def list_to_batch(data):
r"""Constructs a batch object from a python list holding
:class:`torch_geometric.data.Data` objects.
"""
keys = ['x','y','pos','grid_size']
batch = SimpleBatch()
batch.__data_class__ = data.__class__
for key in data[0].keys:
if key in ['y','grid_size']:
item=torch.cat([data[i][key] for i in range(len(data))])
batch[key]=item
else:
item=torch.cat([data[i][key] for i in range(len(data))])
batch[key]=item
return batch.contiguous()
def batch_to_batch3(data,l,j):
r"""Constructs a batch object from a python list holding
:class:`torch_geometric.data.Data` objects.
"""
keys = ['x','y','pos','grid_size']
batch = SimpleBatch()
batch.__data_class__ = data.__class__
for key in data.keys:
if key in ['y','grid_size']:
item = data[key]
batch[key]=item[[0,j]]
else:
item = data[key]
batch[key]=torch.cat((torch.unsqueeze(item[0,l,:],0),torch.unsqueeze(item[j,l,:],0)),axis=0)
#batch[key]=item[:,:128,:]
return batch.contiguous()
def batch_to_batch2(data,random):
r"""Constructs a batch object from a python list holding
:class:`torch_geometric.data.Data` objects.
"""
keys = ['x','y','pos','grid_size']
batch = SimpleBatch()
batch.__data_class__ = data.__class__
l1,l2,l3=get_list_random(random,len(data['x'][0]))
for key in data.keys:
if key in ['y','grid_size']:
item = data[key]
batch[key]=item[[0,1]]
else:
item = data[key]
batch[key]=torch.cat((torch.unsqueeze(item[0,l1,:],0),torch.unsqueeze(item[1,l1,:],0)),axis=0)
#batch[key]=item[:,:128,:]
return batch.contiguous(),l1
