def __init__(self,
d_in,
d_degree,
degree_as_tag,
retain_features,
d_msg,
d_up,
d_h,
seed,
activation_name,
bn,
aggr='add',
msg_kind='general',
eps=0,
train_eps=False,
flow='source_to_target',
**kwargs):
super(MPNN_sparse, self).__init__()
d_msg = d_in if d_msg is None else d_msg
self.flow = flow
self.aggr = aggr
self.msg_kind = msg_kind
self.degree_as_tag = degree_as_tag
self.retain_features = retain_features
if degree_as_tag:
d_in = d_in + d_degree if retain_features else d_degree
if msg_kind == 'gin':
msg_input_dim = None
self.initial_eps = eps
if train_eps:
self.eps = torch.nn.Parameter(torch.Tensor([eps]))
else:
self.register_buffer('eps', torch.Tensor([eps]))
self.eps.data.fill_(self.initial_eps)
self.msg_fn = None
update_input_dim = d_in
elif msg_kind == 'general':
msg_input_dim = 2 * d_in
self.msg_fn = mlp(msg_input_dim, d_msg, d_h, seed, activation_name,
bn)
update_input_dim = d_in + d_msg
self.update_fn = mlp(update_input_dim, d_up, d_h, seed,
activation_name, bn)
return
def __init__(self,
d_in,
d_ef,
d_id,
d_degree,
degree_as_tag,
retain_features,
id_scope,
d_msg,
d_up,
d_h,
seed,
activation_name,
bn,
aggr='add',
msg_kind='ogb',
eps=0,
train_eps=False,
flow='source_to_target',
**kwargs):
super(GSN_edge_sparse_ogb, self).__init__()
self.flow = flow
self.aggr = aggr
self.msg_kind = msg_kind
self.id_scope = id_scope
self.degree_as_tag = degree_as_tag
self.retain_features = retain_features
if degree_as_tag:
d_in = d_in + d_degree if retain_features else d_degree
# INPUT_DIMS
if msg_kind == 'ogb':
self.initial_eps = eps
if train_eps:
self.eps = torch.nn.Parameter(torch.Tensor([eps]))
else:
self.register_buffer('eps', torch.Tensor([eps]))
self.eps.data.fill_(self.initial_eps)
update_input_dim = d_in
else:
raise NotImplementedError('msg kind {} is not currently supported.'.format(msg_kind))
self.update_fn = mlp(update_input_dim, d_up, d_h, seed, activation_name, bn)
return
def __init__(self,
d_in,
d_ef,
d_id,
d_degree,
degree_as_tag,
retain_features,
id_scope,
d_msg,
d_up,
d_h,
seed,
activation_name,
bn,
aggr='add',
msg_kind='general',
eps=0,
train_eps=False,
flow='source_to_target',
**kwargs):
super(GSN_edge_sparse, self).__init__()
d_msg = d_in if d_msg is None else d_msg
self.flow = flow
self.aggr = aggr
self.msg_kind = msg_kind
self.id_scope = id_scope
self.degree_as_tag = degree_as_tag
self.retain_features = retain_features
if degree_as_tag:
d_in = d_in + d_degree if retain_features else d_degree
# INPUT_DIMS
if msg_kind == 'gin':
# dummy variable for self loop edge features
self.central_node_edge_encoder = central_encoder(
kwargs['edge_embedding'], d_ef, extend=kwargs['extend_dims'])
d_ef = self.central_node_edge_encoder.d_out
if self.id_scope == 'local':
# dummy variable for self loop edge counts
self.central_node_id_encoder = central_encoder(
kwargs['id_embedding'], d_id, extend=kwargs['extend_dims'])
d_id = self.central_node_id_encoder.d_out
msg_input_dim = None
self.initial_eps = eps
if train_eps:
self.eps = torch.nn.Parameter(torch.Tensor([eps]))
else:
self.register_buffer('eps', torch.Tensor([eps]))
self.eps.data.fill_(self.initial_eps)
self.msg_fn = None
update_input_dim = d_in + d_id + d_ef
elif msg_kind == 'general':
msg_input_dim = 2 * d_in + d_id + d_ef if id_scope == 'local' else 2 * (
d_in + d_id) + d_ef
# MSG_FUN
self.msg_fn = mlp(msg_input_dim, d_msg, d_h, seed, activation_name,
bn)
update_input_dim = d_in + d_msg
else:
raise NotImplementedError(
'msg kind {} is not currently supported.'.format(msg_kind))
self.update_fn = mlp(update_input_dim, d_up, d_h, seed,
