (line 519) | def deserialize_from_payload(cls, data, tensors): class RPCMessage (line 560) | class RPCMessage(ObjectBase): method __init__ (line 583) | def __init__( method service_id (line 605) | def service_id(self): method msg_seq (line 610) | def msg_seq(self): method client_id (line 615) | def client_id(self): method server_id (line 620) | def server_id(self): method data (line 625) | def data(self): method tensors (line 630) | def tensors(self): method group_id (line 636) | def group_id(self): function send_request (line 641) | def send_request(target, request): function send_request_to_machine (line 680) | def send_request_to_machine(target, request): function send_response (line 714) | def send_response(target, response, group_id): function recv_request (line 749) | def recv_request(timeout=0): function recv_response (line 794) | def recv_response(timeout=0): function remote_call (line 839) | def remote_call(target_and_requests, timeout=0): function send_requests_to_machine (line 919) | def send_requests_to_machine(target_and_requests): function recv_responses (line 965) | def recv_responses(msgseq2pos, timeout=0): function remote_call_to_machine (line 1016) | def remote_call_to_machine(target_and_requests, timeout=0): function send_rpc_message (line 1047) | def send_rpc_message(msg, target): function recv_rpc_message (line 1074) | def recv_rpc_message(timeout=0): function client_barrier (line 1099) | def client_barrier(): function finalize_server (line 1107) | def finalize_server(): function fast_pull (line 1114) | def fast_pull( function register_sig_handler (line 1177) | def register_sig_handler(): function copy_data_to_shared_memory (line 1182) | def copy_data_to_shared_memory(dst, source): class ClientRegisterRequest (line 1192) | class ClientRegisterRequest(Request): method __init__ (line 1201) | def __init__(self, ip_addr): method __getstate__ (line 1204) | def __getstate__(self): method __setstate__ (line 1207) | def __setstate__(self, state): method process_request (line 1210) | def process_request(self, server_state): class ClientRegisterResponse (line 1214) | class ClientRegisterResponse(Response): method __init__ (line 1223) | def __init__(self, client_id): method __getstate__ (line 1226) | def __getstate__(self): method __setstate__ (line 1229) | def __setstate__(self, state): class ShutDownRequest (line 1236) | class ShutDownRequest(Request): method __init__ (line 1247) | def __init__(self, client_id, force_shutdown_server=False): method __getstate__ (line 1251) | def __getstate__(self): method __setstate__ (line 1254) | def __setstate__(self, state): method process_request (line 1257) | def process_request(self, server_state): class GetNumberClientsResponse (line 1266) | class GetNumberClientsResponse(Response): method __init__ (line 1275) | def __init__(self, num_client): method __getstate__ (line 1278) | def __getstate__(self): method __setstate__ (line 1281) | def __setstate__(self, state): class GetNumberClientsRequest (line 1285) | class GetNumberClientsRequest(Request): method __init__ (line 1294) | def __init__(self, client_id): method __getstate__ (line 1297) | def __getstate__(self): method __setstate__ (line 1300) | def __setstate__(self, state): method process_request (line 1303) | def process_request(self, server_state): class ClientBarrierResponse (line 1311) | class ClientBarrierResponse(Response): method __init__ (line 1320) | def __init__(self, msg="barrier"): method __getstate__ (line 1323) | def __getstate__(self): method __setstate__ (line 1326) | def __setstate__(self, state): class ClientBarrierRequest (line 1330) | class ClientBarrierRequest(Request): method __init__ (line 1339) | def __init__(self, msg="barrier"): method __getstate__ (line 1343) | def __getstate__(self): method __setstate__ (line 1346) | def __setstate__(self, state): method process_request (line 1349) | def process_request(self, server_state): function set_group_id (line 1362) | def set_group_id(group_id): function get_group_id (line 1373) | def get_group_id(): function register_client (line 1384) | def register_client(client_id, group_id): function get_client (line 1395) | def get_client(client_id, group_id): class DistConnectError (line 1413) | class DistConnectError(DGLError): method __init__ (line 1422) | def __init__(self, max_try_times, ip="", port=""): FILE: python/dgl/distributed/rpc_client.py function local_ip4_addr_list (line 17) | def local_ip4_addr_list(): function get_local_machine_id (line 51) | def get_local_machine_id(server_namebook): function get_local_usable_addr (line 86) | def get_local_usable_addr(probe_addr): function connect_to_server (line 112) | def connect_to_server( FILE: python/dgl/distributed/rpc_server.py function start_server (line 11) | def start_server( FILE: python/dgl/distributed/server_state.py class ServerState (line 8) | class ServerState: method __init__ (line 45) | def __init__(self, kv_store, local_g, partition_book, use_graphbolt=Fa... method roles (line 53) | def roles(self): method kv_store (line 58) | def kv_store(self): method kv_store (line 63) | def kv_store(self, kv_store): method graph (line 67) | def graph(self): method graph (line 72) | def graph(self, graph): method use_graphbolt (line 76) | def use_graphbolt(self): FILE: python/dgl/distributed/shared_mem_utils.py function _get_ndata_path (line 10) | def _get_ndata_path(graph_name, ndata_name): function _get_edata_path (line 14) | def _get_edata_path(graph_name, edata_name): function _to_shared_mem (line 18) | def _to_shared_mem(arr, name): FILE: python/dgl/distributed/standalone_kvstore.py class KVClient (line 9) | class KVClient(object): method __init__ (line 16) | def __init__(self): method all_possible_part_policy (line 25) | def all_possible_part_policy(self): method num_servers (line 30) | def num_servers(self): method barrier (line 34) | def barrier(self): method register_push_handler (line 37) | def register_push_handler(self, name, func): method register_pull_handler (line 41) | def register_pull_handler(self, name, func): method add_data (line 45) | def add_data(self, name, tensor, part_policy): method init_data (line 52) | def init_data( method delete_data (line 62) | def delete_data(self, name): method data_name_list (line 68) | def data_name_list(self): method gdata_name_list (line 72) | def gdata_name_list(self): method get_data_meta (line 76) | def get_data_meta(self, name): method push (line 80) | def push(self, name, id_tensor, data_tensor): method pull (line 87) | def pull(self, name, id_tensor): method map_shared_data (line 94) | def map_shared_data(self, partition_book): method count_nonzero (line 97) | def count_nonzero(self, name): method data_store (line 113) | def data_store(self): method union (line 123) | def union(self, operand1_name, operand2_name, output_name): FILE: python/dgl/frame.py class _LazyIndex (line 14) | class _LazyIndex(object): method __init__ (line 15) | def __init__(self, index): method __len__ (line 21) | def __len__(self): method slice (line 24) | def slice(self, index): method flatten (line 34) | def flatten(self): method record_stream (line 44) | def record_stream(self, stream): class LazyFeature (line 56) | class LazyFeature(object): method __init__ (line 100) | def __init__(self, name=None, id_=None): method to (line 104) | def to( method data (line 111) | def data(self): method pin_memory_ (line 115) | def pin_memory_(self): method unpin_memory_ (line 118) | def unpin_memory_(self): method record_stream (line 121) | def record_stream(self, stream): class Scheme (line 125) | class Scheme(namedtuple("Scheme", ["shape", "dtype"])): method __reduce__ (line 141) | def __reduce__(self): method _reconstruct_scheme (line 146) | def _reconstruct_scheme(cls, shape, dtype_str): function infer_scheme (line 151) | def infer_scheme(tensor): class Column (line 167) | class Column(TensorStorage): method __init__ (line 210) | def __init__(self, storage, *args, **kwargs): method __len__ (line 214) | def __len__(self): method shape (line 222) | def shape(self): method data (line 227) | def data(self): method data (line 271) | def data(self, val): method to (line 280) | def to(self, device, **kwargs): # pylint: disable=invalid-name method dtype (line 300) | def dtype(self): method astype (line 306) | def astype(self, new_dtype): method __getitem__ (line 342) | def __getitem__(self, rowids): method __setitem__ (line 359) | def __setitem__(self, rowids, feats): method update (line 374) | def update(self, rowids, feats): method extend (line 392) | def extend(self, feats, feat_scheme=None): method clone (line 415) | def clone(self): method deepclone (line 425) | def deepclone(self): method subcolumn (line 432) | def subcolumn(self, rowids): method create (line 473) | def create(data): method __repr__ (line 480) | def __repr__(self): method __getstate__ (line 483) | def __getstate__(self): method __setstate__ (line 494) | def __setstate__(self, state): method _init (line 517) | def _init(self, scheme=None, index=None, device=None, deferred_dtype=N... method __copy__ (line 525) | def __copy__(self): method fetch (line 528) | def fetch(self, indices, device, pin_memory=False, **kwargs): method pin_memory_ (line 532) | def pin_memory_(self): method unpin_memory_ (line 541) | def unpin_memory_(self): method record_stream (line 552) | def record_stream(self, stream): class Frame (line 571) | class Frame(MutableMapping): method __init__ (line 588) | def __init__(self, data=None, num_rows=None): method _set_zero_default_initializer (line 619) | def _set_zero_default_initializer(self): method get_initializer (line 623) | def get_initializer(self, column=None): method set_initializer (line 638) | def set_initializer(self, initializer, column=None): method schemes (line 657) | def schemes(self): method num_columns (line 662) | def num_columns(self): method num_rows (line 667) | def num_rows(self): method __contains__ (line 671) | def __contains__(self, name): method __getitem__ (line 675) | def __getitem__(self, name): method __setitem__ (line 690) | def __setitem__(self, name, data): method __delitem__ (line 702) | def __delitem__(self, name): method add_column (line 712) | def add_column(self, name, scheme, ctx): method add_rows (line 744) | def add_rows(self, num_rows): method update_column (line 772) | def update_column(self, name, data): method update_row (line 794) | def update_row(self, rowids, data): method _append (line 818) | def _append(self, other): method append (line 843) | def append(self, other): method clear (line 860) | def clear(self): method __iter__ (line 865) | def __iter__(self): method __len__ (line 869) | def __len__(self): method keys (line 873) | def keys(self): method values (line 877) | def values(self): method clone (line 881) | def clone(self): method deepclone (line 901) | def deepclone(self): method subframe (line 922) | def subframe(self, rowids): method to (line 944) | def to(self, device, **kwargs): # pylint: disable=invalid-name method __repr__ (line 967) | def __repr__(self): method pin_memory_ (line 970) | def pin_memory_(self): method unpin_memory_ (line 976) | def unpin_memory_(self): method record_stream (line 982) | def record_stream(self, stream): method _astype_float (line 988) | def _astype_float(self, new_type): method bfloat16 (line 1011) | def bfloat16(self): method half (line 1016) | def half(self): method float (line 1021) | def float(self): method double (line 1026) | def double(self): FILE: python/dgl/function/base.py class TargetCode (line 7) | class TargetCode(object): class BuiltinFunction (line 25) | class BuiltinFunction(object): method name (line 29) | def name(self): FILE: python/dgl/function/message.py class MessageFunction (line 13) | class MessageFunction(BuiltinFunction): method name (line 17) | def name(self): class BinaryMessageFunction (line 22) | class BinaryMessageFunction(MessageFunction): method __init__ (line 30) | def __init__(self, binary_op, lhs, rhs, lhs_field, rhs_field, out_field): method name (line 39) | def name(self): class CopyMessageFunction (line 45) | class CopyMessageFunction(MessageFunction): method __init__ (line 53) | def __init__(self, target, in_field, out_field): method name (line 59) | def name(self): function copy_u (line 63) | def copy_u(u, out): function copy_e (line 87) | def copy_e(e, out): function _gen_message_builtin (line 131) | def _gen_message_builtin(lhs, rhs, binary_op): function _register_builtin_message_func (line 179) | def _register_builtin_message_func(): FILE: python/dgl/function/reducer.py class ReduceFunction (line 10) | class ReduceFunction(BuiltinFunction): method name (line 14) | def name(self): class SimpleReduceFunction (line 19) | class SimpleReduceFunction(ReduceFunction): method __init__ (line 23) | def __init__(self, name, msg_field, out_field): method name (line 29) | def name(self): function _gen_reduce_builtin (line 38) | def _gen_reduce_builtin(reducer): function _register_builtin_reduce_func (line 74) | def _register_builtin_reduce_func(): FILE: python/dgl/generators.py function rand_graph (line 8) | def rand_graph(num_nodes, num_edges, idtype=F.int64, device=F.cpu()): function rand_bipartite (line 60) | def rand_bipartite( FILE: python/dgl/geometry/capi.py function _farthest_point_sampler (line 9) | def _farthest_point_sampler( function _neighbor_matching (line 47) | def _neighbor_matching( FILE: python/dgl/geometry/edge_coarsening.py function neighbor_matching (line 9) | def neighbor_matching(graph, e_weights=None, relabel_idx=True): FILE: python/dgl/geometry/fps.py function farthest_point_sampler (line 11) | def farthest_point_sampler(pos, npoints, start_idx=None): FILE: python/dgl/global_config.py function use_libxsmm (line 7) | def use_libxsmm(flag): function is_libxsmm_enabled (line 25) | def is_libxsmm_enabled(): FILE: python/dgl/graph_index.py class BoolFlag (line 14) | class BoolFlag(object): class GraphIndex (line 23) | class GraphIndex(ObjectBase): method __new__ (line 39) | def __new__(cls): method __getstate__ (line 45) | def __getstate__(self): method __setstate__ (line 52) | def __setstate__(self, state): method add_nodes (line 79) | def add_nodes(self, num): method add_edge (line 90) | def add_edge(self, u, v): method add_edges (line 103) | def add_edges(self, u, v): method clear (line 118) | def clear(self): method clear_cache (line 123) | def clear_cache(self): method is_multigraph (line 127) | def is_multigraph(self): method is_readonly (line 138) | def is_readonly(self): method readonly (line 150) | def readonly(self, readonly_state=True): method num_nodes (line 164) | def num_nodes(self): method num_edges (line 174) | def num_edges(self): method number_of_nodes (line 185) | def number_of_nodes(self): method number_of_edges (line 196) | def number_of_edges(self): method has_node (line 206) | def has_node(self, vid): method has_nodes (line 221) | def has_nodes(self, vids): method has_edge_between (line 237) | def has_edge_between(self, u, v): method has_edges_between (line 254) | def has_edges_between(self, u, v): method predecessors (line 275) | def predecessors(self, v, radius=1): method successors (line 294) | def successors(self, v, radius=1): method edge_id (line 313) | def edge_id(self, u, v): method edge_ids (line 330) | def edge_ids(self, u, v): method find_edge (line 359) | def find_edge(self, eid): method find_edges (line 377) | def find_edges(self, eid): method in_edges (line 403) | def in_edges(self, v): method out_edges (line 430) | def out_edges(self, v): method sort_csr (line 457) | def sort_csr(self): method edges (line 468) | def edges(self, order=None): method in_degree (line 500) | def in_degree(self, v): method in_degrees (line 515) | def in_degrees(self, v): method out_degree (line 531) | def out_degree(self, v): method out_degrees (line 546) | def out_degrees(self, v): method node_subgraph (line 562) | def node_subgraph(self, v): method node_halo_subgraph (line 578) | def node_halo_subgraph(self, v, num_hops): method node_subgraphs (line 603) | def node_subgraphs(self, vs_arr): method edge_subgraph (line 621) | def edge_subgraph(self, e, preserve_nodes=False): method adjacency_matrix_scipy (line 642) | def adjacency_matrix_scipy(self, transpose, fmt, return_edge_ids=None): method get_immutable_gidx (line 705) | def get_immutable_gidx(self, ctx): method get_csr_shuffle_order (line 721) | def get_csr_shuffle_order(self): method adjacency_matrix (line 736) | def adjacency_matrix(self, transpose, ctx): method incidence_matrix (line 793) | def incidence_matrix(self, typestr, ctx): method to_networkx (line 871) | def to_networkx(self): method line_graph (line 889) | def line_graph(self, backtracking=True): method to_immutable (line 905) | def to_immutable(self): method ctx (line 915) | def ctx(self): method dtype (line 926) | def dtype(self): method copy_to (line 940) | def copy_to(self, ctx): method copyto_shared_mem (line 959) | def copyto_shared_mem(self, shared_mem_name): method nbits (line 976) | def nbits(self): method bits_needed (line 986) | def bits_needed(self): method asbits (line 999) | def asbits(self, bits): class SubgraphIndex (line 1018) | class SubgraphIndex(ObjectBase): method graph (line 1022) | def graph(self): method induced_nodes (line 1033) | def induced_nodes(self): method induced_edges (line 1046) | def induced_edges(self): function from_coo (line 1062) | def from_coo(num_nodes, src, dst, readonly): function from_csr (line 1094) | def from_csr(indptr, indices, direction): function from_shared_mem_graph_index (line 1119) | def from_shared_mem_graph_index(shared_mem_name): function from_networkx (line 1135) | def from_networkx(nx_graph, readonly): function from_scipy_sparse_matrix (line 1189) | def from_scipy_sparse_matrix(adj, readonly): function from_edge_list (line 1212) | def from_edge_list(elist, readonly): function map_to_subgraph_nid (line 1232) | def map_to_subgraph_nid(induced_nodes, parent_nids): function transform_ids (line 1255) | def transform_ids(mapping, ids): function disjoint_union (line 1275) | def disjoint_union(graphs): function disjoint_partition (line 1297) | def disjoint_partition(graph, num_or_size_splits): function create_graph_index (line 1326) | def create_graph_index(graph_data, readonly): function _get_halo_subgraph_inner_node (line 1362) | def _get_halo_subgraph_inner_node(halo_subg): FILE: python/dgl/graphbolt/__init__.py function load_graphbolt (line 50) | def load_graphbolt(): FILE: python/dgl/graphbolt/base.py function get_host_to_device_uva_stream (line 54) | def get_host_to_device_uva_stream(): function get_device_to_host_uva_stream (line 61) | def get_device_to_host_uva_stream(): function seed (line 68) | def seed(val): function isin (line 79) | def isin(elements, test_elements): function expand_indptr_fake (line 110) | def expand_indptr_fake(indptr, dtype, node_ids, output_size): function expand_indptr (line 119) | def expand_indptr(indptr, dtype=None, node_ids=None, output_size=None): function indptr_edge_ids_fake (line 173) | def indptr_edge_ids_fake(indptr, dtype, offset, output_size): function indptr_edge_ids (line 182) | def indptr_edge_ids(indptr, dtype=None, offset=None, output_size=None): function index_select (line 215) | def index_select(tensor, index): function etype_tuple_to_str (line 243) | def etype_tuple_to_str(c_etype): function etype_str_to_tuple (line 260) | def etype_str_to_tuple(c_etype): function seed_type_str_to_ntypes (line 280) | def seed_type_str_to_ntypes(seed_type, seed_size): function apply_to (line 311) | def apply_to(x, device, non_blocking=False): function is_object_pinned (line 323) | def is_object_pinned(obj): class CopyTo (line 338) | class CopyTo(IterDataPipe): method __init__ (line 364) | def __init__(self, datapipe, device, non_blocking=False): method __iter__ (line 370) | def __iter__(self): class EndMarker (line 378) | class EndMarker(IterDataPipe): method __init__ (line 381) | def __init__(self, datapipe): method __iter__ (line 384) | def __iter__(self): class Bufferer (line 389) | class Bufferer(IterDataPipe): method __init__ (line 402) | def __init__(self, datapipe, buffer_size=1): method __iter__ (line 410) | def __iter__(self): method __getstate__ (line 421) | def __getstate__(self): method __setstate__ (line 427) | def __setstate__(self, state): method reset (line 431) | def reset(self): class Waiter (line 437) | class Waiter(IterDataPipe): method __init__ (line 440) | def __init__(self, datapipe): method __iter__ (line 443) | def __iter__(self): class CSCFormatBase (line 450) | class CSCFormatBase: method __init__ (line 467) | def __init__(self, indptr: torch.Tensor, indices: torch.Tensor): method __repr__ (line 475) | def __repr__(self) -> str: method to (line 478) | def to( # pylint: disable=invalid-name method pin_memory (line 499) | def pin_memory(self): method is_pinned (line 504) | def is_pinned(self) -> bool: function _csc_format_base_str (line 510) | def _csc_format_base_str(csc_format_base: CSCFormatBase) -> str: FILE: python/dgl/graphbolt/dataloader.py function _find_and_wrap_parent (line 25) | def _find_and_wrap_parent(datapipe_graph, target_datapipe, wrapper, **kw... function _set_worker_id (line 44) | def _set_worker_id(worked_id): class MultiprocessingWrapper (line 48) | class MultiprocessingWrapper(torch_data.IterDataPipe): method __init__ (line 64) | def __init__(self, datapipe, num_workers=0, persistent_workers=True): method __iter__ (line 74) | def __iter__(self): class DataLoader (line 78) | class DataLoader(MiniBatchTransformer): method __init__ (line 108) | def __init__( FILE: python/dgl/graphbolt/datapipes/utils.py function _get_parents (line 27) | def _get_parents(result_dict, datapipe_graph): function datapipe_graph_to_adjlist (line 34) | def datapipe_graph_to_adjlist(datapipe_graph): function find_dps (line 79) | def find_dps(graph: DataPipeGraph, dp_type: Type[DataPipe]) -> List[Data... function replace_dp (line 105) | def replace_dp( function _replace_dp (line 128) | def _replace_dp( function _assign_attr (line 142) | def _assign_attr(obj, old_dp, new_dp, inner_dp: bool = False): class _PrefetchData (line 198) | class _PrefetchData: method __init__ (line 199) | def __init__(self, source_datapipe, buffer_size: int): class PrefetcherIterDataPipe (line 211) | class PrefetcherIterDataPipe(IterDataPipe): method __init__ (line 236) | def __init__(self, source_datapipe, buffer_size: int = 10): method thread_worker (line 247) | def thread_worker( method __iter__ (line 272) | def __iter__(self): method __getstate__ (line 307) | def __getstate__(self): method __setstate__ (line 322) | def __setstate__(self, state): method reset (line 328) | def reset(self): # pylint: disable=missing-function-docstring method pause (line 331) | def pause(self): # pylint: disable=missing-function-docstring method resume (line 341) | def resume(self): # pylint: disable=missing-function-docstring method shutdown (line 354) | def shutdown(self): # pylint: disable=missing-function-docstring method __del__ (line 364) | def __del__(self): method __len__ (line 367) | def __len__(self) -> int: FILE: python/dgl/graphbolt/datapipes/visualization.py class Node (line 28) | class Node: method __init__ (line 29) | def __init__(self, dp, *, name=None): method add_child (line 35) | def add_child(self, child): method remove_child (line 39) | def remove_child(self, child): method add_parent (line 43) | def add_parent(self, parent): method remove_parent (line 47) | def remove_parent(self, parent): method __eq__ (line 51) | def __eq__(self, other): method __hash__ (line 57) | def __hash__(self): method __str__ (line 60) | def __str__(self): method __repr__ (line 63) | def __repr__(self): function to_nodes (line 67) | def to_nodes(dp, *, debug: bool) -> Set[Node]: function to_graph (line 140) | def to_graph(dp, *, debug: bool = False) -> "graphviz.Digraph": FILE: python/dgl/graphbolt/dataset.py class Task (line 15) | class Task: method metadata (line 31) | def metadata(self) -> Dict: method train_set (line 36) | def train_set(self) -> Union[ItemSet, HeteroItemSet]: method validation_set (line 41) | def validation_set(self) -> Union[ItemSet, HeteroItemSet]: method test_set (line 46) | def test_set(self) -> Union[ItemSet, HeteroItemSet]: class Dataset (line 51) | class Dataset: method tasks (line 73) | def tasks(self) -> List[Task]: method graph (line 78) | def graph(self) -> SamplingGraph: method feature (line 83) | def feature(self) -> FeatureStore: method dataset_name (line 88) | def dataset_name(self) -> str: method all_nodes_set (line 93) | def all_nodes_set(self) -> Union[ItemSet, HeteroItemSet]: FILE: python/dgl/graphbolt/external_utils.py class SeedEdgesExcluder (line 14) | class SeedEdgesExcluder(MiniBatchTransformer): method __init__ (line 33) | def __init__( method _wait_for_sampled_subgraphs (line 53) | def _wait_for_sampled_subgraphs(minibatch): function add_reverse_edges (line 60) | def add_reverse_edges( function exclude_seed_edges (line 128) | def exclude_seed_edges( FILE: python/dgl/graphbolt/feature_fetcher.py function get_feature_key_list (line 22) | def get_feature_key_list(feature_keys, domain): class FeatureFetcherStartMarker (line 37) | class FeatureFetcherStartMarker(MiniBatchTransformer): method __init__ (line 44) | def __init__(self, datapipe): class FeatureFetcher (line 49) | class FeatureFetcher(MiniBatchTransformer): method __init__ (line 89) | def __init__( method _execute_stage (line 138) | def _execute_stage(current_stage, data): method _final_stage (line 152) | def _final_stage(data): method _cooperative_exchange (line 163) | def _cooperative_exchange(self, data): method _read (line 183) | def _read(self, data): FILE: python/dgl/graphbolt/feature_store.py class FeatureKey (line 16) | class FeatureKey(NamedTuple): class Feature (line 26) | class Feature: method __init__ (line 29) | def __init__(self): method read (line 32) | def read(self, ids: torch.Tensor = None): method read_async (line 47) | def read_async(self, ids: torch.Tensor): method read_async_num_stages (line 75) | def read_async_num_stages(self, ids_device: torch.device): method size (line 92) | def size(self): method count (line 102) | def count(self): method update (line 112) | def update(self, value: torch.Tensor, ids: torch.Tensor = None): method metadata (line 128) | def metadata(self): class FeatureStore (line 139) | class FeatureStore: method __init__ (line 142) | def __init__(self): method __getitem__ (line 145) | def __getitem__(self, feature_key: FeatureKey) -> Feature: method __setitem__ (line 151) | def __setitem__(self, feature_key: FeatureKey, feature: Feature): method __contains__ (line 157) | def __contains__(self, feature_key: FeatureKey) -> bool: method read (line 162) | def read( method size (line 190) | def size( method count (line 213) | def count( method metadata (line 236) | def metadata( method update (line 259) | def update( method keys (line 288) | def keys(self): function bytes_to_number_of_items (line 300) | def bytes_to_number_of_items(cache_capacity_in_bytes, single_item): function wrap_with_cached_feature (line 307) | def wrap_with_cached_feature( FILE: python/dgl/graphbolt/impl/basic_feature_store.py class BasicFeatureStore (line 10) | class BasicFeatureStore(FeatureStore): method __init__ (line 13) | def __init__(self, features: Dict[Tuple[str, str, str], Feature]): method __getitem__ (line 30) | def __getitem__(self, feature_key: FeatureKey) -> Feature: method __setitem__ (line 36) | def __setitem__(self, feature_key: FeatureKey, feature: Feature): method __contains__ (line 42) | def __contains__(self, feature_key: FeatureKey) -> bool: method __len__ (line 47) | def __len__(self): method keys (line 51) | def keys(self): FILE: python/dgl/graphbolt/impl/cooperative_conv.py class CooperativeConvFunction (line 12) | class CooperativeConvFunction(torch.autograd.Function): method forward (line 28) | def forward( method backward (line 60) | def backward( class CooperativeConv (line 96) | class CooperativeConv(torch.nn.Module): method forward (line 111) | def forward( FILE: python/dgl/graphbolt/impl/cpu_cached_feature.py class CPUCachedFeature (line 19) | class CPUCachedFeature(Feature): method __init__ (line 37) | def __init__( method read (line 52) | def read(self, ids: torch.Tensor = None): method read_async (line 72) | def read_async(self, ids: torch.Tensor): method read_async_num_stages (line 379) | def read_async_num_stages(self, ids_device: torch.device): method size (line 401) | def size(self): method count (line 411) | def count(self): method update (line 421) | def update(self, value: torch.Tensor, ids: torch.Tensor = None): method is_pinned (line 450) | def is_pinned(self): method cache_size_in_bytes (line 455) | def cache_size_in_bytes(self): method miss_rate (line 460) | def miss_rate(self): function cpu_cached_feature (line 465) | def cpu_cached_feature( FILE: python/dgl/graphbolt/impl/cpu_feature_cache.py class CPUFeatureCache (line 14) | class CPUFeatureCache(object): method __init__ (line 33) | def __init__( method is_pinned (line 62) | def is_pinned(self): method max_size_in_bytes (line 67) | def max_size_in_bytes(self): method query (line 71) | def query(self, keys, offset=0): method query_and_replace (line 106) | def query_and_replace(self, keys, reader_fn, offset=0): method replace (line 152) | def replace(self, keys, values, offsets=None, offset=0): method miss_rate (line 174) | def miss_rate(self): FILE: python/dgl/graphbolt/impl/fused_csc_sampling_graph.py class _SampleNeighborsWaiter (line 25) | class _SampleNeighborsWaiter: method __init__ (line 26) | def __init__( method wait (line 36) | def wait(self): class FusedCSCSamplingGraph (line 54) | class FusedCSCSamplingGraph(SamplingGraph): method __repr__ (line 57) | def __repr__(self): method __init__ (line 91) | def __init__( method __del__ (line 98) | def __del__(self): method total_num_nodes (line 108) | def total_num_nodes(self) -> int: method total_num_edges (line 119) | def total_num_edges(self) -> int: method num_nodes (line 130) | def num_nodes(self) -> Union[int, Dict[str, int]]: method num_edges (line 180) | def num_edges(self) -> Union[int, Dict[str, int]]: method csc_indptr (line 229) | def csc_indptr(self) -> torch.tensor: method csc_indptr (line 241) | def csc_indptr(self, csc_indptr: torch.tensor) -> None: method indices (line 246) | def indices(self) -> torch.tensor: method indices (line 263) | def indices(self, indices: torch.tensor) -> None: method node_type_offset (line 268) | def node_type_offset(self) -> Optional[torch.Tensor]: method _node_type_offset_list (line 286) | def _node_type_offset_list(self) -> Optional[list]: method node_type_offset (line 312) | def node_type_offset( method _indptr_node_type_offset_list (line 320) | def _indptr_node_type_offset_list(self) -> Optional[list]: method _indptr_node_type_offset_list (line 342) | def _indptr_node_type_offset_list( method _gpu_graph_cache (line 349) | def _gpu_graph_cache(self) -> Optional[GPUGraphCache]: method type_per_edge (line 357) | def type_per_edge(self) -> Optional[torch.Tensor]: method type_per_edge (line 369) | def type_per_edge(self, type_per_edge: Optional[torch.Tensor]) -> None: method node_type_to_id (line 374) | def node_type_to_id(self) -> Optional[Dict[str, int]]: method node_type_to_id (line 386) | def node_type_to_id( method edge_type_to_id (line 393) | def edge_type_to_id(self) -> Optional[Dict[str, int]]: method edge_type_to_id (line 405) | def edge_type_to_id( method node_attributes (line 412) | def node_attributes(self) -> Optional[Dict[str, torch.Tensor]]: method node_attributes (line 426) | def node_attributes( method edge_attributes (line 433) | def edge_attributes(self) -> Optional[Dict[str, torch.Tensor]]: method edge_attributes (line 447) | def edge_attributes( method node_attribute (line 453) | def node_attribute(self, name: str) -> Optional[torch.Tensor]: method add_node_attribute (line 468) | def add_node_attribute(self, name: str, tensor: torch.Tensor) -> None: method edge_attribute (line 480) | def edge_attribute(self, name: str) -> Optional[torch.Tensor]: method add_edge_attribute (line 495) | def add_edge_attribute(self, name: str, tensor: torch.Tensor) -> None: method in_subgraph (line 507) | def in_subgraph( method _convert_to_homogeneous_nodes (line 571) | def _convert_to_homogeneous_nodes( method _convert_to_sampled_subgraph (line 604) | def _convert_to_sampled_subgraph( method sample_neighbors (line 750) | def sample_neighbors( method _check_sampler_arguments (line 866) | def _check_sampler_arguments(self, nodes, fanouts, probs_or_mask): method _sample_neighbors (line 905) | def _sample_neighbors( method sample_layer_neighbors (line 984) | def sample_layer_neighbors( method temporal_sample_neighbors (line 1147) | def temporal_sample_neighbors( method temporal_sample_layer_neighbors (line 1250) | def temporal_sample_layer_neighbors( method sample_negative_edges_uniform (line 1384) | def sample_negative_edges_uniform( method copy_to_shared_memory (line 1448) | def copy_to_shared_memory(self, shared_memory_name: str): method _apply_to_members (line 1465) | def _apply_to_members(self, fn): method to (line 1476) | def to(self, device: torch.device) -> None: # pylint: disable=invalid... method pin_memory_ (line 1498) | def pin_memory_(self): method _initialize_gpu_graph_cache (line 1540) | def _initialize_gpu_graph_cache( function fused_csc_sampling_graph (line 1569) | def fused_csc_sampling_graph( function load_from_shared_memory (line 1684) | def load_from_shared_memory( function from_dglgraph (line 1704) | def from_dglgraph( FILE: python/dgl/graphbolt/impl/gpu_cached_feature.py class GPUCachedFeature (line 18) | class GPUCachedFeature(Feature): method __init__ (line 60) | def __init__( method read (line 75) | def read(self, ids: torch.Tensor = None): method read_async (line 104) | def read_async(self, ids: torch.Tensor): method read_async_num_stages (line 183) | def read_async_num_stages(self, ids_device: torch.device): method size (line 201) | def size(self): method count (line 211) | def count(self): method update (line 221) | def update(self, value: torch.Tensor, ids: torch.Tensor = None): method cache_size_in_bytes (line 251) | def cache_size_in_bytes(self): method miss_rate (line 256) | def miss_rate(self): function gpu_cached_feature (line 261) | def gpu_cached_feature( FILE: python/dgl/graphbolt/impl/gpu_feature_cache.py class GPUFeatureCache (line 8) | class GPUFeatureCache(object): method __init__ (line 11) | def __init__(self, cache_shape, dtype): method query (line 22) | def query(self, keys, async_op=False): method replace (line 64) | def replace(self, keys, values): method miss_rate (line 78) | def miss_rate(self): FILE: python/dgl/graphbolt/impl/gpu_graph_cache.py class GPUGraphCache (line 5) | class GPUGraphCache(object): method __init__ (line 24) | def __init__( method query (line 37) | def query(self, keys): method query_async (line 76) | def query_async(self, keys): method miss_rate (line 116) | def miss_rate(self): FILE: python/dgl/graphbolt/impl/in_subgraph_sampler.py class InSubgraphSampler (line 15) | class InSubgraphSampler(SubgraphSampler): method __init__ (line 61) | def __init__( method sample_subgraphs (line 70) | def sample_subgraphs( FILE: python/dgl/graphbolt/impl/legacy_dataset.py class LegacyDataset (line 15) | class LegacyDataset(Dataset): method __init__ (line 18) | def __init__(self, legacy): method _init_as_heterogeneous_node_pred (line 30) | def _init_as_heterogeneous_node_pred(self, legacy): method _init_as_homogeneous_node_pred (line 89) | def _init_as_homogeneous_node_pred(self, legacy): method tasks (line 136) | def tasks(self) -> List[Task]: method graph (line 141) | def graph(self) -> SamplingGraph: method feature (line 146) | def feature(self) -> BasicFeatureStore: method dataset_name (line 151) | def dataset_name(self) -> str: method all_nodes_set (line 156) | def all_nodes_set(self) -> Union[ItemSet, HeteroItemSet]: FILE: python/dgl/graphbolt/impl/neighbor_sampler.py class FetchCachedInsubgraphData (line 38) | class FetchCachedInsubgraphData(Mapper): method __init__ (line 43) | def __init__(self, datapipe, gpu_graph_cache): method _fetch_per_layer (line 48) | def _fetch_per_layer(self, minibatch): method _wait_query_future (line 56) | def _wait_query_future(minibatch): class CombineCachedAndFetchedInSubgraph (line 63) | class CombineCachedAndFetchedInSubgraph(Mapper): method __init__ (line 68) | def __init__(self, datapipe, prob_name): method _combine_per_layer (line 73) | def _combine_per_layer(self, minibatch): method _wait_replace_future (line 91) | def _wait_replace_future(self, minibatch): class FetchInsubgraphData (line 113) | class FetchInsubgraphData(MiniBatchTransformer): method __init__ (line 120) | def __init__( method _concat_hetero_seeds (line 140) | def _concat_hetero_seeds(self, minibatch): method _fetch_per_layer_stage_1 (line 155) | def _fetch_per_layer_stage_1(self, minibatch): method _fetch_per_layer_stage_2 (line 160) | def _fetch_per_layer_stage_2(self, minibatch): method _fetch_per_layer_async (line 165) | def _fetch_per_layer_async(self, minibatch): class SamplePerLayer (line 257) | class SamplePerLayer(MiniBatchTransformer): method __init__ (line 260) | def __init__( method _sample_per_layer (line 334) | def _sample_per_layer(self, minibatch): method _sample_per_layer_from_fetched_subgraph (line 352) | def _sample_per_layer_from_fetched_subgraph(self, minibatch): method _wait_subgraph_future (line 372) | def _wait_subgraph_future(minibatch): method _fetch_indices_and_original_edge_ids (line 377) | def _fetch_indices_and_original_edge_ids(indices, orig_edge_ids, minib... method _subtract_hetero_indices_offset (line 445) | def _subtract_hetero_indices_offset( class CompactPerLayer (line 460) | class CompactPerLayer(MiniBatchTransformer): method __init__ (line 463) | def __init__( method _compact_per_layer (line 492) | def _compact_per_layer(self, minibatch): method _compact_per_layer_async (line 522) | def _compact_per_layer_async(self, minibatch): method _compact_per_layer_wait_future (line 533) | def _compact_per_layer_wait_future(self, minibatch): method _seeds_cooperative_exchange_1 (line 555) | def _seeds_cooperative_exchange_1(minibatch): method _seeds_cooperative_exchange_2 (line 579) | def _seeds_cooperative_exchange_2(minibatch): method _seeds_cooperative_exchange_3 (line 612) | def _seeds_cooperative_exchange_3(minibatch): method _seeds_cooperative_exchange_4 (line 623) | def _seeds_cooperative_exchange_4(minibatch): class NeighborSamplerImpl (line 640) | class NeighborSamplerImpl(SubgraphSampler): method __init__ (line 645) | def __init__( method _init_seed (line 683) | def _init_seed(self, batch_dependency): method _set_seed (line 693) | def _set_seed(self, minibatch): method _increment_seed (line 708) | def _increment_seed(minibatch): method _delattr_dependency (line 713) | def _delattr_dependency(minibatch): method _prepare (line 719) | def _prepare(node_type_to_id, minibatch): method _set_input_nodes (line 740) | def _set_input_nodes(minibatch): method sampling_stages (line 745) | def sampling_stages( class NeighborSampler (line 783) | class NeighborSampler(NeighborSamplerImpl): method __init__ (line 895) | def __init__( class LayerNeighborSampler (line 926) | class LayerNeighborSampler(NeighborSamplerImpl): method __init__ (line 1067) | def __init__( FILE: python/dgl/graphbolt/impl/ondisk_dataset.py function _graph_data_to_fused_csc_sampling_graph (line 52) | def _graph_data_to_fused_csc_sampling_graph( function preprocess_ondisk_dataset (line 321) | def preprocess_ondisk_dataset( class OnDiskTask (line 518) | class OnDiskTask: method __init__ (line 525) | def __init__( method metadata (line 551) | def metadata(self) -> Dict: method train_set (line 556) | def train_set(self) -> Union[ItemSet, HeteroItemSet]: method validation_set (line 561) | def validation_set(self) -> Union[ItemSet, HeteroItemSet]: method test_set (line 566) | def test_set(self) -> Union[ItemSet, HeteroItemSet]: method __repr__ (line 570) | def __repr__(self) -> str: class OnDiskDataset (line 591) | class OnDiskDataset(Dataset): method __init__ (line 678) | def __init__( method _convert_yaml_path_to_absolute_path (line 698) | def _convert_yaml_path_to_absolute_path(self): method load (line 720) | def load(self, tasks: List[str] = None): method yaml_data (line 773) | def yaml_data(self) -> Dict: method tasks (line 778) | def tasks(self) -> List[Task]: method graph (line 784) | def graph(self) -> SamplingGraph: method feature (line 790) | def feature(self) -> TorchBasedFeatureStore: method dataset_name (line 796) | def dataset_name(self) -> str: method all_nodes_set (line 802) | def all_nodes_set(self) -> Union[ItemSet, HeteroItemSet]: method _init_tasks (line 807) | def _init_tasks( method _check_loaded (line 842) | def _check_loaded(self): method _load_graph (line 848) | def _load_graph( method _init_tvt_set (line 860) | def _init_tvt_set( method _init_all_nodes_set (line 891) | def _init_all_nodes_set(self, graph) -> Union[ItemSet, HeteroItemSet]: class BuiltinDataset (line 915) | class BuiltinDataset(OnDiskDataset): method __init__ (line 1053) | def __init__(self, name: str, root: str = "datasets") -> OnDiskDataset: FILE: python/dgl/graphbolt/impl/ondisk_metadata.py class ExtraMetaData (line 24) | class ExtraMetaData(pydantic.BaseModel, extra="allow"): method build_extra (line 36) | def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: method build_extra (line 48) | def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: class OnDiskFeatureDataFormat (line 57) | class OnDiskFeatureDataFormat(str, Enum): class OnDiskTVTSetData (line 64) | class OnDiskTVTSetData(pydantic.BaseModel): class OnDiskTVTSet (line 73) | class OnDiskTVTSet(pydantic.BaseModel): class OnDiskFeatureDataDomain (line 80) | class OnDiskFeatureDataDomain(str, Enum): class OnDiskFeatureData (line 88) | class OnDiskFeatureData(ExtraMetaData): class OnDiskGraphTopologyType (line 98) | class OnDiskGraphTopologyType(str, Enum): class OnDiskGraphTopology (line 104) | class OnDiskGraphTopology(pydantic.BaseModel): class OnDiskTaskData (line 111) | class OnDiskTaskData(ExtraMetaData): class OnDiskMetaData (line 119) | class OnDiskMetaData(pydantic.BaseModel): FILE: python/dgl/graphbolt/impl/sampled_subgraph_impl.py class SampledSubgraphImpl (line 16) | class SampledSubgraphImpl(SampledSubgraph): method __post_init__ (line 53) | def __post_init__(self): method __repr__ (line 92) | def __repr__(self) -> str: function _sampled_subgraph_str (line 96) | def _sampled_subgraph_str(sampled_subgraph: SampledSubgraph, classname) ... FILE: python/dgl/graphbolt/impl/temporal_neighbor_sampler.py class TemporalNeighborSamplerImpl (line 14) | class TemporalNeighborSamplerImpl(SubgraphSampler): method __init__ (line 17) | def __init__( method sample_subgraphs (line 42) | def sample_subgraphs( class TemporalNeighborSampler (line 107) | class TemporalNeighborSampler(TemporalNeighborSamplerImpl): method __init__ (line 160) | def __init__( class TemporalLayerNeighborSampler (line 183) | class TemporalLayerNeighborSampler(TemporalNeighborSamplerImpl): method __init__ (line 251) | def __init__( FILE: python/dgl/graphbolt/impl/torch_based_feature_store.py class _Waiter (line 23) | class _Waiter: method __init__ (line 24) | def __init__(self, event, values): method wait (line 28) | def wait(self): class TorchBasedFeature (line 37) | class TorchBasedFeature(Feature): method __init__ (line 95) | def __init__(self, torch_feature: torch.Tensor, metadata: Dict = None): method __del__ (line 110) | def __del__(self): method read (line 118) | def read(self, ids: torch.Tensor = None): method read_async (line 142) | def read_async(self, ids: torch.Tensor): method read_async_num_stages (line 209) | def read_async_num_stages(self, ids_device: torch.device): method size (line 232) | def size(self): method count (line 242) | def count(self): method update (line 252) | def update(self, value: torch.Tensor, ids: torch.Tensor = None): method metadata (line 284) | def metadata(self): method pin_memory_ (line 296) | def pin_memory_(self): method is_pinned (line 321) | def is_pinned(self): method to (line 325) | def to(self, device): # pylint: disable=invalid-name method __repr__ (line 335) | def __repr__(self) -> str: class DiskBasedFeature (line 357) | class DiskBasedFeature(Feature): method __init__ (line 387) | def __init__(self, path: str, metadata: Dict = None, num_threads=None): method read (line 402) | def read(self, ids: torch.Tensor = None): method read_async (line 425) | def read_async(self, ids: torch.Tensor): method read_async_num_stages (line 480) | def read_async_num_stages(self, ids_device: torch.device): method size (line 497) | def size(self): method count (line 506) | def count(self): method update (line 516) | def update(self, value: torch.Tensor, ids: torch.Tensor = None): method metadata (line 520) | def metadata(self): method read_into_memory (line 531) | def read_into_memory(self) -> TorchBasedFeature: method to (line 535) | def to(self, _): # pylint: disable=invalid-name method pin_memory_ (line 542) | def pin_memory_(self): # pylint: disable=invalid-name method __repr__ (line 549) | def __repr__(self) -> str: class TorchBasedFeatureStore (line 571) | class TorchBasedFeatureStore(BasicFeatureStore): method __init__ (line 607) | def __init__(self, feat_data: List[OnDiskFeatureData]): method pin_memory_ (line 635) | def pin_memory_(self): method is_pinned (line 649) | def is_pinned(self): method to (line 653) | def to(self, device): # pylint: disable=invalid-name method __repr__ (line 660) | def __repr__(self) -> str: FILE: python/dgl/graphbolt/impl/uniform_negative_sampler.py class UniformNegativeSampler (line 12) | class UniformNegativeSampler(NegativeSampler): method __init__ (line 55) | def __init__( method _sample_with_etype (line 64) | def _sample_with_etype(self, seeds, etype=None): FILE: python/dgl/graphbolt/internal/item_sampler_utils.py function count_split (line 4) | def count_split(total, num_workers, worker_id, batch_size=1): function calculate_range (line 25) | def calculate_range( FILE: python/dgl/graphbolt/internal/sample_utils.py function unique_and_compact (line 11) | def unique_and_compact( function compact_temporal_nodes (line 126) | def compact_temporal_nodes(nodes, nodes_timestamp): function unique_and_compact_csc_formats (line 181) | def unique_and_compact_csc_formats( function _broadcast_timestamps (line 363) | def _broadcast_timestamps(csc, dst_timestamps): function compact_csc_format (line 371) | def compact_csc_format( FILE: python/dgl/graphbolt/internal/utils.py function numpy_save_aligned (line 15) | def numpy_save_aligned(*args, **kwargs): function _read_torch_data (line 30) | def _read_torch_data(path): function _read_numpy_data (line 34) | def _read_numpy_data(path, in_memory=True): function read_data (line 40) | def read_data(path, fmt, in_memory=True): function save_data (line 50) | def save_data(data, path, fmt): function get_npy_dim (line 83) | def get_npy_dim(npy_path): function _to_int32 (line 103) | def _to_int32(data): function copy_or_convert_data (line 114) | def copy_or_convert_data( function read_edges (line 147) | def read_edges(dataset_dir, edge_fmt, edge_path): function calculate_file_hash (line 171) | def calculate_file_hash(file_path, hash_algo="md5"): function calculate_dir_hash (line 186) | def calculate_dir_hash( function check_dataset_change (line 201) | def check_dataset_change(dataset_dir, processed_dir): FILE: python/dgl/graphbolt/internal_utils.py function is_wsl (line 21) | def is_wsl(v: str = platform.uname().release) -> int: function built_with_cuda (line 36) | def built_with_cuda(): class GBWarning (line 42) | class GBWarning(UserWarning): function gb_warning_format (line 47) | def gb_warning_format(message, category, filename, lineno, line=None): function gb_warning (line 57) | def gb_warning(message, category=GBWarning, stacklevel=2): function is_listlike (line 67) | def is_listlike(data): function recursive_apply (line 72) | def recursive_apply(data, fn, *args, **kwargs): function recursive_apply_reduce_all (line 116) | def recursive_apply_reduce_all(data, fn, *args, **kwargs): function get_nonproperty_attributes (line 154) | def get_nonproperty_attributes(_obj) -> list: function get_attributes (line 169) | def get_attributes(_obj) -> list: function download (line 180) | def download( function check_sha1 (line 288) | def check_sha1(filename, sha1_hash): function extract_archive (line 316) | def extract_archive(file, target_dir, overwrite=True): FILE: python/dgl/graphbolt/item_sampler.py function minibatcher_default (line 19) | def minibatcher_default(batch, names): class ItemSampler (line 108) | class ItemSampler(IterDataPipe): method __init__ (line 281) | def __init__( method __iter__ (line 312) | def __iter__(self) -> Iterator: class DistributedItemSampler (line 352) | class DistributedItemSampler(ItemSampler): method __init__ (line 500) | def __init__( method _align_seeds (line 531) | def _align_seeds( function _construct_seeds (line 562) | def _construct_seeds(pos_seeds, neg_srcs=None, neg_dsts=None): FILE: python/dgl/graphbolt/itemset.py function is_scalar (line 13) | def is_scalar(x): class ItemSet (line 20) | class ItemSet: method __init__ (line 132) | def __init__( method __len__ (line 163) | def __len__(self) -> int: method __getitem__ (line 166) | def __getitem__(self, index: Union[int, slice, Iterable[int]]): method names (line 193) | def names(self) -> Tuple[str]: method num_items (line 198) | def num_items(self) -> int: method __repr__ (line 202) | def __repr__(self) -> str: class HeteroItemSet (line 212) | class HeteroItemSet: method __init__ (line 324) | def __init__(self, itemsets: Dict[str, ItemSet]) -> None: method __len__ (line 335) | def __len__(self) -> int: method __getitem__ (line 338) | def __getitem__(self, index: Union[int, slice, Iterable[int]]): method names (line 390) | def names(self) -> Tuple[str]: method __repr__ (line 394) | def __repr__(self) -> str: class ItemSetDict (line 413) | class ItemSetDict: method __init__ (line 423) | def __init__(self, itemsets: Dict[str, ItemSet]) -> None: method __getattr__ (line 431) | def __getattr__(self, name: str): method __getitem__ (line 434) | def __getitem__(self, index): method __len__ (line 437) | def __len__(self) -> int: method __repr__ (line 440) | def __repr__(self) -> str: FILE: python/dgl/graphbolt/minibatch.py class MiniBatch (line 26) | class MiniBatch: method __repr__ (line 129) | def __repr__(self) -> str: method node_ids (line 132) | def node_ids(self) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: method num_layers (line 141) | def num_layers(self) -> int: method edge_ids (line 147) | def edge_ids( method set_node_features (line 153) | def set_node_features( method set_edge_features (line 162) | def set_edge_features( method blocks (line 172) | def blocks(self) -> list: method compute_blocks (line 183) | def compute_blocks(self) -> list: method to_pyg_data (line 306) | def to_pyg_data(self): method to (line 359) | def to( method pin_memory (line 374) | def pin_memory(self): method is_pinned (line 379) | def is_pinned(self) -> bool: function _minibatch_str (line 385) | def _minibatch_str(minibatch: MiniBatch) -> str: FILE: python/dgl/graphbolt/minibatch_transformer.py class MiniBatchTransformer (line 15) | class MiniBatchTransformer(Mapper): method __init__ (line 29) | def __init__( method _transformer (line 37) | def _transformer(self, minibatch): method _identity (line 45) | def _identity(minibatch): FILE: python/dgl/graphbolt/negative_sampler.py class NegativeSampler (line 15) | class NegativeSampler(MiniBatchTransformer): method __init__ (line 30) | def __init__( method _sample (line 39) | def _sample(self, minibatch): method _sample_with_etype (line 78) | def _sample_with_etype(self, seeds, etype=None): FILE: python/dgl/graphbolt/sampled_subgraph.py class _ExcludeEdgesWaiter (line 23) | class _ExcludeEdgesWaiter: method __init__ (line 24) | def __init__(self, sampled_subgraph, index): method wait (line 28) | def wait(self): class PyGLayerData (line 44) | class PyGLayerData(NamedTuple): class PyGLayerHeteroData (line 55) | class PyGLayerHeteroData(NamedTuple): class SampledSubgraph (line 66) | class SampledSubgraph: method sampled_csc (line 73) | def sampled_csc( method original_column_node_ids (line 112) | def original_column_node_ids( method original_row_node_ids (line 130) | def original_row_node_ids( method original_edge_ids (line 147) | def original_edge_ids(self) -> Union[torch.Tensor, Dict[str, torch.Ten... method exclude_edges (line 159) | def exclude_edges( method to_pyg (line 286) | def to_pyg( method to (line 342) | def to( method pin_memory (line 363) | def pin_memory(self): method is_pinned (line 368) | def is_pinned(self) -> bool: function _to_reverse_ids (line 374) | def _to_reverse_ids(node_pair, original_row_node_ids, original_column_no... function _relabel_two_arrays (line 387) | def _relabel_two_arrays(lhs_array, rhs_array): function _exclude_homo_edges (line 394) | def _exclude_homo_edges( function _slice_subgraph (line 420) | def _slice_subgraph(subgraph: SampledSubgraph, index: torch.Tensor): FILE: python/dgl/graphbolt/sampling_graph.py class SamplingGraph (line 11) | class SamplingGraph: method __init__ (line 14) | def __init__(self): method __repr__ (line 17) | def __repr__(self) -> str: method num_nodes (line 28) | def num_nodes(self) -> Union[int, Dict[str, int]]: method num_edges (line 43) | def num_edges(self) -> Union[int, Dict[str, int]]: method copy_to_shared_memory (line 57) | def copy_to_shared_memory(self, shared_memory_name: str) -> "SamplingG... method to (line 73) | def to(self, device: torch.device) -> "SamplingGraph": FILE: python/dgl/graphbolt/subgraph_sampler.py class _NoOpWaiter (line 24) | class _NoOpWaiter: method __init__ (line 25) | def __init__(self, result): method wait (line 28) | def wait(self): function _shift (line 36) | def _shift(inputs: list, group=None): function all_to_all (line 41) | def all_to_all(outputs, inputs, group=None, async_op=False): function revert_to_homo (line 87) | def revert_to_homo(d: dict): function convert_to_hetero (line 93) | def convert_to_hetero(item): class SubgraphSampler (line 101) | class SubgraphSampler(MiniBatchTransformer): method __init__ (line 125) | def __init__( method _postprocess (line 156) | def _postprocess(minibatch): method _preprocess (line 162) | def _preprocess(minibatch, cooperative: bool, async_op: bool): method _wait_preprocess_future (line 186) | def _wait_preprocess_future(minibatch, cooperative: bool): method _seeds_cooperative_exchange_1 (line 199) | def _seeds_cooperative_exchange_1(minibatch): method _seeds_cooperative_exchange_1_wait_future (line 214) | def _seeds_cooperative_exchange_1_wait_future(minibatch): method _seeds_cooperative_exchange_2 (line 255) | def _seeds_cooperative_exchange_2(minibatch): method _seeds_cooperative_exchange_3 (line 284) | def _seeds_cooperative_exchange_3(minibatch): method _seeds_cooperative_exchange_4 (line 295) | def _seeds_cooperative_exchange_4(minibatch): method _sample (line 310) | def _sample(self, minibatch): method sampling_stages (line 319) | def sampling_stages(self, datapipe): method _seeds_preprocess (line 328) | def _seeds_preprocess( method sample_subgraphs (line 542) | def sample_subgraphs( FILE: python/dgl/heterograph.py class DGLGraph (line 40) | class DGLGraph(object): method __init__ (line 57) | def __init__( method _init (line 126) | def _init(self, gidx, ntypes, etypes, node_frames, edge_frames): method __setstate__ (line 212) | def __setstate__(self, state): method __repr__ (line 241) | def __repr__(self): method __copy__ (line 271) | def __copy__(self): method add_nodes (line 283) | def add_nodes(self, num, data=None, ntype=None): method add_edges (line 435) | def add_edges(self, u, v, data=None, etype=None): method remove_edges (line 629) | def remove_edges(self, eids, etype=None, store_ids=False): method remove_nodes (line 763) | def remove_nodes(self, nids, ntype=None, store_ids=False): method _reset_cached_info (line 948) | def _reset_cached_info(self): method is_unibipartite (line 960) | def is_unibipartite(self): method ntypes (line 978) | def ntypes(self): method etypes (line 1009) | def etypes(self): method canonical_etypes (line 1050) | def canonical_etypes(self): method srctypes (line 1090) | def srctypes(self): method dsttypes (line 1141) | def dsttypes(self): method metagraph (line 1191) | def metagraph(self): method to_canonical_etype (line 1230) | def to_canonical_etype(self, etype): method get_ntype_id (line 1297) | def get_ntype_id(self, ntype): method get_ntype_id_from_src (line 1335) | def get_ntype_id_from_src(self, ntype): method get_ntype_id_from_dst (line 1362) | def get_ntype_id_from_dst(self, ntype): method get_etype_id (line 1389) | def get_etype_id(self, etype): method batch_size (line 1420) | def batch_size(self): method batch_num_nodes (line 1461) | def batch_num_nodes(self, ntype=None): method set_batch_num_nodes (line 1526) | def set_batch_num_nodes(self, val): method batch_num_edges (line 1613) | def batch_num_edges(self, etype=None): method set_batch_num_edges (line 1678) | def set_batch_num_edges(self, val): method get_node_storage (line 1769) | def get_node_storage(self, key, ntype=None): method get_edge_storage (line 1773) | def get_edge_storage(self, key, etype=None): method nodes (line 1778) | def nodes(self): method srcnodes (line 1828) | def srcnodes(self): method dstnodes (line 1895) | def dstnodes(self): method ndata (line 1961) | def ndata(self): method srcdata (line 2024) | def srcdata(self): method dstdata (line 2090) | def dstdata(self): method edges (line 2157) | def edges(self): method edata (line 2245) | def edata(self): method _find_etypes (line 2304) | def _find_etypes(self, key): method __getitem__ (line 2316) | def __getitem__(self, key): method number_of_nodes (line 2489) | def number_of_nodes(self, ntype=None): method num_nodes (line 2493) | def num_nodes(self, ntype=None): method number_of_src_nodes (line 2541) | def number_of_src_nodes(self, ntype=None): method num_src_nodes (line 2545) | def num_src_nodes(self, ntype=None): method number_of_dst_nodes (line 2611) | def number_of_dst_nodes(self, ntype=None): method num_dst_nodes (line 2615) | def num_dst_nodes(self, ntype=None): method number_of_edges (line 2681) | def number_of_edges(self, etype=None): method num_edges (line 2685) | def num_edges(self, etype=None): method is_multigraph (line 2746) | def is_multigraph(self): method is_homogeneous (line 2798) | def is_homogeneous(self): method idtype (line 2834) | def idtype(self): method _idtype_str (line 2868) | def _idtype_str(self): method has_nodes (line 2878) | def has_nodes(self, vid, ntype=None): method has_edges_between (line 2936) | def has_edges_between(self, u, v, etype=None): method predecessors (line 3030) | def predecessors(self, v, etype=None): method successors (line 3089) | def successors(self, v, etype=None): method edge_ids (line 3147) | def edge_ids(self, u, v, return_uv=False, etype=None): method find_edges (line 3273) | def find_edges(self, eid, etype=None): method in_edges (line 3344) | def in_edges(self, v, form="uv", etype=None): method out_edges (line 3430) | def out_edges(self, u, form="uv", etype=None): method all_edges (line 3521) | def all_edges(self, form="uv", order="eid", etype=None): method in_degrees (line 3603) | def in_degrees(self, v=ALL, etype=None): method out_degrees (line 3679) | def out_degrees(self, u=ALL, etype=None): method adjacency_matrix (line 3759) | def adjacency_matrix(self, etype=None): method adj (line 3763) | def adj(self, etype=None, eweight_name=None): method adj_external (line 3838) | def adj_external( method adj_tensors (line 3910) | def adj_tensors(self, fmt, etype=None): method inc (line 3950) | def inc(self, typestr, ctx=F.cpu(), etype=None): method node_attr_schemes (line 4032) | def node_attr_schemes(self, ntype=None): method edge_attr_schemes (line 4080) | def edge_attr_schemes(self, etype=None): method set_n_initializer (line 4136) | def set_n_initializer(self, initializer, field=None, ntype=None): method set_e_initializer (line 4224) | def set_e_initializer(self, initializer, field=None, etype=None): method _set_n_repr (line 4317) | def _set_n_repr(self, ntid, u, data): method _get_n_repr (line 4375) | def _get_n_repr(self, ntid, u): method _pop_n_repr (line 4398) | def _pop_n_repr(self, ntid, key): method _set_e_repr (line 4415) | def _set_e_repr(self, etid, edges, data): method _get_e_repr (line 4489) | def _get_e_repr(self, etid, edges): method _pop_e_repr (line 4512) | def _pop_e_repr(self, etid, key): method apply_nodes (line 4533) | def apply_nodes(self, func, v=ALL, ntype=None): method apply_edges (line 4597) | def apply_edges(self, func, edges=ALL, etype=None): method send_and_recv (line 4714) | def send_and_recv( method pull (line 4842) | def pull( method push (line 4940) | def push( method update_all (line 5018) | def update_all( method multi_update_all (line 5161) | def multi_update_all(self, etype_dict, cross_reducer, apply_node_func=... method prop_nodes (line 5283) | def prop_nodes( method prop_edges (line 5354) | def prop_edges( method filter_nodes (line 5430) | def filter_nodes(self, predicate, nodes=ALL, ntype=None): method filter_edges (line 5514) | def filter_edges(self, predicate, edges=ALL, etype=None): method device (line 5632) | def device(self): method to (line 5658) | def to(self, device, **kwargs): # pylint: disable=invalid-name method cpu (line 5744) | def cpu(self): method materialize_data (line 5758) | def materialize_data(self): method pin_memory_ (line 5773) | def pin_memory_(self): method unpin_memory_ (line 5845) | def unpin_memory_(self): method is_pinned (line 5864) | def is_pinned(self): method record_stream (line 5874) | def record_stream(self, stream): method clone (line 5899) | def clone(self): method local_var (line 5943) | def local_var(self): method local_scope (line 6017) | def local_scope(self): method formats (line 6090) | def formats(self, formats=None): method create_formats_ (line 6201) | def create_formats_(self): method astype (line 6241) | def astype(self, idtype): method shared_memory (line 6267) | def shared_memory(self, name, formats=("coo", "csr", "csc")): method long (line 6301) | def long(self): method int (line 6351) | def int(self): function make_canonical_etypes (line 6407) | def make_canonical_etypes(etypes, ntypes, metagraph): function find_src_dst_ntypes (line 6448) | def find_src_dst_ntypes(ntypes, metagraph): function pad_tuple (line 6481) | def pad_tuple(tup, length, pad_val=None): function reduce_dict_data (line 6497) | def reduce_dict_data(frames, reducer, order=None): function combine_frames (line 6561) | def combine_frames(frames, ids, col_names=None): function combine_names (line 6610) | def combine_names(names, ids=None): class DGLBlock (line 6631) | class DGLBlock(DGLGraph): method __repr__ (line 6640) | def __repr__(self): function _create_compute_graph (line 6679) | def _create_compute_graph(graph, u, v, eid, recv_nodes=None): FILE: python/dgl/heterograph_index.py class HeteroGraphIndex (line 19) | class HeteroGraphIndex(ObjectBase): method __new__ (line 27) | def __new__(cls): method __getstate__ (line 32) | def __getstate__(self): method __setstate__ (line 40) | def __setstate__(self, state): method metagraph (line 75) | def metagraph(self): method is_metagraph_unibipartite (line 85) | def is_metagraph_unibipartite(self): method number_of_ntypes (line 89) | def number_of_ntypes(self): method number_of_etypes (line 93) | def number_of_etypes(self): method get_relation_graph (line 97) | def get_relation_graph(self, etype): method flatten_relations (line 112) | def flatten_relations(self, etypes): method add_nodes (line 128) | def add_nodes(self, ntype, num): method add_edge (line 141) | def add_edge(self, etype, u, v): method add_edges (line 156) | def add_edges(self, etype, u, v): method clear (line 173) | def clear(self): method dtype (line 179) | def dtype(self): method ctx (line 190) | def ctx(self): method bits_needed (line 200) | def bits_needed(self, etype): method asbits (line 223) | def asbits(self, bits): method copy_to (line 240) | def copy_to(self, ctx): method pin_memory (line 257) | def pin_memory(self): method pin_memory_ (line 275) | def pin_memory_(self): method unpin_memory_ (line 291) | def unpin_memory_(self): method is_pinned (line 305) | def is_pinned(self): method record_stream (line 315) | def record_stream(self, stream): method shared_memory (line 330) | def shared_memory( method is_multigraph (line 361) | def is_multigraph(self): method is_readonly (line 372) | def is_readonly(self): method num_nodes (line 382) | def num_nodes(self, ntype): method num_edges (line 397) | def num_edges(self, etype): method number_of_nodes (line 413) | def number_of_nodes(self, ntype): method number_of_edges (line 429) | def number_of_edges(self, etype): method has_nodes (line 444) | def has_nodes(self, ntype, vids): method has_edges_between (line 463) | def has_edges_between(self, etype, u, v): method predecessors (line 486) | def predecessors(self, etype, v): method successors (line 507) | def successors(self, etype, v): method edge_ids_all (line 528) | def edge_ids_all(self, etype, u, v): method edge_ids_one (line 559) | def edge_ids_one(self, etype, u, v): method find_edges (line 583) | def find_edges(self, etype, eid): method in_edges (line 612) | def in_edges(self, etype, v): method out_edges (line 639) | def out_edges(self, etype, v): method edges (line 666) | def edges(self, etype, order=None): method in_degrees (line 702) | def in_degrees(self, etype, v): method out_degrees (line 723) | def out_degrees(self, etype, v): method adjacency_matrix (line 744) | def adjacency_matrix(self, etype, transpose, ctx): method adjacency_matrix_tensors (line 808) | def adjacency_matrix_tensors(self, etype, transpose, fmt): method adjacency_matrix_scipy (line 868) | def adjacency_matrix_scipy( method incidence_matrix (line 939) | def incidence_matrix(self, etype, typestr, ctx): method node_subgraph (line 1020) | def node_subgraph(self, induced_nodes): method edge_subgraph (line 1037) | def edge_subgraph(self, induced_edges, preserve_nodes): method get_unitgraph (line 1058) | def get_unitgraph(self, etype, ctx): method get_csr_shuffle_order (line 1079) | def get_csr_shuffle_order(self, etype): method formats (line 1101) | def formats(self, formats=None): method create_formats_ (line 1150) | def create_formats_(self): method reverse (line 1154) | def reverse(self): class HeteroSubgraphIndex (line 1167) | class HeteroSubgraphIndex(ObjectBase): method graph (line 1171) | def graph(self): method induced_nodes (line 1182) | def induced_nodes(self): method induced_edges (line 1195) | def induced_edges(self): function create_metagraph_index (line 1213) | def create_metagraph_index(ntypes, canonical_etypes): function create_unitgraph_from_coo (line 1254) | def create_unitgraph_from_coo( function create_unitgraph_from_csr (line 1304) | def create_unitgraph_from_csr( function create_heterograph_from_relations (line 1353) | def create_heterograph_from_relations( function create_heterograph_from_shared_memory (line 1379) | def create_heterograph_from_shared_memory(name): function joint_union (line 1399) | def joint_union(metagraph, gidx_list): function disjoint_union (line 1417) | def disjoint_union(metagraph, graphs): function disjoint_partition (line 1435) | def disjoint_partition(graph, bnn_all_types, bne_all_types): function slice_gidx (line 1463) | def slice_gidx(graph, num_nodes, start_nid, num_edges, start_eid): class FlattenedHeteroGraph (line 1499) | class FlattenedHeteroGraph(ObjectBase): class HeteroPickleStates (line 1504) | class HeteroPickleStates(ObjectBase): method version (line 1508) | def version(self): method meta (line 1519) | def meta(self): method arrays (line 1530) | def arrays(self): method __getstate__ (line 1542) | def __getstate__(self): method __setstate__ (line 1552) | def __setstate__(self, state): function _forking_rebuild (line 1570) | def _forking_rebuild(pk_state): function _forking_reduce (line 1579) | def _forking_reduce(graph_index): FILE: python/dgl/homophily.py function check_pytorch (line 20) | def check_pytorch(): function get_long_edges (line 28) | def get_long_edges(graph): function node_homophily (line 34) | def node_homophily(graph, y): function edge_homophily (line 86) | def edge_homophily(graph, y): function linkx_homophily (line 131) | def linkx_homophily(graph, y): function adjusted_homophily (line 197) | def adjusted_homophily(graph, y): FILE: python/dgl/init.py function base_initializer (line 9) | def base_initializer( function zero_initializer (line 51) | def zero_initializer( FILE: python/dgl/label_informativeness.py function check_pytorch (line 14) | def check_pytorch(): function edge_label_informativeness (line 22) | def edge_label_informativeness(graph, y, eps=1e-8): function node_label_informativeness (line 113) | def node_label_informativeness(graph, y, eps=1e-8): FILE: python/dgl/logging.py function enable_verbose_logging (line 6) | def enable_verbose_logging(): function _setup_logger (line 16) | def _setup_logger(): FILE: python/dgl/merge.py function merge (line 11) | def merge(graphs): FILE: python/dgl/mpops/edgewise.py function copy_u (line 13) | def copy_u(g, x_node, etype=None): function copy_v (line 67) | def copy_v(g, x_node, etype=None): function _gen_u_op_v (line 126) | def _gen_u_op_v(op): function _register_func (line 226) | def _register_func(func): FILE: python/dgl/multiprocessing/pytorch.py function thread_wrapped_func (line 13) | def thread_wrapped_func(func): class Process (line 43) | class Process(mp.Process): method __init__ (line 45) | def __init__( function _get_shared_mem_name (line 59) | def _get_shared_mem_name(id_): function call_once_and_share (line 63) | def call_once_and_share(func, shape, dtype, rank=0): function shared_tensor (line 109) | def shared_tensor(shape, dtype=torch.float32): FILE: python/dgl/ndarray.py class NDArray (line 31) | class NDArray(NDArrayBase): method __len__ (line 34) | def __len__(self): method shared_memory (line 37) | def shared_memory(self, name): function cpu (line 54) | def cpu(dev_id=0): function gpu (line 70) | def gpu(dev_id=0): function array (line 86) | def array(arr, ctx=cpu(0)): function zerocopy_from_numpy (line 107) | def zerocopy_from_numpy(np_data): function cast_to_signed (line 125) | def cast_to_signed(arr): function get_shared_mem_array (line 146) | def get_shared_mem_array(name, shape, dtype): function create_shared_mem_array (line 170) | def create_shared_mem_array(name, shape, dtype): function exist_shared_mem_array (line 194) | def exist_shared_mem_array(name): class SparseFormat (line 210) | class SparseFormat: class SparseMatrix (line 227) | class SparseMatrix(ObjectBase): method format (line 231) | def format(self): method num_rows (line 241) | def num_rows(self): method num_cols (line 251) | def num_cols(self): method indices (line 261) | def indices(self): method flags (line 272) | def flags(self): method __getstate__ (line 281) | def __getstate__(self): method __setstate__ (line 290) | def __setstate__(self, state): method __repr__ (line 297) | def __repr__(self): FILE: python/dgl/nn/__init__.py function _load_backend (line 28) | def _load_backend(mod_name): FILE: python/dgl/nn/mxnet/conv/agnnconv.py class AGNNConv (line 13) | class AGNNConv(nn.Block): method __init__ (line 79) | def __init__( method set_allow_zero_in_degree (line 92) | def set_allow_zero_in_degree(self, set_value): method forward (line 106) | def forward(self, graph, feat): FILE: python/dgl/nn/mxnet/conv/appnpconv.py class APPNPConv (line 10) | class APPNPConv(nn.Block): method __init__ (line 61) | def __init__(self, k, alpha, edge_drop=0.0): method forward (line 68) | def forward(self, graph, feat): FILE: python/dgl/nn/mxnet/conv/chebconv.py class ChebConv (line 13) | class ChebConv(nn.Block): method __init__ (line 65) | def __init__(self, in_feats, out_feats, k, bias=True): method forward (line 90) | def forward(self, graph, feat, lambda_max=None): FILE: python/dgl/nn/mxnet/conv/densechebconv.py class DenseChebConv (line 10) | class DenseChebConv(nn.Block): method __init__ (line 34) | def __init__(self, in_feats, out_feats, k, bias=True): method forward (line 59) | def forward(self, adj, feat, lambda_max=None): FILE: python/dgl/nn/mxnet/conv/densegraphconv.py class DenseGraphConv (line 10) | class DenseGraphConv(nn.Block): method __init__ (line 45) | def __init__( method forward (line 66) | def forward(self, adj, feat): FILE: python/dgl/nn/mxnet/conv/densesageconv.py class DenseSAGEConv (line 12) | class DenseSAGEConv(nn.Block): method __init__ (line 41) | def __init__( method forward (line 64) | def forward(self, adj, feat): FILE: python/dgl/nn/mxnet/conv/edgeconv.py class EdgeConv (line 11) | class EdgeConv(nn.Block): method __init__ (line 102) | def __init__( method set_allow_zero_in_degree (line 120) | def set_allow_zero_in_degree(self, set_value): method forward (line 134) | def forward(self, g, h): FILE: python/dgl/nn/mxnet/conv/gatconv.py class GATConv (line 16) | class GATConv(nn.Block): method __init__ (line 140) | def __init__( method set_allow_zero_in_degree (line 208) | def set_allow_zero_in_degree(self, set_value): method forward (line 222) | def forward(self, graph, feat, get_attention=False): FILE: python/dgl/nn/mxnet/conv/gatedgraphconv.py class GatedGraphConv (line 10) | class GatedGraphConv(nn.Block): method __init__ (line 64) | def __init__(self, in_feats, out_feats, n_steps, n_etypes, bias=True): method forward (line 84) | def forward(self, graph, feat, etypes): FILE: python/dgl/nn/mxnet/conv/ginconv.py class GINConv (line 10) | class GINConv(nn.Block): method __init__ (line 62) | def __init__( method forward (line 86) | def forward(self, graph, feat): FILE: python/dgl/nn/mxnet/conv/gmmconv.py class GMMConv (line 15) | class GMMConv(nn.Block): method __init__ (line 112) | def __init__( method set_allow_zero_in_degree (line 171) | def set_allow_zero_in_degree(self, set_value): method forward (line 185) | def forward(self, graph, feat, pseudo): FILE: python/dgl/nn/mxnet/conv/graphconv.py class GraphConv (line 13) | class GraphConv(gluon.Block): method __init__ (line 138) | def __init__( method set_allow_zero_in_degree (line 178) | def set_allow_zero_in_degree(self, set_value): method forward (line 192) | def forward(self, graph, feat, weight=None): method __repr__ (line 318) | def __repr__(self): FILE: python/dgl/nn/mxnet/conv/nnconv.py class NNConv (line 11) | class NNConv(nn.Block): method __init__ (line 93) | def __init__( method forward (line 139) | def forward(self, graph, feat, efeat): FILE: python/dgl/nn/mxnet/conv/relgraphconv.py class RelGraphConv (line 14) | class RelGraphConv(gluon.Block): method __init__ (line 103) | def __init__( method basis_message_func (line 195) | def basis_message_func(self, edges): method bdd_message_func (line 214) | def bdd_message_func(self, edges): method forward (line 233) | def forward(self, g, x, etypes, norm=None): FILE: python/dgl/nn/mxnet/conv/sageconv.py class SAGEConv (line 14) | class SAGEConv(nn.Block): method __init__ (line 95) | def __init__( method forward (line 145) | def forward(self, graph, feat): FILE: python/dgl/nn/mxnet/conv/sgconv.py class SGConv (line 12) | class SGConv(nn.Block): method __init__ (line 88) | def __init__( method set_allow_zero_in_degree (line 112) | def set_allow_zero_in_degree(self, set_value): method forward (line 126) | def forward(self, graph, feat): FILE: python/dgl/nn/mxnet/conv/tagconv.py class TAGConv (line 11) | class TAGConv(gluon.Block): method __init__ (line 64) | def __init__(self, in_feats, out_feats, k=2, bias=True, activation=None): method forward (line 82) | def forward(self, graph, feat): FILE: python/dgl/nn/mxnet/glob.py class SumPooling (line 25) | class SumPooling(nn.Block): method __init__ (line 32) | def __init__(self): method forward (line 35) | def forward(self, graph, feat): method __repr__ (line 58) | def __repr__(self): class AvgPooling (line 62) | class AvgPooling(nn.Block): method __init__ (line 69) | def __init__(self): method forward (line 72) | def forward(self, graph, feat): method __repr__ (line 95) | def __repr__(self): class MaxPooling (line 99) | class MaxPooling(nn.Block): method __init__ (line 106) | def __init__(self): method forward (line 109) | def forward(self, graph, feat): method __repr__ (line 132) | def __repr__(self): class SortPooling (line 136) | class SortPooling(nn.Block): method __init__ (line 146) | def __init__(self, k): method forward (line 150) | def forward(self, graph, feat): method __repr__ (line 177) | def __repr__(self): class GlobalAttentionPooling (line 181) | class GlobalAttentionPooling(nn.Block): method __init__ (line 198) | def __init__(self, gate_nn, feat_nn=None): method forward (line 204) | def forward(self, graph, feat): class Set2Set (line 237) | class Set2Set(nn.Block): method __init__ (line 264) | def __init__(self, input_dim, n_iters, n_layers): method forward (line 275) | def forward(self, graph, feat): method __repr__ (line 321) | def __repr__(self): FILE: python/dgl/nn/mxnet/hetero.py class HeteroGraphConv (line 8) | class HeteroGraphConv(nn.Block): method __init__ (line 123) | def __init__(self, mods, aggregate="sum"): method forward (line 142) | def forward(self, g, inputs, mod_args=None, mod_kwargs=None): method __repr__ (line 199) | def __repr__(self): function get_aggregate_fn (line 207) | def get_aggregate_fn(agg): FILE: python/dgl/nn/mxnet/utils.py function matmul_maybe_select (line 10) | def matmul_maybe_select(A, B): function bmm_maybe_select (line 52) | def bmm_maybe_select(A, B, index): function normalize (line 93) | def normalize(x, p=2, axis=1, eps=1e-12): class Sequential (line 117) | class Sequential(gluon.nn.Sequential): method __init__ (line 208) | def __init__(self, prefix=None, params=None): method forward (line 211) | def forward(self, graph, *feats): FILE: python/dgl/nn/pytorch/conv/agnnconv.py class AGNNConv (line 13) | class AGNNConv(nn.Module): method __init__ (line 78) | def __init__( method set_allow_zero_in_degree (line 88) | def set_allow_zero_in_degree(self, set_value): method forward (line 102) | def forward(self, graph, feat): FILE: python/dgl/nn/pytorch/conv/appnpconv.py class APPNPConv (line 10) | class APPNPConv(nn.Module): method __init__ (line 59) | def __init__(self, k, alpha, edge_drop=0.0): method forward (line 65) | def forward(self, graph, feat, edge_weight=None): FILE: python/dgl/nn/pytorch/conv/atomicconv.py class RadialPooling (line 8) | class RadialPooling(nn.Module): method __init__ (line 47) | def __init__( method forward (line 62) | def forward(self, distances): function msg_func (line 100) | def msg_func(edges): function reduce_func (line 126) | def reduce_func(nodes): class AtomicConv (line 148) | class AtomicConv(nn.Module): method __init__ (line 241) | def __init__( method forward (line 264) | def forward(self, graph, feat, distances): FILE: python/dgl/nn/pytorch/conv/cfconv.py class ShiftedSoftplus (line 9) | class ShiftedSoftplus(nn.Module): method __init__ (line 23) | def __init__(self, beta=1, shift=2, threshold=20): method forward (line 29) | def forward(self, inputs): class CFConv (line 49) | class CFConv(nn.Module): method __init__ (line 94) | def __init__(self, node_in_feats, edge_in_feats, hidden_feats, out_fea... method forward (line 108) | def forward(self, g, node_feats, edge_feats): FILE: python/dgl/nn/pytorch/conv/chebconv.py class ChebConv (line 11) | class ChebConv(nn.Module): method __init__ (line 63) | def __init__(self, in_feats, out_feats, k, activation=F.relu, bias=True): method forward (line 71) | def forward(self, graph, feat, lambda_max=None): FILE: python/dgl/nn/pytorch/conv/cugraph_base.py class CuGraphBaseConv (line 6) | class CuGraphBaseConv(nn.Module): method __init__ (line 9) | def __init__(self): method reset_parameters (line 13) | def reset_parameters(self): method forward (line 17) | def forward(self, *args): method pad_offsets (line 21) | def pad_offsets(self, offsets: torch.Tensor, size: int) -> torch.Tensor: FILE: python/dgl/nn/pytorch/conv/cugraph_gatconv.py class CuGraphGATConv (line 19) | class CuGraphGATConv(CuGraphBaseConv): method __init__ (line 86) | def __init__( method reset_parameters (line 133) | def reset_parameters(self): method forward (line 147) | def forward(self, g, feat, max_in_degree=None): FILE: python/dgl/nn/pytorch/conv/cugraph_relgraphconv.py class CuGraphRelGraphConv (line 22) | class CuGraphRelGraphConv(CuGraphBaseConv): method __init__ (line 86) | def __init__( method reset_parameters (line 142) | def reset_parameters(self): method forward (line 156) | def forward(self, g, feat, etypes, max_in_degree=None): FILE: python/dgl/nn/pytorch/conv/cugraph_sageconv.py class CuGraphSAGEConv (line 18) | class CuGraphSAGEConv(CuGraphBaseConv): method __init__ (line 71) | def __init__( method reset_parameters (line 100) | def reset_parameters(self): method forward (line 104) | def forward(self, g, feat, max_in_degree=None): FILE: python/dgl/nn/pytorch/conv/densechebconv.py class DenseChebConv (line 8) | class DenseChebConv(nn.Module): method __init__ (line 57) | def __init__(self, in_feats, out_feats, k, bias=True): method reset_parameters (line 69) | def reset_parameters(self): method forward (line 76) | def forward(self, adj, feat, lambda_max=None): FILE: python/dgl/nn/pytorch/conv/densegraphconv.py class DenseGraphConv (line 8) | class DenseGraphConv(nn.Module): method __init__ (line 67) | def __init__( method reset_parameters (line 83) | def reset_parameters(self): method forward (line 89) | def forward(self, adj, feat): FILE: python/dgl/nn/pytorch/conv/densesageconv.py class DenseSAGEConv (line 8) | class DenseSAGEConv(nn.Module): method __init__ (line 61) | def __init__( method reset_parameters (line 79) | def reset_parameters(self): method forward (line 93) | def forward(self, adj, feat): FILE: python/dgl/nn/pytorch/conv/dgnconv.py function aggregate_dir_av (line 11) | def aggregate_dir_av(h, eig_s, eig_d, eig_idx): function aggregate_dir_dx (line 30) | def aggregate_dir_dx(h, eig_s, eig_d, h_in, eig_idx): class DGNConvTower (line 52) | class DGNConvTower(PNAConvTower): method message (line 55) | def message(self, edges): method reduce_func (line 69) | def reduce_func(self, nodes): class DGNConv (line 99) | class DGNConv(PNAConv): method __init__ (line 191) | def __init__( method forward (line 236) | def forward(self, graph, node_feat, edge_feat=None, eig_vec=None): FILE: python/dgl/nn/pytorch/conv/dotgatconv.py class DotGatConv (line 11) | class DotGatConv(nn.Module): method __init__ (line 122) | def __init__( method forward (line 149) | def forward(self, graph, feat, get_attention=False): FILE: python/dgl/nn/pytorch/conv/edgeconv.py class EdgeConv (line 11) | class EdgeConv(nn.Module): method __init__ (line 97) | def __init__( method set_allow_zero_in_degree (line 110) | def set_allow_zero_in_degree(self, set_value): method forward (line 124) | def forward(self, g, feat): FILE: python/dgl/nn/pytorch/conv/edgegatconv.py class EdgeGATConv (line 12) | class EdgeGATConv(nn.Module): method __init__ (line 136) | def __init__( method reset_parameters (line 197) | def reset_parameters(self): method set_allow_zero_in_degree (line 225) | def set_allow_zero_in_degree(self, set_value): method forward (line 239) | def forward(self, graph, feat, edge_feat, get_attention=False): FILE: python/dgl/nn/pytorch/conv/egatconv.py class EGATConv (line 14) | class EGATConv(nn.Module): method __init__ (line 99) | def __init__( method reset_parameters (line 150) | def reset_parameters(self): method forward (line 162) | def forward( FILE: python/dgl/nn/pytorch/conv/egnnconv.py class EGNNConv (line 9) | class EGNNConv(nn.Module): method __init__ (line 52) | def __init__(self, in_size, hidden_size, out_size, edge_feat_size=0): method message (line 84) | def message(self, edges): method forward (line 107) | def forward(self, graph, node_feat, coord_feat, edge_feat=None): FILE: python/dgl/nn/pytorch/conv/gatconv.py class GATConv (line 14) | class GATConv(nn.Module): method __init__ (line 135) | def __init__( method reset_parameters (line 198) | def reset_parameters(self): method set_allow_zero_in_degree (line 225) | def set_allow_zero_in_degree(self, set_value): method forward (line 239) | def forward(self, graph, feat, edge_weight=None, get_attention=False): FILE: python/dgl/nn/pytorch/conv/gatedgcnconv.py class GatedGCNConv (line 10) | class GatedGCNConv(nn.Module): method __init__ (line 64) | def __init__( method forward (line 97) | def forward(self, graph, feat, edge_feat): FILE: python/dgl/nn/pytorch/conv/gatedgraphconv.py class GatedGraphConv (line 10) | class GatedGraphConv(nn.Module): method __init__ (line 61) | def __init__(self, in_feats, out_feats, n_steps, n_etypes, bias=True): method reset_parameters (line 74) | def reset_parameters(self): method set_allow_zero_in_degree (line 92) | def set_allow_zero_in_degree(self, set_value): method forward (line 106) | def forward(self, graph, feat, etypes=None): FILE: python/dgl/nn/pytorch/conv/gatv2conv.py class GATv2Conv (line 14) | class GATv2Conv(nn.Module): method __init__ (line 139) | def __init__( method reset_parameters (line 193) | def reset_parameters(self): method set_allow_zero_in_degree (line 218) | def set_allow_zero_in_degree(self, set_value): method forward (line 231) | def forward(self, graph, feat, get_attention=False): FILE: python/dgl/nn/pytorch/conv/gcn2conv.py class GCN2Conv (line 14) | class GCN2Conv(nn.Module): method __init__ (line 107) | def __init__( method reset_parameters (line 146) | def reset_parameters(self): method set_allow_zero_in_degree (line 160) | def set_allow_zero_in_degree(self, set_value): method forward (line 174) | def forward(self, graph, feat, feat_0, edge_weight=None): method extra_repr (line 274) | def extra_repr(self): FILE: python/dgl/nn/pytorch/conv/ginconv.py class GINConv (line 10) | class GINConv(nn.Module): method __init__ (line 89) | def __init__( method forward (line 111) | def forward(self, graph, feat, edge_weight=None): FILE: python/dgl/nn/pytorch/conv/gineconv.py class GINEConv (line 11) | class GINEConv(nn.Module): method __init__ (line 50) | def __init__(self, apply_func=None, init_eps=0, learn_eps=False): method message (line 59) | def message(self, edges): method forward (line 63) | def forward(self, graph, node_feat, edge_feat): FILE: python/dgl/nn/pytorch/conv/gmmconv.py class GMMConv (line 13) | class GMMConv(nn.Module): method __init__ (line 107) | def __init__( method reset_parameters (line 156) | def reset_parameters(self): method set_allow_zero_in_degree (line 179) | def set_allow_zero_in_degree(self, set_value): method forward (line 193) | def forward(self, graph, feat, pseudo): FILE: python/dgl/nn/pytorch/conv/graphconv.py class EdgeWeightNorm (line 15) | class EdgeWeightNorm(nn.Module): method __init__ (line 64) | def __init__(self, norm="both", eps=0.0): method forward (line 69) | def forward(self, graph, edge_weight): class GraphConv (line 157) | class GraphConv(nn.Module): method __init__ (line 287) | def __init__( method reset_parameters (line 322) | def reset_parameters(self): method set_allow_zero_in_degree (line 342) | def set_allow_zero_in_degree(self, set_value): method forward (line 356) | def forward(self, graph, feat, weight=None, edge_weight=None): method extra_repr (line 480) | def extra_repr(self): FILE: python/dgl/nn/pytorch/conv/grouprevres.py class InvertibleCheckpoint (line 10) | class InvertibleCheckpoint(torch.autograd.Function): method forward (line 14) | def forward(ctx, fn, fn_inverse, num_inputs, *inputs_and_weights): method backward (line 44) | def backward(ctx, *grad_outputs): class GroupRevRes (line 109) | class GroupRevRes(nn.Module): method __init__ (line 175) | def __init__(self, gnn_module, groups=2): method _forward (line 185) | def _forward(self, g, x, *args): method _inverse (line 206) | def _inverse(self, g, y, *args): method forward (line 231) | def forward(self, g, x, *args): FILE: python/dgl/nn/pytorch/conv/hgtconv.py class HGTConv (line 13) | class HGTConv(nn.Module): method __init__ (line 71) | def __init__( method forward (line 120) | def forward(self, g, x, ntype, etype, *, presorted=False): method message (line 186) | def message(self, edges): FILE: python/dgl/nn/pytorch/conv/nnconv.py class NNConv (line 12) | class NNConv(nn.Module): method __init__ (line 88) | def __init__( method reset_parameters (line 127) | def reset_parameters(self): method forward (line 145) | def forward(self, graph, feat, efeat): FILE: python/dgl/nn/pytorch/conv/pnaconv.py function aggregate_mean (line 8) | def aggregate_mean(h): function aggregate_max (line 13) | def aggregate_max(h): function aggregate_min (line 18) | def aggregate_min(h): function aggregate_sum (line 23) | def aggregate_sum(h): function aggregate_std (line 28) | def aggregate_std(h): function aggregate_var (line 33) | def aggregate_var(h): function _aggregate_moment (line 41) | def _aggregate_moment(h, n): function aggregate_moment_3 (line 49) | def aggregate_moment_3(h): function aggregate_moment_4 (line 54) | def aggregate_moment_4(h): function aggregate_moment_5 (line 59) | def aggregate_moment_5(h): function scale_identity (line 64) | def scale_identity(h): function scale_amplification (line 69) | def scale_amplification(h, D, delta): function scale_attenuation (line 74) | def scale_attenuation(h, D, delta): class PNAConvTower (line 97) | class PNAConvTower(nn.Module): method __init__ (line 100) | def __init__( method reduce_func (line 125) | def reduce_func(self, nodes): method message (line 144) | def message(self, edges): method forward (line 154) | def forward(self, graph, node_feat, edge_feat=None): class PNAConv (line 176) | class PNAConv(nn.Module): method __init__ (line 257) | def __init__( method forward (line 305) | def forward(self, graph, node_feat, edge_feat=None): FILE: python/dgl/nn/pytorch/conv/relgraphconv.py class RelGraphConv (line 10) | class RelGraphConv(nn.Module): method __init__ (line 99) | def __init__( method message (line 145) | def message(self, edges): method forward (line 152) | def forward(self, g, feat, etypes, norm=None, *, presorted=False): FILE: python/dgl/nn/pytorch/conv/sageconv.py class SAGEConv (line 12) | class SAGEConv(nn.Module): method __init__ (line 98) | def __init__( method reset_parameters (line 144) | def reset_parameters(self): method _lstm_reducer (line 165) | def _lstm_reducer(self, nodes): method forward (line 179) | def forward(self, graph, feat, edge_weight=None): FILE: python/dgl/nn/pytorch/conv/sgconv.py class SGConv (line 11) | class SGConv(nn.Module): method __init__ (line 85) | def __init__( method reset_parameters (line 104) | def reset_parameters(self): method set_allow_zero_in_degree (line 120) | def set_allow_zero_in_degree(self, set_value): method forward (line 134) | def forward(self, graph, feat, edge_weight=None): FILE: python/dgl/nn/pytorch/conv/tagconv.py class TAGConv (line 10) | class TAGConv(nn.Module): method __init__ (line 60) | def __init__( method reset_parameters (line 77) | def reset_parameters(self): method forward (line 91) | def forward(self, graph, feat, edge_weight=None): FILE: python/dgl/nn/pytorch/conv/twirlsconv.py class TWIRLSConv (line 11) | class TWIRLSConv(nn.Module): method __init__ (line 79) | def __init__( method forward (line 181) | def forward(self, graph, feat): class Propagate (line 228) | class Propagate(nn.Module): method __init__ (line 238) | def __init__(self): method _prop (line 241) | def _prop(self, graph, Y, lam): method forward (line 249) | def forward(self, graph, Y, X, alp, lam): class PropagateNoPrecond (line 281) | class PropagateNoPrecond(nn.Module): method __init__ (line 291) | def __init__(self): method forward (line 294) | def forward(self, graph, Y, X, alp, lam): class Attention (line 326) | class Attention(nn.Module): method __init__ (line 350) | def __init__(self, tau, T, p, attn_dropout=0.0): method reweighting (line 358) | def reweighting(self, graph): method forward (line 380) | def forward(self, graph, Y, etas=None): function normalized_AX (line 432) | def normalized_AX(graph, X): function AX (line 442) | def AX(graph, X): function D_power_X (line 455) | def D_power_X(graph, X, power): function D_power_bias_X (line 464) | def D_power_bias_X(graph, X, power, coeff, bias): class TWIRLSUnfoldingAndAttention (line 473) | class TWIRLSUnfoldingAndAttention(nn.Module): method __init__ (line 529) | def __init__( method forward (line 569) | def forward(self, g, X): class MLP (line 607) | class MLP(nn.Module): method __init__ (line 633) | def __init__( method reset_params (line 672) | def reset_params(self): method activate (line 678) | def activate(self, x): method forward (line 687) | def forward(self, x): FILE: python/dgl/nn/pytorch/explain/gnnexplainer.py class GNNExplainer (line 16) | class GNNExplainer(nn.Module): method __init__ (line 68) | def __init__( method _init_masks (line 92) | def _init_masks(self, graph, feat): method _loss_regularize (line 123) | def _loss_regularize(self, loss, feat_mask, edge_mask): method explain_node (line 165) | def explain_node(self, node_id, graph, feat, **kwargs): method explain_graph (line 319) | def explain_graph(self, graph, feat, **kwargs): class HeteroGNNExplainer (line 442) | class HeteroGNNExplainer(nn.Module): method __init__ (line 494) | def __init__( method _init_masks (line 518) | def _init_masks(self, graph, feat): method _loss_regularize (line 564) | def _loss_regularize(self, loss, feat_masks, edge_masks): method explain_node (line 610) | def explain_node(self, ntype, node_id, graph, feat, **kwargs): method explain_graph (line 793) | def explain_graph(self, graph, feat, **kwargs): FILE: python/dgl/nn/pytorch/explain/pgexplainer.py class PGExplainer (line 13) | class PGExplainer(nn.Module): method __init__ (line 48) | def __init__( method set_masks (line 78) | def set_masks(self, graph, edge_mask=None): method clear_masks (line 107) | def clear_masks(self): method parameters (line 113) | def parameters(self): method loss (line 128) | def loss(self, prob, ori_pred): method concrete_sample (line 176) | def concrete_sample(self, w, beta=1.0, training=True): method train_step (line 211) | def train_step(self, graph, feat, temperature, **kwargs): method train_step_node (line 248) | def train_step_node(self, nodes, graph, feat, temperature, **kwargs): method explain_graph (line 295) | def explain_graph( method explain_node (line 431) | def explain_node( class HeteroPGExplainer (line 609) | class HeteroPGExplainer(PGExplainer): method train_step (line 639) | def train_step(self, graph, feat, temperature, **kwargs): method train_step_node (line 664) | def train_step_node(self, nodes, graph, feat, temperature, **kwargs): method explain_graph (line 714) | def explain_graph( method explain_node (line 874) | def explain_node( method _edge_mask_to_heterogeneous (line 1110) | def _edge_mask_to_heterogeneous(self, edge_mask, homograph, heterograph): FILE: python/dgl/nn/pytorch/explain/subgraphx.py class MCTSNode (line 18) | class MCTSNode: method __init__ (line 27) | def __init__(self, nodes): method __repr__ (line 34) | def __repr__(self): class SubgraphX (line 45) | class SubgraphX(nn.Module): method __init__ (line 90) | def __init__( method shapley (line 114) | def shapley(self, subgraph_nodes): method get_mcts_children (line 177) | def get_mcts_children(self, mcts_node): method mcts_rollout (line 230) | def mcts_rollout(self, mcts_node): method explain_graph (line 263) | def explain_graph(self, graph, feat, target_class, **kwargs): class HeteroSubgraphX (line 370) | class HeteroSubgraphX(nn.Module): method __init__ (line 415) | def __init__( method shapley (line 439) | def shapley(self, subgraph_nodes): method get_mcts_children (line 538) | def get_mcts_children(self, mcts_node): method mcts_rollout (line 647) | def mcts_rollout(self, mcts_node): method explain_graph (line 683) | def explain_graph(self, graph, feat, target_class, **kwargs): FILE: python/dgl/nn/pytorch/factory.py function pairwise_squared_distance (line 7) | def pairwise_squared_distance(x): class KNNGraph (line 16) | class KNNGraph(nn.Module): method __init__ (line 64) | def __init__(self, k): method forward (line 69) | def forward( class SegmentedKNNGraph (line 140) | class SegmentedKNNGraph(nn.Module): method __init__ (line 185) | def __init__(self, k): method forward (line 190) | def forward( class RadiusGraph (line 269) | class RadiusGraph(nn.Module): method __init__ (line 343) | def __init__( method forward (line 357) | def forward(self, x, get_distances=False): FILE: python/dgl/nn/pytorch/glob.py class SumPooling (line 31) | class SumPooling(nn.Module): method __init__ (line 85) | def __init__(self): method forward (line 88) | def forward(self, graph, feat): class AvgPooling (line 113) | class AvgPooling(nn.Module): method __init__ (line 167) | def __init__(self): method forward (line 170) | def forward(self, graph, feat): class MaxPooling (line 195) | class MaxPooling(nn.Module): method __init__ (line 249) | def __init__(self): method forward (line 252) | def forward(self, graph, feat): class SortPooling (line 275) | class SortPooling(nn.Module): method __init__ (line 337) | def __init__(self, k): method forward (line 341) | def forward(self, graph, feat): class GlobalAttentionPooling (line 371) | class GlobalAttentionPooling(nn.Module): method __init__ (line 437) | def __init__(self, gate_nn, feat_nn=None): method forward (line 442) | def forward(self, graph, feat, get_attention=False): class Set2Set (line 487) | class Set2Set(nn.Module): method __init__ (line 566) | def __init__(self, input_dim, n_iters, n_layers): method reset_parameters (line 575) | def reset_parameters(self): method forward (line 579) | def forward(self, graph, feat): method extra_repr (line 619) | def extra_repr(self): function _gen_mask (line 627) | def _gen_mask(lengths_x, lengths_y, max_len_x, max_len_y): class MultiHeadAttention (line 660) | class MultiHeadAttention(nn.Module): method __init__ (line 683) | def __init__( method reset_parameters (line 707) | def reset_parameters(self): method forward (line 713) | def forward(self, x, mem, lengths_x, lengths_mem): class SetAttentionBlock (line 779) | class SetAttentionBlock(nn.Module): method __init__ (line 803) | def __init__( method forward (line 816) | def forward(self, feat, lengths): class InducedSetAttentionBlock (line 830) | class InducedSetAttentionBlock(nn.Module): method __init__ (line 856) | def __init__( method reset_parameters (line 883) | def reset_parameters(self): method forward (line 887) | def forward(self, feat, lengths): method extra_repr (line 908) | def extra_repr(self): class PMALayer (line 918) | class PMALayer(nn.Module): method __init__ (line 944) | def __init__( method reset_parameters (line 972) | def reset_parameters(self): method forward (line 976) | def forward(self, feat, lengths): method extra_repr (line 996) | def extra_repr(self): class SetTransformerEncoder (line 1006) | class SetTransformerEncoder(nn.Module): method __init__ (line 1092) | def __init__( method forward (line 1145) | def forward(self, graph, feat): class SetTransformerDecoder (line 1168) | class SetTransformerDecoder(nn.Module): method __init__ (line 1240) | def __init__( method forward (line 1279) | def forward(self, graph, feat): class WeightAndSum (line 1305) | class WeightAndSum(nn.Module): method __init__ (line 1365) | def __init__(self, in_feats): method forward (line 1372) | def forward(self, g, feats): FILE: python/dgl/nn/pytorch/gt/biased_mha.py class BiasedMHA (line 8) | class BiasedMHA(nn.Module): method __init__ (line 51) | def __init__( method reset_parameters (line 78) | def reset_parameters(self): method forward (line 91) | def forward(self, ndata, attn_bias=None, attn_mask=None): FILE: python/dgl/nn/pytorch/gt/degree_encoder.py class DegreeEncoder (line 7) | class DegreeEncoder(nn.Module): method __init__ (line 47) | def __init__(self, max_degree, embedding_dim, direction="both"): method forward (line 63) | def forward(self, degrees): FILE: python/dgl/nn/pytorch/gt/egt.py class EGTLayer (line 8) | class EGTLayer(nn.Module): method __init__ (line 51) | def __init__( method forward (line 99) | def forward(self, nfeat, efeat, mask=None): FILE: python/dgl/nn/pytorch/gt/graphormer.py class GraphormerLayer (line 8) | class GraphormerLayer(nn.Module): method __init__ (line 57) | def __init__( method forward (line 90) | def forward(self, nfeat, attn_bias=None, attn_mask=None): FILE: python/dgl/nn/pytorch/gt/lap_pos_encoder.py class LapPosEncoder (line 7) | class LapPosEncoder(nn.Module): method __init__ (line 55) | def __init__( method forward (line 117) | def forward(self, eigvals, eigvecs): FILE: python/dgl/nn/pytorch/gt/path_encoder.py class PathEncoder (line 6) | class PathEncoder(nn.Module): method __init__ (line 48) | def __init__(self, max_len, feat_dim, num_heads=1): method forward (line 55) | def forward(self, dist, path_data): FILE: python/dgl/nn/pytorch/gt/spatial_encoder.py function gaussian (line 8) | def gaussian(x, mean, std): class SpatialEncoder (line 15) | class SpatialEncoder(nn.Module): method __init__ (line 53) | def __init__(self, max_dist, num_heads=1): method forward (line 62) | def forward(self, dist): class SpatialEncoder3d (line 88) | class SpatialEncoder3d(nn.Module): method __init__ (line 136) | def __init__(self, num_kernels, num_heads=1, max_node_type=100): method forward (line 161) | def forward(self, coord, node_type=None): FILE: python/dgl/nn/pytorch/hetero.py class HeteroGraphConv (line 12) | class HeteroGraphConv(nn.Module): method __init__ (line 127) | def __init__(self, mods, aggregate="sum"): method _get_module (line 149) | def _get_module(self, etype): method forward (line 159) | def forward(self, g, inputs, mod_args=None, mod_kwargs=None): function _max_reduce_func (line 224) | def _max_reduce_func(inputs, dim): function _min_reduce_func (line 228) | def _min_reduce_func(inputs, dim): function _sum_reduce_func (line 232) | def _sum_reduce_func(inputs, dim): function _mean_reduce_func (line 236) | def _mean_reduce_func(inputs, dim): function _stack_agg_func (line 240) | def _stack_agg_func(inputs, dsttype): # pylint: disable=unused-argument function _agg_func (line 246) | def _agg_func(inputs, dsttype, fn): # pylint: disable=unused-argument function get_aggregate_fn (line 253) | def get_aggregate_fn(agg): class HeteroLinear (line 290) | class HeteroLinear(nn.Module): method __init__ (line 318) | def __init__(self, in_size, out_size, bias=True): method forward (line 325) | def forward(self, feat): class HeteroEmbedding (line 345) | class HeteroEmbedding(nn.Module): method __init__ (line 382) | def __init__(self, num_embeddings, embedding_dim): method weight (line 392) | def weight(self): method reset_parameters (line 404) | def reset_parameters(self): method forward (line 411) | def forward(self, input_ids): FILE: python/dgl/nn/pytorch/linear.py class TypedLinear (line 13) | class TypedLinear(nn.Module): method __init__ (line 87) | def __init__( method reset_parameters (line 126) | def reset_parameters(self): method get_weight (line 156) | def get_weight(self): method forward (line 172) | def forward(self, x, x_type, sorted_by_type=False): method __repr__ (line 212) | def __repr__(self): FILE: python/dgl/nn/pytorch/link/edgepred.py class EdgePredictor (line 8) | class EdgePredictor(nn.Module): method __init__ (line 107) | def __init__(self, op, in_feats=None, out_feats=None, bias=False): method reset_parameters (line 126) | def reset_parameters(self): method forward (line 136) | def forward(self, h_src, h_dst): FILE: python/dgl/nn/pytorch/link/transe.py class TransE (line 7) | class TransE(nn.Module): method __init__ (line 58) | def __init__(self, num_rels, feats, p=1): method reset_parameters (line 64) | def reset_parameters(self): method forward (line 73) | def forward(self, h_head, h_tail, rels): FILE: python/dgl/nn/pytorch/link/transr.py class TransR (line 7) | class TransR(nn.Module): method __init__ (line 63) | def __init__(self, num_rels, rfeats, nfeats, p=1): method reset_parameters (line 72) | def reset_parameters(self): method forward (line 82) | def forward(self, h_head, h_tail, rels): FILE: python/dgl/nn/pytorch/network_emb.py class DeepWalk (line 21) | class DeepWalk(nn.Module): method __init__ (line 88) | def __init__( method reset_parameters (line 142) | def reset_parameters(self): method sample (line 148) | def sample(self, indices): method forward (line 164) | def forward(self, batch_walk): class MetaPath2Vec (line 231) | class MetaPath2Vec(nn.Module): method __init__ (line 302) | def __init__( method reset_parameters (line 365) | def reset_parameters(self): method sample (line 371) | def sample(self, indices): method forward (line 415) | def forward(self, pos_u, pos_v, neg_v): FILE: python/dgl/nn/pytorch/sparse_emb.py class NodeEmbedding (line 14) | class NodeEmbedding: # NodeEmbedding method __init__ (line 70) | def __init__( method __call__ (line 163) | def __call__(self, node_ids, device=th.device("cpu")): method store (line 190) | def store(self): method partition (line 202) | def partition(self): method rank (line 215) | def rank(self): method world_size (line 226) | def world_size(self): method name (line 237) | def name(self): method num_embeddings (line 248) | def num_embeddings(self): method embedding_dim (line 259) | def embedding_dim(self): method set_optm_state (line 269) | def set_optm_state(self, state): method optm_state (line 280) | def optm_state(self): method trace (line 291) | def trace(self): method reset_trace (line 302) | def reset_trace(self): method weight (line 309) | def weight(self): method all_set_embedding (line 319) | def all_set_embedding(self, values): method _all_get_tensor (line 351) | def _all_get_tensor(self, shared_name, tensor, shape): method all_get_embedding (line 386) | def all_get_embedding(self): method _all_get_optm_state (line 414) | def _all_get_optm_state(self): method _all_set_optm_state (line 447) | def _all_set_optm_state(self, states): FILE: python/dgl/nn/pytorch/utils.py function matmul_maybe_select (line 12) | def matmul_maybe_select(A, B): function bmm_maybe_select (line 54) | def bmm_maybe_select(A, B, index): class Identity (line 99) | class Identity(nn.Module): method __init__ (line 105) | def __init__(self): method forward (line 108) | def forward(self, x): class Sequential (line 113) | class Sequential(nn.Sequential): method __init__ (line 199) | def __init__(self, *args): method forward (line 202) | def