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 collate_fn_withpad(data_list):
'''
Modified based on PyTorch-Geometric's implementation
:param data_list:
:return:
'''
keys = [set(data.keys) for data in data_list]
keys = list(set.union(*keys))
assert 'batch' not in keys
batch = Batch()
for key in keys:
batch[key] = []
batch.batch = []
cumsum = 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]
item = item + cumsum if data.__cumsum__(key, item) else item
batch[key].append(item)
cumsum += num_nodes
for key in keys:
item = batch[key][0]
if torch.is_tensor(item):
if (len(item.shape) == 3):
tlens = [x.shape[1] for x in batch[key]]
maxtlens = np.max(tlens)
to_cat = []
for x in batch[key]:
to_cat.append(
torch.cat([
x,
x.new_zeros(x.shape[0], maxtlens - x.shape[1],
x.shape[2])
],
dim=1))
batch[key] = torch.cat(to_cat, dim=0)
if 'tlens' not in batch.keys:
batch['tlens'] = item.new_tensor(tlens, dtype=torch.long)
else:
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.')
batch.batch = torch.cat(batch.batch, dim=-1)
return batch.contiguous()
def collate(data_list):
batch = Batch()
batch.batch = []
for key in data_list[0].keys:
batch[key] = default_collate([d[key] for d in data_list])
for i, data in enumerate(data_list):
num_nodes = data.num_nodes
if num_nodes is not None:
item = torch.full((num_nodes, ), i, dtype=torch.long)
batch.batch.append(item)
batch.batch = torch.cat(batch.batch, dim=0)
return batch
def forward(self, data):
batch_obj = Batch()
x, pos, batch = data.x, data.pos, data.batch
if self._precompute_multi_scale:
idx = getattr(data, "idx_{}".format(self._index), None)
else:
idx = self.sampler(pos, batch)
batch_obj.idx = idx
ms_x = []
for scale_idx in range(self.neighbour_finder.num_scales):
if self._precompute_multi_scale:
edge_index = getattr(
data, "edge_index_{}_{}".format(self._index, scale_idx),
None)
else:
row, col = self.neighbour_finder(
pos,
pos[idx],
batch_x=batch,
batch_y=batch[idx],
scale_idx=scale_idx,
)
edge_index = torch.stack([col, row], dim=0)
ms_x.append(self.conv(x, (pos, pos[idx]), edge_index, batch))
batch_obj.x = torch.cat(ms_x, -1)
batch_obj.pos = pos[idx]
batch_obj.batch = batch[idx]
copy_from_to(data, batch_obj)
return batch_obj
def forward(self, data):
batch_obj = Batch()
x, pos, batch = data.x, data.pos, data.batch
idx_sampler = self.sampler(pos=pos, x=x, batch=batch)
idx_neighbour, _ = self.neighbour_finder(pos,
pos,
batch_x=batch,
batch_y=batch)
shadow_x = torch.full((1, ) + x.shape[1:],
self.shadow_features_fill).to(x.device)
shadow_points = torch.full((1, ) + pos.shape[1:],
self.shadow_points_fill_).to(x.device)
x = torch.cat([x, shadow_x], dim=0)
pos = torch.cat([pos, shadow_points], dim=0)
x_neighbour = x[idx_neighbour]
pos_centered_neighbour = pos[idx_neighbour] - pos[:-1].unsqueeze(
1) # Centered the points
batch_obj.x = self.conv(x, pos, x_neighbour, pos_centered_neighbour,