activation_name, bn)
return
def __init__(self,
in_features,
out_features,
encoder_ids,
d_in_id,
in_edge_features=None,
d_in_node_encoder=None,
d_in_edge_encoder=None,
encoder_degrees=None,
d_degree=None,
**kwargs):
super(GNN_OGB, self).__init__()
seed = kwargs['seed']
#-------------- Initializations
self.model_name = kwargs['model_name']
self.readout = kwargs['readout'] if kwargs[
'readout'] is not None else 'sum'
self.dropout_features = kwargs['dropout_features']
self.bn = kwargs['bn']
self.final_projection = kwargs['final_projection']
self.residual = kwargs['residual']
self.inject_ids = kwargs['inject_ids']
self.vn = kwargs['vn']
id_scope = kwargs['id_scope']
d_msg = kwargs['d_msg']
d_out = kwargs['d_out']
d_h = kwargs['d_h']
aggr = kwargs['aggr'] if kwargs['aggr'] is not None else 'add'
flow = kwargs['flow'] if kwargs[
'flow'] is not None else 'target_to_source'
msg_kind = kwargs['msg_kind'] if kwargs[
'msg_kind'] is not None else 'general'
train_eps = kwargs['train_eps'] if kwargs[
'train_eps'] is not None else [False for _ in range(len(d_out))]
activation_mlp = kwargs['activation_mlp']
bn_mlp = kwargs['bn_mlp']
jk_mlp = kwargs['jk_mlp']
degree_embedding = kwargs['degree_embedding'] if kwargs[
'degree_as_tag'][0] else 'None'
degree_as_tag = kwargs['degree_as_tag']
retain_features = kwargs['retain_features']
encoders_kwargs = {
'seed': seed,
'activation_mlp': activation_mlp,
'bn_mlp': bn_mlp,
'aggr': kwargs['multi_embedding_aggr'],
'features_scope': kwargs['features_scope']
}
#-------------- Input node embedding
self.input_node_encoder = DiscreteEmbedding(
kwargs['input_node_encoder'], in_features, d_in_node_encoder,
kwargs['d_out_node_encoder'], **encoders_kwargs)
d_in = self.input_node_encoder.d_out
#-------------- Virtual node embedding
if self.vn:
vn_encoder_kwargs = copy.deepcopy(encoders_kwargs)
vn_encoder_kwargs['init'] = 'zeros'
self.vn_encoder = DiscreteEmbedding(kwargs['input_vn_encoder'], 1,
[1],
kwargs['d_out_vn_encoder'],
**vn_encoder_kwargs)
d_in_vn = self.vn_encoder.d_out
#-------------- Edge embedding (for each GNN layer)
self.edge_encoder = []
d_ef = []
for i in range(len(d_out)):
edge_encoder_layer = DiscreteEmbedding(
kwargs['edge_encoder'], in_edge_features, d_in_edge_encoder,
kwargs['d_out_edge_encoder'][i], **encoders_kwargs)
self.edge_encoder.append(edge_encoder_layer)
d_ef.append(edge_encoder_layer.d_out)
self.edge_encoder = nn.ModuleList(self.edge_encoder)
# -------------- Identifier embedding (for each GNN layer)
self.id_encoder = []
d_id = []
num_id_encoders = len(d_out) if kwargs['inject_ids'] else 1
for i in range(num_id_encoders):
id_encoder_layer = DiscreteEmbedding(kwargs['id_embedding'],
len(d_in_id), d_in_id,
kwargs['d_out_id_embedding'],
**encoders_kwargs)
self.id_encoder.append(id_encoder_layer)
d_id.append(id_encoder_layer.d_out)
self.id_encoder = nn.ModuleList(self.id_encoder)
#-------------- Degree embedding
self.degree_encoder = DiscreteEmbedding(
degree_embedding, 1, d_degree, kwargs['d_out_degree_embedding'],
**encoders_kwargs)
d_degree = self.degree_encoder.d_out
#-------------- GNN layers w/ bn
self.conv = []
self.batch_norms = []
self.mlp_vn = []
for i in range(len(d_out)):
if i > 0 and self.vn:
#-------------- vn msg function
mlp_vn_temp = mlp(d_in_vn, kwargs['d_out_vn'][i - 1], d_h[i],
seed, activation_mlp, bn_mlp)
self.mlp_vn.append(mlp_vn_temp)
d_in_vn = kwargs['d_out_vn'][i - 1]
kwargs_filter = {
'd_in': d_in,
'd_degree': d_degree,
'degree_as_tag': degree_as_tag[i],