forward(self, graph, *feats): class WeightBasis (line 235) | class WeightBasis(nn.Module): method __init__ (line 261) | def __init__(self, shape, num_bases, num_outputs): method forward (line 283) | def forward(self): class JumpingKnowledge (line 296) | class JumpingKnowledge(nn.Module): method __init__ (line 362) | def __init__(self, mode="cat", in_feats=None, num_layers=None): method reset_parameters (line 382) | def reset_parameters(self): method forward (line 393) | def forward(self, feat_list): class LabelPropagation (line 425) | class LabelPropagation(nn.Module): method __init__ (line 475) | def __init__( method forward (line 492) | def forward(self, g, labels, mask=None): FILE: python/dgl/nn/tensorflow/conv/appnpconv.py class APPNPConv (line 10) | class APPNPConv(layers.Layer): method __init__ (line 32) | def __init__(self, k, alpha, edge_drop=0.0): method call (line 38) | def call(self, graph, feat): FILE: python/dgl/nn/tensorflow/conv/chebconv.py class ChebConv (line 11) | class ChebConv(layers.Layer): method __init__ (line 63) | def __init__( method call (line 73) | def call(self, graph, feat, lambda_max=None): FILE: python/dgl/nn/tensorflow/conv/densechebconv.py class DenseChebConv (line 8) | class DenseChebConv(layers.Layer): method __init__ (line 33) | def __init__(self, in_feats, out_feats, k, bias=True): method call (line 57) | def call(self, adj, feat, lambda_max=None): FILE: python/dgl/nn/tensorflow/conv/edgeconv.py class EdgeConv (line 11) | class EdgeConv(layers.Layer): method __init__ (line 64) | def __init__(self, out_feats, batch_norm=False, allow_zero_in_degree=F... method set_allow_zero_in_degree (line 74) | def set_allow_zero_in_degree(self, set_value): method call (line 84) | def call(self, g, feat): FILE: python/dgl/nn/tensorflow/conv/gatconv.py class GATConv (line 16) | class GATConv(layers.Layer): method __init__ (line 139) | def __init__( method set_allow_zero_in_degree (line 199) | def set_allow_zero_in_degree(self, set_value): method call (line 209) | def call(self, graph, feat, get_attention=False): FILE: python/dgl/nn/tensorflow/conv/ginconv.py class GINConv (line 10) | class GINConv(layers.Layer): method __init__ (line 61) | def __init__( method call (line 81) | def call(self, graph, feat): FILE: python/dgl/nn/tensorflow/conv/graphconv.py class GraphConv (line 15) | class GraphConv(layers.Layer): method __init__ (line 139) | def __init__( method set_allow_zero_in_degree (line 182) | def set_allow_zero_in_degree(self, set_value): method call (line 192) | def call(self, graph, feat, weight=None): method extra_repr (line 310) | def extra_repr(self): FILE: python/dgl/nn/tensorflow/conv/relgraphconv.py class RelGraphConv (line 10) | class RelGraphConv(layers.Layer): method __init__ (line 112) | def __init__( method basis_message_func (line 210) | def basis_message_func(self, edges): method bdd_message_func (line 245) | def bdd_message_func(self, edges): method call (line 285) | def call(self, g, x, etypes, norm=None): FILE: python/dgl/nn/tensorflow/conv/sageconv.py class SAGEConv (line 11) | class SAGEConv(layers.Layer): method __init__ (line 92) | def __init__( method _lstm_reducer (line 127) | def _lstm_reducer(self, nodes): method call (line 136) | def call(self, graph, feat): FILE: python/dgl/nn/tensorflow/conv/sgconv.py class SGConv (line 11) | class SGConv(layers.Layer): method __init__ (line 87) | def __init__( method set_allow_zero_in_degree (line 105) | def set_allow_zero_in_degree(self, set_value): method call (line 115) | def call(self, graph, feat): FILE: python/dgl/nn/tensorflow/glob.py class SumPooling (line 24) | class SumPooling(layers.Layer): method __init__ (line 31) | def __init__(self): method call (line 34) | def call(self, graph, feat): class AvgPooling (line 58) | class AvgPooling(layers.Layer): method __init__ (line 65) | def __init__(self): method call (line 68) | def call(self, graph, feat): class MaxPooling (line 91) | class MaxPooling(layers.Layer): method __init__ (line 98) | def __init__(self): method call (line 101) | def call(self, graph, feat): class SortPooling (line 124) | class SortPooling(layers.Layer): method __init__ (line 134) | def __init__(self, k): method call (line 138) | def call(self, graph, feat): class GlobalAttentionPooling (line 167) | class GlobalAttentionPooling(layers.Layer): method __init__ (line 184) | def __init__(self, gate_nn, feat_nn=None): method call (line 189) | def call(self, graph, feat): class WeightAndSum (line 224) | class WeightAndSum(layers.Layer): method __init__ (line 233) | def __init__(self, in_feats): method call (line 240) | def call(self, g, feats): FILE: python/dgl/nn/tensorflow/hetero.py class HeteroGraphConv (line 8) | class HeteroGraphConv(layers.Layer): method __init__ (line 130) | def __init__(self, mods, aggregate="sum"): method call (line 146) | def call(self, g, inputs, mod_args=None, mod_kwargs=None): function get_aggregate_fn (line 204) | def get_aggregate_fn(agg): FILE: python/dgl/nn/tensorflow/utils.py function matmul_maybe_select (line 7) | def matmul_maybe_select(A, B): function bmm_maybe_select (line 49) | def bmm_maybe_select(A, B, index): class Identity (line 93) | class Identity(layers.Layer): method call (line 96) | def call(self, x): FILE: python/dgl/ops/edge_softmax.py function edge_softmax (line 12) | def edge_softmax(graph, logits, eids=ALL, norm_by="dst"): FILE: python/dgl/ops/gather_mm.py function gather_mm (line 7) | def gather_mm(a, b, *, idx_b): FILE: python/dgl/ops/sddmm.py function reshape_lhs_rhs (line 14) | def reshape_lhs_rhs(lhs_data, rhs_data): function gsddmm (line 40) | def gsddmm(g, op, lhs_data, rhs_data, lhs_target="u", rhs_target="v"): function _gen_sddmm_func (line 101) | def _gen_sddmm_func(lhs_target, rhs_target, binary_op): function _register_sddmm_func (line 146) | def _register_sddmm_func(): function copy_u (line 157) | def copy_u(g, x): function copy_v (line 179) | def copy_v(g, x): function copy_e (line 202) | def copy_e(g, x): FILE: python/dgl/ops/segment.py function segment_reduce (line 9) | def segment_reduce(seglen, value, reducer="sum"): function segment_softmax (line 62) | def segment_softmax(seglen, value): function segment_mm (line 106) | def segment_mm(a, b, seglen_a): FILE: python/dgl/ops/spmm.py function reshape_lhs_rhs (line 13) | def reshape_lhs_rhs(lhs_data, rhs_data): function gspmm (line 39) | def gspmm(g, op, reduce_op, lhs_data, rhs_data): function _attach_zerodeg_note (line 119) | def _attach_zerodeg_note(docstring, reducer): function _gen_spmm_func (line 136) | def _gen_spmm_func(binary_op, reduce_op): function _gen_copy_reduce_func (line 180) | def _gen_copy_reduce_func(binary_op, reduce_op): function _register_spmm_func (line 224) | def _register_spmm_func(): FILE: python/dgl/optim/__init__.py function _load_backend (line 10) | def _load_backend(mod_name): FILE: python/dgl/optim/pytorch/sparse_optim.py class SparseGradOptimizer (line 19) | class SparseGradOptimizer(abc.ABC): method __init__ (line 32) | def __init__(self, params, lr): method step (line 67) | def step(self): method setup (line 106) | def setup(self, params): method _comm_setup (line 117) | def _comm_setup(self): method _shared_setup (line 120) | def _shared_setup(self): method _comm_step (line 143) | def _comm_step(self): method _shared_step (line 202) | def _shared_step(self): method update (line 436) | def update(self, idx, grad, emb): method zero_grad (line 451) | def zero_grad(self): method state_dict (line 455) | def state_dict(self, **kwargs): # pylint: disable=unused-argument method load_state_dict (line 476) | def load_state_dict( method param_groups (line 497) | def param_groups(self): method _set_param_groups (line 505) | def _set_param_groups(self, groups): class SparseAdagrad (line 509) | class SparseAdagrad(SparseGradOptimizer): method __init__ (line 548) | def __init__(self, params, lr, eps=1e-10): method setup (line 555) | def setup(self, params): method update (line 593) | def update(self, idx, grad, emb): method param_groups (line 633) | def param_groups(self): method _set_param_groups (line 641) | def _set_param_groups(self, groups): class SparseAdam (line 647) | class SparseAdam(SparseGradOptimizer): method __init__ (line 703) | def __init__( method _setup_uva (line 729) | def _setup_uva(self, name, mem, power): method setup (line 735) | def setup(self, params): method update (line 814) | def update(self, idx, grad, emb): method param_groups (line 941) | def param_groups(self): method _set_param_groups (line 955) | def _set_param_groups(self, groups): FILE: python/dgl/partition.py function reorder_nodes (line 22) | def reorder_nodes(g, new_node_ids): function _get_halo_heterosubgraph_inner_node (line 56) | def _get_halo_heterosubgraph_inner_node(halo_subg): function reshuffle_graph (line 60) | def reshuffle_graph(g, node_part=None): function partition_graph_with_halo (line 139) | def partition_graph_with_halo(g, node_part, extra_cached_hops, reshuffle... function get_peak_mem (line 261) | def get_peak_mem(): function metis_partition_assignment (line 278) | def metis_partition_assignment( function metis_partition (line 400) | def metis_partition( class NDArrayPartition (line 474) | class NDArrayPartition(object): method __init__ (line 523) | def __init__( method num_parts (line 557) | def num_parts(self): method array_size (line 561) | def array_size(self): method get (line 565) | def get(self): method get_local_indices (line 569) | def get_local_indices(self, part, ctx): method local_size (line 575) | def local_size(self, part): method map_to_local (line 579) | def map_to_local(self, idxs): method map_to_global (line 587) | def map_to_global(self, idxs, part_id): method generate_permutation (line 595) | def generate_permutation(self, idxs): FILE: python/dgl/propagate.py function prop_nodes (line 16) | def prop_nodes( function prop_edges (line 45) | def prop_edges( function prop_nodes_bfs (line 74) | def prop_nodes_bfs( function prop_nodes_topo (line 117) | def prop_nodes_topo( function prop_edges_dfs (line 153) | def prop_edges_dfs( FILE: python/dgl/random.py function seed (line 10) | def seed(val): function choice (line 21) | def choice(a, size, replace=True, prob=None): # pylint: disable=invalid... FILE: python/dgl/readout.py function readout_nodes (line 26) | def readout_nodes(graph, feat, weight=None, *, op="sum", ntype=None): function readout_edges (line 104) | def readout_edges(graph, feat, weight=None, *, op="sum", etype=None): function sum_nodes (line 188) | def sum_nodes(graph, feat, weight=None, *, ntype=None): function sum_edges (line 198) | def sum_edges(graph, feat, weight=None, *, etype=None): function mean_nodes (line 208) | def mean_nodes(graph, feat, weight=None, *, ntype=None): function mean_edges (line 218) | def mean_edges(graph, feat, weight=None, *, etype=None): function max_nodes (line 228) | def max_nodes(graph, feat, weight=None, *, ntype=None): function max_edges (line 238) | def max_edges(graph, feat, weight=None, *, etype=None): function softmax_nodes (line 248) | def softmax_nodes(graph, feat, *, ntype=None): function softmax_edges (line 308) | def softmax_edges(graph, feat, *, etype=None): function broadcast_nodes (line 374) | def broadcast_nodes(graph, graph_feat, *, ntype=None): function broadcast_edges (line 451) | def broadcast_edges(graph, graph_feat, *, etype=None): function _topk_torch (line 531) | def _topk_torch(keys, k, descending, x): function _topk_on (line 567) | def _topk_on(graph, typestr, feat, k, descending, sortby, ntype_or_etype): function topk_nodes (line 660) | def topk_nodes(graph, feat, k, *, descending=True, sortby=None, ntype=No... function topk_edges (line 775) | def topk_edges(graph, feat, k, *, descending=True, sortby=None, etype=No... FILE: python/dgl/sampling/labor.py function sample_labors (line 32) | def sample_labors( function _sample_labors (line 244) | def _sample_labors( FILE: python/dgl/sampling/negative.py function _calc_redundancy (line 12) | def _calc_redundancy( function global_uniform_negative_sampling (line 39) | def global_uniform_negative_sampling( FILE: python/dgl/sampling/neighbor.py function _prepare_edge_arrays (line 22) | def _prepare_edge_arrays(g, arg): function sample_etype_neighbors (line 69) | def sample_etype_neighbors( function sample_neighbors (line 222) | def sample_neighbors( function sample_neighbors_fused (line 399) | def sample_neighbors_fused( function _sample_neighbors (line 519) | def _sample_neighbors( function sample_neighbors_biased (line 690) | def sample_neighbors_biased( function select_topk (line 880) | def select_topk( FILE: python/dgl/sampling/node2vec_randomwalk.py function node2vec_random_walk (line 11) | def node2vec_random_walk( FILE: python/dgl/sampling/pinsage.py function _select_pinsage_neighbors (line 10) | def _select_pinsage_neighbors(src, dst, num_samples_per_node, k): class RandomWalkNeighborSampler (line 27) | class RandomWalkNeighborSampler(object): method __init__ (line 78) | def __init__( method __call__ (line 119) | def __call__(self, seed_nodes): class PinSAGESampler (line 166) | class PinSAGESampler(RandomWalkNeighborSampler): method __init__ (line 250) | def __init__( FILE: python/dgl/sampling/randomwalks.py function random_walk (line 11) | def random_walk( function pack_traces (line 229) | def pack_traces(traces, types): FILE: python/dgl/sampling/utils.py function _locate_eids_to_exclude (line 12) | def _locate_eids_to_exclude(frontier_parent_eids, exclude_eids): class EidExcluder (line 26) | class EidExcluder(object): method __init__ (line 32) | def __init__(self, exclude_eids): method _find_indices (line 55) | def _find_indices(self, parent_eids): method __call__ (line 65) | def __call__(self, frontier, weights=None): FILE: python/dgl/sparse/__init__.py function load_dgl_sparse (line 19) | def load_dgl_sparse(): FILE: python/dgl/sparse/broadcast.py function sp_broadcast_v (line 10) | def sp_broadcast_v(A: SparseMatrix, v: torch.Tensor, op: str) -> SparseM... function sp_add_v (line 104) | def sp_add_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix: function sp_sub_v (line 112) | def sp_sub_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix: function sp_mul_v (line 120) | def sp_mul_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix: function sp_div_v (line 128) | def sp_div_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix: FILE: python/dgl/sparse/elementwise_op.py function add (line 11) | def add(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix: function sub (line 41) | def sub(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix: function mul (line 71) | def mul( function div (line 120) | def div(A: SparseMatrix, B: Union[SparseMatrix, Scalar]) -> SparseMatrix: function power (line 167) | def power(A: SparseMatrix, scalar: Scalar) -> SparseMatrix: FILE: python/dgl/sparse/elementwise_op_sp.py function spsp_add (line 10) | def spsp_add(A, B): function spsp_mul (line 17) | def spsp_mul(A, B): function spsp_div (line 24) | def spsp_div(A, B): function sp_add (line 31) | def sp_add(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix: function sp_sub (line 63) | def sp_sub(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix: function sp_mul (line 97) | def sp_mul(A: SparseMatrix, B: Union[SparseMatrix, Scalar]) -> SparseMat... function sp_div (line 148) | def sp_div(A: SparseMatrix, B: Union[SparseMatrix, Scalar]) -> SparseMat... function sp_power (line 183) | def sp_power(A: SparseMatrix, scalar: Scalar) -> SparseMatrix: FILE: python/dgl/sparse/matmul.py function spmm (line 12) | def spmm(A: SparseMatrix, X: torch.Tensor) -> torch.Tensor: function bspmm (line 50) | def bspmm(A: SparseMatrix, X: torch.Tensor) -> torch.Tensor: function spspmm (line 88) | def spspmm(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix: function matmul (line 132) | def matmul( FILE: python/dgl/sparse/reduction.py function reduce (line 11) | def reduce(input: SparseMatrix, dim: Optional[int] = None, rtype: str = ... function sum (line 85) | def sum(input: SparseMatrix, dim: Optional[int] = None): function smax (line 139) | def smax(input: SparseMatrix, dim: Optional[int] = None): function smin (line 197) | def smin(input: SparseMatrix, dim: Optional[int] = None): function smean (line 259) | def smean(input: SparseMatrix, dim: Optional[int] = None): function sprod (line 321) | def sprod(input: SparseMatrix, dim: Optional[int] = None): FILE: python/dgl/sparse/sddmm.py function sddmm (line 10) | def sddmm(A: SparseMatrix, X1: torch.Tensor, X2: torch.Tensor) -> Sparse... function bsddmm (line 57) | def bsddmm(A: SparseMatrix, X1: torch.Tensor, X2: torch.Tensor) -> Spars... FILE: python/dgl/sparse/softmax.py function softmax (line 11) | def softmax(input: SparseMatrix, dim: int = 1) -> SparseMatrix: FILE: python/dgl/sparse/sparse_matrix.py class SparseMatrix (line 8) | class SparseMatrix: method __init__ (line 11) | def __init__(self, c_sparse_matrix: torch.ScriptObject): method __repr__ (line 14) | def __repr__(self): method val (line 18) | def val(self) -> torch.Tensor: method shape (line 29) | def shape(self) -> Tuple[int]: method nnz (line 40) | def nnz(self) -> int: method dtype (line 51) | def dtype(self) -> torch.dtype: method device (line 62) | def device(self) -> torch.device: method row (line 73) | def row(self) -> torch.Tensor: method col (line 84) | def col(self) -> torch.Tensor: method coo (line 94) | def coo(self) -> Tuple[torch.Tensor, torch.Tensor]: method indices (line 118) | def indices(self) -> torch.Tensor: method csr (line 141) | def csr(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: method csc (line 172) | def csc(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: method to_dense (line 203) | def to_dense(self) -> torch.Tensor: method t (line 218) | def t(self): method T (line 223) | def T(self): # pylint: disable=C0103 method transpose (line 227) | def transpose(self): method to (line 249) | def to(self, device=None, dtype=None): method cuda (line 297) | def cuda(self): method cpu (line 320) | def cpu(self): method float (line 343) | def float(self): method double (line 366) | def double(self): method int (line 388) | def int(self): method long (line 410) | def long(self): method coalesce (line 432) | def coalesce(self): method has_duplicate (line 463) | def has_duplicate(self): method is_diag (line 478) | def is_diag(self): method index_select (line 482) | def index_select(self, dim: int, index: torch.Tensor): method range_select (line 533) | def range_select(self, dim: int, index: slice): method sample (line 589) | def sample( method compact (line 683) | def compact( function spmatrix (line 763) | def spmatrix( function from_coo (line 845) | def from_coo( function from_csr (line 924) | def from_csr( function from_csc (line 1016) | def from_csc( function val_like (line 1108) | def val_like(mat: SparseMatrix, val: torch.Tensor) -> SparseMatrix: function diag (line 1146) | def diag( function identity (line 1209) | def identity( function from_torch_sparse (line 1284) | def from_torch_sparse(torch_sparse_tensor: torch.Tensor) -> SparseMatrix: function to_torch_sparse_coo (line 1342) | def to_torch_sparse_coo(spmat: SparseMatrix) -> torch.Tensor: function to_torch_sparse_csr (line 1373) | def to_torch_sparse_csr(spmat: SparseMatrix) -> torch.Tensor: function to_torch_sparse_csc (line 1411) | def to_torch_sparse_csc(spmat: SparseMatrix) -> torch.Tensor: function _sparse_matrix_str (line 1449) | def _sparse_matrix_str(spmat: SparseMatrix) -> str: FILE: python/dgl/sparse/unary_op.py function neg (line 5) | def neg(A: SparseMatrix) -> SparseMatrix: function inv (line 29) | def inv(A: SparseMatrix) -> SparseMatrix: FILE: python/dgl/sparse/utils.py function is_scalar (line 8) | def is_scalar(x): FILE: python/dgl/storages/base.py function register_storage_wrapper (line 8) | def register_storage_wrapper(type_): function wrap_storage (line 18) | def wrap_storage(storage): class _FuncWrapper (line 34) | class _FuncWrapper(object): method __init__ (line 35) | def __init__(self, func): method __call__ (line 38) | def __call__(self, buf, *args): class ThreadedFuture (line 42) | class ThreadedFuture(object): method __init__ (line 48) | def __init__(self, target, args): method wait (line 59) | def wait(self): class FeatureStorage (line 65) | class FeatureStorage(object): method requires_ddp (line 71) | def requires_ddp(self): method fetch (line 75) | def fetch(self, indices, device, pin_memory=False, **kwargs): FILE: python/dgl/storages/numpy.py class NumpyStorage (line 9) | class NumpyStorage(FeatureStorage): method __init__ (line 12) | def __init__(self, arr): method _fetch (line 16) | def _fetch(self, indices, device, pin_memory=False): method fetch (line 22) | def fetch(self, indices, device, pin_memory=False, **kwargs): FILE: python/dgl/storages/pytorch_tensor.py function _fetch_cpu (line 10) | def _fetch_cpu(indices, tensor, feature_shape, device, pin_memory, **kwa... function _fetch_cuda (line 23) | def _fetch_cuda(indices, tensor, device, **kwargs): class PyTorchTensorStorage (line 28) | class PyTorchTensorStorage(BaseTensorStorage): method fetch (line 31) | def fetch(self, indices, device, pin_memory=False, **kwargs): FILE: python/dgl/storages/tensor.py class BaseTensorStorage (line 6) | class BaseTensorStorage(FeatureStorage): method __init__ (line 11) | def __init__(self, tensor): method fetch (line 14) | def fetch( FILE: python/dgl/subgraph.py function node_subgraph (line 26) | def node_subgraph( function edge_subgraph (line 180) | def edge_subgraph( function in_subgraph (line 342) | def in_subgraph( function out_subgraph (line 480) | def out_subgraph( function khop_in_subgraph (line 618) | def khop_in_subgraph( function khop_out_subgraph (line 803) | def khop_out_subgraph( function node_type_subgraph (line 988) | def node_type_subgraph(graph, ntypes, output_device=None): function edge_type_subgraph (line 1069) | def edge_type_subgraph(graph, etypes, output_device=None): function _create_hetero_subgraph (line 1182) | def _create_hetero_subgraph( FILE: python/dgl/transforms/functional.py function pairwise_squared_distance (line 100) | def pairwise_squared_distance(x): function knn_graph (line 111) | def knn_graph( function _knn_graph_blas (line 281) | def _knn_graph_blas(x, k, dist="euclidean"): function segmented_knn_graph (line 337) | def segmented_knn_graph( function _segmented_knn_graph_blas (line 490) | def _segmented_knn_graph_blas(x, k, segs, dist="euclidean"): function _nndescent_knn_graph (line 542) | def _nndescent_knn_graph( function knn (line 641) | def knn( function to_bidirected (line 788) | def to_bidirected(g, copy_ndata=False, readonly=None): function add_reverse_edges (line 878) | def add_reverse_edges( function line_graph (line 1057) | def line_graph(g, backtracking=True, shared=False): function khop_adj (line 1135) | def khop_adj(g, k): function khop_graph (line 1178) | def khop_graph(g, k, copy_ndata=True): function reverse (line 1266) | def reverse( function to_simple_graph (line 1399) | def to_simple_graph(g): function laplacian_lambda_max (line 1426) | def laplacian_lambda_max(g): function metapath_reachable_graph (line 1474) | def metapath_reachable_graph(g, metapath): function add_nodes (line 1546) | def add_nodes(g, num, data=None, ntype=None): function add_edges (line 1640) | def add_edges(g, u, v, data=None, etype=None): function remove_edges (line 1751) | def remove_edges(g, eids, etype=None, store_ids=False): function remove_nodes (line 1826) | def remove_nodes(g, nids, ntype=None, store_ids=False): function add_self_loop (line 1906) | def add_self_loop(g, edge_feat_names=None, fill_data=1.0, etype=None): function remove_self_loop (line 2048) | def remove_self_loop(g, etype=None): function compact_graphs (line 2126) | def compact_graphs( function _coalesce_edge_frame (line 2315) | def _coalesce_edge_frame(g, edge_maps, counts, aggregator): function to_simple (line 2371) | def to_simple( function _unitgraph_less_than_int32 (line 2555) | def _unitgraph_less_than_int32(g): function adj_product_graph (line 2564) | def adj_product_graph(A, B, weight_name, etype="_E"): function adj_sum_graph (line 2717) | def adj_sum_graph(graphs, weight_name): function sort_csr_by_tag (line 2824) | def sort_csr_by_tag(g, tag, tag_offset_name="_TAG_OFFSET", tag_type="nod... function sort_csc_by_tag (line 2952) | def sort_csc_by_tag(g, tag, tag_offset_name="_TAG_OFFSET", tag_type="nod... function reorder_graph (line 3078) | def reorder_graph( function metis_perm (line 3347) | def metis_perm(g, k): function rcmk_perm (line 3371) | def rcmk_perm(g): function norm_by_dst (line 3395) | def norm_by_dst(g, etype=None): function radius_graph (line 3433) | def radius_graph( function random_walk_pe (line 3553) | def random_walk_pe(g, k, eweight_name=None): function lap_pe (line 3614) | def lap_pe(g, k, padding=False, return_eigval=False): function laplacian_pe (line 3723) | def laplacian_pe(g, k, padding=False, return_eigval=False): function to_bfloat16 (line 3729) | def to_bfloat16(g): function to_half (line 3747) | def to_half(g): function to_float (line 3765) | def to_float(g): function to_double (line 3783) | def to_double(g): function double_radius_node_labeling (line 3801) | def double_radius_node_labeling(g, src, dst): function shortest_dist (line 3871) | def shortest_dist(g, root=None, return_paths=False): function svd_pe (line 4012) | def svd_pe(g, k, padding=False, random_flip=True): FILE: python/dgl/transforms/module.py function update_graph_structure (line 60) | def update_graph_structure(g, data_dict, copy_edata=True): class BaseTransform (line 101) | class BaseTransform: method __call__ (line 104) | def __call__(self, g): method __repr__ (line 107) | def __repr__(self): class RowFeatNormalizer (line 111) | class RowFeatNormalizer(BaseTransform): method __init__ (line 179) | def __init__( method row_normalize (line 190) | def row_normalize(self, feat): method __call__ (line 212) | def __call__(self, g): class FeatMask (line 238) | class FeatMask(BaseTransform): method __init__ (line 311) | def __init__(self, p=0.5, node_feat_names=None, edge_feat_names=None): method __call__ (line 321) | def __call__(self, g): class RandomWalkPE (line 378) | class RandomWalkPE(BaseTransform): method __init__ (line 409) | def __init__(self, k, feat_name="PE", eweight_name=None): method __call__ (line 414) | def __call__(self, g): class LapPE (line 423) | class LapPE(BaseTransform): method __init__ (line 482) | def __init__(self, k, feat_name="PE", eigval_name=None, padding=False): method __call__ (line 488) | def __call__(self, g): class LaplacianPE (line 502) | class LaplacianPE(LapPE): method __init__ (line 505) | def __init__(self, k, feat_name="PE", eigval_name=None, padding=False): class AddSelfLoop (line 510) | class AddSelfLoop(BaseTransform): method __init__ (line 586) | def __init__( method transform_etype (line 598) | def transform_etype(self, c_etype, g): method __call__ (line 630) | def __call__(self, g): class RemoveSelfLoop (line 653) | class RemoveSelfLoop(BaseTransform): method transform_etype (line 686) | def transform_etype(self, c_etype, g): method __call__ (line 706) | def __call__(self, g): class AddReverse (line 712) | class AddReverse(BaseTransform): method __init__ (line 773) | def __init__(self, copy_edata=False, sym_new_etype=False): method transform_symmetric_etype (line 777) | def transform_symmetric_etype(self, c_etype, g, data_dict): method transform_asymmetric_etype (line 796) | def transform_asymmetric_etype(self, c_etype, g, data_dict): method transform_etype (line 817) | def transform_etype(self, c_etype, g, data_dict): method __call__ (line 835) | def __call__(self, g): class ToSimple (line 866) | class ToSimple(BaseTransform): method __init__ (line 915) | def __init__(self, return_counts="count", aggregator="arbitrary"): method __call__ (line 919) | def __call__(self, g): class LineGraph (line 928) | class LineGraph(BaseTransform): method __init__ (line 976) | def __init__(self, backtracking=True): method __call__ (line 979) | def __call__(self, g): class KHopGraph (line 985) | class KHopGraph(BaseTransform): method __init__ (line 1008) | def __init__(self, k): method __call__ (line 1011) | def __call__(self, g): class AddMetaPaths (line 1015) | class AddMetaPaths(BaseTransform): method __init__ (line 1061) | def __init__(self, metapaths, keep_orig_edges=True): method __call__ (line 1065) | def __call__(self, g): class Compose (line 1084) | class Compose(BaseTransform): method __init__ (line 1106) | def __init__(self, transforms): method __call__ (line 1109) | def __call__(self, g): method __repr__ (line 1114) | def __repr__(self): class GCNNorm (line 1119) | class GCNNorm(BaseTransform): method __init__ (line 1155) | def __init__(self, eweight_name="w"): method calc_etype (line 1158) | def calc_etype(self, c_etype, g): method __call__ (line 1193) | def __call__(self, g): class PPR (line 1205) | class PPR(BaseTransform): method __init__ (line 1248) | def __init__(self, alpha=0.15, eweight_name="w", eps=None, avg_degree=5): method get_eps (line 1254) | def get_eps(self, num_nodes, mat): method __call__ (line 1265) | def __call__(self, g): function is_bidirected (line 1294) | def is_bidirected(g): class HeatKernel (line 1317) | class HeatKernel(BaseTransform): method __init__ (line 1360) | def __init__(self, t=2.0, eweight_name="w", eps=None, avg_degree=5): method get_eps (line 1366) | def get_eps(self, num_nodes, mat): method __call__ (line 1377) | def __call__(self, g): class GDC (line 1411) | class GDC(BaseTransform): method __init__ (line 1453) | def __init__(self, coefs, eweight_name="w", eps=None, avg_degree=5): method get_eps (line 1459) | def get_eps(self, num_nodes, mat): method __call__ (line 1470) | def __call__(self, g): class NodeShuffle (line 1502) | class NodeShuffle(BaseTransform): method __call__ (line 1527) | def __call__(self, g): class DropNode (line 1538) | class DropNode(BaseTransform): method __init__ (line 1569) | def __init__(self, p=0.5): method __call__ (line 1573) | def __call__(self, g): class DropEdge (line 1588) | class DropEdge(BaseTransform): method __init__ (line 1618) | def __init__(self, p=0.5): method __call__ (line 1622) | def __call__(self, g): class AddEdge (line 1638) | class AddEdge(BaseTransform): method __init__ (line 1660) | def __init__(self, ratio=0.2): method __call__ (line 1663) | def __call__(self, g): class SIGNDiffusion (line 1692) | class SIGNDiffusion(BaseTransform): method __init__ (line 1759) | def __init__( method __call__ (line 1791) | def __call__(self, g): method raw (line 1798) | def raw(self, g): method rw (line 1816) | def rw(self, g): method gcn (line 1844) | def gcn(self, g): method ppr (line 1865) | def ppr(self, g): class ToLevi (line 1890) | class ToLevi(BaseTransform): method __init__ (line 1921) | def __init__(self): method __call__ (line 1924) | def __call__(self, g): class SVDPE (line 1961) | class SVDPE(BaseTransform): method __init__ (line 2004) | def __init__(self, k, feat_name="svd_pe", padding=False, random_flip=T... method __call__ (line 2010) | def __call__(self, g): FILE: python/dgl/transforms/to_block.py function to_block (line 28) | def to_block(g, dst_nodes=None, include_dst_in_src=True, src_nodes=None): FILE: python/dgl/traversal.py function bfs_nodes_generator (line 17) | def bfs_nodes_generator(graph, source, reverse=False): function bfs_edges_generator (line 64) | def bfs_edges_generator(graph, source, reverse=False): function topological_nodes_generator (line 112) | def topological_nodes_generator(graph, reverse=False): function dfs_edges_generator (line 155) | def dfs_edges_generator(graph, source, reverse=False): function dfs_labeled_edges_generator (line 208) | def dfs_labeled_edges_generator( FILE: python/dgl/udf.py class EdgeBatch (line 5) | class EdgeBatch(object): method __init__ (line 24) | def __init__(self, graph, eid, etype, src_data, edge_data, dst_data): method src (line 33) | def src(self): method dst (line 71) | def dst(self): method data (line 109) | def data(self): method edges (line 146) | def edges(self): method batch_size (line 188) | def batch_size(self): method __len__ (line 225) | def __len__(self): method canonical_etype (line 235) | def canonical_etype(self): class NodeBatch (line 241) | class NodeBatch(object): method __init__ (line 258) | def __init__(self, graph, nodes, ntype, data, msgs=None): method data (line 266) | def data(self): method mailbox (line 299) | def mailbox(self): method nodes (line 331) | def nodes(self): method batch_size (line 370) | def batch_size(self): method __len__ (line 403) | def __len__(self): method ntype (line 413) | def ntype(self): FILE: python/dgl/utils/checks.py function prepare_tensor (line 12) | def prepare_tensor(g, data, name): function prepare_tensor_dict (line 73) | def prepare_tensor_dict(g, data, name): function prepare_tensor_or_dict (line 97) | def prepare_tensor_or_dict(g, data, name): function parse_edges_arg_to_eid (line 120) | def parse_edges_arg_to_eid(g, edges, etid, argname="edges"): function check_all_same_idtype (line 151) | def check_all_same_idtype(glist, name): function check_device (line 165) | def check_device(data, device): function check_all_same_device (line 186) | def check_all_same_device(glist, name): function check_all_same_schema (line 200) | def check_all_same_schema(schemas, name): function check_all_same_schema_for_keys (line 215) | def check_all_same_schema_for_keys(schemas, keys, name): function check_valid_idtype (line 242) | def check_valid_idtype(idtype): function is_sorted_srcdst (line 257) | def is_sorted_srcdst(src, dst, num_src=None, num_dst=None): FILE: python/dgl/utils/data.py function elist2tensor (line 13) | def elist2tensor(elist, idtype): function scipy2tensor (line 37) | def scipy2tensor(spmat, idtype): function networkx2tensor (line 67) | def networkx2tensor(nx_graph, idtype, edge_id_attr_name=None): function graphdata2tensors (line 119) | def graphdata2tensors( function networkxbipartite2tensors (line 238) | def networkxbipartite2tensors( function infer_num_nodes (line 311) | def infer_num_nodes(data, bipartite=False): function to_device (line 389) | def to_device(data, device): FILE: python/dgl/utils/exception.py class KeyErrorMessage (line 20) | class KeyErrorMessage(str): method __repr__ (line 23) | def __repr__(self): # pylint: disable=invalid-repr-returned class ExceptionWrapper (line 27) | class ExceptionWrapper(object): method __init__ (line 30) | def __init__(self, exc_info=None, where="in background"): method reraise (line 39) | def reraise(self): FILE: python/dgl/utils/filter.py class Filter (line 7) | class Filter(object): method __init__ (line 23) | def __init__(self, ids): method find_included_indices (line 36) | def find_included_indices(self, test): method find_excluded_indices (line 55) | def find_excluded_indices(self, test): FILE: python/dgl/utils/internal.py function is_listlike (line 17) | def is_listlike(data): class InconsistentDtypeException (line 22) | class InconsistentDtypeException(DGLError): method __init__ (line 25) | def __init__(self, msg="", *args, **kwargs): # pylint: disable=W1113 class Index (line 31) | class Index(object): method __init__ (line 34) | def __init__(self, data, dtype="int64"): method _initialize_data (line 39) | def _initialize_data(self, data): method __iter__ (line 46) | def __iter__(self): method __len__ (line 50) | def __len__(self): method __getitem__ (line 62) | def __getitem__(self, i): method _dispatch (line 65) | def _dispatch(self, data): method tonumpy (line 114) | def tonumpy(self): method tousertensor (line 127) | def tousertensor(self, ctx=None): method todgltensor (line 147) | def todgltensor(self): method slice_data (line 156) | def slice_data(self): method is_slice (line 163) | def is_slice(self, start, stop): method __getstate__ (line 167) | def __getstate__(self): method __setstate__ (line 174) | def __setstate__(self, state): method get_items (line 189) | def get_items(self, index): method set_items (line 232) | def set_items(self, index, value): method append_zeros (line 257) | def append_zeros(self, num): method nonzero (line 275) | def nonzero(self): method has_nonzero (line 281) | def has_nonzero(self): function toindex (line 287) | def toindex(data, dtype="int64"): function zero_index (line 307) | def zero_index(size, dtype="int64"): function set_diff (line 320) | def set_diff(ar1, ar2): class LazyDict (line 344) | class LazyDict(Mapping): method __init__ (line 347) | def __init__(self, fn, keys): method __getitem__ (line 351) | def __getitem__(self, key): method __contains__ (line 356) | def __contains__(self, key): method __iter__ (line 359) | def __iter__(self): method __len__ (line 362) | def __len__(self): method keys (line 365) | def keys(self): class HybridDict (line 369) | class HybridDict(Mapping): method __init__ (line 375) | def __init__(self, *dict_like_list): method keys (line 381) | def keys(self): method __getitem__ (line 384) | def __getitem__(self, key): method __contains__ (line 390) | def __contains__(self, key): method __iter__ (line 393) | def __iter__(self): method __len__ (line 396) | def __len__(self): class ReadOnlyDict (line 400) | class ReadOnlyDict(Mapping): method __init__ (line 403) | def __init__(self, dict_like): method keys (line 406) | def keys(self): method __getitem__ (line 409) | def __getitem__(self, key): method __contains__ (line 412) | def __contains__(self, key): method __iter__ (line 415) | def __iter__(self): method __len__ (line 418) | def __len__(self): function build_relabel_map (line 422) | def build_relabel_map(x, is_sorted=False): function build_relabel_dict (line 465) | def build_relabel_dict(x): class CtxCachedObject (line 486) | class CtxCachedObject(object): method __init__ (line 497) | def __init__(self, generator): method __call__ (line 501) | def __call__(self, ctx): function cached_member (line 507) | def cached_member(cache, prefix): function is_dict_like (line 540) | def is_dict_like(obj): function reorder (line 545) | def reorder(dict_like, index): function reorder_index (line 562) | def reorder_index(idx, order): function is_iterable (line 578) | def is_iterable(obj): function to_dgl_context (line 583) | def to_dgl_context(ctx): function to_nbits_int (line 590) | def to_nbits_int(tensor, nbits): function make_invmap (line 601) | def make_invmap(array, use_numpy=True): function expand_as_pair (line 613) | def expand_as_pair(input_, g=None): function check_eq_shape (line 647) | def check_eq_shape(input_): function retry_method_with_fix (line 664) | def retry_method_with_fix(fix_method): function group_as_dict (line 697) | def group_as_dict(pairs): class FlattenedDict (line 718) | class FlattenedDict(object): method __init__ (line 737) | def __init__(self, groups): method __len__ (line 750) | def __len__(self): method __iter__ (line 754) | def __iter__(self): method __getitem__ (line 760) | def __getitem__(self, idx): function maybe_flatten_dict (line 769) | def maybe_flatten_dict(data): function compensate (line 774) | def compensate(ids, origin_ids): function relabel (line 801) | def relabel(x): function extract_node_subframes (line 842) | def extract_node_subframes(graph, nodes_or_device, store_ids=True): function extract_node_subframes_for_block (line 883) | def extract_node_subframes_for_block(graph, srcnodes, dstnodes): function extract_edge_subframes (line 920) | def extract_edge_subframes(graph, edges_or_device, store_ids=True): function set_new_frames (line 961) | def set_new_frames(graph, *, node_frames=None, edge_frames=None): function set_num_threads (line 989) | def set_num_threads(num_threads): function get_num_threads (line 1000) | def get_num_threads(): function get_numa_nodes_cores (line 1005) | def get_numa_nodes_cores(): function alias_func (line 1046) | def alias_func(func): function apply_each (line 1057) | def apply_each(data, fn, *args, **kwargs): function recursive_apply (line 1094) | def recursive_apply(data, fn, *args, **kwargs): function recursive_apply_pair (line 1138) | def recursive_apply_pair(data1, data2, fn, *args, **kwargs): function context_of (line 1161) | def context_of(data): function dtype_of (line 1171) | def dtype_of(data): FILE: python/dgl/utils/pin_memory.py function pin_memory_inplace (line 8) | def pin_memory_inplace(tensor): function gather_pinned_tensor_rows (line 38) | def gather_pinned_tensor_rows(tensor, rows): function scatter_pinned_tensor_rows (line 59) | def scatter_pinned_tensor_rows(dest, rows, source): FILE: python/dgl/view.py class HeteroNodeView (line 15) | class HeteroNodeView(object): method __init__ (line 20) | def __init__(self, graph, typeid_getter): method __getitem__ (line 24) | def __getitem__(self, key): method __call__ (line 46) | def __call__(self, ntype=None): class HeteroNodeDataView (line 58) | class HeteroNodeDataView(MutableMapping): method __init__ (line 63) | def __init__(self, graph, ntype, ntid, nodes): method __getitem__ (line 69) | def __getitem__(self, key): method __setitem__ (line 82) | def __setitem__(self, key, val): method __delitem__ (line 101) | def __delitem__(self, key): method _transpose (line 110) | def _transpose(self, as_dict=False): method __len__ (line 126) | def __len__(self): method __iter__ (line 129) | def __iter__(self): method keys (line 132) | def keys(self): method values (line 135) | def values(self): method __repr__ (line 138) | def __repr__(self): class HeteroEdgeView (line 142) | class HeteroEdgeView(object): method __init__ (line 147) | def __init__(self, graph): method __getitem__ (line 150) | def __getitem__(self, key): method __call__ (line 177) | def __call__(self, *args, **kwargs): class HeteroEdgeDataView (line 182) | class HeteroEdgeDataView(MutableMapping): method __init__ (line 187) | def __init__(self, graph, etype, edges): method __getitem__ (line 197) | def __getitem__(self, key): method __setitem__ (line 210) | def __setitem__(self, key, val): method __delitem__ (line 229) | def __delitem__(self, key): method _transpose (line 238) | def _transpose(self, as_dict=False): method __len__ (line 254) | def __len__(self): method __iter__ (line 257) | def __iter__(self): method keys (line 260) | def keys(self): method values (line 263) | def values(self): method __repr__ (line 266) | def __repr__(self): FILE: python/setup.py class BinaryDistribution (line 14) | class BinaryDistribution(Distribution): method has_ext_modules (line 15) | def has_ext_modules(self): function get_lib_path (line 22) | def get_lib_path(): function get_lib_pattern (line 41) | def get_lib_pattern(lib_name): function remove_lib (line 57) | def remove_lib(lib_name): function cleanup (line 67) | def cleanup(): function config_cython (line 95) | def config_cython(): function copy_lib (line 150) | def copy_lib(lib_name, backend=""): function get_lib_file_path (line 194) | def get_lib_file_path(lib_name, backend=""): FILE: python/update_version.py function update (line 26) | def update(file_name, pattern, repl): function main (line 52) | def main(): FILE: src/api/api_container.cc type dgl (line 11) | namespace dgl { type runtime (line 12) | namespace runtime { FILE: src/api/api_test.cc type dgl (line 13) | namespace dgl { type runtime (line 14) | namespace runtime { FILE: src/array/arith.h function namespace (line 17) | namespace dgl { FILE: src/array/array.cc type dgl (line 23) | namespace dgl { type aten (line 24) | namespace aten { function IdArray (line 26) | IdArray NewIdArray(int64_t length, DGLContext ctx, uint8_t nbits) { function FloatArray (line 30) | FloatArray NewFloatArray(int64_t length, DGLContext ctx, uint8_t nbi... function IdArray (line 34) | IdArray Clone(IdArray arr) { function IdArray (line 40) | IdArray Range(int64_t low, int64_t high, uint8_t nbits, DGLContext c... function IdArray (line 54) | IdArray Full(int64_t val, int64_t length, uint8_t nbits, DGLContext ... function NDArray (line 69) | NDArray Full(DType val, int64_t length, DGLContext ctx) { function IdArray (line 82) | IdArray AsNumBits(IdArray arr, uint8_t bits) { function IdArray (line 96) | IdArray HStack(IdArray lhs, IdArray rhs) { function NDArray (line 116) | NDArray IndexSelect(NDArray array, IdArray index) { function ValueType (line 134) | ValueType IndexSelect(NDArray array, int64_t index) { function NDArray (line 153) | NDArray IndexSelect(NDArray array, int64_t start, int64_t end) { function NDArray (line 171) | NDArray Scatter(NDArray array, IdArray indices) { function Scatter_ (line 183) | void Scatter_(IdArray index, NDArray value, NDArray out) { function NDArray (line 198) | NDArray Repeat(NDArray array, IdArray repeats) { function IdArray (line 210) | IdArray Relabel_(const std::vector& arrays) { function NDArray (line 220) | NDArray Concat(const std::vector& arrays) { function Pack (line 249) | std::tuple Pack(NDArray array, ValueType ... function ConcatSlices (line 268) | std::pair ConcatSlices(NDArray array, IdArray leng... function IdArray (line 280) | IdArray CumSum(IdArray array, bool prepend_zero) { function IdArray (line 290) | IdArray NonZero(NDArray array) { function Sort (line 299) | std::pair Sort(IdArray array, const int num_bits) { function ToDebugString (line 313) | std::string ToDebugString(NDArray array) { function CSRIsNonZero (line 329) | bool CSRIsNonZero(CSRMatrix csr, int64_t row, int64_t col) { function NDArray (line 339) | NDArray CSRIsNonZero(CSRMatrix csr, NDArray row, NDArray col) { function CSRHasDuplicate (line 350) | bool CSRHasDuplicate(CSRMatrix csr) { function CSRGetRowNNZ (line 358) | int64_t CSRGetRowNNZ(CSRMatrix csr, int64_t row) { function NDArray (line 367) | NDArray CSRGetRowNNZ(CSRMatrix csr, NDArray row) { function NDArray (line 376) | NDArray CSRGetRowColumnIndices(CSRMatrix csr, int64_t row) { function NDArray (line 385) | NDArray CSRGetRowData(CSRMatrix csr, int64_t row) { function CSRIsSorted (line 394) | bool CSRIsSorted(CSRMatrix csr) { function NDArray (line 403) | NDArray CSRGetData(CSRMatrix csr, NDArray rows, NDArray cols) { function NDArray (line 415) | NDArray CSRGetData( function CSRGetFloatingData (line 429) | runtime::NDArray CSRGetFloatingData( function CSRGetDataAndIndices (line 446) | std::vector CSRGetDataAndIndices( function CSRMatrix (line 458) | CSRMatrix CSRTranspose(CSRMatrix csr) { function COOMatrix (line 468) | COOMatrix CSRToCOO(CSRMatrix csr, bool data_as_order) { function CSRMatrix (line 487) | CSRMatrix CSRSliceRows(CSRMatrix csr, int64_t start, int64_t end) { function CSRMatrix (line 498) | CSRMatrix CSRSliceRows(CSRMatrix csr, NDArray rows) { function CSRMatrix (line 507) | CSRMatrix CSRSliceMatrix(CSRMatrix csr, NDArray rows, NDArray cols) { function CSRSort_ (line 518) | void CSRSort_(CSRMatrix* csr) { function CSRSortByTag (line 524) | std::pair CSRSortByTag( function CSRMatrix (line 540) | CSRMatrix CSRReorder( function CSRMatrix (line 549) | CSRMatrix CSRRemove(CSRMatrix csr, IdArray entries) { function CSRLaborSampling (line 557) | std::pair CSRLaborSampling( function COOMatrix (line 574) | COOMatrix CSRRowWiseSampling( function CSRRowWiseSamplingFused (line 601) | std::pair CSRRowWiseSamplingFused( function COOMatrix (line 641) | COOMatrix CSRRowWisePerEtypeSampling( function COOMatrix (line 665) | COOMatrix CSRRowWiseTopk( function COOMatrix (line 677) | COOMatrix CSRRowWiseSamplingBiased( function CSRGlobalUniformNegativeSampling (line 690) | std::pair CSRGlobalUniformNegativeSampling( function CSRMatrix (line 703) | CSRMatrix UnionCsr(const std::vector& csrs) { function CSRToSimple (line 723) | std::tuple CSRToSimple(const CSRMatrix&... function COOIsNonZero (line 735) | bool COOIsNonZero(COOMatrix coo, int64_t row, int64_t col) { function NDArray (line 743) | NDArray COOIsNonZero(COOMatrix coo, NDArray row, NDArray col) { function COOHasDuplicate (line 751) | bool COOHasDuplicate(COOMatrix coo) { function COOGetRowNNZ (line 759) | int64_t COOGetRowNNZ(COOMatrix coo, int64_t row) { function NDArray (line 767) | NDArray COOGetRowNNZ(COOMatrix coo, NDArray row) { function COOGetRowDataAndIndices (line 775) | std::pair COOGetRowDataAndIndices( function COOGetDataAndIndices (line 784) | std::vector COOGetDataAndIndices( function NDArray (line 793) | NDArray COOGetData(COOMatrix coo, NDArray rows, NDArray cols) { function COOMatrix (line 801) | COOMatrix COOTranspose(COOMatrix coo) { function CSRMatrix (line 805) | CSRMatrix COOToCSR(COOMatrix coo) { function COOMatrix (line 814) | COOMatrix COOSliceRows(COOMatrix coo, int64_t start, int64_t end) { function COOMatrix (line 822) | COOMatrix COOSliceRows(COOMatrix coo, NDArray rows) { function COOMatrix (line 830) | COOMatrix COOSliceMatrix(COOMatrix coo, NDArray rows, NDArray cols) { function COOSort_ (line 838) | void COOSort_(COOMatrix* mat, bool sort_column) { function COOIsSorted (line 847) | std::pair COOIsSorted(COOMatrix coo) { function COOMatrix (line 856) | COOMatrix COOReorder( function COOMatrix (line 865) | COOMatrix COORemove(COOMatrix coo, IdArray entries) { function COOLaborSampling (line 873) | std::pair COOLaborSampling( function COOMatrix (line 890) | COOMatrix COORowWiseSampling( function COOMatrix (line 909) | COOMatrix COORowWisePerEtypeSampling( function COOMatrix (line 931) | COOMatrix COORowWiseTopk( function COOCoalesce (line 943) | std::pair COOCoalesce(COOMatrix coo) { function COOMatrix (line 951) | COOMatrix DisjointUnionCoo(const std::vector& coos) { function COOMatrix (line 961) | COOMatrix COOLineGraph(const COOMatrix& coo, bool backtracking) { function COOMatrix (line 969) | COOMatrix UnionCoo(const std::vector& coos) { function COOToSimple (line 1023) | std::tuple COOToSimple(const COOMatrix&... function Frontiers (line 1068) | Frontiers BFSNodesFrontiers(const CSRMatrix& csr, IdArray source) { function Frontiers (line 1084) | Frontiers BFSEdgesFrontiers(const CSRMatrix& csr, IdArray source) { function Frontiers (line 1100) | Frontiers TopologicalNodesFrontiers(const CSRMatrix& csr) { function Frontiers (line 1113) | Frontiers DGLDFSEdges(const CSRMatrix& csr, IdArray source) { function Frontiers (line 1129) | Frontiers DGLDFSLabeledEdges( function CSRSpMM (line 1148) | void CSRSpMM( function CSRSpMM (line 1163) | void CSRSpMM( function CSRSDDMM (line 1170) | void CSRSDDMM( function CSRSDDMM (line 1185) | void CSRSDDMM( function COOSpMM (line 1192) | void COOSpMM( function COOSpMM (line 1207) | void COOSpMM( function COOSDDMM (line 1214) | void COOSDDMM( function COOSDDMM (line 1229) | void COOSDDMM( FILE: src/array/array_arith.cc type dgl (line 16) | namespace dgl { type aten (line 17) | namespace aten { function NDArray (line 111) | NDArray operator+(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 114) | NDArray operator-(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 117) | NDArray operator*(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 120) | NDArray operator/(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 123) | NDArray operator%(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 126) | NDArray operator+(const NDArray& lhs, int64_t rhs) { function NDArray (line 129) | NDArray operator-(const NDArray& lhs, int64_t rhs) { function NDArray (line 132) | NDArray operator*(const NDArray& lhs, int64_t rhs) { function NDArray (line 135) | NDArray operator/(const NDArray& lhs, int64_t rhs) { function NDArray (line 138) | NDArray operator%(const NDArray& lhs, int64_t rhs) { function NDArray (line 141) | NDArray operator+(int64_t lhs, const NDArray& rhs) { function NDArray (line 144) | NDArray operator-(int64_t lhs, const NDArray& rhs) { function NDArray (line 147) | NDArray operator*(int64_t lhs, const NDArray& rhs) { function NDArray (line 150) | NDArray operator/(int64_t lhs, const NDArray& rhs) { function NDArray (line 153) | NDArray operator%(int64_t lhs, const NDArray& rhs) { function NDArray (line 156) | NDArray operator-(const NDArray& array) { return dgl::aten::Neg(array); } function NDArray (line 158) | NDArray operator>(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 161) | NDArray operator<(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 164) | NDArray operator>=(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 167) | NDArray operator<=(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 170) | NDArray operator==(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 173) | NDArray operator!=(const NDArray& lhs, const NDArray& rhs) { function NDArray (line 176) | NDArray operator>(const NDArray& lhs, int64_t rhs) { function NDArray (line 179) | NDArray operator<(const NDArray& lhs, int64_t rhs) { function NDArray (line 182) | NDArray operator>=(const NDArray& lhs, int64_t rhs) { function NDArray (line 185) | NDArray operator<=(const NDArray& lhs, int64_t rhs) { function NDArray (line 188) | NDArray operator==(const NDArray& lhs, int64_t rhs) { function NDArray (line 191) | NDArray operator!=(const NDArray& lhs, int64_t rhs) { function NDArray (line 194) | NDArray operator>(int64_t lhs, const NDArray& rhs) { function NDArray (line 197) | NDArray operator<(int64_t lhs, const NDArray& rhs) { function NDArray (line 200) | NDArray operator>=(int64_t lhs, const NDArray& rhs) { function NDArray (line 203) | NDArray operator<=(int64_t lhs, const NDArray& rhs) { function NDArray (line 206) | NDArray operator==(int64_t lhs, const NDArray& rhs) { function NDArray (line 209) | NDArray operator!