idx_neighbour, idx_sampler)
batch_obj.pos = pos[idx_sampler]
batch_obj.batch = batch[idx_sampler]
copy_from_to(data, batch_obj)
return batch_obj
def forward(self, data, **kwargs):
batch_obj = Batch()
x, pos, batch = data.x, data.pos, data.batch
x = self.nn(torch.cat([x, pos], dim=1))
x = self.pool(x, batch)
batch_obj.x = x
batch_obj.pos = pos.new_zeros((x.size(0), 3))
batch_obj.batch = torch.arange(x.size(0), device=batch.device)
copy_from_to(data, batch_obj)
return batch_obj
def forward(self, data, **kwargs):
batch_obj = Batch()
x, pos, batch = data.x, data.pos, data.batch
idx = self.sampler(pos, batch)
row, col = self.neighbour_finder(pos, pos[idx], batch_x=batch, batch_y=batch[idx])
edge_index = torch.stack([col, row], dim=0)
batch_obj.idx = idx
batch_obj.edge_index = edge_index
batch_obj.x = self.conv(x, (pos[idx], pos), edge_index, batch)
batch_obj.pos = pos[idx]
batch_obj.batch = batch[idx]
copy_from_to(data, batch_obj)
return batch_obj
def forward(self, data, **kwargs):
batch_obj = Batch()
x, pos, batch = data.x, data.pos, data.batch
idx = self.sampler(pos, batch)
batch_obj.idx = idx
ms_x = []
for scale_idx in range(self.neighbour_finder.num_scales):
row, col = self.neighbour_finder(pos, pos[idx], batch_x=batch, batch_y=batch[idx], scale_idx=scale_idx,)
edge_index = torch.stack([col, row], dim=0)
ms_x.append(self.conv(x, (pos, pos[idx]), edge_index, batch))
batch_obj.x = torch.cat(ms_x, -1)
batch_obj.pos = pos[idx]
batch_obj.batch = batch[idx]
copy_from_to(data, batch_obj)
return batch_obj
def forward(self, data, **kwargs):
batch_obj = Batch()
x = data.x # (N, indim)
shortcut = x # (N, indim)
x = self.features_downsample_nn(x) # (N, outdim//4)
# if this is an identity resnet block, idx will be None
data = self.convs(data) # (N', convdim)
x = data.x
idx = data.idx
x = self.features_upsample_nn(x) # (N', outdim)
if idx is not None:
shortcut = shortcut[idx] # (N', indim)
shortcut = self.shortcut_feature_resize_nn(shortcut) # (N', outdim)
x = shortcut + x
batch_obj.x = x
batch_obj.pos = data.pos
batch_obj.batch = data.batch
copy_from_to(data, batch_obj)
return batch_obj
def forward(self, data):
batch_obj = Batch()
x, pos, batch = data.x, data.pos, data.batch
if self._precompute_multi_scale:
idx = getattr(data, "index_{}".format(self._index), None)
edge_index = getattr(data, "edge_index_{}".format(self._index),
None)
else:
idx = self.sampler(pos, batch)
row, col = self.neighbour_finder(pos,
pos[idx],
batch_x=batch,
batch_y=batch[idx])
edge_index = torch.stack([col, row], dim=0)
batch_obj.idx = idx
batch_obj.edge_index = edge_index
batch_obj.x = self.conv(x, (pos, pos[idx]), edge_index, batch)
batch_obj.pos = pos[idx]
batch_obj.batch = batch[idx]
copy_from_to(data, batch_obj)
return batch_obj
def data_loader(database, latent_graph_dict, latent_graph_list, batch_size=4, shuffle=True):