'retain_features': retain_features[i],
'd_msg': d_msg[i],
'd_up': d_out[i],
'd_h': d_h[i],
'seed': seed,
'activation_name': activation_mlp,
'bn': bn_mlp,
'aggr': aggr,
'msg_kind': msg_kind,
'eps': 0,
'train_eps': train_eps[i],
'flow': flow,
'd_ef': d_ef[i],
'edge_embedding': kwargs['edge_encoder'],
'id_embedding': kwargs['id_embedding'],
'extend_dims': kwargs['extend_dims']
}
use_ids = ((i > 0 and kwargs['inject_ids']) or
(i == 0)) and (self.model_name == 'GSN_edge_sparse_ogb')
if use_ids:
filter_fn = GSN_edge_sparse_ogb
kwargs_filter['d_id'] = d_id[i] if self.inject_ids else d_id[0]
kwargs_filter['id_scope'] = id_scope
else:
filter_fn = MPNN_edge_sparse_ogb
self.conv.append(filter_fn(**kwargs_filter))
bn_layer = nn.BatchNorm1d(d_out[i]) if self.bn[i] else None
self.batch_norms.append(bn_layer)
d_in = d_out[i]
self.conv = nn.ModuleList(self.conv)
self.batch_norms = nn.ModuleList(self.batch_norms)
if kwargs['vn']:
self.mlp_vn = nn.ModuleList(self.mlp_vn)
#-------------- Readout
if self.readout == 'sum':
self.global_pool = global_add_pool_sparse
elif self.readout == 'mean':
self.global_pool = global_mean_pool_sparse
else:
raise ValueError("Invalid graph pooling type.")
#-------------- Virtual node aggregation operator
if self.vn:
if kwargs['vn_pooling'] == 'sum':
self.global_vn_pool = global_add_pool_sparse
elif kwargs['vn_pooling'] == 'mean':
self.global_vn_pool = global_mean_pool_sparse
else:
raise ValueError("Invalid graph virtual node pooling type.")
self.lin_proj = nn.Linear(d_out[-1], out_features)
#-------------- Activation fn (same across the network)
self.activation = choose_activation(kwargs['activation'])
return
def __init__(self, encoder_name, d_in_features, d_in_encoder,
d_out_encoder, **kwargs):
super(DiscreteEmbedding, self).__init__()
#-------------- various different embedding layers
kwargs['init'] = None if 'init' not in kwargs else kwargs['init']
self.encoder_name = encoder_name
# d_in_features: input feature size (e.g. if already one hot encoded),
# d_in_encoder: number of unique values that will be encoded (size of embedding vocabulary)
#-------------- fill embedding with zeros
if encoder_name == 'zero_encoder':
self.encoder = zero_encoder(d_out_encoder)
d_out = d_out_encoder
#-------------- linear pojection
elif encoder_name == 'linear':
self.encoder = nn.Linear(d_in_features, d_out_encoder, bias=True)
d_out = d_out_encoder
#-------------- mlp
elif encoder_name == 'mlp':
self.encoder = mlp(d_in_features, d_out_encoder, d_out_encoder,
kwargs['seed'], kwargs['activation_mlp'],
kwargs['bn_mlp'])
d_out = d_out_encoder
#-------------- multi hot encoding of categorical data
elif encoder_name == 'one_hot_encoder':
self.encoder = one_hot_encoder(d_in_encoder)
d_out = sum(d_in_encoder)
#-------------- embedding of categorical data (linear projection without bias of one hot encodings)
elif encoder_name == 'embedding':
self.encoder = multi_embedding(d_in_encoder, d_out_encoder,
kwargs['aggr'], kwargs['init'])
if kwargs['aggr'] == 'concat':
d_out = len(d_in_encoder) * d_out_encoder
else:
d_out = d_out_encoder
#-------------- for ogb: multi hot encoding of node features
elif encoder_name == 'atom_one_hot_encoder':
full_atom_feature_dims = get_atom_feature_dims(
) if kwargs['features_scope'] == 'full' else get_atom_feature_dims(
)[:2]
self.encoder = one_hot_encoder(full_atom_feature_dims)
d_out = sum(full_atom_feature_dims)
#-------------- for ogb: multi hot encoding of edge features
elif encoder_name == 'bond_one_hot_encoder':
full_bond_feature_dims = get_bond_feature_dims(