=(int64_t lhs, const NDArray& rhs) { FILE: src/array/array_op.h function namespace (line 16) | namespace dgl { FILE: src/array/check.h function namespace (line 15) | namespace dgl { FILE: src/array/cpu/array_cumsum.cc type dgl (line 8) | namespace dgl { type aten (line 10) | namespace aten { type impl (line 11) | namespace impl { function IdArray (line 14) | IdArray CumSum(IdArray array, bool prepend_zero) { FILE: src/array/cpu/array_index_select.cc type dgl (line 8) | namespace dgl { type aten (line 10) | namespace aten { type impl (line 11) | namespace impl { function NDArray (line 14) | NDArray IndexSelect(NDArray array, IdArray index) { function DType (line 42) | DType IndexSelect(NDArray array, int64_t index) { FILE: src/array/cpu/array_nonzero.cc type dgl (line 8) | namespace dgl { type aten (line 10) | namespace aten { type impl (line 11) | namespace impl { function IdArray (line 14) | IdArray NonZero(IdArray array) { FILE: src/array/cpu/array_op_impl.cc type dgl (line 14) | namespace dgl { type aten (line 17) | namespace aten { type impl (line 18) | namespace impl { function IdArray (line 23) | IdArray AsNumBits(IdArray arr, uint8_t bits) { function IdArray (line 51) | IdArray BinaryElewise(IdArray lhs, IdArray rhs) { function IdArray (line 111) | IdArray BinaryElewise(IdArray lhs, IdType rhs) { function IdArray (line 170) | IdArray BinaryElewise(IdType lhs, IdArray rhs) { function IdArray (line 229) | IdArray UnaryElewise(IdArray lhs) { function NDArray (line 248) | NDArray Full(DType val, int64_t length, DGLContext ctx) { function IdArray (line 267) | IdArray Range(IdType low, IdType high, DGLContext ctx) { function IdArray (line 281) | IdArray Relabel_(const std::vector& arrays) { FILE: src/array/cpu/array_pack.cc type dgl (line 12) | namespace dgl { type aten (line 15) | namespace aten { type impl (line 16) | namespace impl { function ConcatSlices (line 19) | std::pair ConcatSlices(NDArray array, IdArray le... function Pack (line 63) | std::tuple Pack(NDArray array, DType pa... FILE: src/array/cpu/array_repeat.cc type dgl (line 10) | namespace dgl { type aten (line 12) | namespace aten { type impl (line 13) | namespace impl { function NDArray (line 16) | NDArray Repeat(NDArray array, IdArray repeats) { FILE: src/array/cpu/array_scatter.cc type dgl (line 9) | namespace dgl { type aten (line 11) | namespace aten { type impl (line 12) | namespace impl { function NDArray (line 15) | NDArray Scatter(NDArray array, IdArray indices) { function Scatter_ (line 39) | void Scatter_(IdArray index, NDArray value, NDArray out) { FILE: src/array/cpu/array_sort.cc type PairRef (line 16) | struct PairRef { method PairRef (line 17) | PairRef() = delete; method PairRef (line 18) | PairRef(const PairRef& other) = default; method PairRef (line 19) | PairRef(PairRef&& other) = default; method PairRef (line 20) | PairRef(V1* const r, V2* const c) : row(r), col(c) {} method PairRef (line 22) | PairRef& operator=(const PairRef& other) { method PairRef (line 27) | PairRef& operator=(const std::pair& val) { method Swap (line 35) | void Swap(const PairRef& other) const { function swap (line 46) | void swap(const PairRef& r1, const PairRef& r2) { type PairIterator (line 51) | struct PairIterator method PairIterator (line 55) | PairIterator() = default; method PairIterator (line 56) | PairIterator(const PairIterator& other) = default; method PairIterator (line 57) | PairIterator(PairIterator&& other) = default; method PairIterator (line 58) | PairIterator(V1* r, V2* c) : row(r), col(c) {} method PairIterator (line 60) | PairIterator& operator=(const PairIterator& other) = default; method PairIterator (line 61) | PairIterator& operator=(PairIterator&& other) = default; method PairIterator (line 76) | PairIterator& operator+=(const std::ptrdiff_t& movement) { method PairIterator (line 82) | PairIterator& operator-=(const std::ptrdiff_t& movement) { method PairIterator (line 88) | PairIterator& operator++() { return operator+=(1); } method PairIterator (line 90) | PairIterator& operator--() { return operator-=(1); } method PairIterator (line 92) | PairIterator operator++(int) { method PairIterator (line 98) | PairIterator operator--(int) { method PairIterator (line 104) | PairIterator operator+(const std::ptrdiff_t& movement) const { method PairIterator (line 110) | PairIterator operator-(const std::ptrdiff_t& movement) const { type dgl (line 134) | namespace dgl { type aten (line 136) | namespace aten { type impl (line 137) | namespace impl { function Sort (line 140) | std::pair Sort(IdArray array, int /* num_bits */) { FILE: src/array/cpu/array_utils.h function namespace (line 18) | namespace dgl { FILE: src/array/cpu/concurrent_id_hash_map.cc function GetMapSize (line 28) | inline size_t GetMapSize(size_t num) { type dgl (line 34) | namespace dgl { type aten (line 35) | namespace aten { function IdType (line 38) | IdType ConcurrentIdHashMap::CompareAndSwap( function IdArray (line 72) | IdArray ConcurrentIdHashMap::Init( function IdArray (line 141) | IdArray ConcurrentIdHashMap::MapIds(const IdArray& ids) const { function IdType (line 168) | inline IdType ConcurrentIdHashMap::MapId(IdType id) const { class ConcurrentIdHashMap (line 223) | class ConcurrentIdHashMap class ConcurrentIdHashMap (line 224) | class ConcurrentIdHashMap function BoolCompareAndSwap (line 227) | bool BoolCompareAndSwap(IdType* ptr) { FILE: src/array/cpu/concurrent_id_hash_map.h function namespace (line 16) | namespace dgl { FILE: src/array/cpu/coo_coalesce.cc type dgl (line 11) | namespace dgl { type aten (line 13) | namespace aten { type impl (line 15) | namespace impl { function COOCoalesce (line 18) | std::pair COOCoalesce(COOMatrix coo) { FILE: src/array/cpu/coo_linegraph.cc type dgl (line 14) | namespace dgl { type aten (line 15) | namespace aten { type impl (line 16) | namespace impl { function COOMatrix (line 19) | COOMatrix COOLineGraph(const COOMatrix& coo, bool backtracking) { FILE: src/array/cpu/coo_remove.cc type dgl (line 13) | namespace dgl { type aten (line 15) | namespace aten { type impl (line 16) | namespace impl { function COORemoveConsecutive (line 23) | void COORemoveConsecutive( function COORemoveShuffled (line 50) | void COORemoveShuffled( function COOMatrix (line 72) | COOMatrix COORemove(COOMatrix coo, IdArray entries) { FILE: src/array/cpu/coo_sort.cc type TupleRef (line 19) | struct TupleRef { method TupleRef (line 20) | TupleRef() = delete; method TupleRef (line 21) | TupleRef(const TupleRef& other) = default; method TupleRef (line 22) | TupleRef(TupleRef&& other) = default; method TupleRef (line 23) | TupleRef(IdType* const r, IdType* const c, IdType* const d) method TupleRef (line 26) | TupleRef& operator=(const TupleRef& other) { method TupleRef (line 32) | TupleRef& operator=(const std::tuple& val) { method Swap (line 43) | void Swap(const TupleRef& other) const { function swap (line 54) | void swap(const TupleRef& r1, const TupleRef& r2) { type CooIterator (line 59) | struct CooIterator method CooIterator (line 64) | CooIterator() = default; method CooIterator (line 65) | CooIterator(const CooIterator& other) = default; method CooIterator (line 66) | CooIterator(CooIterator&& other) = default; method CooIterator (line 67) | CooIterator(IdType* r, IdType* c, IdType* d) : row(r), col(c), data(d) {} method CooIterator (line 69) | CooIterator& operator=(const CooIterator& other) = default; method CooIterator (line 70) | CooIterator& operator=(CooIterator&& other) = default; method CooIterator (line 85) | CooIterator& operator+=(const std::ptrdiff_t& movement) { method CooIterator (line 92) | CooIterator& operator-=(const std::ptrdiff_t& movement) { method CooIterator (line 99) | CooIterator& operator++() { return operator+=(1); } method CooIterator (line 101) | CooIterator& operator--() { return operator-=(1); } method CooIterator (line 103) | CooIterator operator++(int) { method CooIterator (line 109) | CooIterator operator--(int) { method CooIterator (line 115) | CooIterator operator+(const std::ptrdiff_t& movement) const { method CooIterator (line 121) | CooIterator operator-(const std::ptrdiff_t& movement) const { type impl (line 148) | namespace impl { function COOSort_ (line 153) | void COOSort_(COOMatrix* coo, bool sort_column) { FILE: src/array/cpu/csr_get_data.cc type dgl (line 15) | namespace dgl { type aten (line 19) | namespace aten { type impl (line 20) | namespace impl { function CollectDataFromSorted (line 23) | void CollectDataFromSorted( function NDArray (line 44) | NDArray CSRGetData( FILE: src/array/cpu/csr_mm.cc type dgl (line 16) | namespace dgl { type aten (line 21) | namespace aten { function CountNNZPerRow (line 27) | void CountNNZPerRow( function ComputeIndptrInPlace (line 44) | int64_t ComputeIndptrInPlace(IdType* C_indptr_data, int64_t M) { function ComputeIndicesAndData (line 57) | void ComputeIndicesAndData( function CSRMM (line 88) | std::pair CSRMM( FILE: src/array/cpu/csr_remove.cc type dgl (line 13) | namespace dgl { type aten (line 15) | namespace aten { type impl (line 16) | namespace impl { function CSRRemoveConsecutive (line 21) | void CSRRemoveConsecutive( function CSRRemoveShuffled (line 50) | void CSRRemoveShuffled( function CSRMatrix (line 75) | CSRMatrix CSRRemove(CSRMatrix csr, IdArray entries) { FILE: src/array/cpu/csr_sort.cc type dgl (line 13) | namespace dgl { type aten (line 14) | namespace aten { type impl (line 15) | namespace impl { function CSRIsSorted (line 19) | bool CSRIsSorted(CSRMatrix csr) { function CSRSort_ (line 41) | void CSRSort_(CSRMatrix *csr) { function CSRSortByTag (line 88) | std::pair CSRSortByTag( FILE: src/array/cpu/csr_sum.cc type dgl (line 16) | namespace dgl { type aten (line 20) | namespace aten { function CountNNZPerRow (line 26) | void CountNNZPerRow( function ComputeIndptrInPlace (line 45) | int64_t ComputeIndptrInPlace(IdType* C_indptr_data, int64_t M) { function ComputeIndicesAndData (line 58) | void ComputeIndicesAndData( function CSRSum (line 88) | std::pair CSRSum( FILE: src/array/cpu/csr_to_simple.cc type dgl (line 12) | namespace dgl { type aten (line 13) | namespace aten { type impl (line 14) | namespace impl { function CSRToSimple (line 17) | std::tuple CSRToSimple(CSRMatrix csr) { FILE: src/array/cpu/csr_union.cc type dgl (line 14) | namespace dgl { type aten (line 15) | namespace aten { type impl (line 16) | namespace impl { function CSRMatrix (line 19) | CSRMatrix UnionCsr(const std::vector &csrs) { FILE: src/array/cpu/disjoint_union.cc type dgl (line 25) | namespace dgl { type aten (line 27) | namespace aten { type impl (line 28) | namespace impl { function _ComputePrefixSums (line 31) | std::tuple _ComputePrefixSums( function COOMatrix (line 58) | COOMatrix DisjointUnionCoo(const std::vector& coos) { FILE: src/array/cpu/gather_mm.cc type dgl (line 10) | namespace dgl { type aten (line 11) | namespace aten { function SegmentMM (line 15) | void SegmentMM( function SegmentMMBackwardB (line 22) | void SegmentMMBackwardB( function GatherMM (line 29) | void GatherMM( function GatherMMScatter (line 37) | void GatherMMScatter( FILE: src/array/cpu/gather_mm.h function namespace (line 14) | namespace dgl { FILE: src/array/cpu/labor_pick.h function namespace (line 42) | namespace dgl { FILE: src/array/cpu/labor_sampling.cc type dgl (line 23) | namespace dgl { type aten (line 24) | namespace aten { type impl (line 25) | namespace impl { function CSRLaborSampling (line 30) | std::pair CSRLaborSampling( function COOLaborSampling (line 55) | std::pair COOLaborSampling( FILE: src/array/cpu/negative_sampling.cc type dgl (line 17) | namespace dgl { type aten (line 18) | namespace aten { type impl (line 19) | namespace impl { function CSRGlobalUniformNegativeSampling (line 22) | std::pair CSRGlobalUniformNegativeSampling( FILE: src/array/cpu/rowwise_pick.h function namespace (line 20) | namespace dgl { FILE: src/array/cpu/rowwise_sampling.cc type dgl (line 12) | namespace dgl { type aten (line 13) | namespace aten { type impl (line 14) | namespace impl { function FloatArray (line 18) | inline FloatArray DoubleSlice( function GetSamplingNumPicksFn (line 33) | inline NumPicksFn GetSamplingNumPicksFn( function GetSamplingPickFn (line 59) | inline PickFn GetSamplingPickFn( function GetSamplingRangePickFn (line 77) | inline EtypeRangePickFn GetSamplingRangePickFn( function GetSamplingUniformNumPicksFn (line 102) | inline NumPicksFn GetSamplingUniformNumPicksFn( function GetSamplingUniformPickFn (line 119) | inline PickFn GetSamplingUniformPickFn( function GetSamplingUniformRangePickFn (line 135) | inline EtypeRangePickFn GetSamplingUniformRangePickFn( function GetSamplingBiasedNumPicksFn (line 150) | inline NumPicksFn GetSamplingBiasedNumPicksFn( function GetSamplingBiasedPickFn (line 177) | inline PickFn GetSamplingBiasedPickFn( function COOMatrix (line 198) | COOMatrix CSRRowWiseSampling( function CSRRowWiseSamplingFused (line 230) | std::pair CSRRowWiseSamplingFused( function COOMatrix (line 297) | COOMatrix CSRRowWisePerEtypeSampling( function COOMatrix (line 339) | COOMatrix CSRRowWiseSamplingUniform( function CSRRowWiseSamplingUniformFused (line 355) | std::pair CSRRowWiseSamplingUniformFused( function COOMatrix (line 382) | COOMatrix CSRRowWisePerEtypeSamplingUniform( function COOMatrix (line 400) | COOMatrix CSRRowWiseSamplingBiased( function COOMatrix (line 427) | COOMatrix COORowWiseSampling( function COOMatrix (line 458) | COOMatrix COORowWisePerEtypeSampling( function COOMatrix (line 498) | COOMatrix COORowWiseSamplingUniform( function COOMatrix (line 514) | COOMatrix COORowWisePerEtypeSamplingUniform( FILE: src/array/cpu/rowwise_topk.cc type dgl (line 11) | namespace dgl { type aten (line 12) | namespace aten { type impl (line 13) | namespace impl { function GetTopkNumPicksFn (line 17) | inline NumPicksFn GetTopkNumPicksFn(int64_t k) { function GetTopkPickFn (line 28) | inline PickFn GetTopkPickFn(NDArray weight, bool ascendin... function COOMatrix (line 71) | COOMatrix CSRRowWiseTopk( function COOMatrix (line 96) | COOMatrix COORowWiseTopk( FILE: src/array/cpu/sddmm.cc type dgl (line 10) | namespace dgl { type aten (line 11) | namespace aten { function SDDMMCsr (line 47) | void SDDMMCsr( function SDDMMCsrHetero (line 60) | void SDDMMCsrHetero( function SDDMMCoo (line 139) | void SDDMMCoo( function SDDMMCooHetero (line 152) | void SDDMMCooHetero( FILE: src/array/cpu/sddmm.h function namespace (line 15) | namespace dgl { FILE: src/array/cpu/segment_reduce.cc type dgl (line 14) | namespace dgl { type aten (line 15) | namespace aten { function SegmentReduce (line 19) | void SegmentReduce( function ScatterAdd (line 39) | void ScatterAdd(NDArray feat, NDArray idx, NDArray out) { function UpdateGradMinMax_hetero (line 46) | void UpdateGradMinMax_hetero( function BackwardSegmentCmp (line 55) | void BackwardSegmentCmp(NDArray feat, NDArray arg, NDArray out) { FILE: src/array/cpu/segment_reduce.h function namespace (line 16) | namespace dgl { FILE: src/array/cpu/spmat_op_impl_coo.cc type dgl (line 17) | namespace dgl { type aten (line 22) | namespace aten { type impl (line 23) | namespace impl { function COOIsNonZero (line 35) | bool COOIsNonZero(COOMatrix coo, int64_t row, int64_t col) { function NDArray (line 50) | NDArray COOIsNonZero(COOMatrix coo, NDArray row, NDArray col) { function COOHasDuplicate (line 78) | bool COOHasDuplicate(COOMatrix coo) { function COOGetRowNNZ (line 100) | int64_t COOGetRowNNZ(COOMatrix coo, int64_t row) { function NDArray (line 114) | NDArray COOGetRowNNZ(COOMatrix coo, NDArray rows) { function COOGetRowDataAndIndices (line 133) | std::pair COOGetRowDataAndIndices( function IdArray (line 164) | IdArray COOGetData(COOMatrix coo, IdArray rows, IdArray cols) { function COOGetDataAndIndices (line 225) | std::vector COOGetDataAndIndices( function COOMatrix (line 312) | COOMatrix COOTranspose(COOMatrix coo) { function CSRMatrix (line 323) | CSRMatrix SortedCOOToCSR(const COOMatrix &coo) { function CSRMatrix (line 414) | CSRMatrix UnSortedSparseCOOToCSR(const COOMatrix &coo) { function CSRMatrix (line 538) | CSRMatrix UnSortedDenseCOOToCSR(const COOMatrix &coo) { function CSRMatrix (line 637) | CSRMatrix UnSortedSmallCOOToCSR(COOMatrix coo) { type COOToCSRAlg (line 682) | enum class COOToCSRAlg { function COOToCSRAlg (line 711) | inline COOToCSRAlg WhichCOOToCSR(const COOMatrix &coo) { function CSRMatrix (line 748) | CSRMatrix COOToCSR(COOMatrix coo) { function COOMatrix (line 769) | COOMatrix COOSliceRows(COOMatrix coo, int64_t start, int64_t end) { function COOMatrix (line 801) | COOMatrix COOSliceRows(COOMatrix coo, NDArray rows) { function COOMatrix (line 839) | COOMatrix COOSliceMatrix( function COOMatrix (line 879) | COOMatrix COOReorder( FILE: src/array/cpu/spmat_op_impl_csr.cc type dgl (line 16) | namespace dgl { type aten (line 21) | namespace aten { type impl (line 22) | namespace impl { function CSRIsNonZero (line 27) | bool CSRIsNonZero(CSRMatrix csr, int64_t row, int64_t col) { function NDArray (line 48) | NDArray CSRIsNonZero(CSRMatrix csr, NDArray row, NDArray col) { function CSRHasDuplicate (line 76) | bool CSRHasDuplicate(CSRMatrix csr) { function CSRGetRowNNZ (line 99) | int64_t CSRGetRowNNZ(CSRMatrix csr, int64_t row) { function NDArray (line 108) | NDArray CSRGetRowNNZ(CSRMatrix csr, NDArray rows) { function NDArray (line 128) | NDArray CSRGetRowColumnIndices(CSRMatrix csr, int64_t row) { function NDArray (line 141) | NDArray CSRGetRowData(CSRMatrix csr, int64_t row) { function CollectDataIndicesFromSorted (line 159) | void CollectDataIndicesFromSorted( function CSRGetDataAndIndices (line 182) | std::vector CSRGetDataAndIndices( function CSRMatrix (line 248) | CSRMatrix CSRTranspose(CSRMatrix csr) { function COOMatrix (line 304) | COOMatrix CSRToCOO(CSRMatrix csr) { function COOMatrix (line 325) | COOMatrix CSRToCOODataAsOrder(CSRMatrix csr) { function CSRMatrix (line 357) | CSRMatrix CSRSliceRows(CSRMatrix csr, int64_t start, int64_t end) { function CSRMatrix (line 385) | CSRMatrix CSRSliceRows(CSRMatrix csr, NDArray rows) { function CSRMatrix (line 494) | CSRMatrix CSRSliceMatrix( function CSRMatrix (line 552) | CSRMatrix CSRReorder( FILE: src/array/cpu/spmm.cc type dgl (line 10) | namespace dgl { type aten (line 11) | namespace aten { function SpMMCsr (line 15) | void SpMMCsr( function SpMMCsrHetero (line 46) | void SpMMCsrHetero( function Edge_softmax_csr_forward (line 197) | void Edge_softmax_csr_forward( function Edge_softmax_csr_backward (line 208) | void Edge_softmax_csr_backward( function SpMMCoo (line 256) | void SpMMCoo( FILE: src/array/cpu/spmm.h type typename (line 536) | typedef typename std::conditional< FILE: src/array/cpu/spmm_binary_ops.h function namespace (line 12) | namespace dgl { FILE: src/array/cpu/spmm_blocking_libxsmm.h function namespace (line 31) | namespace dgl { FILE: src/array/cpu/traversal.cc type dgl (line 14) | namespace dgl { type aten (line 15) | namespace aten { type impl (line 16) | namespace impl { type VectorQueueWrapper (line 20) | struct VectorQueueWrapper { method VectorQueueWrapper (line 24) | explicit VectorQueueWrapper(std::vector* vec) : vec(vec) {} method push (line 26) | void push(const DType& elem) { vec->push_back(elem); } method DType (line 28) | DType top() const { return vec->operator[](head); } method pop (line 30) | void pop() { ++head; } method empty (line 32) | bool empty() const { return head == vec->size(); } method size (line 34) | size_t size() const { return vec->size() - head; } function IdArray (line 40) | IdArray MergeMultipleTraversals(const std::vector... function Frontiers (line 91) | Frontiers BFSNodesFrontiers(const CSRMatrix& csr, IdArray source) { function Frontiers (line 116) | Frontiers BFSEdgesFrontiers(const CSRMatrix& csr, IdArray source) { function Frontiers (line 146) | Frontiers TopologicalNodesFrontiers(const CSRMatrix& csr) { function Frontiers (line 171) | Frontiers DGLDFSEdges(const CSRMatrix& csr, IdArray source) { function Frontiers (line 191) | Frontiers DGLDFSLabeledEdges( FILE: src/array/cpu/traversal.h function namespace (line 20) | namespace dgl { FILE: src/array/cuda/csr_transpose.cc type dgl (line 10) | namespace dgl { type aten (line 14) | namespace aten { type impl (line 15) | namespace impl { function CSRMatrix (line 18) | CSRMatrix CSRTranspose(CSRMatrix csr) { function CSRMatrix (line 24) | CSRMatrix CSRTranspose(CSRMatrix csr) { function CSRMatrix (line 86) | CSRMatrix CSRTranspose(CSRMatrix csr) { FILE: src/array/cuda/utils.h function namespace (line 19) | namespace dgl { FILE: src/array/filter.cc type dgl (line 13) | namespace dgl { type array (line 14) | namespace array { FILE: src/array/filter.h function namespace (line 13) | namespace dgl { FILE: src/array/kernel.cc type dgl (line 15) | namespace dgl { type aten (line 16) | namespace aten { function SpMM (line 20) | void SpMM( function SegmentMM (line 47) | void SegmentMM( function SegmentMMBackwardB (line 76) | void SegmentMMBackwardB( function GatherMM (line 95) | void GatherMM( function GatherMMScatter (line 133) | void GatherMMScatter( function SpMMHetero (line 173) | void SpMMHetero( function SDDMM (line 224) | void SDDMM( function get_typeid_by_target (line 257) | int get_typeid_by_target(HeteroGraphPtr graph, int target, dgl_type_... function SDDMMHetero (line 265) | void SDDMMHetero( function Edge_softmax_forward (line 310) | void Edge_softmax_forward( function Edge_softmax_backward (line 328) | void Edge_softmax_backward( function NDArray (line 345) | NDArray GetEdgeMapping(HeteroGraphRef graph) { function SegmentReduceDispatch (line 355) | void SegmentReduceDispatch( function ScatterAddDispatch (line 368) | void ScatterAddDispatch(NDArray feat, NDArray idx, NDArray out) { function UpdateGradMinMaxDispatchHetero (line 380) | void UpdateGradMinMaxDispatchHetero( function BackwardSegmentCmpDispatch (line 398) | void BackwardSegmentCmpDispatch(NDArray feat, NDArray arg, NDArray o... function CSRMM (line 408) | std::pair CSRMM( function CSRSum (line 434) | std::pair CSRSum( FILE: src/array/kernel_decl.h function namespace (line 17) | namespace dgl { FILE: src/array/libra_partition.cc type dgl (line 29) | namespace dgl { type aten (line 30) | namespace aten { function Ver2partition (line 33) | int32_t Ver2partition(IdType in_val, int64_t *node_map, int32_t num_... function LeastLoad (line 47) | int32_t LeastLoad(int64_t *community_edges, int32_t nc) { function LibraVertexCut (line 86) | void LibraVertexCut( function Libra2dglBuildDict (line 353) | List Libra2dglBuildDict( function Libra2dglSetLR (line 468) | void Libra2dglSetLR( function Libra2dglBuildAdjlist (line 541) | void Libra2dglBuildAdjlist( FILE: src/array/selector.h function namespace (line 11) | namespace dgl { FILE: src/array/union_partition.cc type dgl (line 10) | namespace dgl { type aten (line 11) | namespace aten { function DisjointPartitionCooBySizes (line 13) | std::vector DisjointPartitionCooBySizes( function COOMatrix (line 48) | COOMatrix COOSliceContiguousChunk( function CSRMatrix (line 78) | CSRMatrix DisjointUnionCsr(const std::vector &csrs) { function DisjointPartitionCsrBySizes (line 132) | std::vector DisjointPartitionCsrBySizes( function CSRMatrix (line 176) | CSRMatrix CSRSliceContiguousChunk( FILE: src/array/uvm_array.cc type dgl (line 15) | namespace dgl { type aten (line 16) | namespace aten { function NDArray (line 18) | NDArray IndexSelectCPUFromGPU(NDArray array, IdArray index) { function IndexScatterGPUToCPU (line 36) | void IndexScatterGPUToCPU(NDArray dest, IdArray index, NDArray sourc... FILE: src/array/uvm_array_op.h function namespace (line 13) | namespace dgl { FILE: src/bcast.cc type dgl (line 11) | namespace dgl { function UseBcast (line 18) | bool UseBcast(const std::string& op, NDArray lhs, NDArray rhs) { function BcastOff (line 36) | BcastOff CalcBcastOff(const std::string& op, NDArray lhs, NDArray rhs) { FILE: src/c_api_common.cc type dgl (line 16) | namespace dgl { function PackedFunc (line 18) | PackedFunc ConvertNDArrayVectorToPackedFunc(const std::vector... function PackedFunc (line 30) | PackedFunc ConvertEdgeArrayToPackedFunc(const EdgeArray& ea) { FILE: src/geometry/cpu/geometry_op_impl.cc type dgl (line 14) | namespace dgl { type geometry (line 16) | namespace geometry { type impl (line 17) | namespace impl { function IndexShuffle (line 21) | void IndexShuffle(IdType *idxs, int64_t num_elems) { function GroupIndexShuffle (line 40) | void GroupIndexShuffle( function IdArray (line 59) | IdArray RandomPerm(int64_t num_nodes) { function IdArray (line 69) | IdArray GroupRandomPerm( function FarthestPointSampler (line 92) | void FarthestPointSampler( function WeightedNeighborMatching (line 164) | void WeightedNeighborMatching( function NeighborMatching (line 207) | void NeighborMatching(const aten::CSRMatrix &csr, IdArray result) { FILE: src/geometry/geometry.cc type dgl (line 17) | namespace dgl { type geometry (line 18) | namespace geometry { function FarthestPointSampler (line 20) | void FarthestPointSampler( function NeighborMatching (line 43) | void NeighborMatching( FILE: src/geometry/geometry_op.h function namespace (line 11) | namespace dgl { FILE: src/graph/creators.cc type dgl (line 9) | namespace dgl { function HeteroGraphPtr (line 12) | HeteroGraphPtr CreateHeteroGraph( function HeteroGraphPtr (line 19) | HeteroGraphPtr CreateFromCOO( function HeteroGraphPtr (line 27) | HeteroGraphPtr CreateFromCOO( function HeteroGraphPtr (line 33) | HeteroGraphPtr CreateFromCSR( function HeteroGraphPtr (line 41) | HeteroGraphPtr CreateFromCSR( function HeteroGraphPtr (line 48) | HeteroGraphPtr CreateFromCSC( function HeteroGraphPtr (line 56) | HeteroGraphPtr CreateFromCSC( FILE: src/graph/gk_ops.cc type dgl (line 13) | namespace dgl { function gk_csr_t (line 24) | gk_csr_t *Convert2GKCsr(const aten::CSRMatrix mat, bool is_row) { function Convert2DGLCsr (line 70) | aten::CSRMatrix Convert2DGLCsr(gk_csr_t *gk_csr, bool is_row) { function GraphPtr (line 109) | GraphPtr GraphOp::ToBidirectedSimpleImmutableGraph(ImmutableGraphPtr i... FILE: src/graph/graph.cc type dgl (line 17) | namespace dgl { function BoolArray (line 122) | BoolArray Graph::HasVertices(IdArray vids) const { function BoolArray (line 143) | BoolArray Graph::HasEdgesBetween(IdArray src_ids, IdArray dst_ids) con... function IdArray (line 174) | IdArray Graph::Predecessors(dgl_id_t vid, uint64_t radius) const { function IdArray (line 191) | IdArray Graph::Successors(dgl_id_t vid, uint64_t radius) const { function IdArray (line 208) | IdArray Graph::EdgeId(dgl_id_t src, dgl_id_t dst) const { function EdgeArray (line 232) | EdgeArray Graph::EdgeIds(IdArray src_ids, IdArray dst_ids) const { function EdgeArray (line 279) | EdgeArray Graph::FindEdges(IdArray eids) const { function EdgeArray (line 304) | EdgeArray Graph::InEdges(dgl_id_t vid) const { function EdgeArray (line 325) | EdgeArray Graph::InEdges(IdArray vids) const { function EdgeArray (line 353) | EdgeArray Graph::OutEdges(dgl_id_t vid) const { function EdgeArray (line 374) | EdgeArray Graph::OutEdges(IdArray vids) const { function EdgeArray (line 402) | EdgeArray Graph::Edges(const std::string& order) const { function DegreeArray (line 450) | DegreeArray Graph::InDegrees(IdArray vids) const { function DegreeArray (line 465) | DegreeArray Graph::OutDegrees(IdArray vids) const { function Subgraph (line 479) | Subgraph Graph::VertexSubgraph(IdArray vids) const { function Subgraph (line 512) | Subgraph Graph::EdgeSubgraph(IdArray eids, bool preserve_nodes) const { FILE: src/graph/graph_apis.cc type dgl (line 21) | namespace dgl { FILE: src/graph/graph_op.cc type dgl (line 19) | namespace dgl { class RangeIter (line 22) | class RangeIter : public std::iterator graphs) { function IdArray (line 257) | IdArray GraphOp::MapParentIdToSubgraphId(IdArray parent_vids, IdArray ... function IdArray (line 304) | IdArray GraphOp::ExpandIds(IdArray ids, IdArray offset) { function GraphPtr (line 323) | GraphPtr GraphOp::ToSimpleGraph(GraphPtr graph) { function GraphPtr (line 342) | GraphPtr GraphOp::ToBidirectedMutableGraph(GraphPtr g) { function GraphPtr (line 374) | GraphPtr GraphOp::ToBidirectedImmutableGraph(GraphPtr g) { function HaloSubgraph (line 413) | HaloSubgraph GraphOp::GetSubgraphWithHalo( function GraphPtr (line 520) | GraphPtr GraphOp::ReorderImmutableGraph( function IdArray (line 743) | IdArray MapIds( FILE: src/graph/graph_traversal.cc type dgl (line 13) | namespace dgl { type traverse (line 14) | namespace traverse { FILE: src/graph/heterograph.cc type dgl (line 20) | namespace dgl { function HeteroSubgraph (line 25) | HeteroSubgraph EdgeSubgraphPreserveNodes( function HeteroSubgraph (line 50) | HeteroSubgraph EdgeSubgraphNoPreserveNodes( function HeteroGraphSanityCheck (line 121) | void HeteroGraphSanityCheck( function InferNumVerticesPerType (line 138) | std::vector InferNumVerticesPerType( function CastToUnitGraphs (line 174) | std::vector CastToUnitGraphs( function BoolArray (line 212) | BoolArray HeteroGraph::HasVertices(dgl_type_t vtype, IdArray vids) con... function HeteroSubgraph (line 217) | HeteroSubgraph HeteroGraph::VertexSubgraph( function HeteroSubgraph (line 246) | HeteroSubgraph HeteroGraph::EdgeSubgraph( function HeteroGraphPtr (line 255) | HeteroGraphPtr HeteroGraph::AsNumBits(HeteroGraphPtr g, uint8_t bits) { function HeteroGraphPtr (line 266) | HeteroGraphPtr HeteroGraph::CopyTo(HeteroGraphPtr g, const DGLContext&... function HeteroGraphPtr (line 280) | HeteroGraphPtr HeteroGraph::PinMemory(HeteroGraphPtr g) { function HeteroGraphPtr (line 321) | HeteroGraphPtr HeteroGraph::CopyToSharedMem( function HeteroGraphPtr (line 435) | HeteroGraphPtr HeteroGraph::GetGraphInFormat(dgl_format_code_t formats... function FlattenedHeteroGraphPtr (line 446) | FlattenedHeteroGraphPtr HeteroGraph::Flatten( function FlattenedHeteroGraphPtr (line 457) | FlattenedHeteroGraphPtr HeteroGraph::FlattenImpl( function GraphPtr (line 586) | GraphPtr HeteroGraph::AsImmutableGraph() const { function HeteroGraphPtr (line 594) | HeteroGraphPtr HeteroGraph::LineGraph(bool backtracking) const { FILE: src/graph/heterograph.h function namespace (line 24) | namespace dgl { function namespace (line 332) | namespace dmlc { FILE: src/graph/heterograph_capi.cc type dgl (line 22) | namespace dgl { FILE: src/graph/immutable_graph.cc type dgl (line 25) | namespace dgl { function GetSharedMemName (line 27) | inline std::string GetSharedMemName( type GraphIndexMetadata (line 35) | struct GraphIndexMetadata { function NDArray (line 48) | NDArray SerializeMetadata(ImmutableGraphPtr gidx, const std::string &n... function GraphIndexMetadata (line 71) | GraphIndexMetadata DeserializeMetadata(const std::string &name) { function MapFromSharedMemory (line 84) | std::tuple MapFromSharedMemory( function EdgeArray (line 169) | EdgeArray CSR::OutEdges(dgl_id_t vid) const { function EdgeArray (line 177) | EdgeArray CSR::OutEdges(IdArray vids) const { function DegreeArray (line 187) | DegreeArray CSR::OutDegrees(IdArray vids) const { function BoolArray (line 198) | BoolArray CSR::HasEdgesBetween(IdArray src_ids, IdArray dst_ids) const { function IdArray (line 204) | IdArray CSR::Successors(dgl_id_t vid, uint64_t radius) const { function IdArray (line 210) | IdArray CSR::EdgeId(dgl_id_t src, dgl_id_t dst) const { function EdgeArray (line 216) | EdgeArray CSR::EdgeIds(IdArray src_ids, IdArray dst_ids) const { function EdgeArray (line 221) | EdgeArray CSR::Edges(const std::string &order) const { function Subgraph (line 229) | Subgraph CSR::VertexSubgraph(IdArray vids) const { function CSRPtr (line 243) | CSRPtr CSR::Transpose() const { function COOPtr (line 248) | COOPtr CSR::ToCOO() const { function CSR (line 253) | CSR CSR::CopyTo(const DGLContext &ctx) const { function CSR (line 264) | CSR CSR::CopyToSharedMem(const std::string &name) const { function CSR (line 274) | CSR CSR::AsNumBits(uint8_t bits) const { function DGLIdIters (line 285) | DGLIdIters CSR::SuccVec(dgl_id_t vid) const { function DGLIdIters (line 295) | DGLIdIters CSR::OutEdgeVec(dgl_id_t vid) const { function EdgeArray (line 336) | EdgeArray COO::FindEdges(IdArray eids) const { function EdgeArray (line 345) | EdgeArray COO::Edges(const std::string &order) const { function Subgraph (line 353) | Subgraph COO::EdgeSubgraph(IdArray eids, bool preserve_nodes) const { function CSRPtr (line 376) | CSRPtr COO::ToCSR() const { function COO (line 381) | COO COO::CopyTo(const DGLContext &ctx) const { function COO (line 390) | COO COO::CopyToSharedMem(const std::string &name) const { function COO (line 395) | COO COO::AsNumBits(uint8_t bits) const { function BoolArray (line 412) | BoolArray ImmutableGraph::HasVertices(IdArray vids) const { function CSRPtr (line 417) | CSRPtr ImmutableGraph::GetInCSR() const { function CSRPtr (line 434) | CSRPtr ImmutableGraph::GetOutCSR() const { function COOPtr (line 451) | COOPtr ImmutableGraph::GetCOO() const { function EdgeArray (line 463) | EdgeArray ImmutableGraph::Edges(const std::string &order) const { function Subgraph (line 485) | Subgraph ImmutableGraph::VertexSubgraph(IdArray vids) const { function Subgraph (line 493) | Subgraph ImmutableGraph::EdgeSubgraph(IdArray eids, bool preserve_node... function ImmutableGraphPtr (line 521) | ImmutableGraphPtr ImmutableGraph::CreateFromCSR( function ImmutableGraphPtr (line 535) | ImmutableGraphPtr ImmutableGraph::CreateFromCSR(const std::string &nam... function ImmutableGraphPtr (line 555) | ImmutableGraphPtr ImmutableGraph::CreateFromCOO( function ImmutableGraphPtr (line 562) | ImmutableGraphPtr ImmutableGraph::ToImmutable(GraphPtr graph) { function ImmutableGraphPtr (line 573) | ImmutableGraphPtr ImmutableGraph::CopyTo( function ImmutableGraphPtr (line 587) | ImmutableGraphPtr ImmutableGraph::CopyToSharedMem( function ImmutableGraphPtr (line 604) | ImmutableGraphPtr ImmutableGraph::AsNumBits(ImmutableGraphPtr g, uint8... function ImmutableGraphPtr (line 618) | ImmutableGraphPtr ImmutableGraph::Reverse() const { function HeteroGraphPtr (line 646) | HeteroGraphPtr ImmutableGraph::AsHeteroGraph() const { FILE: src/graph/metis_partition.cc type dgl (line 15) | namespace dgl { function IdArray (line 19) | IdArray MetisPartition(GraphPtr g, int k, NDArray vwgt_arr, bool obj_c... FILE: src/graph/nodeflow.cc type dgl (line 20) | namespace dgl { function GetNodeFlowSlice (line 22) | std::vector GetNodeFlowSlice( FILE: src/graph/pickle.cc type dgl (line 18) | namespace dgl { function HeteroPickleStates (line 20) | HeteroPickleStates HeteroPickle(HeteroGraphPtr graph) { function HeteroPickleStates (line 57) | HeteroPickleStates HeteroForkingPickle(HeteroGraphPtr graph) { function HeteroGraphPtr (line 95) | HeteroGraphPtr HeteroUnpickle(const HeteroPickleStates &states) { function HeteroGraphPtr (line 163) | HeteroGraphPtr HeteroUnpickleOld(const HeteroPickleStates &states) { function HeteroGraphPtr (line 192) | HeteroGraphPtr HeteroForkingUnpickle(const HeteroPickleStates &states) { FILE: src/graph/sampler.cc type dgl (line 24) | namespace dgl { class ArrayHeap (line 31) | class ArrayHeap { method ArrayHeap (line 33) | explicit ArrayHeap(const std::vector &prob) { method Delete (line 55) | void Delete(size_t index) { method Add (line 70) | void Add(size_t index, ValueType w) { method Sample (line 81) | size_t Sample() { method SampleWithoutReplacement (line 98) | size_t SampleWithoutReplacement(size_t n, std::vector *sampl... class EdgeSamplerObject (line 121) | class EdgeSamplerObject : public Object { method EdgeSamplerObject (line 123) | EdgeSamplerObject( function RandomSample (line 181) | void RandomSample(size_t set_size, size_t num, std::vector *ou... function RandomSample (line 195) | void RandomSample( function NegateArray (line 228) | void NegateArray( function GetUniformSample (line 250) | void GetUniformSample( function GetNonUniformSample (line 291) | void GetNonUniformSample( type neigh_list (line 324) | struct neigh_list { method neigh_list (line 327) | neigh_list( type neighbor_info (line 332) | struct neighbor_info { method neighbor_info (line 337) | neighbor_info(dgl_id_t id, size_t pos, size_t num_edges) { function NodeFlow (line 344) | NodeFlow ConstructNodeFlow( function NodeFlow (line 485) | NodeFlow SampleSubgraph( function NodeFlow (line 631) | NodeFlow SamplerOp::NeighborSample( function ConstructLayers (line 641) | void ConstructLayers( function ConstructFlows (line 701) | void ConstructFlows( function NodeFlow (line 753) | NodeFlow SamplerOp::LayerUniformSample( function BuildCsr (line 801) | void BuildCsr(const ImmutableGraph &g, const std::string neigh_type) { function NeighborSamplingImpl (line 814) | std::vector NeighborSamplingImpl( function BuildCoo (line 982) | void BuildCoo(const ImmutableGraph &g) { function dgl_id_t (line 987) | dgl_id_t global2local_map( function IsNegativeHeadMode (line 999) | inline bool IsNegativeHeadMode(const std::string &mode) { function IdArray (line 1003) | IdArray GetGlobalVid(IdArray induced_nid, IdArray subg_nid) { function IdArray (line 1015) | IdArray CheckExistence( function IdArray (line 1021) | IdArray CheckExistence( function Global2Local (line 1057) | std::vector Global2Local( function NegSubgraph (line 1069) | NegSubgraph EdgeSamplerObject::genNegEdgeSubgraph( function NegSubgraph (line 1242) | NegSubgraph EdgeSamplerObject::genChunkedNegEdgeSubgraph( function SubgraphRef (line 1396) | inline SubgraphRef ConvertRef(const Subgraph &subg) { function SubgraphRef (line 1400) | inline SubgraphRef ConvertRef(const NegSubgraph &subg) { class UniformEdgeSamplerObject (line 1427) | class UniformEdgeSamplerObject : public EdgeSamplerObject { method UniformEdgeSamplerObject (line 1429) | explicit UniformEdgeSamplerObject( method Fetch (line 1448) | void Fetch(DGLRetValue *rv) { method Reset (line 1518) | void Reset() { method randomSample (line 1532) | void randomSample(size_t set_size, size_t num, std::vector *... method randomSample (line 1536) | void randomSample( class UniformEdgeSampler (line 1547) | class UniformEdgeSampler : public ObjectRef { method UniformEdgeSampler (line 1549) | UniformEdgeSampler() {} method UniformEdgeSampler (line 1550) | explicit UniformEdgeSampler(std::shared_ptr obj) method UniformEdgeSamplerObject (line 1553) | UniformEdgeSamplerObject *operator->() const { method sptr (line 1557) | std::shared_ptr sptr() const { class WeightedEdgeSamplerObject (line 1615) | class WeightedEdgeSamplerObject : public EdgeSamplerObject { method WeightedEdgeSamplerObject (line 1617) | explicit WeightedEdgeSamplerObject( method Fetch (line 1660) | void Fetch(DGLRetValue *rv) { method Reset (line 1727) | void Reset() { method randomSample (line 1746) | void randomSample(size_t set_size, size_t num, std::vector *... method randomSample (line 1766) | void randomSample( class WeightedEdgeSamplerObject (line 1809) | class WeightedEdgeSamplerObject class FloatWeightedEdgeSampler (line 1811) | class FloatWeightedEdgeSampler : public ObjectRef { method FloatWeightedEdgeSampler (line 1813) | FloatWeightedEdgeSampler() {} method FloatWeightedEdgeSampler (line 1814) | explicit FloatWeightedEdgeSampler(std::shared_ptr obj) method sptr (line 1821) | std::shared_ptr> sptr() const { FILE: src/graph/sampling/negative/global_uniform.cc type dgl (line 20) | namespace dgl { type sampling (line 21) | namespace sampling { function GlobalUniformNegativeSampling (line 23) | std::pair GlobalUniformNegativeSampling( FILE: src/graph/sampling/neighbor/neighbor.cc type dgl (line 25) | namespace dgl { type sampling (line 26) | namespace sampling { function ExcludeCertainEdgesFused (line 29) | void ExcludeCertainEdgesFused( function ExcludeCertainEdges (line 98) | std::pair> ExcludeCertainEdges( function SampleLabors (line 158) | std::pair> SampleLabors( function HeteroSubgraph (line 257) | HeteroSubgraph SampleNeighbors( function SampleNeighborsFused (line 343) | std::tuple, std::vector> function HeteroSubgraph (line 578) | HeteroSubgraph SampleNeighborsEType( function HeteroSubgraph (line 659) | HeteroSubgraph SampleNeighborsTopk( function HeteroSubgraph (line 736) | HeteroSubgraph SampleNeighborsBiased( FILE: src/graph/sampling/randomwalks/get_node_types_cpu.cc type dgl (line 14) | namespace dgl { type sampling (line 19) | namespace sampling { type impl (line 21) | namespace impl { function TypeArray (line 24) | TypeArray GetNodeTypesFromMetapath( FILE: src/graph/sampling/randomwalks/metapath_randomwalk.h function namespace (line 22) | namespace dgl { FILE: src/graph/sampling/randomwalks/node2vec.cc type dgl (line 17) | namespace dgl { type sampling (line 19) | namespace sampling { function CheckNode2vecInputs (line 23) | void CheckNode2vecInputs( function Node2vec (line 32) | std::pair Node2vec( FILE: src/graph/sampling/randomwalks/node2vec_cpu.cc type dgl (line 14) | namespace dgl { type sampling (line 19) | namespace sampling { type impl (line 21) | namespace impl { function Node2vec (line 24) | std::pair Node2vec( FILE: src/graph/sampling/randomwalks/node2vec_impl.h function namespace (line 19) | namespace dgl { FILE: src/graph/sampling/randomwalks/node2vec_randomwalk.h function namespace (line 25) | namespace dgl { FILE: src/graph/sampling/randomwalks/randomwalk_cpu.cc type dgl (line 20) | namespace dgl { type sampling (line 25) | namespace sampling { type impl (line 27) | namespace impl { function RandomWalk (line 30) | std::pair RandomWalk( function SelectPinSageNeighbors (line 41) | std::tuple SelectPinSageNeighbors( FILE: src/graph/sampling/randomwalks/randomwalk_with_restart_cpu.cc type dgl (line 19) | namespace dgl { type sampling (line 24) | namespace sampling { type impl (line 26) | namespace impl { function RandomWalkWithRestart (line 29) | std::pair RandomWalkWithRestart( function RandomWalkWithStepwiseRestart (line 48) | std::pair RandomWalkWithStepwiseRestart( FILE: src/graph/sampling/randomwalks/randomwalks.cc type dgl (line 22) | namespace dgl { type sampling (line 24) | namespace sampling { function CheckRandomWalkInputs (line 28) | void CheckRandomWalkInputs( function RandomWalk (line 64) | std::tuple RandomWalk( function RandomWalkWithRestart (line 81) | std::tuple RandomWalkWithRestart( function RandomWalkWithStepwiseRestart (line 101) | std::tuple RandomWalkWithStepwiseRestart( function SelectPinSageNeighbors (line 121) | std::tuple SelectPinSageNeighbors( FILE: src/graph/sampling/randomwalks/randomwalks_cpu.h function namespace (line 20) | namespace dgl { FILE: src/graph/sampling/randomwalks/randomwalks_impl.h function namespace (line 18) | namespace dgl { FILE: src/graph/serialize/dglgraph_data.h function namespace (line 32) | namespace dgl { FILE: src/graph/serialize/dglgraph_serialize.cc type dmlc (line 61) | namespace dmlc { type dgl (line 65) | namespace dgl { type serialize (line 66) | namespace serialize { function SaveDGLGraphs (line 68) | bool SaveDGLGraphs( function StorageMetaData (line 114) | StorageMetaData LoadDGLGraphs( function ImmutableGraphPtr (line 225) | ImmutableGraphPtr BatchLoadedGraphs(std::vector gdata_lis... function ImmutableGraphPtr (line 236) | ImmutableGraphPtr ToImmutableGraph(GraphPtr g) { FILE: src/graph/serialize/dglstream.h function namespace (line 15) | namespace dgl { FILE: src/graph/serialize/graph_serialize.cc type dmlc (line 62) | namespace dmlc { type dgl (line 66) | namespace dgl { type serialize (line 67) | namespace serialize { function GetFileVersion (line 121) | uint64_t GetFileVersion(const std::string &filename) { FILE: src/graph/serialize/graph_serialize.h function namespace (line 34) | namespace dgl { FILE: src/graph/serialize/heterograph_data.h type std (line 34) | typedef std::pair NamedTensor; FILE: src/graph/serialize/heterograph_serialize.cc type dgl (line 55) | namespace dgl { type serialize (line 56) | namespace serialize { function SaveHeteroGraphs (line 64) | bool SaveHeteroGraphs( function LoadHeteroGraphs (line 123) | std::vector LoadHeteroGraphs( function LoadLabels_V2 (line 188) | std::vector LoadLabels_V2(const std::string &filename) { FILE: src/graph/serialize/tensor_serialize.cc type dgl (line 17) | namespace dgl { type serialize (line 18) | namespace serialize { FILE: src/graph/serialize/zerocopy_serializer.cc type dgl (line 12) | namespace dgl { function NDArray (line 16) | NDArray CreateNDArrayFromRawData( function NDArray (line 61) | NDArray StreamWithBuffer::PopNDArray() { FILE: src/graph/shared_mem_manager.cc type dgl (line 31) | namespace dgl { function NDArray (line 34) | NDArray SharedMemManager::CopyToSharedMem( function CSRMatrix (line 56) | CSRMatrix SharedMemManager::CopyToSharedMem( function COOMatrix (line 70) | COOMatrix SharedMemManager::CopyToSharedMem( FILE: src/graph/shared_mem_manager.h function namespace (line 22) | namespace dgl { FILE: src/graph/subgraph.cc type dgl (line 9) | namespace dgl { function HeteroSubgraph (line 11) | HeteroSubgraph InEdgeGraphRelabelNodes( function HeteroSubgraph (line 31) | HeteroSubgraph InEdgeGraphNoRelabelNodes( function HeteroSubgraph (line 68) | HeteroSubgraph InEdgeGraph( function HeteroSubgraph (line 78) | HeteroSubgraph OutEdgeGraphRelabelNodes( function HeteroSubgraph (line 98) | HeteroSubgraph OutEdgeGraphNoRelabelNodes( function HeteroSubgraph (line 135) | HeteroSubgraph OutEdgeGraph( FILE: src/graph/transform/compact.cc type dgl (line 38) | namespace dgl { type transform (line 43) | namespace transform { function CompactGraphsCPU (line 48) | std::pair, std::vector> Compact... FILE: src/graph/transform/compact.h function namespace (line 30) | namespace dgl { FILE: src/graph/transform/cpu/kdtree_ndarray_adapter.h function namespace (line 17) | namespace dgl { FILE: src/graph/transform/cpu/knn.cc type dgl (line 23) | namespace dgl { type transform (line 24) | namespace transform { type impl (line 25) | namespace impl { function FloatType (line 36) | FloatType EuclideanDistWithCheck( function FloatType (line 59) | FloatType EuclideanDist( function HeapInsert (line 72) | void HeapInsert( function FlaggedHeapInsert (line 110) | int FlaggedHeapInsert( function BuildHeap (line 149) | void BuildHeap(IdType* index, FloatType* dist, int k) { function UpdateNeighbors (line 179) | int UpdateNeighbors( function KdTreeKNN (line 213) | void KdTreeKNN( function BruteForceKNN (line 266) | void BruteForceKNN( function KNN (line 316) | void KNN( function NNDescent (line 332) | void NNDescent( FILE: src/graph/transform/knn.cc type dgl (line 15) | namespace dgl { type transform (line 16) | namespace transform { FILE: src/graph/transform/knn.h function namespace (line 14) | namespace dgl { FILE: src/graph/transform/line_graph.cc type dgl (line 18) | namespace dgl { type transform (line 23) | namespace transform { function HeteroGraphPtr (line 32) | HeteroGraphPtr CreateLineGraph(HeteroGraphPtr hg, bool backtracking) { FILE: src/graph/transform/metis_partition_hetero.cc type dgl (line 16) | namespace dgl { type transform (line 18) | namespace transform { function IdArray (line 22) | IdArray MetisPartition( FILE: src/graph/transform/partition_hetero.cc type dgl (line 20) | namespace dgl { type transform (line 27) | namespace transform { class HaloHeteroSubgraph (line 29) | class HaloHeteroSubgraph : public HeteroSubgraph { function HeteroGraphPtr (line 34) | HeteroGraphPtr ReorderUnitGraph(UnitGraphPtr ug, IdArray new_order) { function HaloHeteroSubgraph (line 55) | HaloHeteroSubgraph GetSubgraphWithHalo( type EdgeProperty (line 277) | struct EdgeProperty { FILE: src/graph/transform/remove_edges.cc type dgl (line 18) | namespace dgl { type transform (line 23) | namespace transform { function RemoveEdges (line 25) | std::pair> RemoveEdges( FILE: src/graph/transform/to_block.cc type dgl (line 39) | namespace dgl { type transform (line 44) | namespace transform { type CPUIdsMapper (line 49) | struct CPUIdsMapper { function ToBlockCPU (line 125) | std::tuple> ToBlockCPU( function ProcessToBlock (line 136) | std::tuple> ProcessToBlock( FILE: src/graph/transform/to_block.h function namespace (line 31) | namespace dgl { FILE: src/graph/transform/to_simple.cc type dgl (line 19) | namespace dgl { type transform (line 24) | namespace transform { function ToSimpleGraph (line 26) | std::tuple, std::vector> FILE: src/graph/transform/union_partition.cc type dgl (line 9) | namespace dgl { function HeteroGraphPtr (line 11) | HeteroGraphPtr JointUnionHeteroGraph( function HeteroGraphPtr (line 93) | HeteroGraphPtr DisjointUnionHeteroGraph2( function DisjointPartitionHeteroBySizes2 (line 165) | std::vector DisjointPartitionHeteroBySizes2( function HeteroGraphPtr (line 279) | HeteroGraphPtr SliceHeteroGraph( function DisjointPartitionHeteroBySizes (line 350) | std::vector DisjointPartitionHeteroBySizes( FILE: src/graph/traversal.cc type dgl (line 17) | namespace dgl { type traverse (line 18) | namespace traverse { type VectorQueueWrapper (line 22) | struct VectorQueueWrapper { method VectorQueueWrapper (line 26) | explicit VectorQueueWrapper(std::vector* vec) : vec(vec) {} method push (line 28) | void push(const DType& elem) { vec->push_back(elem); } method DType (line 30) | DType top() const { return vec->operator[](head); } method pop (line 32) | void pop() { ++head; } method empty (line 34) | bool empty() const { return head == vec->size(); } method size (line 36) | size_t size() const { return vec->size() - head; } function IdArray (line 42) | IdArray MergeMultipleTraversals(const std::vector... function IdArray (line 67) | IdArray ComputeMergedSections(const std::vector>&... type Frontiers (line 98) | struct Frontiers { function Frontiers (line 111) | Frontiers BFSNodesFrontiers( function Frontiers (line 137) | Frontiers BFSEdgesFrontiers( function Frontiers (line 168) | Frontiers TopologicalNodesFrontiers( FILE: src/graph/traversal.h type std (line 253) | typedef std::tuple StackEntry; FILE: src/graph/unit_graph.cc type dgl (line 16) | namespace dgl { function GraphPtr (line 23) | inline GraphPtr CreateUnitGraphMetaGraph1() { function GraphPtr (line 34) | inline GraphPtr CreateUnitGraphMetaGraph2() { function GraphPtr (line 44) | inline GraphPtr CreateUnitGraphMetaGraph(int num_vtypes) { class UnitGraph::COO (line 64) | class UnitGraph::COO : public BaseHeteroGraph { method COO (line 66) | COO(GraphPtr metagraph, int64_t num_src, int64_t num_dst, IdArray src, method COO (line 77) | COO(GraphPtr metagraph, const aten::COOMatrix& coo) method COO (line 85) | COO() { method defined (line 93) | bool defined() const { return (adj_.num_rows >= 0) && (adj_.num_cols... method dgl_type_t (line 95) | inline dgl_type_t SrcType() const { return 0; } method dgl_type_t (line 97) | inline dgl_type_t DstType() const { return NumVertexTypes() == 1 ? 0... method dgl_type_t (line 99) | inline dgl_type_t EdgeType() const { return 0; } method HeteroGraphPtr (line 101) | HeteroGraphPtr GetRelationGraph(dgl_type_t etype) const override { method AddVertices (line 107) | void AddVertices(dgl_type_t vtype, uint64_t num_vertices) override { method AddEdge (line 111) | void AddEdge(dgl_type_t etype, dgl_id_t src, dgl_id_t dst) override { method AddEdges (line 115) | void AddEdges(dgl_type_t etype, IdArray src_ids, IdArray dst_ids) ov... method Clear (line 119) | void Clear() override { LOG(FATAL) << "UnitGraph graph is not mutabl... method DGLDataType (line 121) | DGLDataType DataType() const override { return adj_.row->dtype; } method DGLContext (line 123) | DGLContext Context() const override { return adj_.row->ctx; } method IsPinned (line 125) | bool IsPinned() const override { return adj_.is_pinned; } method NumBits (line 127) | uint8_t NumBits() const override { return adj_.row->dtype.bits; } method COO (line 129) | COO AsNumBits(uint8_t bits) const { method COO (line 138) | COO CopyTo(const DGLContext& ctx) const { method COO (line 147) | COO PinMemory() { method PinMemory_ (line 153) | void PinMemory_() { adj_.PinMemory_(); } method UnpinMemory_ (line 156) | void UnpinMemory_() { adj_.UnpinMemory_(); } method RecordStream (line 159) | void RecordStream(DGLStreamHandle stream) override { method IsMultigraph (line 163) | bool IsMultigraph() const override { return aten::COOHasDuplicate(ad... method IsReadonly (line 165) | bool IsReadonly() const override { return true; } method NumVertices (line 167) | uint64_t NumVertices(dgl_type_t vtype) const override { method NumEdges (line 178) | uint64_t NumEdges(dgl_type_t etype) const override { method HasVertex (line 182) | bool HasVertex(dgl_type_t vtype, dgl_id_t vid) const override { method BoolArray (line 186) | BoolArray HasVertices(dgl_type_t vtype, IdArray vids) const override { method HasEdgeBetween (line 191) | bool HasEdgeBetween( method BoolArray (line 198) | BoolArray HasEdgesBetween( method IdArray (line 205) | IdArray Predecessors(dgl_type_t etype, dgl_id_t dst) const override { method IdArray (line 210) | IdArray Successors(dgl_type_t etype, dgl_id_t src) const override { method IdArray (line 215) | IdArray EdgeId(dgl_type_t etype, dgl_id_t src, dgl_id_t dst) const o... method EdgeArray (line 221) | EdgeArray EdgeIdsAll( method IdArray (line 229) | IdArray EdgeIdsOne( method FindEdge (line 234) | std::pair FindEdge( method EdgeArray (line 242) | EdgeArray FindEdges(dgl_type_t etype, IdArray eids) const override { method EdgeArray (line 251) | EdgeArray InEdges(dgl_type_t etype, dgl_id_t vid) const override { method EdgeArray (line 260) | EdgeArray InEdges(dgl_type_t etype, IdArray vids) const override { method EdgeArray (line 267) | EdgeArray OutEdges(dgl_type_t etype, dgl_id_t vid) const override { method EdgeArray (line 275) | EdgeArray OutEdges(dgl_type_t etype, IdArray vids) const override { method EdgeArray (line 282) | EdgeArray Edges( method InDegree (line 291) | uint64_t InDegree(dgl_type_t etype, dgl_id_t vid) const override { method DegreeArray (line 296) | DegreeArray InDegrees(dgl_type_t etype, IdArray vids) const override { method OutDegree (line 301) | uint64_t OutDegree(dgl_type_t etype, dgl_id_t vid) const override { method DegreeArray (line 306) | DegreeArray OutDegrees(dgl_type_t etype, IdArray vids) const override { method DGLIdIters (line 311) | DGLIdIters SuccVec(dgl_type_t etype, dgl_id_t vid) const override { method DGLIdIters (line 316) | DGLIdIters OutEdgeVec(dgl_type_t etype, dgl_id_t vid) const override { method DGLIdIters (line 321) | DGLIdIters PredVec(dgl_type_t etype, dgl_id_t vid) const override { method DGLIdIters (line 326) | DGLIdIters InEdgeVec(dgl_type_t etype, dgl_id_t vid) const override { method GetAdj (line 331) | std::vector GetAdj( method GetCOOMatrix (line 341) | aten::COOMatrix GetCOOMatrix(dgl_type_t etype) const override { retu... method GetCSCMatrix (line 343) | aten::CSRMatrix GetCSCMatrix(dgl_type_t etype) const override { method GetCSRMatrix (line 348) | aten::CSRMatrix GetCSRMatrix(dgl_type_t etype) const override { method SparseFormat (line 353) | SparseFormat SelectFormat( method dgl_format_code_t (line 359) | dgl_format_code_t GetAllowedFormats() const override { method dgl_format_code_t (line 364) | dgl_format_code_t GetCreatedFormats() const override { method HeteroSubgraph (line 369) | HeteroSubgraph VertexSubgraph( method HeteroSubgraph (line 387) | HeteroSubgraph EdgeSubgraph( method HeteroGraphPtr (line 417) | HeteroGraphPtr GetGraphInFormat(dgl_format_code_t formats) const ove... method adj (line 422) | aten::COOMatrix adj() const { return adj_; } method IsHypersparse (line 428) | bool IsHypersparse() const { method Load (line 433) | bool Load(dmlc::Stream* fs) { method Save (line 440) | void Save(dmlc::Stream* fs) const { class UnitGraph::CSR (line 460) | class UnitGraph::CSR : public BaseHeteroGraph { method CSR (line 462) | CSR(GraphPtr metagraph, int64_t num_src, int64_t num_dst, IdArray in... method CSR (line 479) | CSR(GraphPtr metagraph, const aten::CSRMatrix& csr) method CSR (line 482) | CSR() { method defined (line 490) | bool defined() const { return (adj_.num_rows >= 0) || (adj_.num_cols... method dgl_type_t (line 492) | inline dgl_type_t SrcType() const { return 0; } method dgl_type_t (line 494) | inline dgl_type_t DstType() const { return NumVertexTypes() == 1 ? 