# -- This loader creates Databatches from a Database, run on either the training or validation set!
# Establish empty lists
graph_list_p1 = []
graph_list_p2 = []
graph_list_pm = []
target_list = []
Temperature_list = []
# Iterate through database and construct latent graphs of the molecules, as well as the target tensors
for itm in range(len(database)):
graph_p1 = latent_graph_list[latent_graph_dict[database["Molecule1"].loc[itm]]]
graph_p2 = latent_graph_list[latent_graph_dict[database["Molecule2"].loc[itm]]]
graph_pm = latent_graph_list[latent_graph_dict[database["Membrane"].loc[itm]]]
graph_p1.y = torch.tensor([float(database["molp1"].loc[itm])])
graph_p2.y = torch.tensor([float(database["molp2"].loc[itm])])
graph_pm.y = torch.tensor([float(database["molpm"].loc[itm])])
graph_list_p1.append(graph_p1)
graph_list_p2.append(graph_p2)
graph_list_pm.append(graph_pm)
target = torch.tensor([database[str(i)].loc[itm]*10 for i in range(2, 17)], dtype=torch.float)
target_list.append(target)
Temperature_list.append(database["Temperature"].loc[itm])
# Generate a shuffled integer list that then produces distinct batches
idxs = torch.randperm(len(database)).tolist()
if shuffle==False:
idxs = sorted(idxs)
# Generate the batches
batch_p1 = []
batch_p2 = []
batch_pm = []
batch_target = []
batch_Temperature = []
for b in range(len(database))[::batch_size]:
# Creating empty Batch objects
B_p1 = Batch()
B_p2 = Batch()
B_pm = Batch()
# Creating empty lists that will be concatenated later (advanced minibatching)
stack_x_p1, stack_x_p2, stack_x_pm = [], [], []
stack_ei_p1, stack_ei_p2, stack_ei_pm = [], [], []
stack_ea_p1, stack_ea_p2, stack_ea_pm = [], [], []
stack_y_p1, stack_y_p2, stack_y_pm = [], [], []
stack_btc_p1, stack_btc_p2, stack_btc_pm = [], [], []
stack_target = []
stack_Temperature = []
# Creating the batch shifts, that shift the edge indices
b_shift_p1 = 0
b_shift_p2 = 0
b_shift_pm = 0
for i in range(batch_size):
if (b+i) < len(database):
stack_x_p1.append(graph_list_p1[idxs[b + i]].x)
stack_x_p2.append(graph_list_p2[idxs[b + i]].x)
stack_x_pm.append(graph_list_pm[idxs[b + i]].x)
stack_ei_p1.append(graph_list_p1[idxs[b + i]].edge_index+b_shift_p1)
stack_ei_p2.append(graph_list_p2[idxs[b + i]].edge_index+b_shift_p2)
stack_ei_pm.append(graph_list_pm[idxs[b + i]].edge_index+b_shift_pm)
# Updating the shifts
b_shift_p1 += graph_list_p1[idxs[b + i]].x.size()[0]
b_shift_p2 += graph_list_p2[idxs[b + i]].x.size()[0]
b_shift_pm += graph_list_pm[idxs[b + i]].x.size()[0]
stack_ea_p1.append(graph_list_p1[idxs[b + i]].edge_attr)
stack_ea_p2.append(graph_list_p2[idxs[b + i]].edge_attr)
stack_ea_pm.append(graph_list_pm[idxs[b + i]].edge_attr)
stack_y_p1.append(graph_list_p1[idxs[b + i]].y)
stack_y_p2.append(graph_list_p2[idxs[b + i]].y)
stack_y_pm.append(graph_list_pm[idxs[b + i]].y)
stack_btc_p1.append(
torch.full([graph_list_p1[idxs[b + i]].x.size()[0]], fill_value=int(i), dtype=torch.long))
# FILL VALUE IS PROBABLY JUST i! (OR NOT)
stack_btc_p2.append(
torch.full([graph_list_p2[idxs[b + i]].x.size()[0]], fill_value=int(i), dtype=torch.long))
stack_btc_pm.append(
torch.full([graph_list_pm[idxs[b + i]].x.size()[0]], fill_value=int(i), dtype=torch.long))