) if kwargs['features_scope'] == 'full' else get_bond_feature_dims(
)[:2]
self.encoder = one_hot_encoder(full_bond_feature_dims)
d_out = sum(full_bond_feature_dims)
#-------------- for ogb: embedding of node features
elif encoder_name == 'atom_encoder':
self.encoder = AtomEncoder(d_out_encoder)
d_out = d_out_encoder
#-------------- for ogb: embedding of edge features
elif encoder_name == 'bond_encoder':
self.encoder = BondEncoder(emb_dim=d_out_encoder)
d_out = d_out_encoder
#-------------- no embedding, use as is
elif encoder_name == 'None':
self.encoder = None
d_out = d_in_features
else:
raise NotImplementedError(
'Encoder {} is not currently supported.'.format(encoder_name))
self.d_out = d_out
return
def __init__(self,
in_features,
out_features,
encoder_ids,
d_in_id,
in_edge_features=None,
d_in_node_encoder=None,
d_in_edge_encoder=None,
encoder_degrees=None,
d_degree=None,
**kwargs):
super(MLPSubstructures, self).__init__()
seed = kwargs['seed']
#-------------- Initializations
self.model_name = kwargs['model_name']
self.readout = kwargs['readout'] if kwargs[
'readout'] is not None else 'sum'
self.dropout_features = kwargs['dropout_features']
self.bn = kwargs['bn']
self.degree_as_tag = kwargs['degree_as_tag']
self.retain_features = kwargs['retain_features']
self.id_scope = kwargs['id_scope']
d_out = kwargs['d_out']
d_h = kwargs['d_h']
activation_mlp = kwargs['activation_mlp']
bn_mlp = kwargs['bn_mlp']
jk_mlp = kwargs['jk_mlp']
degree_embedding = kwargs['degree_embedding'] if kwargs[
'degree_as_tag'][0] else 'None'
encoders_kwargs = {
'seed': seed,
'activation_mlp': activation_mlp,
'bn_mlp': bn_mlp,
'aggr': kwargs['multi_embedding_aggr']
}
#-------------- Input node embedding
self.input_node_encoder = DiscreteEmbedding(
kwargs['input_node_encoder'], in_features, d_in_node_encoder,
kwargs['d_out_node_encoder'], **encoders_kwargs)
d_in = self.input_node_encoder.d_out
#-------------- Edge embedding (for each GNN layer)
self.edge_encoder = DiscreteEmbedding(kwargs['edge_encoder'],
in_edge_features,
d_in_edge_encoder,
kwargs['d_out_edge_encoder'][0],
**encoders_kwargs)
d_ef = self.edge_encoder.d_out
#-------------- Identifier embedding (for each GNN layer)
self.id_encoder = DiscreteEmbedding(kwargs['id_embedding'],
len(d_in_id), d_in_id,
kwargs['d_out_id_embedding'],
**encoders_kwargs)
d_id = self.id_encoder.d_out
#-------------- Degree embedding
self.degree_encoder = DiscreteEmbedding(
degree_embedding, 1, d_degree, kwargs['d_out_degree_embedding'],
**encoders_kwargs)
d_degree = self.degree_encoder.d_out
#-------------- edge-wise MLP w/ bn
if self.degree_as_tag[0] and self.retain_features[0] == True:
mlp_input_dim = 2 * (d_in + d_degree)
elif self.degree_as_tag[0] and self.retain_features[0] == False:
mlp_input_dim = 2 * d_degree
else:
mlp_input_dim = 2 * d_in
if self.id_scope == 'global':
mlp_input_dim += 2 * d_id
else:
mlp_input_dim += d_id
if kwargs['edge_encoder'] != 'None':
mlp_input_dim += d_ef
filter_fn = mlp(mlp_input_dim, d_out[0], d_h[0], seed, activation_mlp,
bn_mlp)
self.conv = filter_fn
self.batch_norms = nn.BatchNorm1d(d_out[0]) if self.bn[0] else None
if jk_mlp:
final_jk_layer = mlp(d_out[0], out_features, d_h[0], seed,
activation_mlp, bn_mlp)
else:
final_jk_layer = nn.Linear(d_out[0], out_features)
self.lin_proj = final_jk_layer
#-------------- Readout
if self.readout == 'sum':
self.global_pool = global_add_pool_sparse
elif self.readout == 'mean':
self.global_pool = global_mean_pool_sparse
else:
raise ValueError("Invalid graph pooling type.")