0... method dgl_type_t (line 496) | inline dgl_type_t EdgeType() const { return 0; } method HeteroGraphPtr (line 498) | HeteroGraphPtr GetRelationGraph(dgl_type_t etype) const override { method AddVertices (line 504) | void AddVertices(dgl_type_t vtype, uint64_t num_vertices) override { method AddEdge (line 508) | void AddEdge(dgl_type_t etype, dgl_id_t src, dgl_id_t dst) override { method AddEdges (line 512) | void AddEdges(dgl_type_t etype, IdArray src_ids, IdArray dst_ids) ov... method Clear (line 516) | void Clear() override { LOG(FATAL) << "UnitGraph graph is not mutabl... method DGLDataType (line 518) | DGLDataType DataType() const override { return adj_.indices->dtype; } method DGLContext (line 520) | DGLContext Context() const override { return adj_.indices->ctx; } method IsPinned (line 522) | bool IsPinned() const override { return adj_.is_pinned; } method NumBits (line 524) | uint8_t NumBits() const override { return adj_.indices->dtype.bits; } method CSR (line 526) | CSR AsNumBits(uint8_t bits) const { method CSR (line 539) | CSR CopyTo(const DGLContext& ctx) const { method CSR (line 551) | CSR PinMemory() { method PinMemory_ (line 557) | void PinMemory_() { adj_.PinMemory_(); } method UnpinMemory_ (line 560) | void UnpinMemory_() { adj_.UnpinMemory_(); } method RecordStream (line 563) | void RecordStream(DGLStreamHandle stream) override { method IsMultigraph (line 567) | bool IsMultigraph() const override { return aten::CSRHasDuplicate(ad... method IsReadonly (line 569) | bool IsReadonly() const override { return true; } method NumVertices (line 571) | uint64_t NumVertices(dgl_type_t vtype) const override { method NumEdges (line 582) | uint64_t NumEdges(dgl_type_t etype) const override { method HasVertex (line 586) | bool HasVertex(dgl_type_t vtype, dgl_id_t vid) const override { method BoolArray (line 590) | BoolArray HasVertices(dgl_type_t vtype, IdArray vids) const override { method HasEdgeBetween (line 595) | bool HasEdgeBetween( method BoolArray (line 602) | BoolArray HasEdgesBetween( method IdArray (line 609) | IdArray Predecessors(dgl_type_t etype, dgl_id_t dst) const override { method IdArray (line 614) | IdArray Successors(dgl_type_t etype, dgl_id_t src) const override { method IdArray (line 619) | IdArray EdgeId(dgl_type_t etype, dgl_id_t src, dgl_id_t dst) const o... method EdgeArray (line 625) | EdgeArray EdgeIdsAll( method IdArray (line 633) | IdArray EdgeIdsOne( method FindEdge (line 638) | std::pair FindEdge( method EdgeArray (line 644) | EdgeArray FindEdges(dgl_type_t etype, IdArray eids) const override { method EdgeArray (line 649) | EdgeArray InEdges(dgl_type_t etype, dgl_id_t vid) const override { method EdgeArray (line 654) | EdgeArray InEdges(dgl_type_t etype, IdArray vids) const override { method EdgeArray (line 659) | EdgeArray OutEdges(dgl_type_t etype, dgl_id_t vid) const override { method EdgeArray (line 668) | EdgeArray OutEdges(dgl_type_t etype, IdArray vids) const override { method EdgeArray (line 678) | EdgeArray Edges( method InDegree (line 691) | uint64_t InDegree(dgl_type_t etype, dgl_id_t vid) const override { method DegreeArray (line 696) | DegreeArray InDegrees(dgl_type_t etype, IdArray vids) const override { method OutDegree (line 701) | uint64_t OutDegree(dgl_type_t etype, dgl_id_t vid) const override { method DegreeArray (line 706) | DegreeArray OutDegrees(dgl_type_t etype, IdArray vids) const override { method DGLIdIters (line 711) | DGLIdIters SuccVec(dgl_type_t etype, dgl_id_t vid) const override { method DGLIdIters32 (line 722) | DGLIdIters32 SuccVec32(dgl_type_t etype, dgl_id_t vid) { method DGLIdIters (line 732) | DGLIdIters OutEdgeVec(dgl_type_t etype, dgl_id_t vid) const override { method DGLIdIters (line 743) | DGLIdIters PredVec(dgl_type_t etype, dgl_id_t vid) const override { method DGLIdIters (line 748) | DGLIdIters InEdgeVec(dgl_type_t etype, dgl_id_t vid) const override { method GetAdj (line 753) | std::vector GetAdj( method GetCOOMatrix (line 759) | aten::COOMatrix GetCOOMatrix(dgl_type_t etype) const override { method GetCSCMatrix (line 764) | aten::CSRMatrix GetCSCMatrix(dgl_type_t etype) const override { method GetCSRMatrix (line 769) | aten::CSRMatrix GetCSRMatrix(dgl_type_t etype) const override { retu... method SparseFormat (line 771) | SparseFormat SelectFormat( method dgl_format_code_t (line 777) | dgl_format_code_t GetAllowedFormats() const override { method dgl_format_code_t (line 782) | dgl_format_code_t GetCreatedFormats() const override { method HeteroSubgraph (line 787) | HeteroSubgraph VertexSubgraph( method HeteroSubgraph (line 806) | HeteroSubgraph EdgeSubgraph( method HeteroGraphPtr (line 813) | HeteroGraphPtr GetGraphInFormat(dgl_format_code_t formats) const ove... method adj (line 818) | aten::CSRMatrix adj() const { return adj_; } method Load (line 820) | bool Load(dmlc::Stream* fs) { method Save (line 827) | void Save(dmlc::Stream* fs) const { function DGLDataType (line 846) | DGLDataType UnitGraph::DataType() const { return GetAny()->DataType(); } function DGLContext (line 848) | DGLContext UnitGraph::Context() const { return GetAny()->Context(); } function BoolArray (line 882) | BoolArray UnitGraph::HasVertices(dgl_type_t vtype, IdArray vids) const { function BoolArray (line 897) | BoolArray UnitGraph::HasEdgesBetween( function IdArray (line 907) | IdArray UnitGraph::Predecessors(dgl_type_t etype, dgl_id_t dst) const { function IdArray (line 916) | IdArray UnitGraph::Successors(dgl_type_t etype, dgl_id_t src) const { function IdArray (line 922) | IdArray UnitGraph::EdgeId(dgl_type_t etype, dgl_id_t src, dgl_id_t dst... function EdgeArray (line 931) | EdgeArray UnitGraph::EdgeIdsAll( function IdArray (line 943) | IdArray UnitGraph::EdgeIdsOne( function EdgeArray (line 961) | EdgeArray UnitGraph::FindEdges(dgl_type_t etype, IdArray eids) const { function EdgeArray (line 967) | EdgeArray UnitGraph::InEdges(dgl_type_t etype, dgl_id_t vid) const { function EdgeArray (line 978) | EdgeArray UnitGraph::InEdges(dgl_type_t etype, IdArray vids) const { function EdgeArray (line 989) | EdgeArray UnitGraph::OutEdges(dgl_type_t etype, dgl_id_t vid) const { function EdgeArray (line 995) | EdgeArray UnitGraph::OutEdges(dgl_type_t etype, IdArray vids) const { function EdgeArray (line 1001) | EdgeArray UnitGraph::Edges(dgl_type_t etype, const std::string& order)... function DegreeArray (line 1032) | DegreeArray UnitGraph::InDegrees(dgl_type_t etype, IdArray vids) const { function DegreeArray (line 1051) | DegreeArray UnitGraph::OutDegrees(dgl_type_t etype, IdArray vids) const { function DGLIdIters (line 1060) | DGLIdIters UnitGraph::SuccVec(dgl_type_t etype, dgl_id_t vid) const { function DGLIdIters32 (line 1066) | DGLIdIters32 UnitGraph::SuccVec32(dgl_type_t etype, dgl_id_t vid) const { function DGLIdIters (line 1073) | DGLIdIters UnitGraph::OutEdgeVec(dgl_type_t etype, dgl_id_t vid) const { function DGLIdIters (line 1079) | DGLIdIters UnitGraph::PredVec(dgl_type_t etype, dgl_id_t vid) const { function DGLIdIters (line 1088) | DGLIdIters UnitGraph::InEdgeVec(dgl_type_t etype, dgl_id_t vid) const { function HeteroSubgraph (line 1118) | HeteroSubgraph UnitGraph::VertexSubgraph( function HeteroSubgraph (line 1150) | HeteroSubgraph UnitGraph::EdgeSubgraph( function HeteroGraphPtr (line 1181) | HeteroGraphPtr UnitGraph::CreateFromCOO( function HeteroGraphPtr (line 1192) | HeteroGraphPtr UnitGraph::CreateFromCOO( function HeteroGraphPtr (line 1202) | HeteroGraphPtr UnitGraph::CreateFromCSR( function HeteroGraphPtr (line 1212) | HeteroGraphPtr UnitGraph::CreateFromCSR( function HeteroGraphPtr (line 1221) | HeteroGraphPtr UnitGraph::CreateFromCSRAndCOO( function HeteroGraphPtr (line 1236) | HeteroGraphPtr UnitGraph::CreateFromCSC( function HeteroGraphPtr (line 1246) | HeteroGraphPtr UnitGraph::CreateFromCSC( function HeteroGraphPtr (line 1255) | HeteroGraphPtr UnitGraph::CreateFromCSCAndCOO( function HeteroGraphPtr (line 1270) | HeteroGraphPtr UnitGraph::AsNumBits(HeteroGraphPtr g, uint8_t bits) { function HeteroGraphPtr (line 1290) | HeteroGraphPtr UnitGraph::CopyTo(HeteroGraphPtr g, const DGLContext& c... function HeteroGraphPtr (line 1310) | HeteroGraphPtr UnitGraph::PinMemory() { function HeteroGraphPtr (line 1391) | HeteroGraphPtr UnitGraph::CreateUnitGraphFrom( function HeteroGraphPtr (line 1530) | HeteroGraphPtr UnitGraph::GetAny() const { function dgl_format_code_t (line 1540) | dgl_format_code_t UnitGraph::GetCreatedFormats() const { function dgl_format_code_t (line 1548) | dgl_format_code_t UnitGraph::GetAllowedFormats() const { return format... function HeteroGraphPtr (line 1550) | HeteroGraphPtr UnitGraph::GetFormat(SparseFormat format) const { function HeteroGraphPtr (line 1561) | HeteroGraphPtr UnitGraph::GetGraphInFormat(dgl_format_code_t formats) ... function SparseFormat (line 1588) | SparseFormat UnitGraph::SelectFormat( function GraphPtr (line 1602) | GraphPtr UnitGraph::AsImmutableGraph() const { function HeteroGraphPtr (line 1627) | HeteroGraphPtr UnitGraph::LineGraph(bool backtracking) const { function UnitGraphPtr (line 1748) | UnitGraphPtr UnitGraph::Reverse() const { FILE: src/graph/unit_graph.h type std (line 28) | typedef std::shared_ptr UnitGraphPtr; function class (line 41) | class UnitGraph : public BaseHeteroGraph { FILE: src/partition/ndarray_partition.cc type dgl (line 20) | namespace dgl { type partition (line 21) | namespace partition { class RemainderPartition (line 31) | class RemainderPartition : public NDArrayPartition { method RemainderPartition (line 33) | RemainderPartition(const int64_t array_size, const int num_parts) method GeneratePermutation (line 38) | std::pair GeneratePermutation( method IdArray (line 56) | IdArray MapToLocal(IdArray in_idx) const override { method IdArray (line 73) | IdArray MapToGlobal(IdArray in_idx, const int part_id) const overr... method PartSize (line 90) | int64_t PartSize(const int part_id) const override { class RangePartition (line 99) | class RangePartition : public NDArrayPartition { method RangePartition (line 101) | RangePartition(const int64_t array_size, const int num_parts, IdAr... method GeneratePermutation (line 117) | std::pair GeneratePermutation( method IdArray (line 144) | IdArray MapToLocal(IdArray in_idx) const override { method IdArray (line 163) | IdArray MapToGlobal(IdArray in_idx, const int part_id) const overr... method PartSize (line 182) | int64_t PartSize(const int part_id) const override { function NDArrayPartitionRef (line 201) | NDArrayPartitionRef CreatePartitionRemainderBased( function NDArrayPartitionRef (line 207) | NDArrayPartitionRef CreatePartitionRangeBased( FILE: src/partition/partition_op.h function namespace (line 14) | namespace dgl { FILE: src/random/continuous_seed.h function namespace (line 39) | namespace dgl { FILE: src/random/cpu/choice.cc type dgl (line 15) | namespace dgl { function IdxType (line 18) | IdxType RandomEngine::Choice(FloatArray prob) { FILE: src/random/cpu/sample_utils.h function namespace (line 21) | namespace dgl { FILE: src/random/random.cc type dgl (line 16) | namespace dgl { FILE: src/rpc/network/common.cc type dgl (line 14) | namespace dgl { type network (line 15) | namespace network { function CalculateReserveForVector (line 20) | static int CalculateReserveForVector( function SplitStringUsing (line 42) | void SplitStringUsing( function SplitStringToSetUsing (line 52) | void SplitStringToSetUsing( function StringAppendV (line 60) | static void StringAppendV(string* dst, const char* format, va_list a... function string (line 104) | string StringPrintf(const char* format, ...) { function SStringPrintf (line 113) | void SStringPrintf(string* dst, const char* format, ...) { function StringAppendF (line 121) | void StringAppendF(string* dst, const char* format, ...) { FILE: src/rpc/network/common.h function namespace (line 16) | namespace dgl { FILE: src/rpc/network/communicator.h function namespace (line 15) | namespace dgl { FILE: src/rpc/network/msg_queue.cc type dgl (line 12) | namespace dgl { type network (line 13) | namespace network { function STATUS (line 25) | STATUS MessageQueue::Add(Message msg, bool is_blocking) { function STATUS (line 52) | STATUS MessageQueue::Remove(Message* msg, bool is_blocking) { FILE: src/rpc/network/msg_queue.h function namespace (line 20) | namespace dgl { FILE: src/rpc/network/socket_communicator.cc type dgl (line 24) | namespace dgl { type network (line 25) | namespace network { function STATUS (line 127) | STATUS SocketSender::Send(Message msg, int recv_id) { function SendCore (line 161) | void SendCore(Message msg, TCPSocket* socket) { function STATUS (line 308) | STATUS SocketReceiver::Recv(Message* msg, int* send_id, int timeout) { function STATUS (line 335) | STATUS SocketReceiver::RecvFrom(Message* msg, int send_id, int timeo... function RecvDataSize (line 367) | int64_t RecvDataSize(TCPSocket* socket) { function RecvData (line 386) | void RecvData( FILE: src/rpc/network/socket_communicator.h function namespace (line 22) | namespace dgl { FILE: src/rpc/network/socket_pool.cc type dgl (line 16) | namespace dgl { type network (line 17) | namespace network { FILE: src/rpc/network/socket_pool.h function namespace (line 13) | namespace dgl { FILE: src/rpc/network/tcp_socket.cc type dgl (line 21) | namespace dgl { type network (line 22) | namespace network { type sockaddr_in (line 24) | struct sockaddr_in type sockaddr (line 25) | struct sockaddr type timeval (line 177) | struct timeval FILE: src/rpc/network/tcp_socket.h function namespace (line 19) | namespace dgl { FILE: src/rpc/rpc.cc type dgl (line 26) | namespace dgl { type rpc (line 27) | namespace rpc { function RPCStatus (line 34) | RPCStatus SendRPCMessage(const RPCMessage& msg, const int32_t target... function RPCStatus (line 39) | RPCStatus RecvRPCMessage(RPCMessage* msg, int32_t timeout) { function SigHandler (line 300) | void SigHandler(int s) { type sigaction (line 309) | struct sigaction FILE: src/rpc/rpc.h function namespace (line 27) | namespace dgl { FILE: src/rpc/rpc_msg.h function namespace (line 16) | namespace dgl { function namespace (line 82) | namespace dmlc { FILE: src/rpc/server_state.h function namespace (line 17) | namespace dgl { FILE: src/runtime/c_object_api.cc type DGLAPIThreadLocalEntry (line 21) | struct DGLAPIThreadLocalEntry { type APIAttrGetter (line 37) | struct APIAttrGetter : public AttrVisitor { method Visit (line 42) | void Visit(const char* key, double* value) final { method Visit (line 45) | void Visit(const char* key, int64_t* value) final { method Visit (line 48) | void Visit(const char* key, uint64_t* value) final { method Visit (line 54) | void Visit(const char* key, int* value) final { method Visit (line 57) | void Visit(const char* key, bool* value) final { method Visit (line 60) | void Visit(const char* key, std::string* value) final { method Visit (line 63) | void Visit(const char* key, ObjectRef* value) final { method Visit (line 69) | void Visit(const char* key, NDArray* value) final { type APIAttrDir (line 74) | struct APIAttrDir : public AttrVisitor { method Visit (line 77) | void Visit(const char* key, double* value) final { names->push_back(ke... method Visit (line 78) | void Visit(const char* key, int64_t* value) final { names->push_back(k... method Visit (line 79) | void Visit(const char* key, uint64_t* value) final { names->push_back(... method Visit (line 80) | void Visit(const char* key, bool* value) final { names->push_back(key); } method Visit (line 81) | void Visit(const char* key, int* value) final { names->push_back(key); } method Visit (line 82) | void Visit(const char* key, std::string* value) final { method Visit (line 85) | void Visit(const char* key, ObjectRef* value) final { names->push_back... method Visit (line 86) | void Visit(const char* key, NDArray* value) final { names->push_back(k... function DGLObjectFree (line 89) | int DGLObjectFree(ObjectHandle handle) { function DGLObjectTypeKey2Index (line 95) | int DGLObjectTypeKey2Index(const char* type_key, int* out_index) { function DGLObjectGetTypeIndex (line 101) | int DGLObjectGetTypeIndex(ObjectHandle handle, int* out_index) { function DGLObjectGetAttr (line 108) | int DGLObjectGetAttr( function DGLObjectListAttrNames (line 136) | int DGLObjectListAttrNames( FILE: src/runtime/c_runtime_api.cc type dgl (line 22) | namespace dgl { type runtime (line 23) | namespace runtime { function DeviceName (line 29) | inline std::string DeviceName(int type) { class DeviceAPIManager (line 42) | class DeviceAPIManager { method DeviceAPI (line 46) | static DeviceAPI* Get(const DGLContext& ctx) { return Get(ctx.devi... method DeviceAPI (line 47) | static DeviceAPI* Get(int dev_type, bool allow_missing = false) { method DeviceAPIManager (line 56) | DeviceAPIManager() { std::fill(api_.begin(), api_.end(), nullptr); } method DeviceAPIManager (line 58) | static DeviceAPIManager* Global() { method DeviceAPI (line 63) | DeviceAPI* GetAPI(int type, bool allow_missing) { method DeviceAPI (line 78) | DeviceAPI* GetAPI(const std::string name, bool allow_missing) { function DeviceAPI (line 92) | DeviceAPI* DeviceAPI::Get(DGLContext ctx, bool allow_missing) { function DeviceAPI (line 97) | DeviceAPI* DeviceAPI::Get(DGLDeviceType dev_type, bool allow_missing) { function DGLStreamHandle (line 110) | DGLStreamHandle DeviceAPI::CreateStream(DGLContext ctx) { type DGLRuntimeEntry (line 147) | struct DGLRuntimeEntry { function DGLAPISetLastError (line 159) | void DGLAPISetLastError(const char* msg) { function DGLModLoadFromFile (line 167) | int DGLModLoadFromFile( function DGLModImport (line 175) | int DGLModImport(DGLModuleHandle mod, DGLModuleHandle dep) { function DGLModGetFunction (line 181) | int DGLModGetFunction( function DGLModFree (line 195) | int DGLModFree(DGLModuleHandle mod) { function DGLBackendGetFuncFromEnv (line 201) | int DGLBackendGetFuncFromEnv( function DGLBackendFreeWorkspace (line 226) | int DGLBackendFreeWorkspace(int device_type, int device_id, void* ptr) { function DGLBackendRunOnce (line 234) | int DGLBackendRunOnce(void** handle, int (*f)(void*), void* cdata, int n... function DGLFuncFree (line 242) | int DGLFuncFree(DGLFunctionHandle func) { function DGLFuncCall (line 248) | int DGLFuncCall( function DGLCFuncSetReturn (line 279) | int DGLCFuncSetReturn( function DGLFuncCreateFromCFunc (line 288) | int DGLFuncCreateFromCFunc( function DGLStreamCreate (line 322) | int DGLStreamCreate(int device_type, int device_id, DGLStreamHandle* out) { function DGLStreamFree (line 331) | int DGLStreamFree(int device_type, int device_id, DGLStreamHandle stream) { function DGLSetStream (line 340) | int DGLSetStream(int device_type, int device_id, DGLStreamHandle stream) { function DGLGetStream (line 349) | int DGLGetStream(int device_type, int device_id, DGLStreamHandle* stream) { function DGLSynchronize (line 358) | int DGLSynchronize(int device_type, int device_id, DGLStreamHandle strea... function DGLStreamStreamSynchronize (line 367) | int DGLStreamStreamSynchronize( function DGLCbArgToReturn (line 377) | int DGLCbArgToReturn(DGLValue* value, int code) { function DGLLoadTensorAdapter (line 387) | int DGLLoadTensorAdapter(const char* path) { FILE: src/runtime/config.cc type dgl (line 15) | namespace dgl { type runtime (line 16) | namespace runtime { FILE: src/runtime/cpu_device_api.cc type dgl (line 16) | namespace dgl { type runtime (line 17) | namespace runtime { class CPUDeviceAPI (line 18) | class CPUDeviceAPI final : public DeviceAPI { method SetDevice (line 20) | void SetDevice(DGLContext ctx) final {} method GetAttr (line 21) | void GetAttr(DGLContext ctx, DeviceAttrKind kind, DGLRetValue* rv)... method FreeDataSpace (line 47) | void FreeDataSpace(DGLContext ctx, void* ptr) final { method CopyDataFromTo (line 59) | void CopyDataFromTo( method RecordedCopyDataFromTo (line 68) | void RecordedCopyDataFromTo( method DGLStreamHandle (line 75) | DGLStreamHandle CreateStream(DGLContext) final { return nullptr; } method StreamSync (line 77) | void StreamSync(DGLContext ctx, DGLStreamHandle stream) final {} type CPUWorkspacePool (line 90) | struct CPUWorkspacePool : public WorkspacePool { method CPUWorkspacePool (line 91) | CPUWorkspacePool() : WorkspacePool(kDGLCPU, CPUDeviceAPI::Global()... FILE: src/runtime/cuda/cuda_common.h function namespace (line 20) | namespace dgl { FILE: src/runtime/cuda/cuda_device_api.cc type dgl (line 14) | namespace dgl { type runtime (line 15) | namespace runtime { class CUDADeviceAPI (line 17) | class CUDADeviceAPI final : public DeviceAPI { method CUDADeviceAPI (line 19) | CUDADeviceAPI() { method IsAvailable (line 32) | bool IsAvailable() final { return is_available_; } method SetDevice (line 34) | void SetDevice(DGLContext ctx) final { method GetAttr (line 37) | void GetAttr(DGLContext ctx, DeviceAttrKind kind, DGLRetValue* rv)... method FreeDataSpace (line 121) | void FreeDataSpace(DGLContext ctx, void* ptr) final { method CopyDataFromTo (line 130) | void CopyDataFromTo( method CopyDataFromTo (line 158) | void CopyDataFromTo( method RecordedCopyDataFromTo (line 172) | void RecordedCopyDataFromTo( method DGLStreamHandle (line 190) | DGLStreamHandle CreateStream(DGLContext ctx) { method FreeStream (line 198) | void FreeStream(DGLContext ctx, DGLStreamHandle stream) { method SyncStreamFromTo (line 204) | void SyncStreamFromTo( method StreamSync (line 216) | void StreamSync(DGLContext ctx, DGLStreamHandle stream) final { method SetStream (line 227) | void SetStream(DGLContext ctx, DGLStreamHandle stream) final {} method DGLStreamHandle (line 229) | DGLStreamHandle GetStream() const final { method PinData (line 238) | bool PinData(void* ptr, size_t nbytes) override { method UnpinData (line 251) | void UnpinData(void* ptr) { method FreePinnedDataSpace (line 267) | void FreePinnedDataSpace(void** deleter) override { method IsPinned (line 275) | bool IsPinned(const void* ptr) override { method FreeWorkspace (line 324) | void FreeWorkspace(DGLContext ctx, void* data) final { method GPUCopy (line 340) | static void GPUCopy( function CUDAThreadEntry (line 358) | CUDAThreadEntry* CUDAThreadEntry::ThreadLocal() { function cudaStream_t (line 362) | cudaStream_t getCurrentCUDAStream() { FILE: src/runtime/dlpack_convert.cc type dgl (line 19) | namespace dgl { type runtime (line 20) | namespace runtime { function NDArrayDLPackDeleter (line 22) | void NDArrayDLPackDeleter(DLManagedTensor* tensor) { function DGLContext (line 27) | inline DGLContext ToDGLContext(const DLDevice& device) { function DGLDataType (line 34) | inline DGLDataType ToDGLDataType(const DLDataType& src) { function DLDevice (line 42) | inline DLDevice ToDLDevice(const DGLContext& ctx) { function DLDataType (line 49) | inline DLDataType ToDLDataType(const DGLDataType& src) { function NDArray (line 57) | NDArray DLPackConvert::FromDLPack(DLManagedTensor* tensor) { function DLManagedTensor (line 82) | DLManagedTensor* ContainerToDLPack(NDArray::Container* from) { function DLManagedTensor (line 99) | DLManagedTensor* DLPackConvert::ToDLPack(const NDArray& from) { function DGLDLManagedTensorCallDeleter (line 108) | void DGLDLManagedTensorCallDeleter(DLManagedTensor* dltensor) { function IsAligned (line 112) | inline bool IsAligned(const void* ptr, std::uintptr_t alignment) noexcept { function DGLArrayFromDLPack (line 117) | int DGLArrayFromDLPack(DLManagedTensor* from, DGLArrayHandle* out) { function DGLArrayToDLPack (line 123) | int DGLArrayToDLPack( FILE: src/runtime/dso_module.cc type dgl (line 18) | namespace dgl { type runtime (line 19) | namespace runtime { class DSOModuleNode (line 23) | class DSOModuleNode final : public ModuleNode { method PackedFunc (line 31) | PackedFunc GetFunction( method Init (line 49) | void Init(const std::string& name) { method Load (line 71) | void Load(const std::string& name) { method Unload (line 82) | void Unload() { FreeLibrary(lib_handle_); } method Load (line 87) | void Load(const std::string& name) { method Unload (line 93) | void Unload() { dlclose(lib_handle_); } FILE: src/runtime/file_util.cc type dgl (line 15) | namespace dgl { type runtime (line 16) | namespace runtime { function GetFileFormat (line 56) | std::string GetFileFormat( function GetCacheDir (line 72) | std::string GetCacheDir() { function GetFileBasename (line 84) | std::string GetFileBasename(const std::string& file_name) { function GetMetaFilePath (line 90) | std::string GetMetaFilePath(const std::string& file_name) { function LoadBinaryFromFile (line 99) | void LoadBinaryFromFile(const std::string& file_name, std::string* d... function SaveBinaryToFile (line 110) | void SaveBinaryToFile(const std::string& file_name, const std::strin... function SaveMetaDataToFile (line 116) | void SaveMetaDataToFile( function LoadMetaDataFromFile (line 130) | void LoadMetaDataFromFile( FILE: src/runtime/file_util.h function namespace (line 14) | namespace dgl { FILE: src/runtime/meta_data.h function namespace (line 18) | namespace dgl { function namespace (line 35) | namespace dmlc { FILE: src/runtime/module.cc type dgl (line 16) | namespace dgl { type runtime (line 17) | namespace runtime { function Module (line 48) | Module Module::LoadFromFile( function PackedFunc (line 81) | const PackedFunc* ModuleNode::GetFuncFromEnv(const std::string& name) { function RuntimeEnabled (line 101) | bool RuntimeEnabled(const std::string& target) { FILE: src/runtime/module_util.cc type dgl (line 17) | namespace dgl { type runtime (line 18) | namespace runtime { function ImportModuleBlob (line 20) | void ImportModuleBlob(const char* mblob, std::vector* mlist) { function PackedFunc (line 48) | PackedFunc WrapPackedFunc( FILE: src/runtime/module_util.h function namespace (line 21) | namespace dgl { FILE: src/runtime/ndarray.cc type dgl (line 17) | namespace dgl { type runtime (line 35) | namespace runtime { function VerifyDataType (line 37) | inline void VerifyDataType(DGLDataType dtype) { function GetDataSize (line 47) | inline size_t GetDataSize(const DGLArray& arr) { function GetDataAlignment (line 56) | inline size_t GetDataAlignment(const DGLArray& arr) { function NDArray (line 85) | NDArray NDArray::Internal::Create( function DGLArray (line 112) | DGLArray* NDArray::Internal::MoveAsDGLArray(NDArray arr) { function NDArray (line 148) | NDArray NDArray::CreateView( function NDArray (line 166) | NDArray NDArray::EmptyShared( function NDArray (line 182) | NDArray NDArray::Empty( function NDArray (line 234) | NDArray NDArray::PinnedEmpty( function NDArray (line 286) | NDArray NDArray::FromVector(const std::vector& vec, DGLContext ct... function NDArray (line 296) | NDArray NDArray::CreateFromRaw( function DGLArrayAlloc (line 416) | int DGLArrayAlloc( function DGLArrayAllocSharedMem (line 432) | int DGLArrayAllocSharedMem( function DGLArrayFree (line 447) | int DGLArrayFree(DGLArrayHandle handle) { function DGLArrayCopyFromTo (line 453) | int DGLArrayCopyFromTo(DGLArrayHandle from, DGLArrayHandle to) { function DGLArrayCopyFromBytes (line 459) | int DGLArrayCopyFromBytes(DGLArrayHandle handle, void* data, size_t nbyt... function DGLArrayCopyToBytes (line 473) | int DGLArrayCopyToBytes(DGLArrayHandle handle, void* data, size_t nbytes) { function DGLArrayPinData (line 487) | int DGLArrayPinData(DGLArrayHandle handle, DGLContext ctx) { function DGLArrayUnpinData (line 494) | int DGLArrayUnpinData(DGLArrayHandle handle, DGLContext ctx) { function DGLArrayRecordStream (line 501) | int DGLArrayRecordStream(DGLArrayHandle handle, DGLStreamHandle stream) { FILE: src/runtime/object.cc type dgl (line 13) | namespace dgl { type runtime (line 14) | namespace runtime { type TypeManager (line 18) | struct TypeManager { method TypeManager (line 26) | static TypeManager* Global() { FILE: src/runtime/pack_args.h function namespace (line 23) | namespace dgl { function PackedFunc (line 261) | PackedFunc PackFuncVoidAddr( function NumBufferArgs (line 278) | inline size_t NumBufferArgs(const std::vector& arg_types) { function PackedFunc (line 294) | PackedFunc PackFuncNonBufferArg( function PackedFunc (line 312) | PackedFunc PackFuncPackedArg( FILE: src/runtime/parallel_for.cpp type dgl (line 7) | namespace dgl { type runtime (line 8) | namespace runtime { FILE: src/runtime/registry.cc type dgl (line 17) | namespace dgl { type runtime (line 18) | namespace runtime { type Registry::Manager (line 20) | struct Registry::Manager { method Manager (line 33) | Manager() { method Manager (line 39) | static Manager* Global() { function Registry (line 45) | Registry& Registry::set_body(PackedFunc f) { // NOLINT(*) function Registry (line 50) | Registry& Registry::Register( function PackedFunc (line 75) | const PackedFunc* Registry::Get(const std::string& name) { function ExtTypeVTable (line 94) | ExtTypeVTable* ExtTypeVTable::Get(int type_code) { function ExtTypeVTable (line 102) | ExtTypeVTable* ExtTypeVTable::RegisterInternal( type DGLFuncThreadLocalEntry (line 115) | struct DGLFuncThreadLocalEntry { function DGLExtTypeFree (line 125) | int DGLExtTypeFree(void* handle, int type_code) { function DGLFuncRegisterGlobal (line 131) | int DGLFuncRegisterGlobal(const char* name, DGLFunctionHandle f, int ove... function DGLFuncGetGlobal (line 138) | int DGLFuncGetGlobal(const char* name, DGLFunctionHandle* out) { function DGLFuncListGlobalNames (line 149) | int DGLFuncListGlobalNames(int* out_size, const char*** out_array) { FILE: src/runtime/resource_manager.cc type dgl (line 13) | namespace dgl { type runtime (line 14) | namespace runtime { class ResourceManager (line 22) | class ResourceManager { method Add (line 26) | void Add(const std::string &key, std::shared_ptr resourc... method Erase (line 33) | void Erase(const std::string &key) { resources.erase(key); } method Cleanup (line 35) | void Cleanup() { function AddResource (line 45) | void AddResource(const std::string &key, std::shared_ptr r... function DeleteResource (line 49) | void DeleteResource(const std::string &key) { manager.Erase(key); } function CleanupResources (line 51) | void CleanupResources() { manager.Cleanup(); } FILE: src/runtime/resource_manager.h function namespace (line 13) | namespace dgl { FILE: src/runtime/runtime_base.h function DGLAPIHandleException (line 42) | inline int DGLAPIHandleException(const std::runtime_error &e) { FILE: src/runtime/semaphore_wrapper.cc type dgl (line 16) | namespace dgl { type runtime (line 17) | namespace runtime { FILE: src/runtime/semaphore_wrapper.h function namespace (line 15) | namespace dgl { FILE: src/runtime/shared_mem.cc type dgl (line 18) | namespace dgl { type runtime (line 19) | namespace runtime { class SharedMemoryResource (line 25) | class SharedMemoryResource : public Resource { method SharedMemoryResource (line 29) | explicit SharedMemoryResource(const std::string &name) { this->nam... method Destroy (line 31) | void Destroy() { FILE: src/runtime/system_lib_module.cc type dgl (line 13) | namespace dgl { type runtime (line 14) | namespace runtime { class SystemLibModuleNode (line 16) | class SystemLibModuleNode : public ModuleNode { method SystemLibModuleNode (line 18) | SystemLibModuleNode() = default; method PackedFunc (line 22) | PackedFunc GetFunction( method RegisterSymbol (line 42) | void RegisterSymbol(const std::string& name, void* ptr) { function DGLBackendRegisterSystemLibSymbol (line 89) | int DGLBackendRegisterSystemLibSymbol(const char* name, void* ptr) { FILE: src/runtime/tensordispatch.cc type dgl (line 17) | namespace dgl { type runtime (line 18) | namespace runtime { FILE: src/runtime/thread_pool.cc type dgl (line 27) | namespace dgl { type runtime (line 28) | namespace runtime { class ParallelLauncher (line 36) | class ParallelLauncher { method Init (line 39) | void Init( method WaitForJobs (line 65) | int WaitForJobs() { method SignalJobError (line 83) | void SignalJobError(int task_id) { method SignalJobFinish (line 89) | void SignalJobFinish() { num_pending_.fetch_sub(1); } method ParallelLauncher (line 91) | static ParallelLauncher* ThreadLocal() { class SpscTaskQueue (line 116) | class SpscTaskQueue { type Task (line 119) | struct Task { method SpscTaskQueue (line 124) | SpscTaskQueue() : buffer_(new Task[kRingSize]), head_(0), tail_(0) {} method Push (line 132) | void Push(const Task& input) { method Pop (line 148) | bool Pop(Task* output, uint32_t spin_count = 300000) { method SignalForKill (line 175) | void SignalForKill() { method Enqueue (line 187) | bool Enqueue(const Task& input) { class ThreadPool (line 233) | class ThreadPool { method ThreadPool (line 235) | ThreadPool() : num_workers_(dgl::runtime::threading::MaxConcurrenc... method Launch (line 253) | int Launch( method ThreadPool (line 287) | static ThreadPool* ThreadLocal() { method UpdateWorkerConfiguration (line 291) | void UpdateWorkerConfiguration( method RunWorker (line 303) | void RunWorker(int worker_id) { function DGLBackendParallelLaunch (line 343) | int DGLBackendParallelLaunch( function DGLBackendParallelBarrier (line 350) | int DGLBackendParallelBarrier(int task_id, DGLParallelGroupEnv* penv) { FILE: src/runtime/thread_storage_scope.h function namespace (line 14) | namespace dgl { function namespace (line 201) | namespace std { FILE: src/runtime/threading_backend.cc type dgl (line 19) | namespace dgl { type runtime (line 20) | namespace runtime { type threading (line 21) | namespace threading { class ThreadGroup::Impl (line 23) | class ThreadGroup::Impl { method Impl (line 25) | Impl( method Join (line 38) | void Join() { method Configure (line 44) | int Configure(AffinityMode mode, int nthreads, bool exclude_work... method SetAffinity (line 82) | void SetAffinity(bool exclude_worker0, bool reverse = false) { method InitSortedOrder (line 135) | void InitSortedOrder() { function YieldThread (line 197) | void YieldThread() { std::this_thread::yield(); } function MaxConcurrency (line 199) | int MaxConcurrency() { FILE: src/runtime/utils.cc type dgl (line 19) | namespace dgl { FILE: src/runtime/workspace.h function namespace (line 14) | namespace dgl { FILE: src/runtime/workspace_pool.cc type dgl (line 10) | namespace dgl { type runtime (line 11) | namespace runtime { class WorkspacePool::Pool (line 16) | class WorkspacePool::Pool { method Pool (line 19) | Pool() { method Free (line 73) | void Free(void* data) { method Release (line 102) | void Release(DGLContext ctx, DeviceAPI* device) { type Entry (line 112) | struct Entry { FILE: src/runtime/workspace_pool.h function namespace (line 14) | namespace dgl { FILE: src/scheduler/scheduler.cc type dgl (line 11) | namespace dgl { type sched (line 12) | namespace sched { function DegreeBucketing (line 15) | std::vector DegreeBucketing( function GroupEdgeByNodeDegree (line 104) | std::vector GroupEdgeByNodeDegree( FILE: src/scheduler/scheduler_apis.cc type dgl (line 17) | namespace dgl { FILE: tensoradapter/include/tensoradapter.h function namespace (line 17) | namespace tensoradapter { FILE: tensoradapter/pytorch/torch.cpp type tensoradapter (line 17) | namespace tensoradapter { function TA_EXPORTS (line 21) | TA_EXPORTS void* CPURawAlloc(size_t nbytes) { function TA_EXPORTS (line 25) | TA_EXPORTS void CPURawDelete(void* ptr) { function TA_EXPORTS (line 30) | TA_EXPORTS void* CUDARawAlloc(size_t nbytes, cudaStream_t stream) { function TA_EXPORTS (line 35) | TA_EXPORTS void CUDARawDelete(void* ptr) { function TA_EXPORTS (line 39) | TA_EXPORTS cudaStream_t CUDACurrentStream() { function TA_EXPORTS (line 43) | TA_EXPORTS void RecordStream(void* ptr, cudaStream_t stream, int devic... class CUDAHostDeleter (line 60) | class CUDAHostDeleter { method CUDAHostDeleter (line 62) | explicit CUDAHostDeleter(std::unique_ptr ptr) function TA_EXPORTS (line 69) | TA_EXPORTS void* CUDARawHostAlloc( function TA_EXPORTS (line 83) | TA_EXPORTS void CUDARawHostDelete(void** raw_deleter) { function TA_EXPORTS (line 88) | TA_EXPORTS void CUDARecordHostAlloc( function TA_EXPORTS (line 100) | TA_EXPORTS void CUDAHostAllocatorEmptyCache() { FILE: tests/backend/__init__.py function ctx (line 40) | def ctx(): function gpu_ctx (line 44) | def gpu_ctx(): function zeros (line 48) | def zeros(shape, dtype=float32, ctx=_default_context): function ones (line 52) | def ones(shape, dtype=float32, ctx=_default_context): function randn (line 56) | def randn(shape): function tensor (line 60) | def tensor(data, dtype=None): function arange (line 64) | def arange(start, stop, dtype=int64, ctx=None): function full (line 70) | def full(shape, fill_value, dtype, ctx=_default_context): function full_1d (line 74) | def full_1d(length, fill_value, dtype, ctx=_default_context): function softmax (line 78) | def softmax(x, dim): FILE: tests/backend/backend_unittest.py function cuda (line 9) | def cuda(): function is_cuda_available (line 14) | def is_cuda_available(): function array_equal (line 26) | def array_equal(a, b): function allclose (line 31) | def allclose(a, b, rtol=1e-4, atol=1e-4): function randn (line 36) | def randn(shape): function full (line 41) | def full(shape, fill_value, dtype, ctx): function narrow_row_set (line 45) | def narrow_row_set(x, start, stop, new): function sparse_to_numpy (line 50) | def sparse_to_numpy(x): function clone (line 55) | def clone(x): function reduce_sum (line 59) | def reduce_sum(x): function softmax (line 64) | def softmax(x, dim): function spmm (line 69) | def spmm(x, y): function add (line 74) | def add(a, b): function sub (line 79) | def sub(a, b): function mul (line 84) | def mul(a, b): function div (line 89) | def div(a, b): function sum (line 94) | def sum(x, dim, keepdims=False): function max (line 99) | def max(x, dim): function min (line 104) | def min(x, dim): function prod (line 109) | def prod(x, dim): function matmul (line 114) | def matmul(a, b): function dot (line 119) | def dot(a, b): function abs (line 124) | def abs(a): function seed (line 129) | def seed(a): FILE: tests/backend/mxnet/__init__.py function cuda (line 8) | def cuda(): function is_cuda_available (line 12) | def is_cuda_available(): function array_equal (line 21) | def array_equal(a, b): function allclose (line 25) | def allclose(a, b, rtol=1e-4, atol=1e-4): function randn (line 29) | def randn(shape): function full (line 33) | def full(shape, fill_value, dtype, ctx): function narrow_row_set (line 37) | def narrow_row_set(x, start, stop, new): function sparse_to_numpy (line 41) | def sparse_to_numpy(x): function clone (line 45) | def clone(x): function reduce_sum (line 49) | def reduce_sum(x): function softmax (line 53) | def softmax(x, dim): function spmm (line 57) | def spmm(x, y): function add (line 61) | def add(a, b): function sub (line 65) | def sub(a, b): function mul (line 69) | def mul(a, b): function div (line 73) | def div(a, b): function sum (line 77) | def sum(x, dim, keepdims=False): function max (line 81) | def max(x, dim): function min (line 85) | def min(x, dim): function prod (line 89) | def prod(x, dim): function matmul (line 93) | def matmul(a, b): function dot (line 97) | def dot(a, b): function abs (line 101) | def abs(a): function seed (line 105) | def seed(a): FILE: tests/backend/pytorch/__init__.py function cuda (line 6) | def cuda(): function is_cuda_available (line 10) | def is_cuda_available(): function array_equal (line 14) | def array_equal(a, b): function allclose (line 18) | def allclose(a, b, rtol=1e-4, atol=1e-4): function randn (line 22) | def randn(shape): function full (line 26) | def full(shape, fill_value, dtype, ctx): function narrow_row_set (line 30) | def narrow_row_set(x, start, stop, new): function sparse_to_numpy (line 34) | def sparse_to_numpy(x): function clone (line 38) | def clone(x): function reduce_sum (line 42) | def reduce_sum(x): function softmax (line 46) | def softmax(x, dim): function spmm (line 50) | def spmm(x, y): function add (line 54) | def add(a, b): function sub (line 58) | def sub(a, b): function mul (line 62) | def mul(a, b): function div (line 66) | def div(a, b): function sum (line 70) | def sum(x, dim, keepdims=False): function max (line 74) | def max(x, dim): function min (line 78) | def min(x, dim): function prod (line 82) | def prod(x, dim): function matmul (line 86) | def matmul(a, b): function dot (line 90) | def dot(a, b): function abs (line 94) | def abs(a): function seed (line 98) | def seed(a): FILE: tests/backend/tensorflow/__init__.py function cuda (line 8) | def cuda(): function is_cuda_available (line 12) | def is_cuda_available(): function array_equal (line 16) | def array_equal(a, b): function allclose (line 20) | def allclose(a, b, rtol=1e-4, atol=1e-4): function randn (line 29) | def randn(shape): function full (line 33) | def full(shape, fill_value, dtype, ctx): function narrow_row_set (line 39) | def narrow_row_set(x, start, stop, new): function sparse_to_numpy (line 44) | def sparse_to_numpy(x): function clone (line 49) | def clone(x): function reduce_sum (line 53) | def reduce_sum(x): function softmax (line 57) | def softmax(x, dim): function spmm (line 61) | def spmm(x, y): function add (line 65) | def add(a, b): function sub (line 69) | def sub(a, b): function mul (line 73) | def mul(a, b): function div (line 77) | def div(a, b): function sum (line 81) | def sum(x, dim, keepdims=False): function max (line 85) | def max(x, dim): function min (line 89) | def min(x, dim): function prod (line 93) | def prod(x, dim): function matmul (line 97) | def matmul(a, b): function dot (line 101) | def dot(a, b): function abs (line 105) | def abs(a): function seed (line 109) | def seed(a): FILE: tests/cpp/common.h function Len (line 25) | inline int64_t Len(dgl::runtime::NDArray nd) { return nd->shape[0]; } function ArrayEQ (line 28) | bool ArrayEQ(dgl::runtime::NDArray a1, dgl::runtime::NDArray a2) { function IsInArray (line 48) | bool IsInArray(dgl::runtime::NDArray a, T x) { FILE: tests/cpp/graph_index_test.cc function TEST (line 9) | TEST(GraphTest, TestNumVertices) { FILE: tests/cpp/message_queue_test.cc function TEST (line 18) | TEST(MessageQueueTest, AddRemove) { function TEST (line 64) | TEST(MessageQueueTest, EmptyAndNoMoreAdd) { function start_add (line 80) | void start_add(MessageQueue* queue, int id) { function TEST (line 88) | TEST(MessageQueueTest, MultiThread) { FILE: tests/cpp/socket_communicator_test.cc function TEST (line 45) | TEST(SocketCommunicatorTest, SendAndRecv) { function TEST (line 64) | TEST(SocketCommunicatorTest, SendAndRecvTimeout) { function start_client (line 107) | void start_client() { function start_server (line 134) | void start_server(int id) { function TEST (line 156) | TEST(SocketCommunicatorTest, TCPSocketBind) { function sleep (line 171) | void sleep(int seconds) { Sleep(seconds * 1000); } function DWORD (line 176) | DWORD WINAPI _ClientThreadFunc(LPVOID param) { function DWORD (line 181) | DWORD WINAPI _ServerThreadFunc(LPVOID param) { return start_server() ? 1... function TEST (line 183) | TEST(SocketCommunicatorTest, SendAndRecv) { function start_client (line 216) | static void start_client() { function start_server (line 232) | static bool start_server() { FILE: tests/cpp/string_test.cc function TEST (line 18) | TEST(SplitStringTest, SplitStringUsingCompoundDelim) { function TEST (line 27) | TEST(SplitStringTest, testSplitStringUsingSingleDelim) { function TEST (line 36) | TEST(SplitStringTest, testSplitingNoDelimString) { function TEST (line 44) | TEST(StringPrintf, normal) { FILE: tests/cpp/test_aten.cc function TEST (line 9) | TEST(ArrayTest, TestCreate) { function _TestRange (line 29) | void _TestRange(DGLContext ctx) { function TEST (line 39) | TEST(ArrayTest, TestRange) { function TEST (line 46) | TEST(ArrayTest, TestFull) { function TEST (line 55) | TEST(ArrayTest, TestClone) { function _TestNumBits (line 71) | void _TestNumBits(DGLContext ctx) { function TEST (line 79) | TEST(ArrayTest, TestAsNumBits) { function _TestArith (line 87) | void _TestArith(DGLContext ctx) { function TEST (line 170) | TEST(ArrayTest, Arith) { function _TestHStack (line 180) | void _TestHStack(DGLContext ctx) { function TEST (line 189) | TEST(ArrayTest, HStack) { function _TestIndexSelect (line 199) | void _TestIndexSelect(DGLContext ctx) { function TEST (line 210) | TEST(ArrayTest, TestIndexSelect) { function _TestRelabel_ (line 220) | void _TestRelabel_(DGLContext ctx) { function TEST (line 239) | TEST(ArrayTest, TestRelabel_) { function _TestConcat (line 249) | void _TestConcat(DGLContext ctx) { function _TestToSimpleCsr (line 266) | void _TestToSimpleCsr(DGLContext ctx) { function TEST (line 337) | TEST(MatrixTest, TestToSimpleCsr) { function _TestToSimpleCoo (line 343) | void _TestToSimpleCoo(DGLContext ctx) { function TEST (line 425) | TEST(MatrixTest, TestToSimpleCoo) { function _TestDisjointUnionPartitionCoo (line 431) | void _TestDisjointUnionPartitionCoo(DGLContext ctx) { function TEST (line 556) | TEST(DisjointUnionTest, TestDisjointUnionPartitionCoo) { function _TestDisjointUnionPartitionCsr (line 566) | void _TestDisjointUnionPartitionCsr(DGLContext ctx) { function TEST (line 682) | TEST(DisjointUnionTest, TestDisjointUnionPartitionCsr) { function _TestSliceContiguousChunkCoo (line 692) | void _TestSliceContiguousChunkCoo(DGLContext ctx) { function TEST (line 750) | TEST(SliceContiguousChunk, TestSliceContiguousChunkCoo) { function _TestSliceContiguousChunkCsr (line 760) | void _TestSliceContiguousChunkCsr(DGLContext ctx) { function TEST (line 817) | TEST(SliceContiguousChunk, TestSliceContiguousChunkCsr) { function _TestMatrixUnionCsr (line 827) | void _TestMatrixUnionCsr(DGLContext ctx) { function TEST (line 1012) | TEST(MatrixUnionTest, TestMatrixUnionCsr) { function _TestMatrixUnionCoo (line 1018) | void _TestMatrixUnionCoo(DGLContext ctx) { function TEST (line 1190) | TEST(MatrixUnionTest, TestMatrixUnionCoo) { function _TestCumSum (line 1196) | void _TestCumSum(DGLContext ctx) { function TEST (line 1224) | TEST(ArrayTest, CumSum) { function _TestScatter_ (line 1234) | void _TestScatter_(DGLContext ctx) { function TEST (line 1247) | TEST(ArrayTest, Scatter_) { function _TestNonZero (line 1261) | void _TestNonZero(DGLContext ctx) { function TEST (line 1285) | TEST(ArrayTest, NonZero) { function _TestLineGraphCOO (line 1295) | void _TestLineGraphCOO(DGLContext ctx) { function TEST (line 1384) | TEST(LineGraphTest, LineGraphCOO) { function _TestSort (line 1390) | void _TestSort(DGLContext ctx) { function TEST (line 1421) | TEST(ArrayTest, Sort) { function TEST (line 1430) | TEST(ArrayTest, BFloatCast) { FILE: tests/cpp/test_concurrent_id_hash_map.cc function ConstructRandomSet (line 18) | size_t ConstructRandomSet( function _TestIdMap (line 33) | void _TestIdMap() { function TEST (line 64) | TEST(ConcurrentIdHashMapTest, TestConcurrentIdHashMap) { FILE: tests/cpp/test_csrmm.cc function COOToMap (line 19) | std::unordered_map, DType, aten::PairHash> COO... function CSRIsClose (line 35) | bool CSRIsClose( function CSR_A (line 54) | std::pair CSR_A(DGLContext ctx = CTX) { function CSR_B (line 70) | std::pair CSR_B(DGLContext ctx = CTX) { function CSR_C (line 89) | std::pair CSR_C(DGLContext ctx = CTX) { function CSR_A_mm_B (line 103) | std::pair CSR_A_mm_B(DGLContext ctx = CTX) { function CSR_A_plus_C (line 124) | std::pair CSR_A_plus_C(DGLContext ctx = CTX) { function NDArray (line 137) | NDArray CSR_A_mask_C(DGLContext ctx = CTX) { function _TestCsrmm (line 143) | void _TestCsrmm(DGLContext ctx = CTX) { function _TestCsrsum (line 154) | void _TestCsrsum(DGLContext ctx = CTX) { function _TestCsrmask (line 166) | void _TestCsrmask(DGLContext ctx = CTX) { function TEST (line 176) | TEST(CsrmmTest, TestCsrmm) { function TEST (line 189) | TEST(CsrmmTest, TestCsrsum) { function TEST (line 202) | TEST(CsrmmTest, TestCsrmask) { FILE: tests/cpp/test_partition.cc function _TestRemainder_GeneratePermutation (line 10) | void _TestRemainder_GeneratePermutation() { function _TestRemainder_MapToX (line 50) | void _TestRemainder_MapToX() { function TEST (line 79) | TEST(PartitionTest, TestRemainderPartition) { function _FindPart (line 91) | int _FindPart(const INDEX idx, const RANGE* const range, const int num_p... function _TestRange_GeneratePermutation (line 102) | void _TestRange_GeneratePermutation() { function _TestRange_MapToX (line 150) | void _TestRange_MapToX() { function TEST (line 187) | TEST(PartitionTest, TestRangePartition) { FILE: tests/cpp/test_rowwise.cc function AllEdgeSet (line 17) | std::set> AllEdgeSet(bool has_data) { function AllEdgePerEtypeSet (line 38) | std::set> AllEdgePerEtypeSet(bool has_data) { function ToEdgeSet (line 61) | std::set> ToEdgeSet(COOMatrix mat) { function CheckSampledResult (line 74) | void CheckSampledResult(COOMatrix mat, IdArray rows, bool has_data) { function CheckSampledPerEtypeResult (line 88) | void CheckSampledPerEtypeResult(COOMatrix mat, IdArray rows, bool has_da... function CSRMatrix (line 103) | CSRMatrix CSR(bool has_data) { function COOMatrix (line 114) | COOMatrix COO(bool has_data) { function CSREtypes (line 125) | std::pair> CSREtypes(bool has_data) { function COOEtypes (line 138) | std::pair> COOEtypes(bool has_data) { function _TestCSRSampling (line 150) | void _TestCSRSampling(bool has_data) { function TEST (line 190) | TEST(RowwiseTest, TestCSRSampling) { function _TestCSRSamplingUniform (line 202) | void _TestCSRSamplingUniform(bool has_data) { function TEST (line 228) | TEST(RowwiseTest, TestCSRSamplingUniform) { function _TestCSRPerEtypeSampling (line 240) | void _TestCSRPerEtypeSampling(bool has_data) { function _TestCSRPerEtypeSamplingSorted (line 320) | void _TestCSRPerEtypeSamplingSorted() { function TEST (line 375) | TEST(RowwiseTest, TestCSRPerEtypeSampling) { function _TestCSRPerEtypeSamplingUniform (line 391) | void _TestCSRPerEtypeSamplingUniform(bool has_data) { function _TestCSRPerEtypeSamplingUniformSorted (line 450) | void _TestCSRPerEtypeSamplingUniformSorted() { function TEST (line 490) | TEST(RowwiseTest, TestCSRPerEtypeSamplingUniform) { function _TestCOOSampling (line 506) | void _TestCOOSampling(bool has_data) { function TEST (line 546) | TEST(RowwiseTest, TestCOOSampling) { function _TestCOOSamplingUniform (line 558) | void _TestCOOSamplingUniform(bool has_data) { function TEST (line 584) | TEST(RowwiseTest, TestCOOSamplingUniform) { function _TestCOOPerEtypeSampling (line 599) | void _TestCOOPerEtypeSampling(bool has_data) { function TEST (line 678) | TEST(RowwiseTest, TestCOOPerEtypeSampling) { function _TestCOOPerEtypeSamplingUniform (line 690) | void _TestCOOPerEtypeSamplingUniform(bool has_data) { function TEST (line 748) | TEST(RowwiseTest, TestCOOPerEtypeSamplingUniform) { function _TestCSRTopk (line 760) | void _TestCSRTopk(bool has_data) { function TEST (line 794) | TEST(RowwiseTest, TestCSRTopk) { function _TestCOOTopk (line 806) | void _TestCOOTopk(bool has_data) { function TEST (line 840) | TEST(RowwiseTest, TestCOOTopk) { function _TestCSRSamplingBiased (line 852) | void _TestCSRSamplingBiased(bool has_data) { function TEST (line 