stack_target.append(target_list[idxs[b + i]])
stack_Temperature.append(Temperature_list[int(b+i)])
# print(stack_y_p1)
# print(stack_x_p1)
B_p1.edge_index=torch.cat(stack_ei_p1,dim=1)
B_p1.x=torch.cat(stack_x_p1,dim=0)
B_p1.edge_attr=torch.cat(stack_ea_p1,dim=0)
B_p1.y=torch.cat(stack_y_p1,dim=0)
B_p1.batch=torch.cat(stack_btc_p1,dim=0)
B_p2.edge_index = torch.cat(stack_ei_p2, dim=1)
B_p2.x = torch.cat(stack_x_p2, dim=0)
B_p2.edge_attr = torch.cat(stack_ea_p2, dim=0)
B_p2.y = torch.cat(stack_y_p2, dim=0)
B_p2.batch = torch.cat(stack_btc_p2, dim=0)
B_pm.edge_index = torch.cat(stack_ei_pm, dim=1)
B_pm.x = torch.cat(stack_x_pm, dim=0)
B_pm.edge_attr = torch.cat(stack_ea_pm, dim=0)
B_pm.y = torch.cat(stack_y_pm, dim=0)
B_pm.batch = torch.cat(stack_btc_pm, dim=0)
B_target = torch.stack(stack_target, dim=0)
B_Temperature = torch.Tensor(stack_Temperature)
# Appending batches
batch_p1.append(B_p1)
batch_p2.append(B_p2)
batch_pm.append(B_pm)
batch_target.append(B_target)
batch_Temperature.append(B_Temperature)
# Return
return [batch_p1, batch_p2, batch_pm, batch_target, batch_Temperature]
def collate(data_list):
keys = data_list[0].keys
assert 'batch' not in keys
batch = Batch()
for key in keys:
batch[key] = []
batch.batch = []
if 'edge_index_2' in keys:
batch.batch_2 = []
if 'edge_index_3' in keys:
batch.batch_3 = []
keys.remove('edge_index')
props = [
'edge_index_2', 'assignment_2', 'edge_index_3', 'assignment_3',
'assignment_2to3'
]
keys = [x for x in keys if x not in props]
cumsum_1 = N_1 = cumsum_2 = N_2 = cumsum_3 = N_3 = 0
for i, data in enumerate(data_list):
for key in keys:
batch[key].append(data[key])
N_1 = data.num_nodes
batch.edge_index.append(data.edge_index + cumsum_1)
batch.batch.append(torch.full((N_1, ), i, dtype=torch.long))
if 'edge_index_2' in data:
N_2 = data.assignment_2[1].max().item() + 1
batch.edge_index_2.append(data.edge_index_2 + cumsum_2)
batch.assignment_2.append(data.assignment_2 +
torch.tensor([[cumsum_1], [cumsum_2]]))
batch.batch_2.append(torch.full((N_2, ), i, dtype=torch.long))
if 'edge_index_3' in data:
N_3 = data.assignment_3[1].max().item() + 1
batch.edge_index_3.append(data.edge_index_3 + cumsum_3)
batch.assignment_3.append(data.assignment_3 +
torch.tensor([[cumsum_1], [cumsum_3]]))
batch.batch_3.append(torch.full((N_3, ), i, dtype=torch.long))
if 'assignment_2to3' in data:
assert 'edge_index_2' in data and 'edge_index_3' in data
batch.assignment_2to3.append(
data.assignment_2to3 + torch.tensor([[cumsum_2], [cumsum_3]]))
cumsum_1 += N_1
cumsum_2 += N_2
cumsum_3 += N_3
keys = [x for x in batch.keys if x not in ['batch', 'batch_2', 'batch_3']]
for key in keys:
batch[key] = torch.cat(batch[key], dim=data_list[0].cat_dim(key))
batch.batch = torch.cat(batch.batch, dim=-1)
if 'batch_2' in batch:
batch.batch_2 = torch.cat(batch.batch_2, dim=-1)
if 'batch_3' in batch:
batch.batch_3 = torch.cat(batch.batch_3, dim=-1)
return batch.contiguous()
def forward(self, plist):
# Here we instantiate the three molecular graphs for the first level GNN
p1, p2, pm, Temperature = plist
x_p1, ei_p1, ea_p1, u_p1, btc_p1 = p1.x, p1.edge_index, p1.edge_attr, p1.y, p1.batch