#-------------- Activation fn (same across the network)
self.activation = choose_activation(kwargs['activation'])
return
def __init__(self,
d_in,
d_ef,
d_degree,
degree_as_tag,
retain_features,
d_msg,
d_up,
d_h,
seed,
activation_name,
bn,
aggr='add',
msg_kind='general',
eps=0,
train_eps=False,
flow='source_to_target',
**kwargs):
super(MPNN_edge_sparse, self).__init__()
d_msg = d_in if d_msg is None else d_msg
self.flow = flow
self.aggr = aggr
self.msg_kind = msg_kind
self.degree_as_tag = degree_as_tag
self.retain_features = retain_features
if degree_as_tag:
d_in = d_in + d_degree if retain_features else d_degree
# INPUT_DIMS
if msg_kind == 'gin':
# dummy variable for self loop edge features
self.central_node_edge_encoder = central_encoder(
kwargs['edge_embedding'], d_ef, extend=kwargs['extend_dims'])
d_ef = self.central_node_edge_encoder.d_out
msg_input_dim = None
self.initial_eps = eps
if train_eps:
self.eps = torch.nn.Parameter(torch.Tensor([eps]))
else:
self.register_buffer('eps', torch.Tensor([eps]))
self.eps.data.fill_(self.initial_eps)
self.msg_fn = None
update_input_dim = d_in + d_ef
elif msg_kind == 'general':
msg_input_dim = 2 * d_in + d_ef
self.msg_fn = mlp(msg_input_dim, d_msg, d_h, seed, activation_name,
bn)
update_input_dim = d_in + d_msg
self.update_fn = mlp(update_input_dim, d_up, d_h, seed,
activation_name, bn)
return
def __init__(self,
in_features,
out_features,
encoder_ids,
d_in_id,
in_edge_features=None,
d_in_node_encoder=None,
d_in_edge_encoder=None,
encoder_degrees=None,
d_degree=None,
**kwargs):
super(GNNSubstructures, self).__init__()
seed = kwargs['seed']
#-------------- Initializations
self.model_name = kwargs['model_name']
self.readout = kwargs['readout'] if kwargs[
'readout'] is not None else 'sum'
self.dropout_features = kwargs['dropout_features']
self.bn = kwargs['bn']
self.final_projection = kwargs['final_projection']
self.inject_ids = kwargs['inject_ids']
self.inject_edge_features = kwargs['inject_edge_features']
self.random_features = kwargs['random_features']
id_scope = kwargs['id_scope']
d_msg = kwargs['d_msg']
d_out = kwargs['d_out']
d_h = kwargs['d_h']
aggr = kwargs['aggr'] if kwargs['aggr'] is not None else 'add'
flow = kwargs['flow'] if kwargs[
'flow'] is not None else 'target_to_source'
msg_kind = kwargs['msg_kind'] if kwargs[
'msg_kind'] is not None else 'general'
train_eps = kwargs['train_eps'] if kwargs[
'train_eps'] is not None else [False for _ in range(len(d_out))]
activation_mlp = kwargs['activation_mlp']
bn_mlp = kwargs['bn_mlp']
jk_mlp = kwargs['jk_mlp']
degree_embedding = kwargs['degree_embedding'] if kwargs[
'degree_as_tag'][0] else 'None'
degree_as_tag = kwargs['degree_as_tag']
retain_features = kwargs['retain_features']
encoders_kwargs = {
'seed': seed,
'activation_mlp': activation_mlp,
'bn_mlp': bn_mlp,
'aggr': kwargs['multi_embedding_aggr']
}
#-------------- Input node embedding
self.input_node_encoder = DiscreteEmbedding(
kwargs['input_node_encoder'], in_features, d_in_node_encoder,
kwargs['d_out_node_encoder'], **encoders_kwargs)
d_in = self.input_node_encoder.d_out
if self.random_features:
self.r_d_out = d_out[0]
d_in = d_in + self.r_d_out
#-------------- Edge embedding (for each GNN layer)
self.edge_encoder = []
d_ef = []
num_edge_encoders = len(d_out) if kwargs['inject_edge_features'] else 1