896) | TEST(RowwiseTest, TestCSRSamplingBiased) { FILE: tests/cpp/test_sampler.cc function _TestWithReplacement (line 16) | void _TestWithReplacement(RandomEngine* re) { function TEST (line 58) | TEST(SampleUtilsTest, TestWithReplacement) { function _TestWithoutReplacementOrder (line 71) | void _TestWithoutReplacementOrder(RandomEngine* re) { function TEST (line 92) | TEST(SampleUtilsTest, TestWithoutReplacementOrder) { function _TestWithoutReplacementUnique (line 105) | void _TestWithoutReplacementUnique(RandomEngine* re) { function TEST (line 128) | TEST(SampleUtilsTest, TestWithoutReplacementUnique) { function _TestChoice (line 141) | void _TestChoice(RandomEngine* re) { function TEST (line 181) | TEST(RandomTest, TestChoice) { function _TestUniformChoice (line 190) | void _TestUniformChoice(RandomEngine* re) { function TEST (line 220) | TEST(RandomTest, TestUniformChoice) { function _TestBiasedChoice (line 229) | void _TestBiasedChoice(RandomEngine* re) { function TEST (line 274) | TEST(RandomTest, TestBiasedChoice) { FILE: tests/cpp/test_serialize.cc function TEST (line 19) | TEST(Serialize, UnitGraph_COO) { function TEST (line 41) | TEST(Serialize, UnitGraph_CSR) { function TEST (line 61) | TEST(Serialize, ImmutableGraph) { function TEST (line 79) | TEST(Serialize, HeteroGraph) { FILE: tests/cpp/test_smart_ptr_serialize.cc class MyClass (line 16) | class MyClass { method MyClass (line 18) | MyClass() {} method MyClass (line 19) | MyClass(std::string data) : data_(data) {} method Save (line 20) | inline void Save(dmlc::Stream *strm) const { strm->Write(this->data_); } method Load (line 21) | inline bool Load(dmlc::Stream *strm) { return strm->Read(&data_); } type dmlc (line 30) | namespace dmlc { class SmartPtrTest (line 35) | class SmartPtrTest : public ::testing::Test { function TYPED_TEST (line 44) | TYPED_TEST(SmartPtrTest, Obj_Test) { function TYPED_TEST (line 57) | TYPED_TEST(SmartPtrTest, Vector_Test1) { function TYPED_TEST (line 80) | TYPED_TEST(SmartPtrTest, Vector_Test2) { FILE: tests/cpp/test_spmat_coo.cc function CSR1 (line 16) | aten::CSRMatrix CSR1(DGLContext ctx = CTX) { function CSR2 (line 34) | aten::CSRMatrix CSR2(DGLContext ctx = CTX) { function COO1 (line 53) | aten::COOMatrix COO1(DGLContext ctx = CTX) { function COO2 (line 72) | aten::COOMatrix COO2(DGLContext ctx = CTX) { function SR_CSR3 (line 92) | aten::CSRMatrix SR_CSR3(DGLContext ctx) { function SRC_CSR3 (line 109) | aten::CSRMatrix SRC_CSR3(DGLContext ctx) { function COO3 (line 126) | aten::COOMatrix COO3(DGLContext ctx) { function COORandomized (line 143) | aten::COOMatrix COORandomized(IDX rows_and_cols, int64_t nnz, int seed) { type SparseCOOCSR (line 177) | struct SparseCOOCSR { method COOSparse (line 182) | static aten::COOMatrix COOSparse(const DGLContext &ctx = CTX) { method CSRSparse (line 192) | static aten::CSRMatrix CSRSparse(const DGLContext &ctx = CTX) { function RowSorted_NullData_COO (line 209) | aten::COOMatrix RowSorted_NullData_COO(DGLContext ctx = CTX) { function RowSorted_NullData_CSR (line 226) | aten::CSRMatrix RowSorted_NullData_CSR(DGLContext ctx = CTX) { function _TestCOOToCSR (line 245) | void _TestCOOToCSR(DGLContext ctx) { function TEST (line 331) | TEST(SpmatTest, COOToCSR) { function _TestCOOHasDuplicate (line 341) | void _TestCOOHasDuplicate() { function TEST (line 348) | TEST(SpmatTest, TestCOOHasDuplicate) { function _TestCOOSort (line 354) | void _TestCOOSort(DGLContext ctx) { function TEST (line 417) | TEST(SpmatTest, COOSort) { function _TestCOOReorder (line 427) | void _TestCOOReorder() { function TEST (line 438) | TEST(SpmatTest, TestCOOReorder) { function _TestCOOGetData (line 444) | void _TestCOOGetData(DGLContext ctx) { function TEST (line 479) | TEST(SpmatTest, COOGetData) { function _TestCOOGetDataAndIndices (line 489) | void _TestCOOGetDataAndIndices() { function TEST (line 507) | TEST(SpmatTest, COOGetDataAndIndices) { function _TestCOOToCSRAlgs (line 513) | void _TestCOOToCSRAlgs() { function TEST (line 573) | TEST(SpmatTest, COOToCSRAlgs) { FILE: tests/cpp/test_spmat_csr.cc function CSR1 (line 12) | aten::CSRMatrix CSR1(DGLContext ctx = CTX) { function CSR2 (line 30) | aten::CSRMatrix CSR2(DGLContext ctx = CTX) { function CSR3 (line 49) | aten::CSRMatrix CSR3(DGLContext ctx = CTX) { function COO1 (line 77) | aten::COOMatrix COO1(DGLContext ctx = CTX) { function COO2 (line 96) | aten::COOMatrix COO2(DGLContext ctx = CTX) { function SR_CSR3 (line 116) | aten::CSRMatrix SR_CSR3(DGLContext ctx) { function SRC_CSR3 (line 133) | aten::CSRMatrix SRC_CSR3(DGLContext ctx) { function COO3 (line 150) | aten::COOMatrix COO3(DGLContext ctx) { function _TestCSRIsNonZero1 (line 169) | void _TestCSRIsNonZero1(DGLContext ctx) { function _TestCSRIsNonZero2 (line 184) | void _TestCSRIsNonZero2(DGLContext ctx) { function TEST (line 212) | TEST(SpmatTest, TestCSRIsNonZero) { function _TestCSRGetRowNNZ (line 226) | void _TestCSRGetRowNNZ(DGLContext ctx) { function TEST (line 238) | TEST(SpmatTest, TestCSRGetRowNNZ) { function _TestCSRGetRowColumnIndices (line 248) | void _TestCSRGetRowColumnIndices(DGLContext ctx) { function TEST (line 262) | TEST(SpmatTest, TestCSRGetRowColumnIndices) { function _TestCSRGetRowData (line 272) | void _TestCSRGetRowData(DGLContext ctx) { function TEST (line 286) | TEST(SpmatTest, TestCSRGetRowData) { function _TestCSRGetData (line 296) | void _TestCSRGetData(DGLContext ctx) { function TEST (line 331) | TEST(SpmatTest, CSRGetData) { function _TestCSRGetDataAndIndices (line 341) | void _TestCSRGetDataAndIndices(DGLContext ctx) { function TEST (line 359) | TEST(SpmatTest, CSRGetDataAndIndices) { function _TestCSRTranspose (line 369) | void _TestCSRTranspose(DGLContext ctx) { function TEST (line 391) | TEST(SpmatTest, CSRTranspose) { function _TestCSRToCOO (line 401) | void _TestCSRToCOO(DGLContext ctx) { function TEST (line 437) | TEST(SpmatTest, CSRToCOO) { function _TestCSRSliceRows (line 447) | void _TestCSRSliceRows(DGLContext ctx) { function TEST (line 545) | TEST(SpmatTest, TestCSRSliceRows) { function _TestCSRSliceMatrix1 (line 555) | void _TestCSRSliceMatrix1(DGLContext ctx) { function _TestCSRSliceMatrix2 (line 623) | void _TestCSRSliceMatrix2(DGLContext ctx) { function TEST (line 691) | TEST(SpmatTest, CSRSliceMatrix) { function _TestCSRHasDuplicate (line 705) | void _TestCSRHasDuplicate(DGLContext ctx) { function TEST (line 712) | TEST(SpmatTest, CSRHasDuplicate) { function _TestCSRSort (line 722) | void _TestCSRSort(DGLContext ctx) { function TEST (line 736) | TEST(SpmatTest, CSRSort) { function _TestCSRReorder (line 746) | void _TestCSRReorder() { function TEST (line 757) | TEST(SpmatTest, TestCSRReorder) { FILE: tests/cpp/test_spmm.cc function GenerateData (line 19) | void GenerateData(T* data, int dim, T mul) { function GenerateRandomData (line 26) | void GenerateRandomData(T* data, int dim) { function GenerateZeroData (line 35) | void GenerateZeroData(T* data, int dim) { function Copy (line 42) | void Copy(T* exp, T* out, T* hs, int dim) { function Add (line 49) | void Add(T* exp, T* out, T* lhs, T* rhs, int dim) { function Sub (line 56) | void Sub(T* exp, T* out, T* lhs, T* rhs, int dim) { function Mul (line 63) | void Mul(T* exp, T* out, T* lhs, T* rhs, int dim) { function Div (line 70) | void Div(T* exp, T* out, T* lhs, T* rhs, int dim) { function CheckResult (line 77) | void CheckResult(T* exp, T* out, int dim) { function _TestSpmmCopyLhs (line 86) | void _TestSpmmCopyLhs() { function TEST (line 105) | TEST(SpmmTest, TestSpmmCopyLhs) { function _TestSpmmCopyRhs (line 112) | void _TestSpmmCopyRhs() { function TEST (line 131) | TEST(SpmmTest, TestSpmmCopyRhs) { function _TestSpmmAdd (line 138) | void _TestSpmmAdd() { function TEST (line 158) | TEST(SpmmTest, TestSpmmAdd) { function _TestSpmmSub (line 165) | void _TestSpmmSub() { function TEST (line 185) | TEST(SpmmTest, TestSpmmSub) { function _TestSpmmMul (line 192) | void _TestSpmmMul() { function TEST (line 212) | TEST(SpmmTest, TestSpmmMul) { function _TestSpmmDiv (line 219) | void _TestSpmmDiv() { function TEST (line 239) | TEST(SpmmTest, TestSpmmDiv) { FILE: tests/cpp/test_unit_graph.cc function CSR1 (line 22) | aten::CSRMatrix CSR1(DGLContext ctx) { function COO1 (line 43) | aten::COOMatrix COO1(DGLContext ctx) { function _TestUnitGraph_InOutDegrees (line 62) | void _TestUnitGraph_InOutDegrees(DGLContext ctx) { function _TestUnitGraph (line 109) | void _TestUnitGraph(DGLContext ctx) { function _TestUnitGraph_GetInCSR (line 151) | void _TestUnitGraph_GetInCSR(DGLContext ctx) { function _TestUnitGraph_GetOutCSR (line 188) | void _TestUnitGraph_GetOutCSR(DGLContext ctx) { function _TestUnitGraph_GetCOO (line 225) | void _TestUnitGraph_GetCOO(DGLContext ctx) { function _TestUnitGraph_Reserve (line 261) | void _TestUnitGraph_Reserve(DGLContext ctx) { function _TestUnitGraph_CopyTo (line 341) | void _TestUnitGraph_CopyTo( function TEST (line 369) | TEST(UniGraphTest, TestUnitGraph_CopyTo) { function TEST (line 382) | TEST(UniGraphTest, TestUnitGraph_InOutDegrees) { function TEST (line 391) | TEST(UniGraphTest, TestUnitGraph_Create) { function TEST (line 400) | TEST(UniGraphTest, TestUnitGraph_GetInCSR) { function TEST (line 409) | TEST(UniGraphTest, TestUnitGraph_GetOutCSR) { function TEST (line 418) | TEST(UniGraphTest, TestUnitGraph_GetCOO) { function TEST (line 427) | TEST(UniGraphTest, TestUnitGraph_Reserve) { FILE: tests/cpp/test_zerocopy_serialize.cc function IdArrayToStr (line 21) | std::string IdArrayToStr(IdArray arr) { function TEST (line 41) | TEST(ZeroCopySerialize, NDArray) { function TEST (line 72) | TEST(ZeroCopySerialize, ZeroShapeNDArray) { function TEST (line 100) | TEST(ZeroCopySerialize, SharedMem) { function TEST (line 125) | TEST(ZeroCopySerialize, HeteroGraph) { FILE: tests/cugraph/cugraph-ops/test_cugraph_gatconv.py function generate_graph (line 20) | def generate_graph(): function test_gatconv_equality (line 28) | def test_gatconv_equality(idtype_int, max_in_degree, num_heads, to_block): FILE: tests/cugraph/cugraph-ops/test_cugraph_relgraphconv.py function generate_graph (line 23) | def generate_graph(): function test_relgraphconv_equality (line 31) | def test_relgraphconv_equality( FILE: tests/cugraph/cugraph-ops/test_cugraph_sageconv.py function generate_graph (line 19) | def generate_graph(): function test_SAGEConv_equality (line 27) | def test_SAGEConv_equality(idtype_int, max_in_degree, to_block): FILE: tests/cugraph/test_basics.py function test_dummy (line 9) | def test_dummy(): function test_to_cugraph_conversion (line 14) | def test_to_cugraph_conversion(): function test_from_cugraph_conversion (line 26) | def test_from_cugraph_conversion(): FILE: tests/dist/python/rpc_basic.py function tensor_func (line 14) | def tensor_func(tensor): class HelloResponse (line 18) | class HelloResponse(dgl.distributed.Response): method __init__ (line 19) | def __init__(self, hello_str, integer, tensor): method __getstate__ (line 24) | def __getstate__(self): method __setstate__ (line 27) | def __setstate__(self, state): class HelloRequest (line 31) | class HelloRequest(dgl.distributed.Request): method __init__ (line 32) | def __init__(self, hello_str, integer, tensor, func): method __getstate__ (line 38) | def __getstate__(self): method __setstate__ (line 41) | def __setstate__(self, state): method process_request (line 44) | def process_request(self, server_state): function start_server (line 52) | def start_server(server_id, ip_config, num_servers, num_clients, keep_al... function start_client (line 69) | def start_client(ip_config, num_servers, group_id): function main (line 113) | def main(): FILE: tests/dist/python/run_dist_objects.py function batched_assert_zero (line 24) | def batched_assert_zero(tensor, size): function zeros_init (line 33) | def zeros_init(shape, dtype): function rand_init (line 37) | def rand_init(shape, dtype): function run_server (line 41) | def run_server( function node_split_test (line 67) | def node_split_test(g, force_even, ntype="_N"): function edge_split_test (line 90) | def edge_split_test(g, force_even, etype="_E"): function test_dist_graph (line 113) | def test_dist_graph(g): function find_edges_test (line 144) | def find_edges_test(g, orig_nid_map): function edge_subgraph_test (line 159) | def edge_subgraph_test(g, etype_eids_uv_map): function sample_neighbors_with_args (line 179) | def sample_neighbors_with_args(g, size, fanout): function sample_neighbors_test (line 201) | def sample_neighbors_test(g): function test_dist_graph_services (line 210) | def test_dist_graph_services(g): function dist_tensor_test_sanity (line 245) | def dist_tensor_test_sanity(data_shape, name=None): function dist_tensor_test_destroy_recreate (line 265) | def dist_tensor_test_destroy_recreate(data_shape, name): function dist_tensor_test_persistent (line 279) | def dist_tensor_test_persistent(data_shape): function test_dist_tensor (line 298) | def test_dist_tensor(g): function dist_embedding_check_sanity (line 312) | def dist_embedding_check_sanity(num_nodes, optimizer, name=None): function dist_embedding_check_existing (line 345) | def dist_embedding_check_existing(num_nodes): function test_dist_embedding (line 359) | def test_dist_embedding(g): function dist_optimizer_check_store (line 377) | def dist_optimizer_check_store(g): function test_dist_optimizer (line 422) | def test_dist_optimizer(g): class NeighborSampler (line 431) | class NeighborSampler(object): method __init__ (line 432) | def __init__(self, g, fanouts, sample_neighbors): method sample_blocks (line 437) | def sample_blocks(self, seeds): function distdataloader_test (line 458) | def distdataloader_test(g, batch_size, drop_last, shuffle): function distnodedataloader_test (line 526) | def distnodedataloader_test( function distedgedataloader_test (line 583) | def distedgedataloader_test( function multi_distdataloader_test (line 659) | def multi_distdataloader_test(g, dataloader_class): function test_dist_dataloader (line 704) | def test_dist_dataloader(g): FILE: tests/dist/test_dist_objects.py function create_graph (line 23) | def create_graph(num_part, dist_graph_path, hetero): function test_dist_objects (line 103) | def test_dist_objects(num_servers, num_clients, hetero, shared_mem): function teardown (line 165) | def teardown(): FILE: tests/dist/test_rpc.py function test_rpc (line 12) | def test_rpc(): FILE: tests/dist/utils.py function run (line 7) | def run(ssh_cmd): function execute_remote (line 11) | def execute_remote( function get_ips (line 57) | def get_ips(ip_config): FILE: tests/distributed/test_dist_graph_store.py function _verify_dist_graph_server_dgl (line 42) | def _verify_dist_graph_server_dgl(g): function _verify_dist_graph_server_graphbolt (line 56) | def _verify_dist_graph_server_graphbolt(g): function run_server (line 62) | def run_server( function emb_init (line 90) | def emb_init(shape, dtype): function rand_init (line 94) | def rand_init(shape, dtype): function check_dist_graph_empty (line 98) | def check_dist_graph_empty(g, num_clients, num_nodes, num_edges): function run_client_empty (line 130) | def run_client_empty( function check_server_client_empty (line 148) | def check_server_client_empty( function run_client (line 209) | def run_client( function run_emb_client (line 231) | def run_emb_client( function run_optim_client (line 250) | def run_optim_client( function check_dist_optim_store (line 274) | def check_dist_optim_store(rank, num_nodes, optimizer_states, save): function run_client_hierarchy (line 311) | def run_client_hierarchy( function check_dist_emb (line 342) | def check_dist_emb(g, num_clients, num_nodes, num_edges): function check_dist_graph (line 403) | def check_dist_graph(g, num_clients, num_nodes, num_edges, use_graphbolt... function check_dist_emb_server_client (line 506) | def check_dist_emb_server_client( function check_server_client (line 570) | def check_server_client( function check_server_client_hierarchy (line 636) | def check_server_client_hierarchy( function run_client_hetero (line 725) | def run_client_hetero( function create_random_hetero (line 745) | def create_random_hetero(): function check_dist_graph_hetero (line 777) | def check_dist_graph_hetero( function check_server_client_hetero (line 905) | def check_server_client_hetero( function test_server_client (line 980) | def test_server_client(shared_mem, num_servers, num_clients, use_graphbo... function test_dist_emb_server_client (line 1005) | def test_dist_emb_server_client(): function test_dist_optim_server_client (line 1026) | def test_dist_optim_server_client(): function check_dist_optim_server_client (line 1038) | def check_dist_optim_server_client( function test_standalone (line 1106) | def test_standalone(): function test_standalone_node_emb (line 1135) | def test_standalone_node_emb(): function test_split (line 1158) | def test_split(hetero, empty_mask): function test_split_even (line 1253) | def test_split_even(empty_mask): function prepare_dist (line 1339) | def prepare_dist(num_servers=1): FILE: tests/distributed/test_dist_tensor.py function rand_mask (line 14) | def rand_mask(shape, dtype): function setup_module (line 25) | def setup_module(): function check_binary_op (line 53) | def check_binary_op(key1, key2, key3, op): function test_op (line 76) | def test_op(): function teardown_module (line 88) | def teardown_module(): FILE: tests/distributed/test_distributed_sampling.py function start_server (line 33) | def start_server( function start_sample_client (line 54) | def start_sample_client(rank, tmpdir, disable_shared_mem): function start_sample_client_shuffle (line 75) | def start_sample_client_shuffle( function start_find_edges_client (line 134) | def start_find_edges_client(rank, tmpdir, disable_shared_mem, eids, etyp... function start_get_degrees_client (line 151) | def start_get_degrees_client(rank, tmpdir, disable_shared_mem, nids=None): function check_rpc_sampling (line 171) | def check_rpc_sampling(tmpdir, num_server): function check_rpc_find_edges_shuffle (line 214) | def check_rpc_find_edges_shuffle(tmpdir, num_server): function create_random_hetero (line 250) | def create_random_hetero(dense=False, empty=False): function check_rpc_hetero_find_edges_shuffle (line 276) | def check_rpc_hetero_find_edges_shuffle(tmpdir, num_server): function test_rpc_find_edges_shuffle (line 334) | def test_rpc_find_edges_shuffle(num_server): function check_rpc_get_degree_shuffle (line 344) | def check_rpc_get_degree_shuffle(tmpdir, num_server): function test_rpc_get_degree_shuffle (line 398) | def test_rpc_get_degree_shuffle(num_server): function test_rpc_sampling (line 410) | def test_rpc_sampling(): function check_rpc_sampling_shuffle (line 419) | def check_rpc_sampling_shuffle( function start_hetero_sample_client (line 496) | def start_hetero_sample_client( function start_hetero_etype_sample_client (line 536) | def start_hetero_etype_sample_client( function check_rpc_hetero_sampling_shuffle (line 593) | def check_rpc_hetero_sampling_shuffle( function get_degrees (line 671) | def get_degrees(g, nids, ntype): function check_rpc_hetero_sampling_empty_shuffle (line 681) | def check_rpc_hetero_sampling_empty_shuffle( function check_rpc_hetero_etype_sampling_shuffle (line 738) | def check_rpc_hetero_etype_sampling_shuffle( function check_rpc_hetero_etype_sampling_empty_shuffle (line 830) | def check_rpc_hetero_etype_sampling_empty_shuffle( function create_random_bipartite (line 890) | def create_random_bipartite(): function start_bipartite_sample_client (line 901) | def start_bipartite_sample_client( function start_bipartite_etype_sample_client (line 936) | def start_bipartite_etype_sample_client( function check_rpc_bipartite_sampling_empty (line 981) | def check_rpc_bipartite_sampling_empty( function check_rpc_bipartite_sampling_shuffle (line 1042) | def check_rpc_bipartite_sampling_shuffle( function check_rpc_bipartite_etype_sampling_empty (line 1122) | def check_rpc_bipartite_etype_sampling_empty( function check_rpc_bipartite_etype_sampling_shuffle (line 1184) | def check_rpc_bipartite_etype_sampling_shuffle( function test_rpc_sampling_shuffle (line 1270) | def test_rpc_sampling_shuffle( function test_rpc_hetero_sampling_shuffle (line 1290) | def test_rpc_hetero_sampling_shuffle( function test_rpc_hetero_sampling_empty_shuffle (line 1308) | def test_rpc_hetero_sampling_empty_shuffle( function test_rpc_hetero_etype_sampling_shuffle_dgl (line 1326) | def test_rpc_hetero_etype_sampling_shuffle_dgl(num_server, graph_formats): function test_rpc_hetero_etype_sampling_shuffle_graphbolt (line 1337) | def test_rpc_hetero_etype_sampling_shuffle_graphbolt(num_server, return_... function test_rpc_hetero_etype_sampling_empty_shuffle (line 1352) | def test_rpc_hetero_etype_sampling_empty_shuffle( function test_rpc_bipartite_sampling_empty_shuffle (line 1369) | def test_rpc_bipartite_sampling_empty_shuffle( function test_rpc_bipartite_sampling_shuffle (line 1383) | def test_rpc_bipartite_sampling_shuffle(num_server, use_graphbolt, retur... function test_rpc_bipartite_etype_sampling_empty_shuffle (line 1395) | def test_rpc_bipartite_etype_sampling_empty_shuffle( function test_rpc_bipartite_etype_sampling_shuffle (line 1412) | def test_rpc_bipartite_etype_sampling_shuffle( function check_standalone_sampling (line 1426) | def check_standalone_sampling(tmpdir): function check_standalone_etype_sampling (line 1482) | def check_standalone_etype_sampling(tmpdir): function check_standalone_etype_sampling_heterograph (line 1538) | def check_standalone_etype_sampling_heterograph(tmpdir): function test_standalone_sampling (line 1584) | def test_standalone_sampling(): function start_in_subgraph_client (line 1593) | def start_in_subgraph_client(rank, tmpdir, disable_shared_mem, nodes): function check_rpc_in_subgraph_shuffle (line 1610) | def check_rpc_in_subgraph_shuffle(tmpdir, num_server): function test_rpc_in_subgraph (line 1663) | def test_rpc_in_subgraph(): function test_standalone_etype_sampling (line 1680) | def test_standalone_etype_sampling(): function test_local_sampling_homograph (line 1695) | def test_local_sampling_homograph(num_parts, use_graphbolt, prob_or_mask): function test_local_sampling_heterograph (line 1767) | def test_local_sampling_heterograph(num_parts, use_graphbolt, prob_or_ma... function check_hetero_dist_edge_dataloader_gb (line 1862) | def check_hetero_dist_edge_dataloader_gb( function test_hetero_dist_edge_dataloader_gb (line 1928) | def test_hetero_dist_edge_dataloader_gb( FILE: tests/distributed/test_mp_dataloader.py function _unique_rand_graph (line 25) | def _unique_rand_graph(num_nodes=1000, num_edges=10 * 1000): class NeighborSampler (line 46) | class NeighborSampler(object): method __init__ (line 47) | def __init__( method sample_blocks (line 61) | def sample_blocks(self, seeds): function start_server (line 84) | def start_server( function start_dist_dataloader (line 106) | def start_dist_dataloader( function test_standalone (line 199) | def test_standalone(): function start_dist_neg_dataloader (line 229) | def start_dist_neg_dataloader( function check_neg_dataloader (line 303) | def check_neg_dataloader(g, num_server, num_workers): function test_dist_dataloader (line 373) | def test_dist_dataloader(num_server, num_workers, use_graphbolt, return_... function start_node_dataloader (line 448) | def start_node_dataloader( function start_edge_dataloader (line 571) | def start_edge_dataloader( function check_dataloader (line 769) | def check_dataloader( function create_random_hetero (line 879) | def create_random_hetero(): function test_dataloader_homograph (line 907) | def test_dataloader_homograph( function test_edge_dataloader_homograph (line 929) | def test_edge_dataloader_homograph( function test_dataloader_homograph_prob_or_mask (line 954) | def test_dataloader_homograph_prob_or_mask( function test_dataloader_heterograph (line 979) | def test_dataloader_heterograph( function test_edge_dataloader_heterograph (line 1001) | def test_edge_dataloader_heterograph( function test_dataloader_heterograph_prob_or_mask (line 1027) | def test_dataloader_heterograph_prob_or_mask( function test_neg_dataloader (line 1051) | def test_neg_dataloader(num_server, num_workers): function start_multiple_dataloaders (line 1059) | def start_multiple_dataloaders( function test_multiple_dist_dataloaders (line 1107) | def test_multiple_dist_dataloaders( function test_deprecated_dataloader (line 1171) | def test_deprecated_dataloader(dataloader_type): FILE: tests/distributed/test_new_kvstore.py function init_zero_func (line 74) | def init_zero_func(shape, dtype): function udf_push (line 78) | def udf_push(target, name, id_tensor, data_tensor): function add_push (line 82) | def add_push(target, name, id_tensor, data_tensor): function test_partition_policy (line 90) | def test_partition_policy(): function start_server (line 115) | def start_server(server_id, num_clients, num_servers): function start_server_mul_role (line 150) | def start_server_mul_role(server_id, num_clients, num_servers): function start_client (line 176) | def start_client(num_clients, num_servers): function start_client_mul_role (line 301) | def start_client_mul_role(i): function test_kv_store (line 327) | def test_kv_store(): function test_kv_multi_role (line 358) | def test_kv_multi_role(): FILE: tests/distributed/test_partition.py function _verify_partition_data_types (line 39) | def _verify_partition_data_types(part_g): function _verify_partition_formats (line 62) | def _verify_partition_formats(part_g, formats): function create_random_graph (line 71) | def create_random_graph(n): function create_random_hetero (line 78) | def create_random_hetero(): function _verify_graphbolt_attributes (line 100) | def _verify_graphbolt_attributes( function _verify_hetero_graph_node_edge_num (line 113) | def _verify_hetero_graph_node_edge_num( function _verify_edge_id_range_hetero (line 169) | def _verify_edge_id_range_hetero( function _verify_node_id_range_hetero (line 206) | def _verify_node_id_range_hetero(g, part, nids): function _verify_graph_attributes_hetero (line 231) | def _verify_graph_attributes_hetero( function _verify_hetero_graph (line 268) | def _verify_hetero_graph( function _verify_node_feats (line 291) | def _verify_node_feats(g, part, gpb, orig_nids, node_feats, is_homo=False): function _verify_edge_feats (line 325) | def _verify_edge_feats(g, part, gpb, orig_eids, edge_feats, is_homo=False): function verify_graph_feats_hetero_dgl (line 357) | def verify_graph_feats_hetero_dgl( function verify_graph_feats_gb (line 375) | def verify_graph_feats_gb( function check_hetero_partition (line 431) | def check_hetero_partition( function check_partition (line 565) | def check_partition( function test_partition (line 747) | def test_partition( function test_RangePartitionBook (line 780) | def test_RangePartitionBook(node_map_dtype, edge_map_dtype): function test_UnknownPartitionBook (line 935) | def test_UnknownPartitionBook(): function test_dgl_partition_to_graphbolt_homo (line 965) | def test_dgl_partition_to_graphbolt_homo( function test_dgl_partition_to_graphbolt_hetero (line 1043) | def test_dgl_partition_to_graphbolt_hetero( function test_not_sorted_node_edge_map (line 1177) | def test_not_sorted_node_edge_map(): function _get_part_IDs (line 1287) | def _get_part_IDs(part_g): function _verify_orig_edge_IDs_gb (line 1300) | def _verify_orig_edge_IDs_gb( function _verify_metadata_gb (line 1331) | def _verify_metadata_gb(gpb, g, num_parts, part_id, part_sizes): function _verify_local_id_gb (line 1351) | def _verify_local_id_gb(part_g, part_id, gpb): function _verify_map_gb (line 1374) | def _verify_map_gb( function _verify_local_and_map_id_gb (line 1416) | def _verify_local_and_map_id_gb( function _verify_orig_IDs_gb (line 1434) | def _verify_orig_IDs_gb( function test_partition_graph_graphbolt_homo (line 1504) | def test_partition_graph_graphbolt_homo( function _verify_constructed_id_gb (line 1556) | def _verify_constructed_id_gb(part_sizes, gpb): function _verify_shuffled_labels_gb (line 1577) | def _verify_shuffled_labels_gb( function _verify_node_type_ID_gb (line 1615) | def _verify_node_type_ID_gb(part_g, gpb): function _verify_IDs_gb (line 1641) | def _verify_IDs_gb( function _collect_data_gb (line 1689) | def _collect_data_gb( function _verify_graphbolt_part (line 1717) | def _verify_graphbolt_part( function _verify_original_IDs_type_hetero (line 1821) | def _verify_original_IDs_type_hetero(hg, orig_nids, orig_eids): function test_partition_graph_graphbolt_hetero (line 1843) | def test_partition_graph_graphbolt_hetero( function test_partition_graph_graphbolt_homo_find_edges (line 1918) | def test_partition_graph_graphbolt_homo_find_edges( function test_partition_graph_graphbolt_hetero_find_edges (line 2005) | def test_partition_graph_graphbolt_hetero_find_edges( function test_partition_graph_graphbolt_hetero_multi (line 2130) | def test_partition_graph_graphbolt_hetero_multi( function test_partition_graph_graphbolt_homo_find_edges_multi (line 2147) | def test_partition_graph_graphbolt_homo_find_edges_multi( function test_partition_graph_graphbolt_hetero_find_edges_multi (line 2159) | def test_partition_graph_graphbolt_hetero_find_edges_multi( function test_partition_hetero_few_edges (line 2174) | def test_partition_hetero_few_edges( function test_partition_hetero_few_nodes (line 2208) | def test_partition_hetero_few_nodes( FILE: tests/distributed/test_rpc.py function foo (line 24) | def foo(x, y): class MyRequest (line 29) | class MyRequest(dgl.distributed.Request): method __init__ (line 30) | def __init__(self): method __getstate__ (line 36) | def __getstate__(self): method __setstate__ (line 39) | def __setstate__(self, state): method process_request (line 42) | def process_request(self, server_state): class MyResponse (line 46) | class MyResponse(dgl.distributed.Response): method __init__ (line 47) | def __init__(self): method __getstate__ (line 50) | def __getstate__(self): method __setstate__ (line 53) | def __setstate__(self, state): function simple_func (line 57) | def simple_func(tensor): class HelloResponse (line 61) | class HelloResponse(dgl.distributed.Response): method __init__ (line 62) | def __init__(self, hello_str, integer, tensor): method __getstate__ (line 67) | def __getstate__(self): method __setstate__ (line 70) | def __setstate__(self, state): class HelloRequest (line 74) | class HelloRequest(dgl.distributed.Request): method __init__ (line 75) | def __init__(self, hello_str, integer, tensor, func): method __getstate__ (line 81) | def __getstate__(self): method __setstate__ (line 84) | def __setstate__(self, state): method process_request (line 87) | def process_request(self, server_state): class TimeoutResponse (line 99) | class TimeoutResponse(dgl.distributed.Response): method __init__ (line 100) | def __init__(self, meta): method __getstate__ (line 103) | def __getstate__(self): method __setstate__ (line 106) | def __setstate__(self, state): class TimeoutRequest (line 110) | class TimeoutRequest(dgl.distributed.Request): method __init__ (line 111) | def __init__(self, meta, timeout, response=True): method __getstate__ (line 116) | def __getstate__(self): method __setstate__ (line 119) | def __setstate__(self, state): method process_request (line 122) | def process_request(self, server_state): function start_server (line 132) | def start_server( function start_client (line 159) | def start_client(ip_config, group_id=0, num_servers=1): function start_client_timeout (line 201) | def start_client_timeout(ip_config, group_id=0, num_servers=1): function test_rpc_timeout (line 254) | def test_rpc_timeout(): function test_serialize (line 268) | def test_serialize(): function test_rpc_msg (line 292) | def test_rpc_msg(): function test_multi_client (line 316) | def test_multi_client(): function test_multi_thread_rpc (line 340) | def test_multi_thread_rpc(): function test_multi_client_connect (line 387) | def test_multi_client_connect(): FILE: tests/distributed/utils.py function generate_ip_config (line 11) | def generate_ip_config(file_name, num_machines, num_servers): function reset_envs (line 46) | def reset_envs(): function create_random_graph (line 61) | def create_random_graph(n): FILE: tests/examples/test_sampling_examples.py function test_node_classification (line 16) | def test_node_classification(): function test_link_prediction (line 26) | def test_link_prediction(): FILE: tests/examples/test_sparse_examples.py function test_gcn (line 14) | def test_gcn(): function test_gcnii (line 24) | def test_gcnii(): function test_appnp (line 34) | def test_appnp(): function test_c_and_s (line 44) | def test_c_and_s(): function test_gat (line 54) | def test_gat(): function test_hgnn (line 64) | def test_hgnn(): function test_hypergraphatt (line 74) | def test_hypergraphatt(): function test_sgc (line 84) | def test_sgc(): function _test_flaky (line 94) | def _test_flaky(test_fn, max_num_success=8, num_tries=10): function _test_sign (line 110) | def _test_sign(): function test_sign (line 120) | def test_sign(): function test_twirls (line 124) | def test_twirls(): FILE: tests/go/test_model.py function test_gcn (line 9) | def test_gcn(g): function test_gcn_block (line 35) | def test_gcn_block(g): function test_gat (line 51) | def test_gat(g): function test_gat_block (line 67) | def test_gat_block(g): function test_gin (line 77) | def test_gin(g): function test_sage (line 93) | def test_sage(g): function test_sage_block (line 112) | def test_sage_block(g): function test_sgc (line 124) | def test_sgc(g): function test_bilinear (line 139) | def test_bilinear(): function test_ele (line 148) | def test_ele(): function test_ogbg_gin (line 158) | def test_ogbg_gin(virtual_node): function test_pna (line 190) | def test_pna(): FILE: tests/go/test_pipeline.py function test_nodepred_data (line 19) | def test_nodepred_data(data): function test_nodepred_model (line 35) | def test_nodepred_model(model): function test_nodepred_ns_data (line 63) | def test_nodepred_ns_data(data): function test_nodepred_ns_model (line 79) | def test_nodepred_ns_model(model): function test_linkpred_data (line 109) | def test_linkpred_data(data): function test_linkpred_node_model (line 131) | def test_linkpred_node_model(node_model): function test_linkpred_edge_model (line 153) | def test_linkpred_edge_model(edge_model): function test_linkpred_neg_sampler (line 175) | def test_linkpred_neg_sampler(neg_sampler): function test_graphpred (line 203) | def test_graphpred(data, model): function test_recipe (line 246) | def test_recipe(recipe): function test_node_cora (line 257) | def test_node_cora(): FILE: tests/integration/test_data.py function test_reddit (line 24) | def test_reddit(): function test_fakenews (line 42) | def test_fakenews(): function test_peptides_structural (line 65) | def test_peptides_structural(): function test_peptides_functional (line 82) | def test_peptides_functional(): function test_VOC_superpixels (line 100) | def test_VOC_superpixels(): function test_COCO_superpixels (line 117) | def test_COCO_superpixels(): function test_MNIST_SuperPixel (line 134) | def test_MNIST_SuperPixel(): function test_CIFAR10_SuperPixel (line 151) | def test_CIFAR10_SuperPixel(): function test_as_graphpred (line 166) | def test_as_graphpred(): function test_as_linkpred_ogb (line 219) | def test_as_linkpred_ogb(): function test_as_nodepred_ogb (line 240) | def test_as_nodepred_ogb(): function test_as_graphpred_ogb (line 262) | def test_as_graphpred_ogb(): FILE: tests/lint/clangformat_linter.py function eprint (line 18) | def eprint(*args: Any, **kwargs: Any) -> None: class LintSeverity (line 22) | class LintSeverity(str, Enum): class LintMessage (line 29) | class LintMessage(NamedTuple): function as_posix (line 41) | def as_posix(name: str) -> str: function _run_command (line 45) | def _run_command( function run_command (line 66) | def run_command( function check_file (line 89) | def check_file( function main (line 178) | def main() -> None: FILE: tests/lint/lint.py function filepath_enumerate (line 25) | def filepath_enumerate(paths): class LintHelper (line 39) | class LintHelper(object): method _print_summary_map (line 43) | def _print_summary_map(strm, result_map, ftype): method __init__ (line 59) | def __init__(self): method process_cpp (line 97) | def process_cpp(self, path, suffix): method process_python (line 109) | def process_python(self, path): method print_summary (line 129) | def print_summary(self, strm): function get_header_guard_dmlc (line 150) | def get_header_guard_dmlc(filename): function process (line 191) | def process(fname, allow_type): function main (line 203) | def main(): FILE: tests/lint/pip_init.py function run_command (line 16) | def run_command(args: List[str]) -> "subprocess.CompletedProcess[bytes]": FILE: tests/lint/ufmt_linter.py function eprint (line 19) | def eprint(*args: Any, **kwargs: Any) -> None: class LintSeverity (line 23) | class LintSeverity(str, Enum): class LintMessage (line 30) | class LintMessage(NamedTuple): function as_posix (line 42) | def as_posix(name: str) -> str: function format_error_message (line 46) | def format_error_message(filename: str, err: Exception) -> LintMessage: function check_file (line 60) | def check_file( function main (line 107) | def main() -> None: FILE: tests/python/common/backend/test_set_default_backend.py function test_set_default_backend (line 7) | def test_set_default_backend(): FILE: tests/python/common/backend/test_tensor.py function test_dlpack (line 14) | def test_dlpack(): FILE: tests/python/common/cuda/test_gpu_cache.py function generate_graph (line 27) | def generate_graph(idtype, grad=False, add_data=True): function test_gpu_cache (line 54) | def test_gpu_cache(idtype): FILE: tests/python/common/data/data/test_heterophilous_graphs.py function test_roman_empire (line 16) | def test_roman_empire(): function test_amazon_ratings (line 34) | def test_amazon_ratings(): function test_minesweeper (line 54) | def test_minesweeper(): function test_tolokers (line 72) | def test_tolokers(): function test_questions (line 90) | def test_questions(): FILE: tests/python/common/data/test_actor.py function test_actor (line 15) | def test_actor(): FILE: tests/python/common/data/test_data.py function test_minigc (line 25) | def test_minigc(): function test_gin (line 41) | def test_gin(): function test_fraud (line 67) | def test_fraud(): function test_tudataset_regression (line 95) | def test_tudataset_regression(): function test_data_hash (line 112) | def test_data_hash(): function test_citation_graph (line 132) | def test_citation_graph(): function test_gnn_benchmark (line 168) | def test_gnn_benchmark(): function test_explain_syn (line 222) | def test_explain_syn(): function test_wiki_cs (line 286) | def test_wiki_cs(): function test_yelp (line 300) | def test_yelp(): function test_flickr (line 317) | def test_flickr(): function test_pattern (line 334) | def test_pattern(): function test_cluster (line 356) | def test_cluster(): function test_zinc (line 380) | def test_zinc(): function test_extract_archive (line 403) | def test_extract_archive(): function _test_construct_graphs_node_ids (line 430) | def _test_construct_graphs_node_ids(): function _test_construct_graphs_homo (line 512) | def _test_construct_graphs_homo(): function _test_construct_graphs_hetero (line 570) | def _test_construct_graphs_hetero(): function _test_construct_graphs_multiple (line 646) | def _test_construct_graphs_multiple(): function _get_data_table (line 740) | def _get_data_table(data_frame, save_index=False): function _test_DefaultDataParser (line 758) | def _test_DefaultDataParser(): function _test_load_yaml_with_sanity_check (line 794) | def _test_load_yaml_with_sanity_check(): function _test_load_node_data_from_csv (line 946) | def _test_load_node_data_from_csv(): function _test_load_edge_data_from_csv (line 1008) | def _test_load_edge_data_from_csv(): function _test_load_graph_data_from_csv (line 1097) | def _test_load_graph_data_from_csv(): function _test_CSVDataset_single (line 1160) | def _test_CSVDataset_single(): function _test_CSVDataset_multiple (line 1252) | def _test_CSVDataset_multiple(): function _test_CSVDataset_customized_data_parser (line 1393) | def _test_CSVDataset_customized_data_parser(): function _test_NodeEdgeGraphData (line 1543) | def _test_NodeEdgeGraphData(): function test_csvdataset (line 1640) | def test_csvdataset(): function test_as_nodepred1 (line 1661) | def test_as_nodepred1(): function test_as_nodepred2 (line 1707) | def test_as_nodepred2(): function test_as_linkpred (line 1766) | def test_as_linkpred(): function test_as_nodepred_csvdataset (line 1798) | def test_as_nodepred_csvdataset(): function test_as_graphpred_reprocess (line 1853) | def test_as_graphpred_reprocess(): FILE: tests/python/common/data/test_geom_gcn.py function test_chameleon (line 15) | def test_chameleon(): function test_squirrel (line 32) | def test_squirrel(): function test_cornell (line 49) | def test_cornell(): function test_texas (line 66) | def test_texas(): function test_wisconsin (line 83) | def test_wisconsin(): FILE: tests/python/common/data/test_movielens.py function test_movielens (line 16) | def test_movielens(): FILE: tests/python/common/data/test_serialize.py function generate_rand_graph (line 19) | def generate_rand_graph(n): function construct_graph (line 26) | def construct_graph(n): function test_graph_serialize_with_feature (line 38) | def test_graph_serialize_with_feature(): function test_graph_serialize_without_feature (line 79) | def test_graph_serialize_without_feature(): function test_graph_serialize_with_labels (line 107) | def test_graph_serialize_with_labels(): function test_serialize_tensors (line 138) | def test_serialize_tensors(): function test_serialize_empty_dict (line 171) | def test_serialize_empty_dict(): function load_old_files (line 188) | def load_old_files(files): function test_load_old_files1 (line 194) | def test_load_old_files1(): function test_load_old_files2 (line 210) | def test_load_old_files2(): function create_heterographs (line 226) | def create_heterographs(idtype): function create_heterographs2 (line 251) | def create_heterographs2(idtype): function test_deserialize_old_heterograph_file (line 280) | def test_deserialize_old_heterograph_file(): function create_old_heterograph_files (line 297) | def create_old_heterograph_files(): function test_serialize_heterograph (line 305) | def test_serialize_heterograph(): function test_serialize_heterograph_s3 (line 344) | def test_serialize_heterograph_s3(): function test_graph_serialize_with_formats (line 376) | def test_graph_serialize_with_formats(formats): function test_graph_serialize_with_restricted_formats (line 411) | def test_graph_serialize_with_restricted_formats(): function test_deserialize_old_graph (line 432) | def test_deserialize_old_graph(): FILE: tests/python/common/data/test_utils.py function test_add_nodepred_split (line 24) | def test_add_nodepred_split(): function test_extract_archive (line 43) | def test_extract_archive(): function test_mask_nodes_by_property (line 75) | def test_mask_nodes_by_property(): function test_add_node_property_split (line 90) | def test_add_node_property_split(): FILE: tests/python/common/dataloading/test_dataloader.py function create_test_graph (line 14) | def create_test_graph(idtype): function test_edge_prediction_sampler (line 39) | def test_edge_prediction_sampler(idtype): FILE: tests/python/common/function/test_basics.py function message_func (line 15) | def message_func(edges): function reduce_func (line 21) | def reduce_func(nodes): function apply_node_func (line 29) | def apply_node_func(nodes): function generate_graph_old (line 33) | def generate_graph_old(grad=False): function generate_graph (line 57) | def generate_graph(idtype, grad=False): function test_compatible (line 95) | def test_compatible(): function test_batch_setter_getter (line 100) | def test_batch_setter_getter(idtype): function test_batch_setter_autograd (line 183) | def test_batch_setter_autograd(idtype): function _test_nx_conversion (line 200) | def _test_nx_conversion(): function test_apply_nodes (line 322) | def test_apply_nodes(idtype): function test_apply_edges (line 336) | def test_apply_edges(idtype): function test_update_routines (line 352) | def test_update_routines(idtype): function test_update_all_0deg (line 390) | def test_update_all_0deg(idtype): function test_pull_0deg (line 434) | def test_pull_0deg(idtype): function test_dynamic_addition (line 476) | def test_dynamic_addition(): function test_repr (line 508) | def test_repr(idtype): function test_local_var (line 521) | def test_local_var(idtype): function test_local_scope (line 594) | def test_local_scope(idtype): function test_isolated_nodes (line 695) | def test_isolated_nodes(idtype): function test_send_multigraph (line 722) | def test_send_multigraph(idtype): function test_issue_1088 (line 753) | def test_issue_1088(idtype): function test_degree_bucket_edge_ordering (line 766) | def test_degree_bucket_edge_ordering(idtype): function test_issue_2484 (line 785) | def test_issue_2484(idtype): FILE: tests/python/common/ops/test_edge_softmax.py function test_edge_softmax (line 29) | def test_edge_softmax(g, norm_by, shp, idtype): function create_test_heterograph (line 59) | def create_test_heterograph(idtype): function test_edge_softmax_unidirectional (line 86) | def test_edge_softmax_unidirectional(): function test_edge_softmax (line 121) | def test_edge_softmax(g, norm_by, idtype): FILE: tests/python/common/ops/test_ops.py function select (line 31) | def select(target, src, edge, dst): function binary_op (line 40) | def binary_op(msg, x, y): function edge_func (line 57) | def edge_func(lhs_target, rhs_target, msg): function test_spmm (line 119) | def test_spmm(idtype, dtype, g, shp, msg, reducer): function test_half_spmm (line 193) | def test_half_spmm(idtype, dtype, rtol, atol): function test_sddmm (line 234) | def test_sddmm(g, shp, lhs_target, rhs_target, msg, idtype): function test_segment_reduce (line 303) | def test_segment_reduce(reducer): function test_segment_mm (line 350) | def test_segment_mm(idtype, feat_size, dtype, tol): function test_gather_mm_idx_b (line 407) | def test_gather_mm_idx_b(feat_size, dtype, tol): function _test_gather_mm_idx_a (line 462) | def _test_gather_mm_idx_a(idtype, feat_size): function test_use_libxsmm_switch (line 498) | def test_use_libxsmm_switch(): FILE: tests/python/common/sampling/test_sampling.py function check_random_walk (line 16) | def check_random_walk(g, metapath, traces, ntypes, prob=None, trace_eids... function test_non_uniform_random_walk (line 42) | def test_non_uniform_random_walk(use_uva): function test_uniform_random_walk (line 150) | def test_uniform_random_walk(use_uva): function test_node2vec (line 272) | def test_node2vec(): function test_pack_traces (line 295) | def test_pack_traces(): function test_pinsage_sampling (line 316) | def test_pinsage_sampling(use_uva): function _gen_neighbor_sampling_test_graph (line 401) | def _gen_neighbor_sampling_test_graph(hypersparse, reverse): function _gen_neighbor_topk_test_graph (line 489) | def _gen_neighbor_topk_test_graph(hypersparse, reverse): function _test_sample_neighbors (line 563) | def _test_sample_neighbors(hypersparse, prob, fused): function _test_sample_labors (line 712) | def _test_sample_labors(hypersparse, prob): function _test_sample_neighbors_outedge (line 826) | def _test_sample_neighbors_outedge(hypersparse, fused): function _test_sample_neighbors_topk (line 974) | def _test_sample_neighbors_topk(hypersparse): function _test_sample_neighbors_topk_outedge (line 1058) | def _test_sample_neighbors_topk_outedge(hypersparse): function test_sample_neighbors_noprob (line 1128) | def test_sample_neighbors_noprob(): function test_sample_labors_noprob (line 1135) | def test_sample_labors_noprob(): function test_sample_neighbors_prob (line 1139) | def test_sample_neighbors_prob(): function test_sample_labors_prob (line 1146) | def test_sample_labors_prob(): function test_sample_neighbors_outedge (line 1150) | def test_sample_neighbors_outedge(): function test_sample_neighbors_mask (line 1164) | def test_sample_neighbors_mask(): function test_sample_neighbors_topk (line 1174) | def test_sample_neighbors_topk(): function test_sample_neighbors_topk_outedge (line 1183) | def test_sample_neighbors_topk_outedge(): function test_sample_neighbors_with_0deg (line 1189) | def test_sample_neighbors_with_0deg(fused): function create_test_graph (line 1213) | def create_test_graph(num_nodes, num_edges_per_node, bipartite=False): function create_etype_test_graph (line 1230) | def create_etype_test_graph(num_nodes, num_edges_per_node, rare_cnt): function test_sample_neighbors_biased_homogeneous (line 1281) | def test_sample_neighbors_biased_homogeneous(): function test_sample_neighbors_biased_bipartite (line 1337) | def test_sample_neighbors_biased_bipartite(): function test_sample_neighbors_etype_homogeneous (line 1401) | def test_sample_neighbors_etype_homogeneous(format_, direction, replace): function test_sample_neighbors_etype_sorted_homogeneous (line 1533) | def test_sample_neighbors_etype_sorted_homogeneous(format_, direction): function test_sample_neighbors_exclude_edges_heteroG (line 1558) | def test_sample_neighbors_exclude_edges_heteroG(dtype, fused): function test_sample_neighbors_exclude_edges_homoG (line 1719) | def test_sample_neighbors_exclude_edges_homoG(dtype, fused): function test_global_uniform_negative_sampling (line 1776) | def test_global_uniform_negative_sampling(dtype): FILE: tests/python/common/test_batch-graph.py function tree1 (line 10) | def tree1(idtype): function tree2 (line 30) | def tree2(idtype): function test_batch_unbatch (line 51) | def test_batch_unbatch(idtype): function test_batch_unbatch1 (line 70) | def test_batch_unbatch1(idtype): function test_batch_unbatch_frame (line 95) | def test_batch_unbatch_frame(idtype): function test_batch_unbatch2 (line 133) | def test_batch_unbatch2(idtype): function test_batch_send_and_recv (line 149) | def test_batch_send_and_recv(idtype): function test_batch_propagate (line 167) | def test_batch_propagate(idtype): function test_batched_edge_ordering (line 196) | def test_batched_edge_ordering(idtype): function test_batch_no_edge (line 214) | def test_batch_no_edge(idtype): function test_batch_keeps_empty_data (line 227) | def test_batch_keeps_empty_data(idtype): function _get_subgraph_batch_info (line 239) | def _get_subgraph_batch_info(keys, induced_indices_arr, batch_num_objs): function test_set_batch_info (line 274) | def test_set_batch_info(idtype): FILE: tests/python/common/test_batch-heterograph.py function check_equivalence_between_heterographs (line 11) | def check_equivalence_between_heterographs( function test_topology (line 54) | def test_topology(gs, idtype): function test_batching_batched (line 125) | def test_batching_batched(idtype): function test_features (line 204) | def test_features(idtype): function test_empty_relation (line 337) | def test_empty_relation(idtype): function test_unbatch2 (line 432) | def test_unbatch2(idtype): function test_slice_batch (line 457) | def test_slice_batch(idtype): function test_batch_keeps_empty_data (line 518) | def test_batch_keeps_empty_data(idtype): function test_batch_netypes (line 534) | def test_batch_netypes(): FILE: tests/python/common/test_convert.py function get_nodes_by_ntype (line 9) | def get_nodes_by_ntype(nodes, ntype): function edge_attrs (line 13) | def edge_attrs(edge): function get_edges_by_etype (line 18) | def get_edges_by_etype(edges, etype): function check_attrs_for_nodes (line 22) | def check_attrs_for_nodes(nodes, attrs): function check_attr_values_for_nodes (line 26) | def check_attr_values_for_nodes(nodes, attr_name, values): function check_attrs_for_edges (line 32) | def check_attrs_for_edges(edges, attrs): function check_attr_values_for_edges (line 36) | def check_attr_values_for_edges(edges, attr_name, values): function test_to_networkx (line 47) | def test_to_networkx(idtype): FILE: tests/python/common/test_ffi.py function test_cython (line 12) | def test_cython(): function test_callback (line 17) | def test_callback(arg): function _test_callback_array (line 26) | def _test_callback_array(dtype): function test_callback_thread (line 38) | def test_callback_thread(arg): function _test_callback_array_thread (line 47) | def _test_callback_array_thread(dtype): FILE: tests/python/common/test_frame.py function test_column_subcolumn (line 13) | def test_column_subcolumn(): function test_serialize_deserialize_plain (line 54) | def test_serialize_deserialize_plain(): function test_serialize_deserialize_subcolumn (line 76) | def test_serialize_deserialize_subcolumn(): function test_serialize_deserialize_dtype (line 101) | def test_serialize_deserialize_dtype(): FILE: tests/python/common/test_generators.py function test_rand_graph (line 12) | def test_rand_graph(): FILE: tests/python/common/test_heterograph-apply-edges.py function create_test_heterograph (line 28) | def create_test_heterograph(idtype): function create_random_hetero_with_single_source_node_type (line 52) | def create_random_hetero_with_single_source_node_type(idtype): function test_unary_copy_u (line 70) | def test_unary_copy_u(idtype): function test_unary_copy_e (line 126) | def test_unary_copy_e(idtype): function test_binary_op (line 185) | def test_binary_op(idtype): function