x_p2, ei_p2, ea_p2, u_p2, btc_p2 = p2.x, p2.edge_index, p2.edge_attr, p2.y, p2.batch
x_pm, ei_pm, ea_pm, u_pm, btc_pm = pm.x, pm.edge_index, pm.edge_attr, pm.y, pm.batch
# Embed the node and edge features
enc_x_p1 = self.encoding_node_1(x_p1)
enc_x_p2 = self.encoding_node_1(x_p2)
enc_x_pm = self.encoding_node_1(x_pm)
enc_ea_p1 = self.encoding_edge_1(ea_p1)
enc_ea_p2 = self.encoding_edge_1(ea_p2)
enc_ea_pm = self.encoding_edge_1(ea_pm)
#Create the empty molecular graphs for feature extraction, graph level one
u1 = torch.full(size=(self.batch_size, NO_GRAPH_FEATURES_ONE),
fill_value=0.1,
dtype=torch.float).to(device)
u2 = torch.full(size=(self.batch_size, NO_GRAPH_FEATURES_ONE),
fill_value=0.1,
dtype=torch.float).to(device)
um = torch.full(size=(self.batch_size, NO_GRAPH_FEATURES_ONE),
fill_value=0.1,
dtype=torch.float).to(device)
# Now the first level rounds of message passing are performed, molecular features are constructed
for _ in range(self.no_mp_one):
enc_x_p1, enc_ea_p1, u1 = self.meta1(x=enc_x_p1,
edge_index=ei_p1,
edge_attr=enc_ea_p1,
u=u1,
batch=btc_p1)
enc_x_p2, enc_ea_p2, u2 = self.meta1(x=enc_x_p2,
edge_index=ei_p2,
edge_attr=enc_ea_p2,
u=u2,
batch=btc_p2)
enc_x_pm, enc_ea_pm, um = self.meta1(x=enc_x_pm,
edge_index=ei_pm,
edge_attr=enc_ea_pm,
u=um,
batch=btc_pm)
# --- GRAPH GENERATION SECOND LEVEL
#Encode the nodes second level
u1 = self.encoding_node_2(u1)
u2 = self.encoding_node_2(u2)
um = self.encoding_node_2(um)
# Instantiate new Batch object for second level graph
nu_Batch = Batch()
# Create edge indices for the second level (no connection between p1 and p2
nu_ei = []
temp_ei = torch.tensor([[0, 2, 1, 2], [2, 0, 2, 1]], dtype=torch.long)
for b in range(self.batch_size):
nu_ei.append(
temp_ei + b * 3
) # +3 because this is the number of nodes in the second stage graph
nu_Batch.edge_index = torch.cat(nu_ei, dim=1)
# Create the edge features for the second level graphs from the first level "u"s
p1_div_pm = u_p1 / u_pm
p2_div_pm = u_p2 / u_pm
#p1_div_p2 = u_p1 / (u_p2-1)
# Concatenate the temperature and molecular percentages
concat_T_p1pm = torch.transpose(torch.stack([Temperature, p1_div_pm]),
0, 1)
concat_T_p2pm = torch.transpose(torch.stack([Temperature, p2_div_pm]),
0, 1)
# Encode the edge features
concat_T_p1pm = self.encoding_edge_2(concat_T_p1pm)
concat_T_p2pm = self.encoding_edge_2(concat_T_p2pm)
nu_ea = []
for b in range(self.batch_size):
temp_ea = []
# Appending twice because of bidirectional edges
temp_ea.append(concat_T_p1pm[b])
temp_ea.append(concat_T_p1pm[b])
temp_ea.append(concat_T_p2pm[b])
temp_ea.append(concat_T_p2pm[b])
nu_ea.append(torch.stack(temp_ea, dim=0))
nu_Batch.edge_attr = torch.cat(nu_ea, dim=0)
# Create new nodes in the batch
nu_x = []
for b in range(self.batch_size):
temp_x = []
temp_x.append(u1[b])
temp_x.append(u2[b])
temp_x.append(um[b])
nu_x.append(torch.stack(temp_x, dim=0))
nu_Batch.x = torch.cat(nu_x, dim=0)
# Create new graph level target
gamma = torch.full(size=(self.batch_size, NO_GRAPH_FEATURES_TWO),
fill_value=0.1,
dtype=torch.float)
nu_Batch.y = gamma
# Create new batch
nu_btc = []
for b in range(self.batch_size):
nu_btc.append(
torch.full(size=[3], fill_value=(int(b)), dtype=torch.long))