for i in range(num_edge_encoders):
edge_encoder_layer = DiscreteEmbedding(
kwargs['edge_encoder'], in_edge_features, d_in_edge_encoder,
kwargs['d_out_edge_encoder'][i], **encoders_kwargs)
self.edge_encoder.append(edge_encoder_layer)
d_ef.append(edge_encoder_layer.d_out)
self.edge_encoder = nn.ModuleList(self.edge_encoder)
#-------------- Identifier embedding (for each GNN layer)
self.id_encoder = []
d_id = []
num_id_encoders = len(d_out) if kwargs['inject_ids'] else 1
for i in range(num_id_encoders):
id_encoder_layer = DiscreteEmbedding(kwargs['id_embedding'],
len(d_in_id), d_in_id,
kwargs['d_out_id_embedding'],
**encoders_kwargs)
self.id_encoder.append(id_encoder_layer)
d_id.append(id_encoder_layer.d_out)
self.id_encoder = nn.ModuleList(self.id_encoder)
#-------------- Degree embedding
self.degree_encoder = DiscreteEmbedding(
degree_embedding, 1, d_degree, kwargs['d_out_degree_embedding'],
**encoders_kwargs)
d_degree = self.degree_encoder.d_out
#-------------- GNN layers w/ bn and jk
self.conv = []
self.batch_norms = []
self.lin_proj = []
for i in range(len(d_out)):
kwargs_filter = {
'd_in': d_in,
'd_degree': d_degree,
'degree_as_tag': degree_as_tag[i],
'retain_features': retain_features[i],
'd_msg': d_msg[i],
'd_up': d_out[i],
'd_h': d_h[i],
'd_ef': d_ef[i] if self.inject_edge_features else d_ef[0],
'seed': seed,
'activation_name': activation_mlp,
'bn': bn_mlp,
'aggr': aggr,
'msg_kind': msg_kind,
'eps': 0,
'train_eps': train_eps[i],
'flow': flow,
'edge_embedding': kwargs['edge_encoder'],
'id_embedding': kwargs['id_embedding'],
'extend_dims': kwargs['extend_dims']
}
use_ids = ((i > 0 and kwargs['inject_ids']) or
(i == 0)) and (self.model_name
in {'GSN_sparse', 'GSN_edge_sparse'})
use_efs = ((i > 0 and kwargs['inject_edge_features']) or
(i == 0)) and (self.model_name in {
'GSN_edge_sparse', 'MPNN_edge_sparse'
})
if use_ids:
filter_fn = GSN_edge_sparse if use_efs else GSN_sparse
kwargs_filter['d_id'] = d_id[i] if self.inject_ids else d_id[0]
kwargs_filter['id_scope'] = id_scope
else:
filter_fn = MPNN_edge_sparse if use_efs else MPNN_sparse
self.conv.append(filter_fn(**kwargs_filter))
if self.final_projection[i]:
# if desired, jk projections can be performed
# by an mlp instead of a simple linear layer;
if jk_mlp:
jk_layer = mlp(d_in, out_features, d_h[i], seed,
activation_mlp, bn_mlp)
else:
jk_layer = nn.Linear(d_in, out_features)
else:
jk_layer = None
self.lin_proj.append(jk_layer)
bn_layer = nn.BatchNorm1d(d_out[i]) if self.bn[i] else None
self.batch_norms.append(bn_layer)
d_in = d_out[i]
if self.final_projection[-1]:
# if desired, jk projections can be performed
# by an mlp instead of a simple linear layer;
if jk_mlp:
final_jk_layer = mlp(d_in, out_features, d_h[-1], seed,
activation_mlp, bn_mlp)
else:
final_jk_layer = nn.Linear(d_in, out_features)
else:
final_jk_layer = None
self.lin_proj.append(final_jk_layer)
self.conv = nn.ModuleList(self.conv)
self.lin_proj = nn.ModuleList(self.lin_proj)
self.batch_norms = nn.ModuleList(self.batch_norms)
#-------------- Readout
if self.readout == 'sum':
self.global_pool = global_add_pool_sparse
elif self.readout == 'mean':
self.global_pool = global_mean_pool_sparse
else:
raise ValueError("Invalid graph pooling type.")
#-------------- Activation fn (same across the network)
self.activation = choose_activation(kwargs['activation'])
return