test_heterograph_with_single_source_node_type_apply_edges (line 288) | def test_heterograph_with_single_source_node_type_apply_edges(idtype): FILE: tests/python/common/test_heterograph-index.py function create_test_heterograph (line 11) | def create_test_heterograph(idtype): function test_pin_memory (line 42) | def test_pin_memory(idtype): FILE: tests/python/common/test_heterograph-kernel.py function udf_copy_src (line 13) | def udf_copy_src(edges): function udf_copy_edge (line 17) | def udf_copy_edge(edges): function udf_mean (line 21) | def udf_mean(nodes): function udf_sum (line 25) | def udf_sum(nodes): function udf_max (line 29) | def udf_max(nodes): function generate_feature (line 42) | def generate_feature(g, broadcast="none", binary_op="none"): function test_copy_src_reduce (line 90) | def test_copy_src_reduce(): function test_copy_edge_reduce (line 158) | def test_copy_edge_reduce(): function test_all_binary_builtins (line 226) | def test_all_binary_builtins(): function test_mean_zero_degree (line 402) | def test_mean_zero_degree(g, idtype): FILE: tests/python/common/test_heterograph-misc.py function edge_pair_input (line 18) | def edge_pair_input(sort=False): function nx_input (line 29) | def nx_input(): function elist_input (line 37) | def elist_input(): function scipy_coo_input (line 42) | def scipy_coo_input(): function scipy_csr_input (line 47) | def scipy_csr_input(): function gen_by_mutation (line 56) | def gen_by_mutation(): function test_query (line 64) | def test_query(): function test_mutation (line 280) | def test_mutation(): function test_scipy_adjmat (line 297) | def test_scipy_adjmat(): function test_incmat (line 311) | def test_incmat(): function test_find_edges (line 360) | def test_find_edges(): function test_ismultigraph (line 387) | def test_ismultigraph(): function test_hypersparse_query (line 399) | def test_hypersparse_query(): function test_empty_data_initialized (line 418) | def test_empty_data_initialized(): function test_is_sorted (line 427) | def test_is_sorted(): function test_default_types (line 453) | def test_default_types(): function test_formats (line 460) | def test_formats(): FILE: tests/python/common/test_heterograph-pickle.py function _assert_is_identical_nodeflow (line 23) | def _assert_is_identical_nodeflow(nf1, nf2): function _assert_is_identical_batchedgraph (line 44) | def _assert_is_identical_batchedgraph(bg1, bg2): function _assert_is_identical_batchedhetero (line 51) | def _assert_is_identical_batchedhetero(bg1, bg2): function _assert_is_identical_index (line 61) | def _assert_is_identical_index(i1, i2): function _reconstruct_pickle (line 66) | def _reconstruct_pickle(obj): function test_pickling_index (line 76) | def test_pickling_index(): function test_pickling_graph_index (line 90) | def test_pickling_graph_index(): function _global_message_func (line 105) | def _global_message_func(nodes): function test_pickling_graph (line 114) | def test_pickling_graph(g, idtype): function test_pickling_batched_heterograph (line 121) | def test_pickling_batched_heterograph(): function test_pickling_subgraph (line 156) | def test_pickling_subgraph(): function test_pickling_is_pinned (line 199) | def test_pickling_is_pinned(idtype): FILE: tests/python/common/test_heterograph-remove.py function create_graph (line 8) | def create_graph(idtype, num_node): function test_node_removal (line 16) | def test_node_removal(idtype): function test_multigraph_node_removal (line 45) | def test_multigraph_node_removal(idtype): function test_multigraph_edge_removal (line 72) | def test_multigraph_edge_removal(idtype): function test_edge_removal (line 98) | def test_edge_removal(idtype): function test_node_and_edge_removal (line 134) | def test_node_and_edge_removal(idtype): function test_node_frame (line 172) | def test_node_frame(idtype): function test_edge_frame (line 184) | def test_edge_frame(idtype): function test_issue1287 (line 197) | def test_issue1287(idtype): FILE: tests/python/common/test_heterograph-shared-memory.py function create_test_graph (line 18) | def create_test_graph(idtype): function _assert_is_identical_hetero (line 33) | def _assert_is_identical_hetero(g, g2): function test_single_process (line 58) | def test_single_process(idtype): function sub_proc (line 68) | def sub_proc(hg_origin, name): function test_multi_process (line 80) | def test_multi_process(idtype): function test_copy_from_gpu (line 95) | def test_copy_from_gpu(): FILE: tests/python/common/test_heterograph-specialization.py function generate_graph (line 12) | def generate_graph(idtype): function test_v2v_update_all (line 29) | def test_v2v_update_all(idtype): function test_v2v_snr (line 75) | def test_v2v_snr(idtype): function test_v2v_pull (line 130) | def test_v2v_pull(idtype): function test_update_all_multi_fallback (line 184) | def test_update_all_multi_fallback(idtype): function test_pull_multi_fallback (line 238) | def test_pull_multi_fallback(idtype): function test_spmv_3d_feat (line 307) | def test_spmv_3d_feat(idtype): FILE: tests/python/common/test_heterograph-update-all.py function create_test_heterograph (line 25) | def create_test_heterograph(idtype): function create_test_heterograph_2 (line 49) | def create_test_heterograph_2(idtype): function create_test_heterograph_large (line 72) | def create_test_heterograph_large(idtype): function test_unary_copy_u (line 91) | def test_unary_copy_u(idtype): function test_unary_copy_e (line 161) | def test_unary_copy_e(idtype): function test_binary_op (line 252) | def test_binary_op(idtype): function test_multi_update_all_minmax_reduce_with_isolated_nodes (line 338) | def test_multi_update_all_minmax_reduce_with_isolated_nodes(): FILE: tests/python/common/test_heterograph.py function create_test_heterograph (line 24) | def create_test_heterograph(idtype): function create_test_heterograph1 (line 48) | def create_test_heterograph1(idtype): function create_test_heterograph2 (line 67) | def create_test_heterograph2(idtype): function create_test_heterograph3 (line 86) | def create_test_heterograph3(idtype): function create_test_heterograph4 (line 117) | def create_test_heterograph4(idtype): function create_test_heterograph5 (line 147) | def create_test_heterograph5(idtype): function get_redfn (line 177) | def get_redfn(name): function test_create (line 182) | def test_create(idtype): function test_create2 (line 336) | def test_create2(): function test_query (line 370) | def test_query(idtype): function test_empty_query (line 544) | def test_empty_query(idtype): function _test_hypersparse (line 597) | def _test_hypersparse(): function _test_edge_ids (line 672) | def _test_edge_ids(): function test_adj (line 715) | def test_adj(idtype): function test_adj_external (line 728) | def test_adj_external(idtype): function test_inc (line 768) | def test_inc(idtype): function test_view (line 792) | def test_view(idtype): function test_view1 (line 879) | def test_view1(idtype): function test_flatten (line 1012) | def test_flatten(idtype): function test_to_device (line 1135) | def test_to_device(idtype): function test_to_device2 (line 1181) | def test_to_device2(g, idtype): function test_pin_memory_ (line 1201) | def test_pin_memory_(idtype): function test_convert_bound (line 1279) | def test_convert_bound(idtype): function test_convert (line 1300) | def test_convert(idtype): function test_to_homo_zero_nodes (line 1463) | def test_to_homo_zero_nodes(idtype): function test_to_homo2 (line 1486) | def test_to_homo2(idtype): function test_invertible_conversion (line 1517) | def test_invertible_conversion(idtype): function test_metagraph_reachable (line 1526) | def test_metagraph_reachable(idtype): function test_subgraph_mask (line 1549) | def test_subgraph_mask(idtype): function test_subgraph (line 1609) | def test_subgraph(idtype): function test_apply (line 1804) | def test_apply(idtype): function test_level2 (line 1845) | def test_level2(idtype): function test_more_nnz (line 1985) | def test_more_nnz(idtype): function test_updates (line 1999) | def test_updates(idtype): function test_backward (line 2048) | def test_backward(idtype): function test_empty_heterograph (line 2077) | def test_empty_heterograph(idtype): function test_types_in_function (line 2113) | def test_types_in_function(idtype): function test_stack_reduce (line 2176) | def test_stack_reduce(idtype): function test_isolated_ntype (line 2213) | def test_isolated_ntype(idtype): function test_ismultigraph (line 2248) | def test_ismultigraph(idtype): function test_graph_index_is_unibipartite (line 2312) | def test_graph_index_is_unibipartite(idtype): function test_bipartite (line 2355) | def test_bipartite(idtype): function test_dtype_cast (line 2431) | def test_dtype_cast(idtype): function test_float_cast (line 2445) | def test_float_cast(): function test_format (line 2530) | def test_format(idtype): function test_edges_order (line 2572) | def test_edges_order(idtype): function test_reverse (line 2587) | def test_reverse(idtype): function test_clone (line 2732) | def test_clone(idtype): function test_add_edges (line 2809) | def test_add_edges(idtype): function test_add_nodes (line 3016) | def test_add_nodes(idtype): function test_remove_edges (line 3076) | def test_remove_edges(idtype): function test_remove_nodes (line 3196) | def test_remove_nodes(idtype): function test_frame (line 3312) | def test_frame(idtype): function test_frame_device (line 3378) | def test_frame_device(idtype): function test_create_block (line 3430) | def test_create_block(idtype): function test_adj_tensors (line 3558) | def test_adj_tensors(idtype, fmt): function _test_forking_pickler_entry (line 3614) | def _test_forking_pickler_entry(g, q): function test_forking_pickler (line 3621) | def test_forking_pickler(): FILE: tests/python/common/test_homophily.py function test_node_homophily (line 14) | def test_node_homophily(idtype): function test_edge_homophily (line 29) | def test_edge_homophily(idtype): function test_linkx_homophily (line 44) | def test_linkx_homophily(idtype): function test_adjusted_homophily (line 60) | def test_adjusted_homophily(idtype): FILE: tests/python/common/test_label_informativeness.py function test_edge_label_informativeness (line 14) | def test_edge_label_informativeness(idtype): function test_node_label_informativeness (line 33) | def test_node_label_informativeness(idtype): FILE: tests/python/common/test_merge.py function test_heterograph_merge (line 8) | def test_heterograph_merge(idtype): FILE: tests/python/common/test_partition.py function test_get_node_partition_from_book (line 15) | def test_get_node_partition_from_book(idtype): FILE: tests/python/common/test_propagate.py function create_graph (line 10) | def create_graph(idtype): function mfunc (line 15) | def mfunc(edges): function rfunc (line 19) | def rfunc(nodes): function test_prop_nodes_bfs (line 26) | def test_prop_nodes_bfs(idtype): function test_prop_edges_dfs (line 41) | def test_prop_edges_dfs(idtype): function test_prop_nodes_topo (line 86) | def test_prop_nodes_topo(idtype): FILE: tests/python/common/test_random.py function test_random_choice (line 12) | def test_random_choice(): FILE: tests/python/common/test_readout.py function test_sum_case1 (line 14) | def test_sum_case1(idtype): function test_reduce_readout (line 31) | def test_reduce_readout(g, idtype, reducer): function test_weighted_reduce_readout (line 78) | def test_weighted_reduce_readout(g, idtype, reducer): function test_topk (line 127) | def test_topk(g, idtype, descending): function test_softmax (line 193) | def test_softmax(g, idtype): function test_broadcast (line 217) | def test_broadcast(idtype, g): FILE: tests/python/common/test_sparse_ops-csr.py function _random_simple_graph (line 15) | def _random_simple_graph( function test_csrmm (line 47) | def test_csrmm(idtype, dtype, return_edge_ids): function test_csrmm_backward (line 67) | def test_csrmm_backward(idtype, dtype, num_vtypes): function test_csrsum (line 116) | def test_csrsum(idtype, dtype, return_edge_ids): function test_csrsum_backward (line 136) | def test_csrsum_backward(idtype, dtype, nelems): function test_csrmask (line 203) | def test_csrmask(idtype, dtype, A_nnz, B_nnz): function test_csrmask_backward (line 220) | def test_csrmask_backward(idtype, dtype): FILE: tests/python/common/test_subgraph.py function generate_graph (line 15) | def generate_graph(grad=False, add_data=True): function test_edge_subgraph (line 35) | def test_edge_subgraph(): function test_subgraph_relabel_nodes (line 58) | def test_subgraph_relabel_nodes(relabel_nodes): function _test_map_to_subgraph (line 113) | def _test_map_to_subgraph(): function create_test_heterograph (line 122) | def create_test_heterograph(idtype): function create_test_heterograph2 (line 148) | def create_test_heterograph2(idtype): function test_subgraph_mask (line 179) | def test_subgraph_mask(idtype): function test_subgraph1 (line 237) | def test_subgraph1(idtype): function test_in_subgraph (line 435) | def test_in_subgraph(idtype): function test_out_subgraph (line 523) | def test_out_subgraph(idtype): function test_subgraph_message_passing (line 621) | def test_subgraph_message_passing(): function test_khop_in_subgraph (line 633) | def test_khop_in_subgraph(idtype): function test_khop_out_subgraph (line 713) | def test_khop_out_subgraph(idtype): function test_subframes (line 794) | def test_subframes(parent_idx_device, child_device): function test_uva_subgraph (line 838) | def test_uva_subgraph(idtype, device): FILE: tests/python/common/test_traversal.py function toset (line 18) | def toset(x): function test_bfs (line 24) | def test_bfs(idtype, n=100): function test_topological_nodes (line 66) | def test_topological_nodes(idtype, n=100): function test_dfs_labeled_edges (line 97) | def test_dfs_labeled_edges(idtype, example=False): FILE: tests/python/common/transforms/test_functional-sort.py function create_test_heterograph (line 17) | def create_test_heterograph(num_nodes, num_adj, idtype): function check_sort (line 32) | def check_sort(spm, tag_arr=None, tag_pos=None): function test_sort_with_tag (line 65) | def test_sort_with_tag(idtype): function test_sort_with_tag_bipartite (line 98) | def test_sort_with_tag_bipartite(idtype): FILE: tests/python/common/transforms/test_to_block.py function test_to_block (line 25) | def test_to_block(idtype): FILE: tests/python/common/transforms/test_transform.py function create_test_heterograph3 (line 36) | def create_test_heterograph3(idtype): function create_test_heterograph4 (line 67) | def create_test_heterograph4(idtype): function create_test_heterograph5 (line 97) | def create_test_heterograph5(idtype): function test_line_graph1 (line 130) | def test_line_graph1(): function test_line_graph2 (line 141) | def test_line_graph2(idtype): function test_no_backtracking (line 186) | def test_no_backtracking(): function test_reverse (line 200) | def test_reverse(idtype): function test_reverse_shared_frames (line 362) | def test_reverse_shared_frames(idtype): function test_to_bidirected (line 379) | def test_to_bidirected(): function test_add_reverse_edges (line 420) | def test_add_reverse_edges(): function test_simple_graph (line 589) | def test_simple_graph(): function _test_bidirected_graph (line 602) | def _test_bidirected_graph(): function test_khop_graph (line 622) | def test_khop_graph(): function test_khop_adj (line 649) | def test_khop_adj(): function test_laplacian_lambda_max (line 666) | def test_laplacian_lambda_max(): function create_large_graph (line 687) | def create_large_graph(num_nodes, idtype=F.int64): function test_partition_with_halo (line 698) | def test_partition_with_halo(): function test_metis_partition (line 724) | def test_metis_partition(idtype): function check_metis_partition_with_constraint (line 741) | def check_metis_partition_with_constraint(g): function check_metis_partition (line 769) | def check_metis_partition(g, extra_hops): function test_reorder_nodes (line 840) | def test_reorder_nodes(): function test_compact (line 876) | def test_compact(idtype): function test_to_simple (line 1012) | def test_to_simple(idtype): function test_remove_edges (line 1130) | def test_remove_edges(idtype): function test_add_edges (line 1207) | def test_add_edges(idtype): function test_add_nodes (line 1423) | def test_add_nodes(idtype): function test_remove_edges (line 1483) | def test_remove_edges(idtype): function test_remove_nodes (line 1727) | def test_remove_nodes(idtype): function test_add_selfloop (line 1998) | def test_add_selfloop(idtype): function test_remove_selfloop (line 2198) | def test_remove_selfloop(idtype): function test_reorder_graph (line 2260) | def test_reorder_graph(idtype): function test_norm_by_dst (line 2392) | def test_norm_by_dst(idtype): function test_module_add_self_loop (line 2412) | def test_module_add_self_loop(idtype): function test_module_remove_self_loop (line 2508) | def test_module_remove_self_loop(idtype): function test_module_add_reverse (line 2556) | def test_module_add_reverse(idtype): function test_module_to_simple (line 2658) | def test_module_to_simple(idtype): function test_module_line_graph (line 2700) | def test_module_line_graph(idtype): function test_module_khop_graph (line 2724) | def test_module_khop_graph(idtype): function test_module_add_metapaths (line 2739) | def test_module_add_metapaths(idtype): function test_module_compose (line 2817) | def test_module_compose(idtype): function test_module_gcnnorm (line 2831) | def test_module_gcnnorm(idtype): function test_module_ppr (line 2859) | def test_module_ppr(idtype): function test_module_heat_kernel (line 2916) | def test_module_heat_kernel(idtype): function test_module_gdc (line 2943) | def test_module_gdc(idtype): function test_module_node_shuffle (line 2988) | def test_module_node_shuffle(idtype): function test_module_drop_node (line 3004) | def test_module_drop_node(idtype): function test_module_drop_edge (line 3024) | def test_module_drop_edge(idtype): function test_module_add_edge (line 3046) | def test_module_add_edge(idtype): function test_module_random_walk_pe (line 3070) | def test_module_random_walk_pe(idtype): function test_module_lap_pe (line 3079) | def test_module_lap_pe(idtype): function test_module_sign (line 3142) | def test_module_sign(g): function test_module_row_feat_normalizer (line 3242) | def test_module_row_feat_normalizer(idtype): function test_module_feat_mask (line 3297) | def test_module_feat_mask(idtype): function test_shortest_dist (line 3333) | def test_shortest_dist(idtype): function test_module_to_levi (line 3367) | def test_module_to_levi(idtype): function test_module_svd_pe (line 3398) | def test_module_svd_pe(idtype): FILE: tests/python/common/utils/test_filter.py function test_graph_filter (line 11) | def test_graph_filter(): function test_array_filter (line 50) | def test_array_filter(idtype): function test_filter_multistream (line 82) | def test_filter_multistream(idtype): FILE: tests/python/common/utils/test_pin_memory.py function test_pin_unpin (line 10) | def test_pin_unpin(): FILE: tests/python/mxnet/test_geometry.py function test_fps (line 8) | def test_fps(): FILE: tests/python/mxnet/test_nn.py function check_close (line 21) | def check_close(a, b): function _AXWb (line 25) | def _AXWb(A, X, W, b): function test_graph_conv (line 33) | def test_graph_conv(idtype, out_dim): function test_graph_conv2 (line 101) | def test_graph_conv2(idtype, g, norm, weight, bias, out_dim): function test_graph_conv2_bi (line 124) | def test_graph_conv2_bi(idtype, g, norm, weight, bias, out_dim): function _S2AXWb (line 140) | def _S2AXWb(A, N, X, W, b): function test_tagconv (line 154) | def test_tagconv(out_dim): function test_gat_conv (line 191) | def test_gat_conv(g, idtype, out_dim, num_heads): function test_gat_conv_bi (line 213) | def test_gat_conv_bi(g, idtype, out_dim, num_heads): function test_sage_conv (line 232) | def test_sage_conv(idtype, g, aggre_type, out_dim): function test_sage_conv_bi (line 246) | def test_sage_conv_bi(idtype, g, aggre_type, out_dim): function test_sage_conv_bi2 (line 264) | def test_sage_conv_bi2(idtype, aggre_type, out_dim): function test_gg_conv (line 284) | def test_gg_conv(): function test_cheb_conv (line 300) | def test_cheb_conv(out_dim): function test_agnn_conv (line 318) | def test_agnn_conv(g, idtype): function test_agnn_conv_bi (line 331) | def test_agnn_conv_bi(g, idtype): function test_appnp_conv (line 345) | def test_appnp_conv(): function test_dense_cheb_conv (line 360) | def test_dense_cheb_conv(out_dim): function test_dense_graph_conv (line 387) | def test_dense_graph_conv(idtype, g, norm_type, out_dim): function test_dense_sage_conv (line 408) | def test_dense_sage_conv(idtype, g, out_dim): function test_edge_conv (line 436) | def test_edge_conv(g, idtype, out_dim): function test_edge_conv_bi (line 451) | def test_edge_conv_bi(g, idtype, out_dim): function test_gin_conv (line 467) | def test_gin_conv(g, idtype, aggregator_type): function test_gin_conv_bi (line 484) | def test_gin_conv_bi(g, idtype, aggregator_type): function test_gmm_conv (line 505) | def test_gmm_conv(g, idtype): function test_gmm_conv_bi (line 518) | def test_gmm_conv_bi(g, idtype): function test_nn_conv (line 533) | def test_nn_conv(g, idtype): function test_nn_conv_bi (line 547) | def test_nn_conv_bi(g, idtype): function test_sg_conv (line 561) | def test_sg_conv(out_dim): function test_set2set (line 576) | def test_set2set(): function test_glob_att_pool (line 596) | def test_glob_att_pool(): function test_simple_pool (line 615) | def test_simple_pool(): function test_rgcn (line 677) | def test_rgcn(O): function test_sequential (line 730) | def test_sequential(): function myagg (line 787) | def myagg(alist, dsttype): function test_hetero_conv (line 796) | def test_hetero_conv(agg, idtype): FILE: tests/python/pytorch/cuda/test_nccl.py function test_nccl_sparse_push_single_remainder (line 14) | def test_nccl_sparse_push_single_remainder(): function test_nccl_sparse_pull_single_remainder (line 38) | def test_nccl_sparse_pull_single_remainder(): function test_nccl_sparse_push_single_range (line 62) | def test_nccl_sparse_push_single_range(): function test_nccl_sparse_pull_single_range (line 89) | def test_nccl_sparse_pull_single_range(): FILE: tests/python/pytorch/dataloading/test_dataloader.py function test_graph_dataloader (line 19) | def test_graph_dataloader(batch_size): function test_cluster_gcn (line 39) | def test_cluster_gcn(num_workers): function test_shadow (line 52) | def test_shadow(num_workers): function test_saint (line 77) | def test_saint(num_workers, mode): function test_neighbor_nonuniform (line 102) | def test_neighbor_nonuniform(idtype, mode, use_ddp, use_mask): function _check_dtype (line 204) | def _check_dtype(data, dtype, attr_name): function _check_device (line 215) | def _check_device(data): function test_ddp_dataloader_decompose_dataset (line 232) | def test_ddp_dataloader_decompose_dataset( function _ddp_runner (line 259) | def _ddp_runner(proc_id, nprocs, g, data, args): function test_node_dataloader (line 330) | def test_node_dataloader(idtype, sampler_name, mode, use_ddp): function test_edge_dataloader (line 449) | def test_edge_dataloader(idtype, sampler_name, neg_sampler, mode, use_ddp): function _create_homogeneous (line 580) | def _create_homogeneous(): function _create_heterogeneous (line 594) | def _create_heterogeneous(): function _remove_duplicates (line 619) | def _remove_duplicates(s, d): function _find_edges_to_exclude (line 624) | def _find_edges_to_exclude(g, exclude, always_exclude, pair_eids): function test_edge_dataloader_excludes (line 670) | def test_edge_dataloader_excludes( function test_edge_dataloader_exclusion_with_reverse_seed_nodes (line 731) | def test_edge_dataloader_exclusion_with_reverse_seed_nodes(): function test_edge_dataloader_exclusion_without_all_reverses (line 770) | def test_edge_dataloader_exclusion_without_all_reverses(): function dummy_worker_init_fn (line 804) | def dummy_worker_init_fn(worker_id): function test_dataloader_worker_init_fn (line 808) | def test_dataloader_worker_init_fn(): function test_distributed_dataloaders (line 824) | def test_distributed_dataloaders(): FILE: tests/python/pytorch/dataloading/test_spot_target.py function _create_homogeneous (line 9) | def _create_homogeneous(): function _find_edges_to_exclude (line 18) | def _find_edges_to_exclude(g, pair_eids, degree_threshold): function test_spot_target_excludes (line 33) | def test_spot_target_excludes(degree_threshold, batch_size): FILE: tests/python/pytorch/distributed/optim/test_dist_optim.py function create_random_graph (line 33) | def create_random_graph(n): function get_local_usable_addr (line 40) | def get_local_usable_addr(): function prepare_dist (line 66) | def prepare_dist(): function run_server (line 73) | def run_server(graph_name, server_id, server_count, num_clients, shared_... function initializer (line 86) | def initializer(shape, dtype): function run_client (line 93) | def run_client(graph_name, cli_id, part_id, server_count): function check_sparse_adam (line 153) | def check_sparse_adam(num_trainer=1, shared_mem=True): function test_sparse_opt (line 192) | def test_sparse_opt(): FILE: tests/python/pytorch/geometry/test_geometry.py function test_fps (line 15) | def test_fps(): function test_fps_start_idx (line 29) | def test_fps_start_idx(): function _test_knn_common (line 41) | def _test_knn_common(device, algorithm, dist, exclude_self): function test_knn_cpu (line 171) | def test_knn_cpu(algorithm, dist, exclude_self): function test_knn_cuda (line 180) | def test_knn_cuda(algorithm, dist, exclude_self): function test_knn_sharedmem_large (line 187) | def test_knn_sharedmem_large(num_points): function test_edge_coarsening (line 217) | def test_edge_coarsening(idtype, g, weight, relabel): FILE: tests/python/pytorch/graphbolt/gb_test_utils.py function rand_csc_graph (line 12) | def rand_csc_graph(N, density, bidirection_edge=False): function random_homo_graph (line 26) | def random_homo_graph(num_nodes, num_edges): function get_type_to_id (line 35) | def get_type_to_id(num_ntypes, num_etypes): function get_ntypes_and_etypes (line 48) | def get_ntypes_and_etypes(num_nodes, num_ntypes, num_etypes): function random_hetero_graph (line 64) | def random_hetero_graph(num_nodes, num_edges, num_ntypes, num_etypes): function random_homo_graphbolt_graph (line 90) | def random_homo_graphbolt_graph( function generate_raw_data_for_hetero_dataset (line 223) | def generate_raw_data_for_hetero_dataset( FILE: tests/python/pytorch/graphbolt/impl/test_basic_feature_store.py function test_basic_feature_store_homo (line 7) | def test_basic_feature_store_homo(): function test_basic_feature_store_hetero (line 69) | def test_basic_feature_store_hetero(): function test_basic_feature_store_errors (line 129) | def test_basic_feature_store_errors(): FILE: tests/python/pytorch/graphbolt/impl/test_cooperative_minibatching_utils.py function test_rank_sort_and_unique_and_compact (line 21) | def test_rank_sort_and_unique_and_compact(dtype, rank): FILE: tests/python/pytorch/graphbolt/impl/test_cpu_cached_feature.py function to_on_disk_numpy (line 13) | def to_on_disk_numpy(test_dir, name, t): function test_cpu_cached_feature (line 35) | def test_cpu_cached_feature(dtype, policy): function test_cpu_cached_feature_read_async (line 127) | def test_cpu_cached_feature_read_async(dtype): function test_cpu_cached_disk_feature_read_async (line 162) | def test_cpu_cached_disk_feature_read_async(dtype): FILE: tests/python/pytorch/graphbolt/impl/test_disk_based_feature_store.py function to_on_disk_numpy (line 15) | def to_on_disk_numpy(test_dir, name, t): function test_disk_based_feature (line 29) | def test_disk_based_feature(): function test_more_disk_based_feature (line 135) | def test_more_disk_based_feature(dtype, idtype, shape, index): function test_disk_based_feature_repr (line 159) | def test_disk_based_feature_repr(): FILE: tests/python/pytorch/graphbolt/impl/test_feature_cache.py function _test_query_and_replace (line 9) | def _test_query_and_replace(policy1, policy2, keys, offset): function test_feature_cache (line 63) | def test_feature_cache(offsets, dtype, feature_size, num_parts, policy, ... FILE: tests/python/pytorch/graphbolt/impl/test_fused_csc_sampling_graph.py function test_empty_graph (line 30) | def test_empty_graph(total_num_nodes): function test_hetero_empty_graph (line 45) | def test_hetero_empty_graph(total_num_nodes): function test_type_to_id_with_ntype_exception (line 87) | def test_type_to_id_with_ntype_exception(ntypes): function test_type_to_id_with_etype_exception (line 110) | def test_type_to_id_with_etype_exception(etypes): function test_homo_graph (line 128) | def test_homo_graph(total_num_nodes, total_num_edges): function test_hetero_graph (line 170) | def test_hetero_graph(total_num_nodes, total_num_edges, num_ntypes, num_... function test_num_nodes_edges_homo (line 221) | def test_num_nodes_edges_homo(total_num_nodes, total_num_edges): function test_num_nodes_hetero (line 241) | def test_num_nodes_hetero(): function test_node_type_offset_wrong_legnth (line 318) | def test_node_type_offset_wrong_legnth(node_type_offset): function test_load_save_homo_graph (line 349) | def test_load_save_homo_graph( function test_load_save_hetero_graph (line 418) | def test_load_save_hetero_graph( function test_pickle_homo_graph (line 501) | def test_pickle_homo_graph( function test_pickle_hetero_graph (line 568) | def test_pickle_hetero_graph( function process_csc_sampling_graph_multiprocessing (line 643) | def process_csc_sampling_graph_multiprocessing(graph): function test_multiprocessing (line 651) | def test_multiprocessing(): function test_in_subgraph_homo (line 686) | def test_in_subgraph_homo(): function test_in_subgraph_hetero (line 726) | def test_in_subgraph_hetero(): function test_temporal_sample_neighbors_homo (line 839) | def test_temporal_sample_neighbors_homo( function test_temporal_sample_neighbors_hetero (line 968) | def test_temporal_sample_neighbors_hetero( function check_tensors_on_the_same_shared_memory (line 1107) | def check_tensors_on_the_same_shared_memory(t1: torch.Tensor, t2: torch.... function check_node_edge_attributes (line 1123) | def check_node_edge_attributes(graph1, graph2, attributes, attr_name): function test_homo_graph_on_shared_memory (line 1145) | def test_homo_graph_on_shared_memory( function test_hetero_graph_on_shared_memory (line 1221) | def test_hetero_graph_on_shared_memory( function process_csc_sampling_graph_on_shared_memory (line 1313) | def process_csc_sampling_graph_on_shared_memory(graph, data_queue, flag_... function test_multiprocessing_with_shared_memory (line 1351) | def test_multiprocessing_with_shared_memory(): function test_from_dglgraph_homogeneous (line 1423) | def test_from_dglgraph_homogeneous(): function test_from_dglgraph_heterogeneous (line 1460) | def test_from_dglgraph_heterogeneous(): function create_fused_csc_sampling_graph (line 1549) | def create_fused_csc_sampling_graph(): function is_graph_on_device_type (line 1586) | def is_graph_on_device_type(graph, device_type): function is_graph_pinned (line 1596) | def is_graph_pinned(graph): function test_csc_sampling_graph_to_device (line 1611) | def test_csc_sampling_graph_to_device(device): function test_csc_sampling_graph_to_pinned_memory (line 1635) | def test_csc_sampling_graph_to_pinned_memory(): function test_sample_neighbors_homo (line 1655) | def test_sample_neighbors_homo( function test_sample_neighbors_hetero_single_fanout (line 1706) | def test_sample_neighbors_hetero_single_fanout(labor): function test_sample_neighbors_hetero (line 1723) | def test_sample_neighbors_hetero(indptr_dtype, indices_dtype, labor): function test_sample_neighbors_fanouts (line 1831) | def test_sample_neighbors_fanouts( function test_sample_neighbors_replace (line 1891) | def test_sample_neighbors_replace( function test_sample_neighbors_return_eids_homo (line 1946) | def test_sample_neighbors_return_eids_homo(labor, is_pinned): function test_sample_neighbors_return_eids_hetero (line 1989) | def test_sample_neighbors_return_eids_hetero(labor): function test_sample_neighbors_probs (line 2054) | def test_sample_neighbors_probs(replace, labor, probs_name): function test_sample_neighbors_zero_probs (line 2112) | def test_sample_neighbors_zero_probs(replace, labor, probs_or_mask): function test_sample_neighbors_homo_pick_number (line 2165) | def test_sample_neighbors_homo_pick_number(fanouts, replace, labor, prob... function test_sample_neighbors_hetero_pick_number (line 2243) | def test_sample_neighbors_hetero_pick_number( function test_graph_attributes (line 2343) | def test_graph_attributes(): FILE: tests/python/pytorch/graphbolt/impl/test_gpu_cached_feature.py function to_on_disk_numpy (line 14) | def to_on_disk_numpy(test_dir, name, t): function _skip_condition_cached_feature (line 20) | def _skip_condition_cached_feature(): function _reason_to_skip_cached_feature (line 26) | def _reason_to_skip_cached_feature(): function test_gpu_cached_feature (line 54) | def test_gpu_cached_feature(dtype, cache_size_a, cache_size_b): function test_gpu_cached_feature_read_async (line 150) | def test_gpu_cached_feature_read_async(dtype, pin_memory): function test_gpu_cached_nested_feature_async (line 190) | def test_gpu_cached_nested_feature_async(dtype): FILE: tests/python/pytorch/graphbolt/impl/test_gpu_graph_cache.py function test_gpu_graph_cache (line 42) | def test_gpu_graph_cache(indptr_dtype, dtype, cache_size, with_edge_ids): FILE: tests/python/pytorch/graphbolt/impl/test_hetero_cached_feature.py function test_hetero_cached_feature (line 12) | def test_hetero_cached_feature(cached_feature_type): FILE: tests/python/pytorch/graphbolt/impl/test_in_subgraph_sampler.py function test_index_select_csc (line 35) | def test_index_select_csc( function test_InSubgraphSampler_homo (line 100) | def test_InSubgraphSampler_homo(): function test_InSubgraphSampler_hetero (line 155) | def test_InSubgraphSampler_hetero(): FILE: tests/python/pytorch/graphbolt/impl/test_legacy_dataset.py function test_LegacyDataset_homo_node_pred (line 8) | def test_LegacyDataset_homo_node_pred(): FILE: tests/python/pytorch/graphbolt/impl/test_negative_sampler.py function test_NegativeSampler_invoke (line 12) | def test_NegativeSampler_invoke(): function test_UniformNegativeSampler_invoke (line 40) | def test_UniformNegativeSampler_invoke(): function test_Uniform_NegativeSampler (line 80) | def test_Uniform_NegativeSampler(negative_ratio): function test_Uniform_NegativeSampler_error_shape (line 125) | def test_Uniform_NegativeSampler_error_shape(): function get_hetero_graph (line 200) | def get_hetero_graph(): function test_NegativeSampler_Hetero_Data (line 222) | def test_NegativeSampler_Hetero_Data(): FILE: tests/python/pytorch/graphbolt/impl/test_neighbor_sampler.py function get_hetero_graph (line 11) | def get_hetero_graph(include_original_edge_ids): function test_NeighborSampler_GraphFetch (line 51) | def test_NeighborSampler_GraphFetch( function test_labor_dependent_minibatching (line 111) | def test_labor_dependent_minibatching(layer_dependency, overlap_graph_fe... FILE: tests/python/pytorch/graphbolt/impl/test_ondisk_dataset.py function write_yaml_file (line 21) | def write_yaml_file(yaml_content, dir): function load_dataset (line 28) | def load_dataset(dataset): function write_yaml_and_load_dataset (line 34) | def write_yaml_and_load_dataset(yaml_content, dir, force_preprocess=False): function load_sampling_graph (line 41) | def load_sampling_graph(test_dir, processed_dataset): function test_OnDiskDataset_TVTSet_exceptions (line 48) | def test_OnDiskDataset_TVTSet_exceptions(): function test_OnDiskDataset_multiple_tasks (line 88) | def test_OnDiskDataset_multiple_tasks(): function test_OnDiskDataset_TVTSet_ItemSet_names (line 154) | def test_OnDiskDataset_TVTSet_ItemSet_names(): function test_OnDiskDataset_TVTSet_HeteroItemSet_names (line 196) | def test_OnDiskDataset_TVTSet_HeteroItemSet_names(): function test_OnDiskDataset_TVTSet_ItemSet_id_label (line 241) | def test_OnDiskDataset_TVTSet_ItemSet_id_label(): function test_OnDiskDataset_TVTSet_ItemSet_node_pairs_labels (line 361) | def test_OnDiskDataset_TVTSet_ItemSet_node_pairs_labels(): function test_OnDiskDataset_TVTSet_ItemSet_node_pairs_labels_indexes (line 458) | def test_OnDiskDataset_TVTSet_ItemSet_node_pairs_labels_indexes(): function test_OnDiskDataset_TVTSet_HeteroItemSet_id_label (line 601) | def test_OnDiskDataset_TVTSet_HeteroItemSet_id_label(): function test_OnDiskDataset_TVTSet_HeteroItemSet_node_pairs_labels (line 710) | def test_OnDiskDataset_TVTSet_HeteroItemSet_node_pairs_labels(): function test_OnDiskDataset_Feature_heterograph (line 847) | def test_OnDiskDataset_Feature_heterograph(): function test_OnDiskDataset_Feature_homograph (line 945) | def test_OnDiskDataset_Feature_homograph(): function test_OnDiskDataset_Graph_Exceptions (line 1033) | def test_OnDiskDataset_Graph_Exceptions(): function test_OnDiskDataset_Graph_homogeneous (line 1051) | def test_OnDiskDataset_Graph_homogeneous(): function test_OnDiskDataset_Graph_heterogeneous (line 1082) | def test_OnDiskDataset_Graph_heterogeneous(): function test_OnDiskDataset_Metadata (line 1124) | def test_OnDiskDataset_Metadata(): function test_OnDiskDataset_preprocess_homogeneous (line 1144) | def test_OnDiskDataset_preprocess_homogeneous(edge_fmt): function test_OnDiskDataset_preprocess_homogeneous_hardcode (line 1241) | def test_OnDiskDataset_preprocess_homogeneous_hardcode( function test_OnDiskDataset_preprocess_heterogeneous_hardcode (line 1426) | def test_OnDiskDataset_preprocess_heterogeneous_hardcode( function test_OnDiskDataset_preprocess_path (line 1631) | def test_OnDiskDataset_preprocess_path(): function test_OnDiskDataset_preprocess_yaml_content (line 1670) | def test_OnDiskDataset_preprocess_yaml_content(): function test_OnDiskDataset_preprocess_force_preprocess (line 1828) | def test_OnDiskDataset_preprocess_force_preprocess(capsys): function test_OnDiskDataset_preprocess_auto_force_preprocess (line 1901) | def test_OnDiskDataset_preprocess_auto_force_preprocess(capsys): function test_OnDiskDataset_preprocess_not_include_eids (line 2014) | def test_OnDiskDataset_preprocess_not_include_eids(): function test_OnDiskDataset_load_name (line 2044) | def test_OnDiskDataset_load_name(edge_fmt): function test_OnDiskDataset_load_feature (line 2075) | def test_OnDiskDataset_load_feature(edge_fmt): function test_OnDiskDataset_load_graph (line 2161) | def test_OnDiskDataset_load_graph(edge_fmt): function test_OnDiskDataset_load_tasks (line 2263) | def test_OnDiskDataset_load_tasks(edge_fmt): function test_OnDiskDataset_all_nodes_set_homo (line 2354) | def test_OnDiskDataset_all_nodes_set_homo(): function test_OnDiskDataset_all_nodes_set_hetero (line 2378) | def test_OnDiskDataset_all_nodes_set_hetero(): function test_OnDiskDataset_load_1D_feature (line 2419) | def test_OnDiskDataset_load_1D_feature(fmt): function test_BuiltinDataset (line 2521) | def test_BuiltinDataset(): function test_OnDiskDataset_homogeneous (line 2557) | def test_OnDiskDataset_homogeneous( function test_OnDiskDataset_heterogeneous (line 2643) | def test_OnDiskDataset_heterogeneous( function test_OnDiskDataset_force_preprocess (line 2728) | def test_OnDiskDataset_force_preprocess(capsys): function test_OnDiskDataset_auto_force_preprocess (line 2795) | def test_OnDiskDataset_auto_force_preprocess(capsys): function test_OnDiskTask_repr_homogeneous (line 2898) | def test_OnDiskTask_repr_homogeneous(): function test_OnDiskDataset_not_include_eids (line 2923) | def test_OnDiskDataset_not_include_eids(): function test_OnDiskTask_repr_heterogeneous (line 2950) | def test_OnDiskTask_repr_heterogeneous(): function test_OnDiskDataset_load_tasks_selectively (line 2995) | def test_OnDiskDataset_load_tasks_selectively(): function test_OnDiskDataset_preprocess_graph_with_single_type (line 3053) | def test_OnDiskDataset_preprocess_graph_with_single_type(): FILE: tests/python/pytorch/graphbolt/impl/test_sampled_subgraph_impl.py function _assert_container_equal (line 12) | def _assert_container_equal(lhs, rhs): function test_exclude_edges_homo_deduplicated (line 37) | def test_exclude_edges_homo_deduplicated(reverse_row, reverse_column): function test_exclude_edges_homo_duplicated (line 86) | def test_exclude_edges_homo_duplicated(reverse_row, reverse_column): function test_exclude_edges_hetero_deduplicated (line 135) | def test_exclude_edges_hetero_deduplicated(reverse_row, reverse_column): function test_exclude_edges_hetero_duplicated (line 207) | def test_exclude_edges_hetero_duplicated(reverse_row, reverse_column): function test_exclude_edges_homo_deduplicated_tensor (line 279) | def test_exclude_edges_homo_deduplicated_tensor(reverse_row, reverse_col... function test_exclude_edges_homo_duplicated_tensor (line 328) | def test_exclude_edges_homo_duplicated_tensor(reverse_row, reverse_column): function test_exclude_edges_hetero_deduplicated_tensor (line 377) | def test_exclude_edges_hetero_deduplicated_tensor(reverse_row, reverse_c... function test_exclude_edges_hetero_duplicated_tensor (line 444) | def test_exclude_edges_hetero_duplicated_tensor(reverse_row, reverse_col... function test_to_pyg_homo (line 509) | def test_to_pyg_homo(): function test_to_pyg_hetero (line 546) | def test_to_pyg_hetero(): function test_sampled_subgraph_to_device (line 612) | def test_sampled_subgraph_to_device(): function test_sampled_subgraph_impl_representation_homo (line 662) | def test_sampled_subgraph_impl_representation_homo(): function test_sampled_subgraph_impl_representation_hetero (line 700) | def test_sampled_subgraph_impl_representation_hetero(): FILE: tests/python/pytorch/graphbolt/impl/test_torch_based_feature_store.py function to_on_disk_tensor (line 15) | def to_on_disk_tensor(test_dir, name, t): function test_torch_based_feature (line 26) | def test_torch_based_feature(in_memory): function is_feature_store_on_cuda (line 139) | def is_feature_store_on_cuda(store): function is_feature_store_on_cpu (line 144) | def is_feature_store_on_cpu(store): function test_feature_store_to_device (line 154) | def test_feature_store_to_device(device): function test_torch_based_pinned_feature (line 206) | def test_torch_based_pinned_feature(dtype, idtype, shape, in_place): function write_tensor_to_disk (line 248) | def write_tensor_to_disk(dir, name, t, fmt="torch"): function test_torch_based_feature_store (line 259) | def test_torch_based_feature_store(in_memory): function test_torch_based_feature_repr (line 354) | def test_torch_based_feature_repr(in_memory): function test_torch_based_feature_store_repr (line 393) | def test_torch_based_feature_store_repr(in_memory): FILE: tests/python/pytorch/graphbolt/internal/test_sample_utils.py function test_unique_and_compact_hetero (line 7) | def test_unique_and_compact_hetero(): function test_unique_and_compact_homo (line 66) | def test_unique_and_compact_homo(): function test_unique_and_compact_csc_formats_hetero (line 96) | def test_unique_and_compact_csc_formats_hetero(): function test_unique_and_compact_csc_formats_homo (line 152) | def test_unique_and_compact_csc_formats_homo(): function test_unique_and_compact_incorrect_indptr (line 173) | def test_unique_and_compact_incorrect_indptr(): function test_compact_csc_format_hetero (line 184) | def test_compact_csc_format_hetero(): function test_compact_csc_format_homo (line 241) | def test_compact_csc_format_homo(): function test_compact_incorrect_indptr (line 265) | def test_compact_incorrect_indptr(): FILE: tests/python/pytorch/graphbolt/internal/test_utils.py function test_read_torch_data (line 16) | def test_read_torch_data(): function test_read_numpy_data (line 27) | def test_read_numpy_data(in_memory): function test_read_data (line 38) | def test_read_data(fmt): function test_save_data (line 64) | def test_save_data(data_fmt, save_fmt, contiguous): function test_get_npy_dim (line 93) | def test_get_npy_dim(fmt): function test_copy_or_convert_data (line 111) | def test_copy_or_convert_data(data_fmt, save_fmt, is_feature): function test_read_edges (line 153) | def test_read_edges(edge_fmt): function test_read_edges_error (line 180) | def test_read_edges_error(): function test_calculate_file_hash (line 209) | def test_calculate_file_hash(): function test_calculate_dir_hash (line 231) | def test_calculate_dir_hash(): function test_check_dataset_change (line 249) | def test_check_dataset_change(): function test_numpy_save_aligned (line 274) | def test_numpy_save_aligned(): FILE: tests/python/pytorch/graphbolt/test_base.py function test_pytorch_cuda_allocator_conf (line 16) | def test_pytorch_cuda_allocator_conf(): function test_CopyTo (line 25) | def test_CopyTo(non_blocking): function test_CopyToWithMiniBatches (line 57) | def test_CopyToWithMiniBatches(task): function test_etype_tuple_to_str (line 138) | def test_etype_tuple_to_str(): function test_etype_str_to_tuple (line 168) | def test_etype_str_to_tuple(): function test_seed_type_str_to_ntypes (line 187) | def test_seed_type_str_to_ntypes(): function test_isin (line 212) | def test_isin(): function test_isin_big_data (line 222) | def test_isin_big_data(): function test_isin_non_1D_dim (line 230) | def test_isin_non_1D_dim(): function test_index_select (line 258) | def test_index_select(dtype, idtype, pinned): function test_scatter_async (line 293) | def test_scatter_async(dtype, idtype): function torch_expand_indptr (line 302) | def torch_expand_indptr(indptr, dtype, nodes=None): function test_expand_indptr (line 310) | def test_expand_indptr(nodes, dtype): function test_indptr_edge_ids (line 352) | def test_indptr_edge_ids(offset, dtype): function test_csc_format_base_representation (line 395) | def test_csc_format_base_representation(): function test_csc_format_base_incorrect_indptr (line 408) | def test_csc_format_base_incorrect_indptr(): FILE: tests/python/pytorch/graphbolt/test_dataloader.py function test_DataLoader (line 20) | def test_DataLoader(overlap_feature_fetch): function test_gpu_sampling_DataLoader (line 74) | def test_gpu_sampling_DataLoader( FILE: tests/python/pytorch/graphbolt/test_dataset.py function test_Dataset (line 6) | def test_Dataset(): FILE: tests/python/pytorch/graphbolt/test_feature_fetcher.py function test_FeatureFetcher_invoke (line 11) | def test_FeatureFetcher_invoke(): function test_FeatureFetcher_homo (line 45) | def test_FeatureFetcher_homo(): function _func (line 70) | def _func(fn, minibatch): function test_FeatureFetcher_with_edges_homo (line 74) | def test_FeatureFetcher_with_edges_homo(): function get_hetero_graph (line 124) | def get_hetero_graph(): function test_FeatureFetcher_hetero (line 146) | def test_FeatureFetcher_hetero(): function test_FeatureFetcher_with_edges_hetero (line 183) | def test_FeatureFetcher_with_edges_hetero(): FILE: tests/python/pytorch/graphbolt/test_graphbolt_utils.py function test_find_reverse_edges_homo (line 7) | def test_find_reverse_edges_homo(): function test_find_reverse_edges_hetero (line 15) | def test_find_reverse_edges_hetero(): function test_find_reverse_edges_bi_reverse_types (line 29) | def test_find_reverse_edges_bi_reverse_types(): function test_find_reverse_edges_circual_reverse_types (line 43) | def test_find_reverse_edges_circual_reverse_types(): FILE: tests/python/pytorch/graphbolt/test_integration.py function test_integration_link_prediction (line 7) | def test_integration_link_prediction(): function test_integration_node_classification (line 216) | def test_integration_node_classification(): FILE: tests/python/pytorch/graphbolt/test_item_sampler.py function test_ItemSampler_minibatcher (line 17) | def test_ItemSampler_minibatcher(): function test_ItemSet_integer (line 74) | def test_ItemSet_integer(batch_size, shuffle, drop_last): function test_ItemSet_seed_nodes (line 102) | def test_ItemSet_seed_nodes(batch_size, shuffle, drop_last): function test_ItemSet_seed_nodes_labels (line 131) | def test_ItemSet_seed_nodes_labels(batch_size, shuffle, drop_last): function test_ItemSet_node_pairs (line 168) | def test_ItemSet_node_pairs(batch_size, shuffle, drop_last): function test_ItemSet_node_pairs_labels (line 207) | def test_ItemSet_node_pairs_labels(batch_size, shuffle, drop_last): function test_ItemSet_node_pairs_labels_indexes (line 256) | def test_ItemSet_node_pairs_labels_indexes(batch_size, shuffle, drop_last): function test_ItemSet_hyperlink (line 329) | def test_ItemSet_hyperlink(batch_size, shuffle, drop_last): function test_ItemSet_seeds_labels (line 365) | def test_ItemSet_seeds_labels(batch_size, shuffle, drop_last): function test_append_with_other_datapipes (line 406) | def test_append_with_other_datapipes(): function test_HeteroItemSet_seed_nodes (line 420) | def test_HeteroItemSet_seed_nodes(batch_size, shuffle, drop_last): function test_HeteroItemSet_seed_nodes_labels (line 459) | def test_HeteroItemSet_seed_nodes_labels(batch_size, shuffle, drop_last): function test_HeteroItemSet_node_pairs (line 516) | def test_HeteroItemSet_node_pairs(batch_size, shuffle, drop_last): function test_HeteroItemSet_node_pairs_labels (line 566) | def test_HeteroItemSet_node_pairs_labels(batch_size, shuffle, drop_last): function test_HeteroItemSet_node_pairs_labels_indexes (line 633) | def test_HeteroItemSet_node_pairs_labels_indexes( function test_HeteroItemSet_hyperlink (line 766) | def test_HeteroItemSet_hyperlink(batch_size, shuffle, drop_last): function test_HeteroItemSet_hyperlink_labels (line 812) | def test_HeteroItemSet_hyperlink_labels(batch_size, shuffle, drop_last): function distributed_item_sampler_subprocess (line 871) | def distributed_item_sampler_subprocess( function test_RangeCalculation (line 995) | def test_RangeCalculation(params): function test_DistributedItemSampler (line 1033) | def test_DistributedItemSampler( FILE: tests/python/pytorch/graphbolt/test_itemset.py function test_ItemSet_names (line 9) | def test_ItemSet_names(): function test_ItemSet_scalar_dtype (line 41) | def test_ItemSet_scalar_dtype(dtype): function test_ItemSet_length (line 52) | def test_ItemSet_length(): function test_ItemSet_seed_nodes (line 92) | def test_ItemSet_seed_nodes(): function test_ItemSet_seed_nodes_labels (line 131) | def test_ItemSet_seed_nodes_labels(): function test_ItemSet_node_pairs (line 151) | def test_ItemSet_node_pairs(): function test_ItemSet_node_pairs_labels (line 168) | def test_ItemSet_node_pairs_labels(): function test_ItemSet_node_pairs_labels_indexes (line 188) | def test_ItemSet_node_pairs_labels_indexes(): function test_ItemSet_graphs (line 215) | def test_ItemSet_graphs(): function test_HeteroItemSet_names (line 228) | def test_HeteroItemSet_names(): function test_HeteroItemSet_length (line 278) | def test_HeteroItemSet_length(): function test_HeteroItemSet_iteration_seed_nodes (line 336) | def test_HeteroItemSet_iteration_seed_nodes(): function test_HeteroItemSet_iteration_seed_nodes_labels (line 403) | def test_HeteroItemSet_iteration_seed_nodes_labels(): function test_HeteroItemSet_iteration_node_pairs (line 432) | def test_HeteroItemSet_iteration_node_pairs(): function test_HeteroItemSet_iteration_node_pairs_labels (line 458) | def test_HeteroItemSet_iteration_node_pairs_labels(): function test_HeteroItemSet_iteration_node_pairs_labels_indexes (line 494) | def test_HeteroItemSet_iteration_node_pairs_labels_indexes(): function test_ItemSet_repr (line 535) | def test_ItemSet_repr(): function test_HeteroItemSet_repr (line 561) | def test_HeteroItemSet_repr(): function test_deprecation_alias (line 611) | def test_deprecation_alias(): FILE: tests/python/pytorch/graphbolt/test_minibatch.py function test_minibatch_representation_homo (line 13) | def test_minibatch_representation_homo(indptr_dtype, indices_dtype): function test_minibatch_representation_hetero (line 114) | def test_minibatch_representation_hetero(indptr_dtype, indices_dtype): function test_get_dgl_blocks_homo (line 233) | def test_get_dgl_blocks_homo(indptr_dtype, indices_dtype): function test_get_dgl_blocks_hetero (line 278) | def test_get_dgl_blocks_hetero(): function test_get_dgl_blocks_hetero_partial_empty_edges (line 349) | def test_get_dgl_blocks_hetero_partial_empty_edges(): function test_get_dgl_blocks_hetero_empty_edges (line 376) | def test_get_dgl_blocks_hetero_empty_edges(): function test_get_dgl_blocks_homo_empty_edges (line 403) | def test_get_dgl_blocks_homo_empty_edges(): function test_seeds_ntype_being_passed (line 416) | def test_seeds_ntype_being_passed(): function create_homo_minibatch (line 431) | def create_homo_minibatch(): function create_hetero_minibatch (line 477) | def create_hetero_minibatch(): function check_dgl_blocks_hetero (line 544) | def check_dgl_blocks_hetero(minibatch, blocks): function check_dgl_blocks_homo (line 581) | def check_dgl_blocks_homo(minibatch, blocks): function test_dgl_node_classification_without_feature (line 602) | def test_dgl_node_classification_without_feature(): function test_dgl_node_classification_homo (line 618) | def test_dgl_node_classification_homo(): function test_dgl_node_classification_hetero (line 631) | def test_dgl_node_classification_hetero(): function test_dgl_link_predication_homo (line 643) | def test_dgl_link_predication_homo(): function test_dgl_link_predication_hetero (line 658) | def test_dgl_link_predication_hetero(): function