def from_data_list_token(data_list, follow_batch=[]):
""" This is pretty a copy paste of the from data list of pytorch geometric
batch object with the difference that indexes that are negative are not incremented
"""
keys = [set(data.keys) for data in data_list]
keys = list(set.union(*keys))
assert "batch" not in keys
batch = Batch()
batch.__data_class__ = data_list[0].__class__
batch.__slices__ = {key: [0] for key in keys}
for key in keys:
batch[key] = []
for key in follow_batch:
batch["{}_batch".format(key)] = []
cumsum = {key: 0 for key in keys}
batch.batch = []
for i, data in enumerate(data_list):
for key in data.keys:
item = data[key]
if torch.is_tensor(item) and item.dtype != torch.bool:
mask = item >= 0
item[mask] = item[mask] + cumsum[key]
if torch.is_tensor(item):
size = item.size(data.__cat_dim__(key, data[key]))
else:
size = 1
batch.__slices__[key].append(size + batch.__slices__[key][-1])
cumsum[key] += data.__inc__(key, item)
batch[key].append(item)
if key in follow_batch:
item = torch.full((size,), i, dtype=torch.long)
batch["{}_batch".format(key)].append(item)
num_nodes = data.num_nodes
if num_nodes is not None:
item = torch.full((num_nodes,), i, dtype=torch.long)
batch.batch.append(item)
if num_nodes is None:
batch.batch = None
for key in batch.keys:
item = batch[key][0]
if torch.is_tensor(item):
batch[key] = torch.cat(batch[key], dim=data_list[0].__cat_dim__(key, item))
elif isinstance(item, int) or isinstance(item, float):
batch[key] = torch.tensor(batch[key])
else:
raise ValueError("Unsupported attribute type {} : {}".format(type(item), item))
if torch_geometric.is_debug_enabled():
batch.debug()
return batch.contiguous()
def from_data_list(data_list, follow_batch=[]):
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.
Additionally, creates assignment batch vectors for each key in
:obj:`follow_batch`."""
keys = [set(data.keys) for data in data_list]
keys = set.union(*keys)
keys.remove('depth_count')
keys = list(keys)
depth = max(data.depth_count.shape[0] for data in data_list)
assert 'batch' not in keys
batch = Batch()
batch.__data_class__ = data_list[0].__class__
batch.__slices__ = {key: [0] for key in keys}
for key in keys:
batch[key] = []
for key in follow_batch:
batch['{}_batch'.format(key)] = []
cumsum = {i: 0 for i in range(depth)}
depth_count = th.zeros((depth, ), dtype=th.long)
batch.batch = []
for i, data in enumerate(data_list):
edges = data['edge_index']
for d in range(1, depth):
mask = data.depth_mask == d
edges[mask] += cumsum[d - 1]
cumsum[d - 1] += data.depth_count[d - 1].item()
batch['edge_index'].append(edges)
depth_count += data['depth_count']
for key in data.keys:
if key == 'edge_index' or key == 'depth_count':
continue
item = data[key]
batch[key].append(item)
num_nodes = data.num_nodes
if num_nodes is not None:
item = torch.full((num_nodes, ), i, dtype=torch.long)
batch.batch.append(item)
if num_nodes is None:
batch.batch = None
for key in batch.keys:
item = batch[key][0]
if torch.is_tensor(item):
batch[key] = torch.cat(batch[key],
dim=data_list[0].__cat_dim__(key, item))
elif isinstance(item, int) or isinstance(item, float):
batch[key] = torch.tensor(batch[key])
batch.depth_count = depth_count
if torch_geometric.is_debug_enabled():
batch.debug()
return batch.contiguous()
def from_data_list(data_list, follow_batch=[]):
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.
Additionally, creates assignment batch vectors for each key in
:obj:`follow_batch`."""
keys = [set(data.keys) for data in data_list]
keys = list(set.union(*keys))
assert 'batch' not in keys
batch = Batch()
batch.__data_class__ = data_list[0].__class__
batch.__slices__ = {key: [0] for key in keys}
for key in keys:
batch[key] = []
for key in follow_batch:
batch['{}_batch'.format(key)] = []
cumsum = {key: 0 for key in keys}
batch.batch = []
for i, data in enumerate(data_list):
for key in data.keys:
# logger.info(f"key={key}")
item = data[key]
if torch.is_tensor(item) and item.dtype != torch.bool:
item = item + cumsum[key]
if torch.is_tensor(item):
size = item.size(data.__cat_dim__(key, data[key]))
else:
size = 1
batch.__slices__[key].append(size + batch.__slices__[key][-1])
cumsum[key] = cumsum[key] + data.__inc__(key, item)
batch[key].append(item)
if key in follow_batch:
item = torch.full((size,), i, dtype=torch.long)
batch['{}_batch'.format(key)].append(item)
num_nodes = data.num_nodes
if num_nodes is not None:
item = torch.full((num_nodes,), i, dtype=torch.long)
batch.batch.append(item)
if num_nodes is None:
batch.batch = None
for key in batch.keys:
item = batch[key][0]
logger.debug(f"key = {key}")
if torch.is_tensor(item):
logger.debug(f"batch[{key}]")
logger.debug(f"item.shape = {item.shape}")
elem = data_list[0] # type(elem) = Data or ClevrData
dim_ = elem.__cat_dim__(key, item) # basically, which dim we want to concat
batch[key] = torch.cat(batch[key], dim=dim_)
# batch[key] = torch.cat(batch[key],
# dim=data_list[0].__cat_dim__(key, item))
elif isinstance(item, int) or isinstance(item, float):
batch[key] = torch.tensor(batch[key])
if torch_geometric.is_debug_enabled():
batch.debug()
return batch.contiguous()
