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Functions303 in github.com/GATECH-EIC/GCoD

⨍ Functions 303◇ Types & classes 42
↓ 1 callersFunctionplot_optimzed_adj
(checkpoint, bd, n_class, n_groups)
plot_adj.py:58
↓ 1 callersFunctionplot_raw_adj
(data)
plot_adj.py:42
↓ 1 callersFunctionremove_chunks
(edge_index, adj_size, n_subgraphs, pre_subgraph_nnz)
tune_sage.py:301
↓ 1 callersFunctionremove_chunks
(edge_index, adj_size, n_subgraphs, pre_subgraph_nnz)
tune.py:328
↓ 1 callersMethodreset_parameters
(self)
models/gat_conv.py:162
↓ 1 callersMethodreset_parameters
(self)
models/sage_conv.py:137
↓ 1 callersMethodreset_parameters
(self)
models/global_gat_conv.py:102
↓ 1 callersMethodreset_parameters
(self)
models/gcn_conv.py:146
↓ 1 callersMethodreset_parameters
(self)
models/gin_conv.py:257
↓ 1 callersMethodreset_params
(self)
models/quantize.py:356
↓ 1 callersFunctionretrain
(model, data)
SGCN/pytorch_retrain_with_graph.py:48
↓ 1 callersFunctionsparse_adj_from_weights
(data, model, device)
network.py:646
↓ 1 callersFunctionsparse_adj_from_weights
(data, model, device)
models/network.py:645
↓ 1 callersFunctiontest
(model, data)
train.py:301
↓ 1 callersFunctiontest
(model, data)
train_reddit.py:282
↓ 1 callersFunctiontest
(model, data, x, y, batch_size)
train_sage_reddit.py:247
↓ 1 callersFunctiontest
(model, data, x, y, batch_size)
train_sage.py:287
↓ 1 callersFunctiontrain
(model, optimizer, data)
train.py:291
↓ 1 callersFunctiontrain
(model, epoch, optimizer, x, y, support1, U1, Z1, support2, U2, Z2, batch_size=64, adjOpt=False, retrain=False
tune_sage.py:629
↓ 1 callersFunctiontrain
(model, optimizer, data)
train_reddit.py:272
↓ 1 callersFunctiontrain
(model, data, epoch, optimizer, x, y)
train_sage_reddit.py:211
↓ 1 callersFunctiontrain
(model, data, epoch, optimizer, x, y)
train_sage.py:251
↓ 1 callersFunctiontrain
(model,data)
SGCN/pytorch_train.py:69
↓ 1 callersFunctionupdate_gradients_adj
(grads_vars, adj_mask)
tune_sage.py:133
↓ 1 callersFunctionupdate_gradients_adj
(grads_vars, adj_mask)
tune.py:160
↓ 1 callersFunctionupdate_gradients_adj
(grads_vars ,adj_p_mask:np.ndarray)
SGCN/pytorch_tune.py:81
Method__call__
(self, data)
datasets.py:14
Method__collate__
(self, node_idx)
sampler.py:227
Method__collate__
(self, node_idx)
models/sampler.py:227
Method__getitem__
(self, idx)
sampler.py:224
Method__getitem__
(self, idx)
models/sampler.py:224
Method__init__
(self, in_features, out_features, bias=True, chunk_q=False)
network.py:27
Method__init__
(self, dataset, data, hidden_unit, heads, dropout=0.5, adj=(), device='cpu', quant=False, num_
network.py:268
Method__init__
(self, dataset, data, num_layers, hidden, adj=(), device='cpu', quant=False, num_act_bits=None
network.py:435
Method__init__
(self, in_channels, hidden_channels, out_channels, data, device='cpu', quant=False, num_act_bi
network.py:655
Method__init__
(self, num_features, num_classes)
train.py:58
Method__init__
(self, mean, std)
datasets.py:10
Method__init__
(self, num_features, num_classes)
tune.py:62
Method__init__
(self, num_features, num_classes)
train_reddit.py:58
Method__init__
(self, num_nodes: int, num_parts: int, shuffle: bool = False)
sampler.py:168
Method__init__
(self, data, num_parts: int, shuffle: bool = False, **kwargs)
sampler.py:205
Method__init__
(self, num_features, num_classes)
train_sage_reddit.py:58
Method__init__
(self, num_features, num_classes)
train_sage.py:58
Method__init__
(self, in_features, out_features, bias=True, chunk_q=False)
models/gat_conv.py:20
Method__init__
(self, in_features, out_features, bias=True, chunk_q=False)
models/network.py:27
Method__init__
(self, dataset, data, hidden_unit, heads, dropout=0.5, adj=(), device='cpu', quant=False, num_
models/network.py:270
Method__init__
(self, dataset, data, num_layers, hidden, adj=(), device='cpu', quant=False, num_act_bits=None
models/network.py:437
Method__init__
(self, in_channels, hidden_channels, out_channels, data, device='cpu', quant=False, num_act_bi