test_to_pyg_data (line 679) | def test_to_pyg_data(): FILE: tests/python/pytorch/graphbolt/test_subgraph_sampler.py function _check_sampler_len (line 17) | def _check_sampler_len(sampler, lenExp): class SamplerType (line 23) | class SamplerType(Enum): function _get_sampler (line 30) | def _get_sampler(sampler_type): function _is_temporal (line 49) | def _is_temporal(sampler_type): function get_hetero_graph (line 53) | def get_hetero_graph(): function _assert_hetero_values (line 75) | def _assert_hetero_values( function _assert_homo_values (line 100) | def _assert_homo_values( function test_SubgraphSampler_invoke (line 120) | def test_SubgraphSampler_invoke(): function test_NeighborSampler_invoke (line 136) | def test_NeighborSampler_invoke(labor): function test_NeighborSampler_fanouts (line 159) | def test_NeighborSampler_fanouts(labor): function test_SubgraphSampler_Node (line 193) | def test_SubgraphSampler_Node(sampler_type): function test_SubgraphSampler_Link (line 224) | def test_SubgraphSampler_Link(sampler_type): function test_SubgraphSampler_Link_With_Negative (line 260) | def test_SubgraphSampler_Link_With_Negative(sampler_type): function test_SubgraphSampler_HyperLink (line 293) | def test_SubgraphSampler_HyperLink(sampler_type): function test_SubgraphSampler_Node_Hetero (line 329) | def test_SubgraphSampler_Node_Hetero(sampler_type): function test_SubgraphSampler_Link_Hetero (line 364) | def test_SubgraphSampler_Link_Hetero(sampler_type): function test_SubgraphSampler_Link_Hetero_With_Negative (line 423) | def test_SubgraphSampler_Link_Hetero_With_Negative(sampler_type): function test_SubgraphSampler_Link_Hetero_Unknown_Etype (line 472) | def test_SubgraphSampler_Link_Hetero_Unknown_Etype(sampler_type): function test_SubgraphSampler_Link_Hetero_With_Negative_Unknown_Etype (line 521) | def test_SubgraphSampler_Link_Hetero_With_Negative_Unknown_Etype(sampler... function test_SubgraphSampler_HyperLink_Hetero (line 571) | def test_SubgraphSampler_HyperLink_Hetero(sampler_type): function test_SubgraphSampler_Random_Hetero_Graph (line 630) | def test_SubgraphSampler_Random_Hetero_Graph(sampler_type, replace): function test_SubgraphSampler_without_deduplication_Homo_Node (line 731) | def test_SubgraphSampler_without_deduplication_Homo_Node(sampler_type): function test_SubgraphSampler_without_deduplication_Hetero_Node (line 808) | def test_SubgraphSampler_without_deduplication_Hetero_Node(sampler_type): function test_SubgraphSampler_unique_csc_format_Homo_Node_cpu (line 892) | def test_SubgraphSampler_unique_csc_format_Homo_Node_cpu(labor): function test_SubgraphSampler_unique_csc_format_Homo_Node_gpu (line 939) | def test_SubgraphSampler_unique_csc_format_Homo_Node_gpu(labor): function test_SubgraphSampler_unique_csc_format_Hetero_Node (line 1001) | def test_SubgraphSampler_unique_csc_format_Hetero_Node(labor): function test_SubgraphSampler_Hetero_multifanout_per_layer (line 1073) | def test_SubgraphSampler_Hetero_multifanout_per_layer(sampler_type): function test_SubgraphSampler_without_deduplication_Homo_Link (line 1148) | def test_SubgraphSampler_without_deduplication_Homo_Link(sampler_type): function test_SubgraphSampler_without_deduplication_Hetero_Link (line 1218) | def test_SubgraphSampler_without_deduplication_Hetero_Link(sampler_type): function test_SubgraphSampler_unique_csc_format_Homo_Link_cpu (line 1314) | def test_SubgraphSampler_unique_csc_format_Homo_Link_cpu(labor): function test_SubgraphSampler_unique_csc_format_Homo_Link_gpu (line 1371) | def test_SubgraphSampler_unique_csc_format_Homo_Link_gpu(labor): function test_SubgraphSampler_unique_csc_format_Hetero_Link (line 1443) | def test_SubgraphSampler_unique_csc_format_Hetero_Link(labor): function test_SubgraphSampler_without_deduplication_Homo_HyperLink (line 1536) | def test_SubgraphSampler_without_deduplication_Homo_HyperLink(sampler_ty... function test_SubgraphSampler_without_deduplication_Hetero_HyperLink (line 1605) | def test_SubgraphSampler_without_deduplication_Hetero_HyperLink(sampler_... function test_SubgraphSampler_unique_csc_format_Homo_HyperLink_cpu (line 1701) | def test_SubgraphSampler_unique_csc_format_Homo_HyperLink_cpu(labor): function test_SubgraphSampler_unique_csc_format_Homo_HyperLink_gpu (line 1758) | def test_SubgraphSampler_unique_csc_format_Homo_HyperLink_gpu(labor): function test_SubgraphSampler_unique_csc_format_Hetero_HyperLink (line 1830) | def test_SubgraphSampler_unique_csc_format_Hetero_HyperLink(labor): FILE: tests/python/pytorch/graphbolt/test_utils.py function test_add_reverse_edges_homo (line 14) | def test_add_reverse_edges_homo(): function test_add_reverse_edges_hetero (line 32) | def test_add_reverse_edges_hetero(): function test_exclude_seed_edges_homo_cpu (line 76) | def test_exclude_seed_edges_homo_cpu(use_datapipe): function test_exclude_seed_edges_gpu (line 130) | def test_exclude_seed_edges_gpu(use_datapipe, async_op): function get_hetero_graph (line 205) | def get_hetero_graph(): function test_exclude_seed_edges_hetero (line 227) | def test_exclude_seed_edges_hetero(): FILE: tests/python/pytorch/mpops/test_edgewise.py function test_copy_u (line 19) | def test_copy_u(idtype, feat_size): function test_copy_u_hetero (line 43) | def test_copy_u_hetero(idtype, feat_size): function test_copy_v (line 73) | def test_copy_v(idtype, feat_size): function test_copy_v_hetero (line 97) | def test_copy_v_hetero(idtype, feat_size): function pad_shape (line 144) | def pad_shape(x, y, x_size, y_size): function test_u_op_v (line 156) | def test_u_op_v(idtype, op, x_size, y_size): function test_u_dot_v (line 186) | def test_u_dot_v(idtype, x_size, y_size): FILE: tests/python/pytorch/nn/conv/test_gatedgcnconv.py function test_gatedgcn_conv (line 15) | def test_gatedgcn_conv(g, idtype): FILE: tests/python/pytorch/nn/test_nn.py function _AXWb (line 40) | def _AXWb(A, X, W, b): function graph_with_nodes (line 46) | def graph_with_nodes(num_nodes, ctx=None): function test_graph_conv0 (line 52) | def test_graph_conv0(out_dim): function test_graph_conv (line 118) | def test_graph_conv(idtype, g, norm, weight, bias, out_dim): function test_graph_conv_e_weight (line 144) | def test_graph_conv_e_weight(idtype, g, norm, weight, bias, out_dim): function test_graph_conv_e_weight_norm (line 170) | def test_graph_conv_e_weight_norm(idtype, g, norm, weight, bias, out_dim): function test_graph_conv_bi (line 200) | def test_graph_conv_bi(idtype, g, norm, weight, bias, out_dim): function _S2AXWb (line 222) | def _S2AXWb(A, N, X, W, b): function test_tagconv (line 236) | def test_tagconv(out_dim): function test_set2set (line 276) | def test_set2set(): function test_glob_att_pool (line 298) | def test_glob_att_pool(): function test_simple_pool (line 321) | def test_simple_pool(): function test_set_trans (line 393) | def test_set_trans(): function test_rgcn (line 430) | def test_rgcn(idtype, O): function test_rgcn_default_nbasis (line 491) | def test_rgcn_default_nbasis(idtype, O): function test_gat_conv (line 555) | def test_gat_conv(g, idtype, out_dim, num_heads): function test_gat_conv_bi (line 580) | def test_gat_conv_bi(g, idtype, out_dim, num_heads): function test_gat_conv_edge_weight (line 599) | def test_gat_conv_edge_weight(g, idtype, out_dim, num_heads): function test_gatv2_conv (line 622) | def test_gatv2_conv(g, idtype, out_dim, num_heads): function test_gatv2_conv_bi (line 647) | def test_gatv2_conv_bi(g, idtype, out_dim, num_heads): function test_egat_conv (line 667) | def test_egat_conv(g, idtype, out_node_feats, out_edge_feats, num_heads): function test_egat_conv_bi (line 695) | def test_egat_conv_bi(g, idtype, out_node_feats, out_edge_feats, num_hea... function test_egat_conv_edge_weight (line 726) | def test_egat_conv_edge_weight( function test_edgegat_conv (line 754) | def test_edgegat_conv(g, idtype, out_feats, num_heads): function test_edgegat_conv_bi (line 776) | def test_edgegat_conv_bi(g, idtype, out_feats, num_heads): function test_sage_conv (line 803) | def test_sage_conv(idtype, g, aggre_type): function test_sage_conv_bi (line 818) | def test_sage_conv_bi(idtype, g, aggre_type, out_dim): function test_sage_conv2 (line 834) | def test_sage_conv2(idtype, out_dim): function test_sgc_conv (line 858) | def test_sgc_conv(g, idtype, out_dim): function test_appnp_conv (line 884) | def test_appnp_conv(g, idtype): function test_appnp_conv_e_weight (line 900) | def test_appnp_conv_e_weight(g, idtype): function test_gcn2conv_e_weight (line 915) | def test_gcn2conv_e_weight(g, idtype, bias): function test_sgconv_e_weight (line 934) | def test_sgconv_e_weight(g, idtype): function test_tagconv_e_weight (line 947) | def test_tagconv_e_weight(g, idtype): function test_gin_conv (line 964) | def test_gin_conv(g, idtype, aggregator_type): function test_gine_conv (line 986) | def test_gine_conv(g, idtype): function test_gin_conv_bi (line 1009) | def test_gin_conv_bi(g, idtype, aggregator_type): function test_agnn_conv (line 1026) | def test_agnn_conv(g, idtype): function test_agnn_conv_bi (line 1038) | def test_agnn_conv_bi(g, idtype): function test_gated_graph_conv (line 1053) | def test_gated_graph_conv(g, idtype): function test_gated_graph_conv_one_etype (line 1069) | def test_gated_graph_conv_one_etype(g, idtype): function test_nn_conv (line 1089) | def test_nn_conv(g, idtype): function test_nn_conv_bi (line 1104) | def test_nn_conv_bi(g, idtype): function test_gmm_conv (line 1120) | def test_gmm_conv(g, idtype): function test_gmm_conv_bi (line 1136) | def test_gmm_conv_bi(g, idtype): function test_dense_graph_conv (line 1155) | def test_dense_graph_conv(norm_type, g, idtype, out_dim): function test_dense_sage_conv (line 1175) | def test_dense_sage_conv(g, idtype, out_dim): function test_edge_conv (line 1202) | def test_edge_conv(g, idtype, out_dim): function test_edge_conv_bi (line 1219) | def test_edge_conv_bi(g, idtype, out_dim): function test_dotgat_conv (line 1236) | def test_dotgat_conv(g, idtype, out_dim, num_heads): function test_dotgat_conv_bi (line 1256) | def test_dotgat_conv_bi(g, idtype, out_dim, num_heads): function test_dense_cheb_conv (line 1272) | def test_dense_cheb_conv(out_dim): function test_sequential (line 1296) | def test_sequential(): function test_atomic_conv (line 1349) | def test_atomic_conv(g, idtype): function test_cf_conv (line 1376) | def test_cf_conv(g, idtype, out_dim): function myagg (line 1399) | def myagg(alist, dsttype): function test_hetero_conv (line 1409) | def test_hetero_conv(agg, idtype, canonical_keys): function test_hetero_linear (line 1548) | def test_hetero_linear(out_dim): function test_hetero_embedding (line 1564) | def test_hetero_embedding(out_dim): function test_gnnexplainer (line 1592) | def test_gnnexplainer(g, idtype, out_dim): function test_heterognnexplainer (line 1639) | def test_heterognnexplainer(g, idtype, input_dim, output_dim): function test_subgraphx (line 1727) | def test_subgraphx(g, idtype, n_classes): function test_heterosubgraphx (line 1754) | def test_heterosubgraphx(g, idtype, input_dim, n_classes): function test_pgexplainer (line 1823) | def test_pgexplainer(g, idtype, n_classes): function test_heteropgexplainer (line 1887) | def test_heteropgexplainer(g, idtype, input_dim, n_classes): function test_jumping_knowledge (line 1965) | def test_jumping_knowledge(): function test_edge_predictor (line 1989) | def test_edge_predictor(op): function test_ke_score_funcs (line 2008) | def test_ke_score_funcs(): function test_twirls (line 2029) | def test_twirls(): function test_typed_linear (line 2041) | def test_typed_linear(feat_size, regularizer, num_bases): function test_hgt (line 2071) | def test_hgt(idtype, in_size, num_heads): function test_radius_graph (line 2132) | def test_radius_graph(self_loop, get_distances): function test_group_rev_res (line 2276) | def test_group_rev_res(idtype): function test_egnn_conv (line 2295) | def test_egnn_conv(in_size, hidden_size, out_size, edge_feat_size): function test_pna_conv (line 2325) | def test_pna_conv( function test_label_prop (line 2362) | def test_label_prop(k, alpha, norm_type, clamp, normalize, reset): function test_dgn_conv (line 2385) | def test_dgn_conv( function test_DeepWalk (line 2421) | def test_DeepWalk(): function test_degree_encoder (line 2454) | def test_degree_encoder(max_degree, embedding_dim, direction): function test_MetaPath2Vec (line 2484) | def test_MetaPath2Vec(idtype): function test_LapPosEncoder (line 2514) | def test_LapPosEncoder( function test_BiasedMHA (line 2544) | def test_BiasedMHA(feat_size, num_heads, bias, attn_bias_type, attn_drop): function test_EGTLayer (line 2556) | def test_EGTLayer(edge_update): function test_GraphormerLayer (line 2588) | def test_GraphormerLayer(attn_bias_type, norm_first): function test_PathEncoder (line 2615) | def test_PathEncoder(max_len, feat_dim, num_heads): function test_SpatialEncoder (line 2635) | def test_SpatialEncoder(max_dist, num_kernels, num_heads): function test_conv_with_zero_nodes_bugfix_7894 (line 2686) | def test_conv_with_zero_nodes_bugfix_7894(residual): FILE: tests/python/pytorch/nn/test_sparse_emb.py function initializer (line 13) | def initializer(emb): function check_all_set_all_get_emb (line 19) | def check_all_set_all_get_emb(device, init_emb): function check_all_set_all_get_optm_state (line 29) | def check_all_set_all_get_optm_state( function start_sparse_worker (line 46) | def start_sparse_worker(rank, world_size, test, args): function test_multiprocess_sparse_emb_get_set (line 70) | def test_multiprocess_sparse_emb_get_set(num_workers): function test_multiprocess_sparse_emb_get_set_optm_state (line 95) | def test_multiprocess_sparse_emb_get_set_optm_state(num_workers): FILE: tests/python/pytorch/optim/test_optim.py function test_sparse_adam (line 15) | def test_sparse_adam(emb_dim): function test_sparse_adam_uva (line 55) | def test_sparse_adam_uva(use_uva, emb_dim): function test_sparse_adam_dtype (line 97) | def test_sparse_adam_dtype(dtype, emb_dim): function test_sparse_adam_zero_step (line 133) | def test_sparse_adam_zero_step(): function initializer (line 172) | def initializer(emb): function start_sparse_adam_worker (line 178) | def start_sparse_adam_worker( function start_torch_adam_worker (line 245) | def start_torch_adam_worker( function test_multiprocess_cpu_sparse_adam (line 310) | def test_multiprocess_cpu_sparse_adam(num_workers): function test_multiprocess_sparse_adam (line 356) | def test_multiprocess_sparse_adam(num_workers, backend, zero_comm): function test_multiprocess_sparse_adam_cuda_tensor (line 418) | def test_multiprocess_sparse_adam_cuda_tensor(num_workers): function test_multiprocess_sparse_adam_cpu_zero_step (line 459) | def test_multiprocess_sparse_adam_cpu_zero_step(num_workers): function test_multiprocess_sparse_adam_zero_step (line 505) | def test_multiprocess_sparse_adam_zero_step(num_workers, backend): function test_multiprocess_sparse_adam_zero_step_cuda_tensor (line 556) | def test_multiprocess_sparse_adam_zero_step_cuda_tensor(num_workers): function start_sparse_adam_state_dict_worker (line 592) | def start_sparse_adam_state_dict_worker( function test_multiprocess_sparse_adam_state_dict (line 652) | def test_multiprocess_sparse_adam_state_dict(num_workers, backend): FILE: tests/python/pytorch/sparse/test_broadcast.py function test_sp_broadcast_v (line 16) | def test_sp_broadcast_v(shape, nnz, nz_dim, op): FILE: tests/python/pytorch/sparse/test_elementwise_op.py function test_diag_op_diag (line 13) | def test_diag_op_diag(opname): function test_diag_op_scalar (line 27) | def test_diag_op_scalar(v_scalar): function test_addsub_coo (line 71) | def test_addsub_coo(val_shape, opname): function test_addsub_csr (line 100) | def test_addsub_csr(val_shape, opname): function test_addsub_csc (line 129) | def test_addsub_csc(val_shape, opname): function test_addsub_diag (line 158) | def test_addsub_diag(val_shape, opname): function test_add_sparse_diag (line 181) | def test_add_sparse_diag(val_shape): function test_sub_sparse_diag (line 205) | def test_sub_sparse_diag(val_shape): function test_error_op_sparse_diag (line 229) | def test_error_op_sparse_diag(op): FILE: tests/python/pytorch/sparse/test_elementwise_op_sp.py function all_close_sparse (line 18) | def all_close_sparse(A, row, col, val, shape): function test_muldiv_scalar (line 30) | def test_muldiv_scalar(v_scalar): function test_pow (line 58) | def test_pow(val_shape): function test_error_op_scalar (line 86) | def test_error_op_scalar(op, v_scalar): function test_spspmul (line 114) | def test_spspmul(create_func1, create_func2, shape, nnz1, nnz2, nz_dim): function test_spspdiv (line 145) | def test_spspdiv(create_func, nnz, shape, nz_dim): FILE: tests/python/pytorch/sparse/test_matmul.py function _torch_sparse_mm (line 22) | def _torch_sparse_mm(torch_A1, torch_A2): function test_spmm (line 32) | def test_spmm(create_func, shape, nnz, out_dim): function test_bspmm (line 63) | def test_bspmm(create_func, shape, nnz): function test_spspmm (line 95) | def test_spspmm(create_func1, create_func2, shape_n_m, shape_k, nnz1, nn... function test_spspmm_duplicate (line 125) | def test_spspmm_duplicate(): function test_sparse_diag_mm (line 158) | def test_sparse_diag_mm(create_func, sparse_shape, nnz): function test_diag_sparse_mm (line 191) | def test_diag_sparse_mm(create_func, sparse_shape, nnz): FILE: tests/python/pytorch/sparse/test_matrix_op.py function test_compact (line 19) | def test_compact(create_func, dim, index): FILE: tests/python/pytorch/sparse/test_reduction.py function _coalesce_dense (line 37) | def _coalesce_dense(row, col, val, nrows, ncols, op): function test_docstring (line 73) | def test_docstring(func): function test_reduce_all (line 85) | def test_reduce_all(shape, op, use_reduce): function test_reduce_along (line 120) | def test_reduce_along(shape, dim, empty_nnz, op, use_reduce): FILE: tests/python/pytorch/sparse/test_sddmm.py function test_sddmm (line 22) | def test_sddmm(create_func, shape, nnz, hidden): function test_bsddmm (line 63) | def test_bsddmm(create_func, shape, nnz, nz_dim): FILE: tests/python/pytorch/sparse/test_softmax.py function test_softmax (line 14) | def test_softmax(val_D, csr, dim): FILE: tests/python/pytorch/sparse/test_sparse_matrix.py function _torch_sparse_csr_tensor (line 30) | def _torch_sparse_csr_tensor(indptr, indices, val, torch_sparse_shape): function test_from_coo (line 40) | def test_from_coo(dense_dim, row, col, shape): function test_from_csr (line 68) | def test_from_csr(dense_dim, indptr, indices, shape): function test_from_csc (line 96) | def test_from_csc(dense_dim, indptr, indices, shape): function test_dense (line 121) | def test_dense(val_shape): function test_csr_to_coo (line 140) | def test_csr_to_coo(dense_dim, indptr, indices, shape): function test_csc_to_coo (line 175) | def test_csc_to_coo(dense_dim, indptr, indices, shape): function _scatter_add (line 206) | def _scatter_add(a, index, v=1): function test_coo_to_csr (line 217) | def test_coo_to_csr(dense_dim, row, col, shape): function test_csc_to_csr (line 249) | def test_csc_to_csr(dense_dim, indptr, indices, shape): function test_coo_to_csc (line 291) | def test_coo_to_csc(dense_dim, row, col, shape): function test_csr_to_csc (line 323) | def test_csr_to_csc(dense_dim, indptr, indices, shape): function test_diag_conversions (line 363) | def test_diag_conversions(shape): function test_val_like (line 386) | def test_val_like(val_shape, shape): function test_coalesce (line 417) | def test_coalesce(): function test_has_duplicate (line 438) | def test_has_duplicate(): function test_index_select (line 468) | def test_index_select(create_func, shape, dense_dim, select_dim, index): function test_range_select (line 490) | def test_range_select(create_func, shape, dense_dim, select_dim, rang): function test_sample_rowwise (line 513) | def test_sample_rowwise(create_func, index, replace, bias): function test_sample_columnwise (line 555) | def test_sample_columnwise(create_func, index, replace, bias): function test_print (line 591) | def test_print(): function test_to_device (line 650) | def test_to_device(device): function test_to_dtype (line 675) | def test_to_dtype(dtype): function test_sparse_matrix_transpose (line 701) | def test_sparse_matrix_transpose(dense_dim, row, col, extra_shape): function test_torch_sparse_coo_conversion (line 724) | def test_torch_sparse_coo_conversion(row, col, nz_dim, shape): function test_torch_sparse_csr_conversion (line 756) | def test_torch_sparse_csr_conversion(indptr, indices, shape): function test_torch_sparse_csc_conversion (line 787) | def test_torch_sparse_csc_conversion(indptr, indices, shape): function test_diag (line 820) | def test_diag(val_shape, mat_shape): function test_identity (line 852) | def test_identity(shape, d): function test_diag_matrix_transpose (line 870) | def test_diag_matrix_transpose(val_shape, mat_shape): FILE: tests/python/pytorch/sparse/test_unary_op.py function test_neg (line 9) | def test_neg(): function test_diag_neg (line 23) | def test_diag_neg(): function test_diag_inv (line 33) | def test_diag_inv(): FILE: tests/python/pytorch/sparse/utils.py function clone_detach_and_grad (line 10) | def clone_detach_and_grad(t): function rand_stride (line 16) | def rand_stride(t): function rand_coo (line 26) | def rand_coo(shape, nnz, dev, nz_dim=None): function rand_csr (line 42) | def rand_csr(shape, nnz, dev, nz_dim=None): function rand_csc (line 64) | def rand_csc(shape, nnz, dev, nz_dim=None): function rand_diag (line 86) | def rand_diag(shape, nnz, dev, nz_dim=None): function rand_coo_uncoalesced (line 95) | def rand_coo_uncoalesced(shape, nnz, dev): function rand_csr_uncoalesced (line 105) | def rand_csr_uncoalesced(shape, nnz, dev): function rand_csc_uncoalesced (line 122) | def rand_csc_uncoalesced(shape, nnz, dev): function sparse_matrix_to_dense (line 139) | def sparse_matrix_to_dense(A: SparseMatrix): function sparse_matrix_to_torch_sparse (line 144) | def sparse_matrix_to_torch_sparse(A: SparseMatrix, val=None): function dense_mask (line 158) | def dense_mask(dense, sparse): FILE: tests/python/pytorch/test_ffi-stream.py function _get_cycles_per_ms (line 17) | def _get_cycles_per_ms() -> float: function test_basics (line 45) | def test_basics(): function test_set_get_stream (line 69) | def test_set_get_stream(): function test_record_stream_ndarray (line 86) | def test_record_stream_ndarray(): function test_record_stream_graph_positive (line 128) | def test_record_stream_graph_positive(): function test_record_stream_graph_negative (line 163) | def test_record_stream_graph_negative(): FILE: tests/python/pytorch/test_heterograph-pickle.py function _reconstruct_pickle (line 10) | def _reconstruct_pickle(obj): function test_pickling_batched_graph (line 19) | def test_pickling_batched_graph(): FILE: tests/python/pytorch/test_multiprocessing-ipc.py function sub_ipc (line 10) | def sub_ipc(g): function test_torch_ipc (line 16) | def test_torch_ipc(): FILE: tests/python/pytorch/utils/test_pin_memory.py function test_pin_noncontiguous (line 11) | def test_pin_noncontiguous(): function test_pin_view (line 24) | def test_pin_view(): function test_unpin_automatically (line 42) | def test_unpin_automatically(): function test_pin_unpin_column (line 59) | def test_pin_unpin_column(): function test_pin_empty (line 81) | def test_pin_empty(): FILE: tests/python/tensorflow/test_basic.py function test (line 1) | def test(): FILE: tests/python/tensorflow/test_nn.py function _AXWb (line 23) | def _AXWb(A, X, W, b): function test_graph_conv (line 30) | def test_graph_conv(out_dim): function test_graph_conv2 (line 95) | def test_graph_conv2(idtype, g, norm, weight, bias, out_dim): function test_graph_conv2_bi (line 118) | def test_graph_conv2_bi(idtype, g, norm, weight, bias, out_dim): function test_simple_pool (line 133) | def test_simple_pool(): function test_glob_att_pool (line 201) | def test_glob_att_pool(): function test_rgcn (line 220) | def test_rgcn(O): function test_gat_conv (line 308) | def test_gat_conv(g, idtype, out_dim, num_heads): function test_gat_conv_bi (line 327) | def test_gat_conv_bi(g, idtype, out_dim, num_heads): function test_sage_conv (line 345) | def test_sage_conv(idtype, g, aggre_type, out_dim): function test_sage_conv_bi (line 357) | def test_sage_conv_bi(idtype, g, aggre_type, out_dim): function test_sage_conv_bi_empty (line 373) | def test_sage_conv_bi_empty(idtype, aggre_type, out_dim): function test_sgc_conv (line 395) | def test_sgc_conv(g, idtype, out_dim): function test_appnp_conv (line 415) | def test_appnp_conv(g, idtype): function test_gin_conv (line 428) | def test_gin_conv(g, idtype, aggregator_type): function test_gin_conv_bi (line 440) | def test_gin_conv_bi(g, idtype, aggregator_type): function test_edge_conv (line 456) | def test_edge_conv(g, idtype, out_dim): function test_edge_conv_bi (line 468) | def test_edge_conv_bi(g, idtype, out_dim): function myagg (line 479) | def myagg(alist, dsttype): function test_hetero_conv (line 488) | def test_hetero_conv(agg, idtype): function test_dense_cheb_conv (line 604) | def test_dense_cheb_conv(out_dim): FILE: tests/python/test_dgl_import.py function test_graphbolt_is_not_imported (line 4) | def test_graphbolt_is_not_imported(): FILE: tests/scripts/ci_report/report.py class JobStatus (line 12) | class JobStatus(enum.Enum): function get_jenkins_json (line 40) | def get_jenkins_json(path): function get_node_full_name (line 54) | def get_node_full_name(node, node_dict): function test_generate_report (line 87) | def test_generate_report(test_name): FILE: tests/tools/pytest_utils.py function _chunk_numpy_array (line 13) | def _chunk_numpy_array(arr, fmt_meta, chunk_sizes, path_fmt, vector_rows... function _initialize_num_chunks (line 38) | def _initialize_num_chunks(g, num_chunks, kwargs=None): function _chunk_graph (line 89) | def _chunk_graph( function chunk_graph (line 279) | def chunk_graph( function create_chunked_dataset (line 342) | def create_chunked_dataset( FILE: tests/tools/test_array_readwriter.py function test_array_readwriter (line 13) | def test_array_readwriter(format, shape): FILE: tests/tools/test_change_etype_to_canonical_etype.py function create_random_hetero (line 15) | def create_random_hetero(type_n, node_n): function test_hetero_graph (line 45) | def test_hetero_graph(type_n, node_n, num_parts): function test_homo_graph (line 52) | def test_homo_graph(node_n, num_parts): function do_convert_and_check (line 57) | def do_convert_and_check(g, graph_name, num_parts, expected_c_etypes): function _get_old_config (line 74) | def _get_old_config(part_config): function _extract_etypes (line 86) | def _extract_etypes(c_etypes): function _extract_edge_map (line 94) | def _extract_edge_map(c_edge_map): FILE: tests/tools/test_convert_partition.py function test_get_unique_invidx_with_numpy (line 26) | def test_get_unique_invidx_with_numpy(num_nodes, num_edges, nid_begin, n... function test_get_unique_invidx (line 105) | def test_get_unique_invidx(num_nodes, num_edges, nid_begin, nid_end): function test_get_unique_invidx_low_mem (line 157) | def test_get_unique_invidx_low_mem(): function test_get_unique_invidx_high_mem (line 190) | def test_get_unique_invidx_high_mem(): function test_get_unique_invidx_low_high_mem (line 223) | def test_get_unique_invidx_low_high_mem(): FILE: tests/tools/test_dist_lookup.py function _init_process_group (line 31) | def _init_process_group(rank, world_size): function _create_lookup_service (line 42) | def _create_lookup_service( function _run (line 56) | def _run( function _single_machine_run (line 94) | def _single_machine_run( function _prepare_test_data (line 122) | def _prepare_test_data(partitions_dir, ntypes, gid_ranges, world_size): function test_lookup_service (line 202) | def test_lookup_service( FILE: tests/tools/test_dist_part.py function _test_chunk_graph (line 33) | def _test_chunk_graph( function test_chunk_graph_basics (line 156) | def test_chunk_graph_basics(num_chunks, data_fmt, edges_fmt): function test_chunk_graph_vector_rows (line 162) | def test_chunk_graph_vector_rows(num_chunks, vector_rows): function test_chunk_graph_arbitrary_chunks (line 191) | def test_chunk_graph_arbitrary_chunks( function create_mini_chunked_dataset (line 207) | def create_mini_chunked_dataset( function _test_pipeline (line 304) | def _test_pipeline( function test_pipeline_basics (line 409) | def test_pipeline_basics(num_chunks, num_parts, world_size): function test_pipeline_formats (line 419) | def test_pipeline_formats(graph_formats): function test_pipeline_arbitrary_chunks (line 447) | def test_pipeline_arbitrary_chunks( function test_pipeline_formats (line 466) | def test_pipeline_formats(graph_formats): function test_pipeline_feature_format (line 471) | def test_pipeline_feature_format(data_fmt): function test_partition_hetero_few_entity (line 480) | def test_partition_hetero_few_entity( function test_utils_generate_read_list (line 567) | def test_utils_generate_read_list(): FILE: tests/tools/test_dist_partition_graphbolt.py function _verify_metadata_gb (line 28) | def _verify_metadata_gb(gpb, g, num_parts, part_id, part_sizes): function _verify_local_id_gb (line 48) | def _verify_local_id_gb(part_g, part_id, gpb): function _verify_map_gb (line 71) | def _verify_map_gb( function _verify_local_and_map_id_gb (line 113) | def _verify_local_and_map_id_gb( function _get_part_IDs (line 131) | def _get_part_IDs(part_g): function _verify_node_type_ID_gb (line 144) | def _verify_node_type_ID_gb(part_g, gpb): function _verify_orig_edge_IDs_gb (line 170) | def _verify_orig_edge_IDs_gb( function _verify_orig_IDs_gb (line 201) | def _verify_orig_IDs_gb( function _verify_constructed_id_gb (line 265) | def _verify_constructed_id_gb(part_sizes, gpb): function _verify_IDs_gb (line 286) | def _verify_IDs_gb( function _collect_data_gb (line 334) | def _collect_data_gb( function _verify_node_feats (line 362) | def _verify_node_feats(g, part, gpb, orig_nids, node_feats, is_homo=False): function _verify_edge_feats (line 396) | def _verify_edge_feats(g, part, gpb, orig_eids, edge_feats, is_homo=False): function _verify_shuffled_labels_gb (line 428) | def _verify_shuffled_labels_gb( function verify_graph_feats_gb (line 466) | def verify_graph_feats_gb( function _verify_graphbolt_attributes (line 522) | def _verify_graphbolt_attributes( function _verify_graphbolt_part (line 535) | def _verify_graphbolt_part( function _verify_hetero_graph_node_edge_num (line 641) | def _verify_hetero_graph_node_edge_num( function _verify_edge_id_range_hetero (line 696) | def _verify_edge_id_range_hetero( function _verify_node_id_range_hetero (line 733) | def _verify_node_id_range_hetero(g, part, nids): function _verify_graph_attributes_hetero (line 758) | def _verify_graph_attributes_hetero( function _verify_hetero_graph (line 795) | def _verify_hetero_graph( function _test_pipeline_graphbolt (line 818) | def _test_pipeline_graphbolt( function test_pipeline_basics (line 955) | def test_pipeline_basics(num_chunks, num_parts, world_size): function test_pipeline_attributes (line 969) | def test_pipeline_attributes(store_inner_node, store_inner_edge, store_e... function test_pipeline_arbitrary_chunks (line 1004) | def test_pipeline_arbitrary_chunks( function test_pipeline_feature_format (line 1022) | def test_pipeline_feature_format(data_fmt): FILE: tests/tools/test_launch.py class TestWrapUdfInTorchDistLauncher (line 9) | class TestWrapUdfInTorchDistLauncher(unittest.TestCase): method test_simple (line 12) | def test_simple(self): method test_chained_udf (line 30) | def test_chained_udf(self): method test_py_versions (line 50) | def test_py_versions(self): class TestWrapCmdWithLocalEnvvars (line 80) | class TestWrapCmdWithLocalEnvvars(unittest.TestCase): method test_simple (line 83) | def test_simple(self): class TestConstructDglServerEnvVars (line 90) | class TestConstructDglServerEnvVars(unittest.TestCase): method test_simple (line 93) | def test_simple(self): class TestConstructDglClientEnvVars (line 117) | class TestConstructDglClientEnvVars(unittest.TestCase): method test_simple (line 120) | def test_simple(self): function test_submit_jobs (line 175) | def test_submit_jobs(): FILE: tests/tools/test_parmetis.py function test_parmetis_preprocessing (line 23) | def test_parmetis_preprocessing(): function test_parmetis_postprocessing (line 117) | def test_parmetis_postprocessing(): function test_parmetis_wrapper (line 187) | def test_parmetis_wrapper(): FILE: tests/tools/test_parmetis_preproc.py function _read_file (line 16) | def _read_file(fname, fmt_name, fmt_delimiter): function _get_test_data (line 37) | def _get_test_data(edges_dir, num_chunks, edge_fmt, edge_fmt_del): function test_gen_edge_files (line 94) | def test_gen_edge_files(num_chunks, num_parts, edges_fmt, edges_delimiter): FILE: tests/utils/checks.py function check_fail (line 15) | def check_fail(fn, *args, **kwargs): function assert_is_identical (line 23) | def assert_is_identical(g, g2): function assert_is_identical_hetero (line 38) | def assert_is_identical_hetero(g, g2, ignore_internal_data=False): function check_graph_equal (line 78) | def check_graph_equal(g1, g2, *, check_idtype=True, check_feature=True): FILE: tests/utils/graph_cases.py function register_case (line 12) | def register_case(labels): function get_cases (line 22) | def get_cases(labels=None, exclude=[]): function bipartite1 (line 36) | def bipartite1(): function bipartite_full (line 43) | def bipartite_full(): function graph0 (line 55) | def graph0(): function bipartite1 (line 65) | def bipartite1(): function graph1 (line 70) | def graph1(): function graph1 (line 84) | def graph1(): function graph2 (line 98) | def graph2(): function graph3 (line 109) | def graph3(): function heterograph0 (line 121) | def heterograph0(): function batched_graph0 (line 148) | def batched_graph0(): function block_graph0 (line 156) | def block_graph0(): function block_graph1 (line 163) | def block_graph1(): function clique (line 176) | def clique(): function random_dglgraph (line 181) | def random_dglgraph(size): function random_graph (line 185) | def random_graph(size): function random_bipartite (line 189) | def random_bipartite(size_src, size_dst): function random_block (line 198) | def random_block(size): function two_hetero_batch (line 204) | def two_hetero_batch(): function two_hetero_batch_with_isolated_ntypes (line 223) | def two_hetero_batch_with_isolated_ntypes(): function batched_heterograph0 (line 244) | def batched_heterograph0(): FILE: third_party/HugeCTR/gpu_cache/include/gpu_cache_api.hpp type gpu_cache (line 22) | namespace gpu_cache { class gpu_cache_api (line 28) | class gpu_cache_api { FILE: third_party/HugeCTR/gpu_cache/include/nv_gpu_cache.hpp type gpu_cache (line 34) | namespace gpu_cache { type static_slab (line 38) | struct static_slab { type slab_set (line 44) | struct slab_set { class gpu_cache (line 54) | class gpu_cache : public gpu_cache_api { method Record (line 79) | void Record(cudaStream_t stream) override {} FILE: third_party/HugeCTR/gpu_cache/include/nv_util.h function namespace (line 28) | namespace nv { FILE: tools/change_etype_to_canonical_etype.py function convert_conf (line 21) | def convert_conf(part_config): function etype2canonical_etype (line 51) | def etype2canonical_etype(part_config, config): function _find_c_etypes_in_partition (line 91) | def _find_c_etypes_in_partition( function is_old_version (line 123) | def is_old_version(config): FILE: tools/chunk_graph.py function chunk_numpy_array (line 15) | def chunk_numpy_array(arr, fmt_meta, chunk_sizes, path_fmt): function _chunk_graph (line 30) | def _chunk_graph( function chunk_graph (line 160) | def chunk_graph( FILE: tools/copy_files.py function copy_file (line 13) | def copy_file(file_name, ip, workspace, param=""): function exec_cmd (line 19) | def exec_cmd(ip, cmd): function main (line 24) | def main(): function signal_handler (line 140) | def signal_handler(signal, frame): FILE: tools/dispatch_data.py function get_launch_cmd (line 28) | def get_launch_cmd(args) -> str: function submit_jobs (line 38) | def submit_jobs(args) -> str: function main (line 97) | def main(): FILE: tools/distgraphlaunch.py function cleanup_proc (line 20) | def cleanup_proc(get_all_remote_pids, conn): function kill_process (line 38) | def kill_process(ip, port, pids): function get_killed_pids (line 78) | def get_killed_pids(ip, port, killed_pids): function execute_remote (line 98) | def execute_remote( function get_remote_pids (line 137) | def get_remote_pids(ip, port, cmd_regex): function get_all_remote_pids (line 178) | def get_all_remote_pids(hosts, ssh_port, udf_command): function construct_torch_dist_launcher_cmd (line 192) | def construct_torch_dist_launcher_cmd( function wrap_udf_in_torch_dist_launcher (line 229) | def wrap_udf_in_torch_dist_launcher( function construct_dgl_server_env_vars (line 305) | def construct_dgl_server_env_vars( function wrap_cmd_with_local_envvars (line 339) | def wrap_cmd_with_local_envvars(cmd: str, env_vars: str) -> str: function wrap_cmd_with_extra_envvars (line 365) | def wrap_cmd_with_extra_envvars(cmd: str, env_vars: list) -> str: function submit_jobs (line 385) | def submit_jobs(args, udf_command): function main (line 445) | def main(): FILE: tools/distpartitioning/array_readwriter/csv.py class CSVArrayParser (line 11) | class CSVArrayParser(object): method __init__ (line 12) | def __init__(self, delimiter=","): method read (line 15) | def read(self, path): method write (line 29) | def write(self, path, arr): FILE: tools/distpartitioning/array_readwriter/numpy_array.py class NumpyArrayParser (line 10) | class NumpyArrayParser(object): method __init__ (line 11) | def __init__(self): method read (line 14) | def read(self, path): method write (line 20) | def write(self, path, arr): FILE: tools/distpartitioning/array_readwriter/parquet.py class ParquetArrayParser (line 12) | class ParquetArrayParser(object): method __init__ (line 13) | def __init__(self): method read (line 16) | def read(self, path): method write (line 47) | def write(self, path, array, vector_rows=False): FILE: tools/distpartitioning/array_readwriter/registry.py function register_array_parser (line 4) | def register_array_parser(name): function get_array_parser (line 12) | def get_array_parser(**fmt_meta): FILE: tools/distpartitioning/convert_partition.py function _get_unique_invidx (line 26) | def _get_unique_invidx(srcids, dstids, nids, low_mem=True): function _is_homogeneous (line 170) | def _is_homogeneous(ntypes, etypes): function _coo2csc (line 175) | def _coo2csc(src_ids, dst_ids): function _create_edge_data (line 186) | def _create_edge_data(edgeid_offset, etype_ids, num_edges): function _create_node_data (line 197) | def _create_node_data(ntype, uniq_ids, reshuffle_nodes, inner_nodes): function _compute_node_ntype (line 207) | def _compute_node_ntype( function _graph_orig_ids (line 217) | def _graph_orig_ids( function _create_edge_attr_gb (line 251) | def _create_edge_attr_gb( function _create_node_attr (line 277) | def _create_node_attr( function remove_attr_gb (line 307) | def remove_attr_gb( function _process_partition_gb (line 325) | def _process_partition_gb( function _update_node_map (line 359) | def _update_node_map(node_map_val, end_ids_per_rank, id_ntypes, prev_las... function create_graph_object (line 387) | def create_graph_object( function create_metadata_json (line 849) | def create_metadata_json( FILE: tools/distpartitioning/data_proc_pipeline.py function log_params (line 12) | def log_params(params): FILE: tools/distpartitioning/data_shuffle.py function gen_node_data (line 46) | def gen_node_data( function exchange_edge_data (line 178) | def exchange_edge_data(rank, world_size, num_parts, edge_data, id_lookup): function exchange_feature (line 331) | def exchange_feature( function exchange_features (line 568) | def exchange_features( function exchange_graph_data (line 714) | def exchange_graph_data( function read_dataset (line 847) | def read_dataset(rank, world_size, id_lookup, params, schema_map, ntype_... function reorder_data (line 935) | def reorder_data(num_parts, world_size, data, key): function gen_dist_partitions (line 970) | def gen_dist_partitions(rank, world_size, params): function single_machine_run (line 1443) | def single_machine_run(params): function run (line 1469) | def run(rank, world_size, func_exec, params, backend="gloo"): function multi_machine_run (line 1501) | def multi_machine_run(params): FILE: tools/distpartitioning/dataset_utils.py function _broadcast_shape (line 24) | def _broadcast_shape( function get_dataset (line 92) | def get_dataset( FILE: tools/distpartitioning/dist_lookup.py class DistLookupService (line 13) | class DistLookupService: method __init__ (line 51) | def __init__(self, input_dir, ntype_names, rank, world_size, num_parts): method set_idMap (line 126) | def set_idMap(self, id_map): method get_partition_ids (line 129) | def get_partition_ids(self, agg_global_nids): method get_shuffle_nids (line 336) | def get_shuffle_nids( FILE: tools/distpartitioning/globalids.py function get_shuffle_global_nids (line 13) | def get_shuffle_global_nids(rank, world_size, global_nids_ranks, node_da... function lookup_shuffle_global_nids_edges (line 75) | def lookup_shuffle_global_nids_edges( function assign_shuffle_global_nids_nodes (line 186) | def assign_shuffle_global_nids_nodes(rank, world_size, num_parts, node_d... function assign_shuffle_global_nids_edges (line 234) | def assign_shuffle_global_nids_edges(rank, world_size, num_parts, edge_d... FILE: tools/distpartitioning/gloo_wrapper.py function allgather_sizes (line 6) | def allgather_sizes(send_data, world_size, num_parts, return_sizes=False): function __alltoall_cpu (line 62) | def __alltoall_cpu(rank, world_size, output_tensor_list, input_tensor_li... function alltoallv_cpu (line 100) | def alltoallv_cpu(rank, world_size, input_tensor_list, retain_nones=True): function gather_metadata_json (line 201) | def gather_metadata_json(metadata, rank, world_size): FILE: tools/distpartitioning/parmetis_postprocess.py function post_process (line 17) | def post_process(params): FILE: tools/distpartitioning/parmetis_preprocess.py function get_proc_info (line 23) | def get_proc_info(): function get_world_size (line 47) | def get_world_size(): function gen_edge_files (line 67) | def gen_edge_files(rank, schema_map, params): function gen_node_weights_files (line 159) | def gen_node_weights_files(schema_map, params): function gen_parmetis_input_args (line 266) | def gen_parmetis_input_args(params, schema_map): function run_preprocess_data (line 381) | def run_preprocess_data(params): FILE: tools/distpartitioning/parmetis_wrapper.py function check_dependencies (line 12) | def check_dependencies(): function run_parmetis_wrapper (line 33) | def run_parmetis_wrapper(params): FILE: tools/distpartitioning/utils.py function read_ntype_partition_files (line 37) | def read_ntype_partition_files(schema_map, input_dir): function read_json (line 79) | def read_json(json_file): function get_etype_featnames (line 98) | def get_etype_featnames(etype_name, schema_map): function get_ntype_featnames (line 120) | def get_ntype_featnames(ntype_name, schema_map): function get_edge_types (line 143) | def get_edge_types(schema_map): function get_node_types (line 168) | def get_node_types(schema_map): function get_gid_offsets (line 194) | def get_gid_offsets(typenames, typecounts): function get_gnid_range_map (line 240) | def get_gnid_range_map(node_tids): function write_metadata_json (line 268) | def write_metadata_json( function augment_edge_data (line 349) | def augment_edge_data( function read_edges_file (line 406) | def read_edges_file(edge_file, edge_data_dict): function read_node_features_file (line 442) | def read_node_features_file(nodes_features_file): function read_edge_features_file (line 461) | def read_edge_features_file(edge_features_file): function write_node_features (line 479) | def write_node_features(node_features, node_file): function write_edge_features (line 493) | def write_edge_features(edge_features, edge_file): function write_graph_graghbolt (line 507) | def write_graph_graghbolt(graph_file, graph_obj): function write_graph_dgl (line 521) | def write_graph_dgl(graph_file, graph_obj, formats, sort_etypes): function _write_graph (line 542) | def _write_graph( function write_dgl_objects (line 555) | def write_dgl_objects( function get_idranges (line 620) | def get_idranges(names, counts, num_chunks=None): function get_ntype_counts_map (line 659) | def get_ntype_counts_map(ntypes, ntype_counts): function memory_snapshot (line 680) | def memory_snapshot(tag, rank): function map_partid_rank (line 706) | def map_partid_rank(partid, world_size): function generate_read_list (line 726) | def generate_read_list(num_files, world_size): function generate_roundrobin_read_list (line 756) | def generate_roundrobin_read_list(num_files, world_size): FILE: tools/files.py function setdir (line 9) | def setdir(path): FILE: tools/launch.py function cleanup_proc (line 18) | def cleanup_proc(get_all_remote_pids, conn): function kill_process (line 36) | def kill_process(ip, port, pids): function get_killed_pids (line 76) | def get_killed_pids(ip, port, killed_pids): function execute_remote (line 96) | def execute_remote( function get_remote_pids (line 155) | def get_remote_pids(ip, port, cmd_regex): function get_all_remote_pids (line 196) | def get_all_remote_pids(hosts, ssh_port, udf_command): function construct_torch_dist_launcher_cmd (line 210) | def construct_torch_dist_launcher_cmd( function wrap_udf_in_torch_dist_launcher (line 247) | def wrap_udf_in_torch_dist_launcher( function construct_dgl_server_env_vars (line 323) | def construct_dgl_server_env_vars( function construct_dgl_client_env_vars (line 380) | def construct_dgl_client_env_vars( function wrap_cmd_with_local_envvars (line 445) | def wrap_cmd_with_local_envvars(cmd: str, env_vars: str) -> str: function wrap_cmd_with_extra_envvars (line 471) | def wrap_cmd_with_extra_envvars(cmd: str, env_vars: list) -> str: function get_available_port (line 491) | def get_available_port(ip): function submit_jobs (line 504) | def submit_jobs(args, udf_command, dry_run=False): function main (line 657) | def main(): FILE: tools/partition_algo/base.py class PartitionMeta (line 8) | class PartitionMeta(dt.BaseModel): function dump_partition_meta (line 36) | def dump_partition_meta(part_meta, meta_file): function load_partition_meta (line 50) | def load_partition_meta(meta_file): FILE: tools/partition_algo/random_partition.py function _random_partition (line 13) | def _random_partition(metadata, num_parts): function random_partition (line 24) | def random_partition(metadata, num_parts, output_path): FILE: tools/verification_utils.py function read_file (line 24) | def read_file(fname, ftype): function verify_partition_data_types (line 44) | def verify_partition_data_types(part_g): function verify_partition_formats (line 59) | def verify_partition_formats(part_g, formats): function verify_graph_feats (line 79) | def verify_graph_feats( function verify_metadata_counts (line 143) | def verify_metadata_counts(part_schema, part_g, graph_schema, g, partid): function get_node_partids (line 182) | def get_node_partids(partitions_dir, graph_schema): function verify_node_partitionids (line 224) | def verify_node_partitionids( function read_orig_ids (line 266) | def read_orig_ids(out_dir, fname, num_parts): FILE: tutorials/blitz/1_introduction.py class GCN (line 119) | class GCN(nn.Module): method __init__ (line 120) | def __init__(self, in_feats, h_feats, num_classes): method forward (line 125) | def forward(self, g, in_feat): function train (line 150) | def train(g, model): FILE: tutorials/blitz/3_message_passing.py class SAGEConv (line 87) | class SAGEConv(nn.Module): method __init__ (line 98) | def __init__(self, in_feat, out_feat): method forward (line 103) | def forward(self, g, h): class Model (line 150) | class Model(nn.Module): method __init__ (line 151) | def __init__(self, in_feats, h_feats, num_classes): method forward (line 156) | def forward(self, g, in_feat): method __init__ (line 294) | def __init__(self, in_feats, h_feats, num_classes): method forward (line 299) | def forward(self, g, in_feat): function train (line 176) | def train(g, model): class WeightedSAGEConv (line 246) | class WeightedSAGEConv(nn.Module): method __init__ (line 257) | def __init__(self, in_feat, out_feat): method forward (line 262) | def forward(self, g, h, w): class Model (line 293) | class Model(nn.Module): method __init__ (line 151) | def __init__(self, in_feats, h_feats, num_classes): method forward (line 156) | def forward(self, g, in_feat): method __init__ (line 294) | def __init__(self, in_feats, h_feats, num_classes): method forward (line 299) | def forward(self, g, in_feat): function u_mul_e_udf (line 320) | def u_mul_e_udf(edges): function mean_udf (line 338) | def mean_udf(nodes): FILE: tutorials/blitz/4_link_predict.py class GraphSAGE (line 144) | class GraphSAGE(nn.Module): method __init__ (line 145) | def __init__(self, in_feats, h_feats): method forward (line 150) | def forward(self, g, in_feat): class DotPredictor (line 215) | class DotPredictor(nn.Module): method forward (line 216) | def forward(self, g, h): class MLPPredictor (line 233) | class MLPPredictor(nn.Module): method __init__ (line 234) | def __init__(self, h_feats): method apply_edges (line 239) | def apply_edges(self, edges): method forward (line 259) | def forward(self, g, h): function compute_loss (line 305) | def compute_loss(pos_score, neg_score): function compute_auc (line 313) | def compute_auc(pos_score, neg_score): FILE: tutorials/blitz/5_graph_classification.py class GCN (line 167) | class GCN(nn.Module): method __init__ (line 168) | def __init__(self, in_feats, h_feats, num_classes): method forward (line 173) | def forward(self, g, in_feat): FILE: tutorials/models/1_gnn/1_gcn.py class GCNLayer (line 72) | class GCNLayer(nn.Module): method __init__ (line 73) | def __init__(self, in_feats, out_feats): method forward (line 77) | def forward(self, g, feature): class Net (line 97) | class Net(nn.Module): method __init__ (line 98) | def __init__(self): method forward (line 103) | def forward(self, g, features): function load_cora_data (line 118) | def load_cora_data(): function evaluate (line 133) | def evaluate(model, g, features, labels, mask): FILE: tutorials/models/3_generative_model/5_dgmg.py function forward_inference (line 121) | def forward_inference(self): function forward_train (line 209) | def forward_train(self, actions): class DGMGSkeleton (line 250) | class DGMGSkeleton(nn.Module): method __init__ (line 251) | def __init__(self, v_max): method add_node_and_update (line 263) | def add_node_and_update(self, a=None): method add_edge_or_not (line 268) | def add_edge_or_not(self, a=None): method choose_dest_and_update (line 272) | def choose_dest_and_update(self, a=None): method forward_train (line 277) | def forward_train(self, actions): method forward_inference (line 282) | def forward_inference(self): method forward (line 287) | def forward(self, actions=None): class GraphEmbed (line 336) | class GraphEmbed(nn.Module): method __init__ (line 337) | def __init__(self, node_hidden_size): method forward (line 349) | def forward(self, g): class GraphProp (line 403) | class GraphProp(nn.Module): method __init__ (line 404) | def __init__(self, num_prop_rounds, node_hidden_size): method dgmg_msg (line 431) | def dgmg_msg(self, edges): method dgmg_reduce (line 435) | def dgmg_reduce(self, nodes, round): method forward (line 445) | def forward(self, g): function bernoulli_action_log_prob (line 488) | def bernoulli_action_log_prob(logit, action): class AddNode (line 497) | class AddNode(nn.Module): method __init__ (line 498) | def __init__(self, graph_embed_func, node_hidden_size): method _initialize_node_repr (line 515) | def _initialize_node_repr(self, g, node_type, graph_embed): method prepare_training (line 530) | def prepare_training(self): method forward (line 533) | def forward(self, g, action=None): class AddEdge (line 570) | class AddEdge(nn.Module): method __init__ (line 571) | def __init__(self, graph_embed_func, node_hidden_size): method prepare_training (line 579) | def prepare_training(self): method forward (line 582) | def forward(self, g, action=None): class ChooseDestAndUpdate (line 616) | class ChooseDestAndUpdate(nn.Module): method __init__ (line 617) | def __init__(self, graph_prop_func, node_hidden_size): method _initialize_edge_repr (line 623) | def _initialize_edge_repr(self, g, src_list, dest_list): method prepare_training (line 630) | def prepare_training(self): method forward (line 633) | def forward(self, g, dest): class DGMG (line 672) | class DGMG(DGMGSkeleton): method __init__ (line 673) | def __init__(self, v_max, node_hidden_size, num_prop_rounds): method action_step (line 694) | def action_step(self): method prepare_for_train (line 700) | def prepare_for_train(self): method add_node_and_update (line 707) | def add_node_and_update(self, a=None): method add_edge_or_not (line 713) | def add_edge_or_not(self, a=None): method choose_dest_and_update (line 718) | def choose_dest_and_update(self, a=None): method get_log_prob (line 724) | def get_log_prob(self): function is_valid (line 753) | def is_valid(g):