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batch.py
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batch.py
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import torch
from torch_geometric.data import Data, Batch
class BatchMasking(Data):
r"""A plain old python object modeling a batch of graphs as one big
(dicconnected) graph. With :class:`torch_geometric.data.Data` being the
base class, all its methods can also be used here.
In addition, single graphs can be reconstructed via the assignment vector
:obj:`batch`, which maps each node to its respective graph identifier.
"""
def __init__(self, batch=None, **kwargs):
super(BatchMasking, self).__init__(**kwargs)
self.batch = batch
@staticmethod
def from_data_list(data_list):
r"""Constructs a batch object from a python list holding
:class:`torch_geometric.data.Data` objects.
The assignment vector :obj:`batch` is created on the fly."""
keys = [set(data.keys) for data in data_list]
keys = list(set.union(*keys))
assert 'batch' not in keys
batch = BatchMasking()
for key in keys:
batch[key] = []
batch.batch = []
cumsum_node = 0
cumsum_edge = 0
for i, data in enumerate(data_list):
num_nodes = data.num_nodes
batch.batch.append(torch.full((num_nodes, ), i, dtype=torch.long))
for key in data.keys:
item = data[key]
if key in ['edge_index', 'masked_atom_indices']:
item = item + cumsum_node
elif key == 'connected_edge_indices':
item = item + cumsum_edge
batch[key].append(item)
cumsum_node += num_nodes
cumsum_edge += data.edge_index.shape[1]
for key in keys:
batch[key] = torch.cat(
batch[key], dim=data_list[0].__cat_dim__(key, batch[key][0]))
batch.batch = torch.cat(batch.batch, dim=-1)
return batch.contiguous()
def cumsum(self, key, item):
r"""If :obj:`True`, the attribute :obj:`key` with content :obj:`item`
should be added up cumulatively before concatenated together.
.. note::
This method is for internal use only, and should only be overridden
if the batch concatenation process is corrupted for a specific data
attribute.
"""
return key in ['edge_index', 'face', 'masked_atom_indices', 'connected_edge_indices']
@property
def num_graphs(self):
"""Returns the number of graphs in the batch."""
return self.batch[-1].item() + 1
class BatchAE(Data):
r"""A plain old python object modeling a batch of graphs as one big
(dicconnected) graph. With :class:`torch_geometric.data.Data` being the
base class, all its methods can also be used here.
In addition, single graphs can be reconstructed via the assignment vector
:obj:`batch`, which maps each node to its respective graph identifier.
"""
def __init__(self, batch=None, **kwargs):
super(BatchAE, self).__init__(**kwargs)
self.batch = batch
@staticmethod
def from_data_list(data_list):
r"""Constructs a batch object from a python list holding
:class:`torch_geometric.data.Data` objects.
The assignment vector :obj:`batch` is created on the fly."""
keys = [set(data.keys) for data in data_list]
keys = list(set.union(*keys))
assert 'batch' not in keys
batch = BatchAE()
for key in keys:
batch[key] = []
batch.batch = []
cumsum_node = 0
for i, data in enumerate(data_list):
num_nodes = data.num_nodes
batch.batch.append(torch.full((num_nodes, ), i, dtype=torch.long))
for key in data.keys:
item = data[key]
if key in ['edge_index', 'negative_edge_index']:
item = item + cumsum_node
batch[key].append(item)
cumsum_node += num_nodes
for key in keys:
batch[key] = torch.cat(
batch[key], dim=batch.cat_dim(key))
batch.batch = torch.cat(batch.batch, dim=-1)
return batch.contiguous()
@property
def num_graphs(self):
"""Returns the number of graphs in the batch."""
return self.batch[-1].item() + 1
def cat_dim(self, key):
return -1 if key in ["edge_index", "negative_edge_index"] else 0
class BatchSubstructContext(Data):
r"""A plain old python object modeling a batch of graphs as one big
(dicconnected) graph. With :class:`torch_geometric.data.Data` being the
base class, all its methods can also be used here.
In addition, single graphs can be reconstructed via the assignment vector
:obj:`batch`, which maps each node to its respective graph identifier.
"""
"""
Specialized batching for substructure context pair!
"""
def __init__(self, batch=None, **kwargs):
super(BatchSubstructContext, self).__init__(**kwargs)
self.batch = batch
@staticmethod
def from_data_list(data_list):
r"""Constructs a batch object from a python list holding
:class:`torch_geometric.data.Data` objects.
The assignment vector :obj:`batch` is created on the fly."""
#keys = [set(data.keys) for data in data_list]
#keys = list(set.union(*keys))
#assert 'batch' not in keys
batch = BatchSubstructContext()
keys = ["center_substruct_idx", "edge_attr_substruct", "edge_index_substruct", "x_substruct", "overlap_context_substruct_idx", "edge_attr_context", "edge_index_context", "x_context"]
for key in keys:
#print(key)
batch[key] = []
#batch.batch = []
#used for pooling the context
batch.batch_overlapped_context = []
batch.overlapped_context_size = []
cumsum_main = 0
cumsum_substruct = 0
cumsum_context = 0
i = 0
for data in data_list:
#If there is no context, just skip!!
if hasattr(data, "x_context"):
num_nodes = data.num_nodes
num_nodes_substruct = len(data.x_substruct)
num_nodes_context = len(data.x_context)
#batch.batch.append(torch.full((num_nodes, ), i, dtype=torch.long))
batch.batch_overlapped_context.append(torch.full((len(data.overlap_context_substruct_idx), ), i, dtype=torch.long))
batch.overlapped_context_size.append(len(data.overlap_context_substruct_idx))
###batching for the main graph
#for key in data.keys:
# if not "context" in key and not "substruct" in key:
# item = data[key]
# item = item + cumsum_main if batch.cumsum(key, item) else item
# batch[key].append(item)
###batching for the substructure graph
for key in ["center_substruct_idx", "edge_attr_substruct", "edge_index_substruct", "x_substruct"]:
item = data[key]
item = item + cumsum_substruct if batch.cumsum(key, item) else item
batch[key].append(item)
###batching for the context graph
for key in ["overlap_context_substruct_idx", "edge_attr_context", "edge_index_context", "x_context"]:
item = data[key]
item = item + cumsum_context if batch.cumsum(key, item) else item
batch[key].append(item)
cumsum_main += num_nodes
cumsum_substruct += num_nodes_substruct
cumsum_context += num_nodes_context
i += 1
for key in keys:
batch[key] = torch.cat(
batch[key], dim=batch.cat_dim(key))
#batch.batch = torch.cat(batch.batch, dim=-1)
batch.batch_overlapped_context = torch.cat(batch.batch_overlapped_context, dim=-1)
batch.overlapped_context_size = torch.LongTensor(batch.overlapped_context_size)
return batch.contiguous()
def cat_dim(self, key):
return -1 if key in ["edge_index", "edge_index_substruct", "edge_index_context"] else 0
def cumsum(self, key, item):
r"""If :obj:`True`, the attribute :obj:`key` with content :obj:`item`
should be added up cumulatively before concatenated together.
.. note::
This method is for internal use only, and should only be overridden
if the batch concatenation process is corrupted for a specific data
attribute.
"""
return key in ["edge_index", "edge_index_substruct", "edge_index_context", "overlap_context_substruct_idx", "center_substruct_idx"]
@property
def num_graphs(self):
"""Returns the number of graphs in the batch."""
return self.batch[-1].item() + 1