nu_Batch.batch = torch.cat(nu_btc, dim=0)
nu_Batch = nu_Batch.to(device)
# --- MESSAGE PASSING LVL 2
for _ in range(self.no_mp_two):
nu_Batch.x, nu_Batch.edge_attr, nu_Batch.y = self.meta3(
x=nu_Batch.x,
edge_index=nu_Batch.edge_index,
edge_attr=nu_Batch.edge_attr,
u=nu_Batch.y,
batch=nu_Batch.batch)
c = self.mlp_last(nu_Batch.y)
return c
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()
for key in keys:
batch[key] = []
for key in follow_batch:
batch['{}_batch'.format(key)] = []
cumsum = {}
batch.batch = []
for i, data in enumerate(data_list):
for key in data.keys:
item = data[key] + cumsum.get(key, 0)
if key in cumsum:
if key == 'edge_index':
cumsum[key] += data['x_numeric'].shape[0]
if key == 'subgraphs_nodes':
cumsum[key] += data['x_numeric'].shape[0]
if key == 'subgraphs_edges':
cumsum[key] += data['edge_attr_numeric'].shape[0]
if key == 'subgraphs_connectivity':
cumsum[key] += data['subgraphs_nodes'].shape[0]
if key == 'subgraphs_nodes_path_batch':
cumsum[key] += data['subgraphs_nodes_path_batch'].max() + 1
if key == 'subgraphs_edges_path_batch':
cumsum[key] += data['subgraphs_edges_path_batch'].max() + 1
if key == 'subgraphs_global_path_batch':
cumsum[key] += 1
if key == 'cycles_id':
if data['cycles_id'].shape[0] > 0:
cumsum[key] += data['cycles_id'].max() + 1
if key == 'cycles_edge_index':
cumsum[key] += data['edge_attr_numeric'].shape[0]
if key == 'edges_connectivity_ids':
cumsum[key] += data['edge_attr_numeric'].shape[0]
else:
if key == 'edge_index':
cumsum[key] = data['x_numeric'].shape[0]
if key == 'subgraphs_nodes':
cumsum[key] = data['x_numeric'].shape[0]
if key == 'subgraphs_edges':
cumsum[key] = data['edge_attr_numeric'].shape[0]
if key == 'subgraphs_connectivity':
cumsum[key] = data['subgraphs_nodes'].shape[0]
if key == 'subgraphs_nodes_path_batch':
cumsum[key] = data['subgraphs_nodes_path_batch'].max() + 1
if key == 'subgraphs_edges_path_batch':
cumsum[key] = data['subgraphs_edges_path_batch'].max() + 1
if key == 'subgraphs_global_path_batch':
cumsum[key] = 1
if key == 'cycles_id':
if data['cycles_id'].shape[0] > 0:
cumsum[key] = data['cycles_id'].max() + 1
if key == 'cycles_edge_index':
cumsum[key] = data['edge_attr_numeric'].shape[0]
if key == 'edges_connectivity_ids':
cumsum[key] = data['edge_attr_numeric'].shape[0]
batch[key].append(item)
for key in follow_batch:
size = data[key].size(data.__cat_dim__(key, data[key]))
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):
if key not in ['subgraphs_connectivity', 'edges_connectivity_ids']:
batch[key] = torch.cat(
batch[key], dim=data_list[0].__cat_dim__(key, item))
else:
batch[key] = torch.cat(
batch[key], dim=-1)
elif isinstance(item, int) or isinstance(item, float):
batch[key] = torch.tensor(batch[key])
else:
raise ValueError('Unsupported attribute type.')
# Copy custom data functions to batch (does not work yet):
# if data_list.__class__ != Data:
# org_funcs = set(Data.__dict__.keys())
# funcs = set(data_list[0].__class__.__dict__.keys())
# batch.__custom_funcs__ = funcs.difference(org_funcs)
# for func in funcs.difference(org_funcs):
# setattr(batch, func, getattr(data_list[0], func))
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()