models/network.py:654
Method__init__
(self, in_features, out_features, bias=True, chunk_q=False)
models/sage_conv.py:37
Method__init__
(self, in_channels: Union[int, Tuple[int, int]], out_channels: int, heads: int = 1, concat: b
models/global_gat_conv.py:62
Method__init__
(self, in_channels: int, out_channels: int, improved: bool = False, cached: bool = False,
models/gcn_conv.py:107
Method__init__
(self, shape_measure=(1,), flatten_dims=_DEFAULT_FLATTEN, inplace=False, dequantize=True, sto
models/quantize.py:164
Method__init__
(self, in_features, out_features, bias=True, num_bits=8, num_bits_weight=8, num_bits_grad=8, biprecision=False
models/quantize.py:305
Method__init__
(self, num_features, dim=1, momentum=0.1, affine=True, num_chunks=16, eps=1e-5, num_bits=8, num_bits_grad=8)
models/quantize.py:339
Method__init__
(self, num_features, dim=1, momentum=0.1, affine=True, num_chunks=16, eps=1e-5, num_bits=8, num_bits_grad=8)
models/quantize.py:415
Method__init__
(self, nn: Callable, eps: float = 0., train_eps: bool = False, **kwargs)
models/gin_conv.py:244
Method__init__
(self, num_nodes: int, num_parts: int, shuffle: bool = False)
models/sampler.py:168
Method__init__
(self, data, num_parts: int, shuffle: bool = False, **kwargs)
models/sampler.py:205
Method__init__
(self, dataset, data, args, adj=())
SGCN/pytorch_train.py:43
Method__iter__
(self)
sampler.py:180
Method__iter__
(self)
models/sampler.py:180
Method__len__
(self)
sampler.py:185
Method__len__
(self)
models/sampler.py:185
Method__repr__
(self)
network.py:642
Method__repr__
(self)
sampler.py:163
Method__repr__
(self)
models/gat_conv.py:340
Method__repr__
(self)
models/network.py:641
Method__repr__
(self)
models/sage_conv.py:216
Method__repr__
(self)
models/global_gat_conv.py:253
Method__repr__
(self)
models/gcn_conv.py:269
Method__repr__
(self)
models/gin_conv.py:218
Method__repr__
(self)
models/gin_conv.py:286
Method__repr__
(self)
models/sampler.py:163
Functionadj_to_edge
(adj:torch.Tensor)
network.py:79
Functionadj_to_edge
(adj:torch.Tensor)
models/network.py:79
Functionall_adj_mask
(adj)
utils.py:177
Functionall_adj_mask
(adj)
models/utils.py:177
Functionall_adj_mask
(adj)
SGCN/utils.py:177
Methodbackward
(ctx, grad_output)
models/quantize.py:109
Functioncalc_dist
(m1,m2)
tune_sage.py:209
Functioncalc_dist
(m1,m2)
tune.py:236
Functionchebyshev_polynomials
Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).
utils.py:149
Functionchebyshev_polynomials
Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).
models/utils.py:149
Functionchebyshev_polynomials
Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).
SGCN/utils.py:149
Functionconstruct_feed_dict
Construct feed dictionary.
utils.py:139
Functionconstruct_feed_dict
Construct feed dictionary.
models/utils.py:139
Functionconstruct_feed_dict
Construct feed dictionary.
SGCN/utils.py:139
Functionconv2d_biprec
(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1, num_bits_grad=None)
models/quantize.py:123
Functionconvertoadj
(admm_adj)
utils.py:285
Functionconvertoadj
(admm_adj)
models/utils.py:285
Functionconvertoadj
(admm_adj)
SGCN/utils.py:285
Functionedge_to_adj
(edge_index, edge_attr=None,num_nodes=None)
network.py:65
Functionedge_to_adj
(edge_index, edge_attr=None,num_nodes=None)
models/network.py:65
Methodforward
(self, input, num_act_bits=None, num_wei_bits=None, act_quant_bits=None, n_classes=None)
network.py:38
Methodforward
(self, return_time=False)
network.py:240
Methodforward
(self, return_time=False)
network.py:405
Methodforward
(self, return_time=False)
network.py:609
Methodforward
(self, x, adjs, n_id)
network.py:785
Methodforward
(self, input, num_act_bits=None, num_wei_bits=None, act_quant_bits=None, n_classes=None)
models/gat_conv.py:31
Methodforward
r""" Args: return_attention_weights (bool, optional): If set to :obj:`True`, will additionally return the tuple
models/gat_conv.py:172
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