23 KiB
23 KiB
0626 dataset.py
class DataModule:
def prepare_data(self) -> None:
dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None)
class Dataset:
Dataset is the subclass of InMemoryDataset from torch_geometric.data module.
super().__init__(root, transform, pre_transform)
def init(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
initiliaze the nasbench api and load the dataset.
self.data and self.slices are loaded from the nasbench api.
try to print the data of self.data and self.slices
get
self.data=Data(x=[125000], edge_index=[2, 93750], edge_attr=[93750, 1], y=[15625, 5]),
self.slices=defaultdict(<class 'dict'>, {'x': tensor([ 0, 8, 16, ..., 124984, 124992, 125000]),
'edge_index': tensor([ 0, 6, 12, ..., 93738, 93744, 93750]),
'edge_attr': tensor([ 0, 6, 12, ..., 93738, 93744, 93750]),
'y': tensor([ 0, 1, 2, ..., 15623, 15624, 15625])})
The original get :
click to expand
self.data: Data(x=[16599], edge_index=[2, 36132], edge_attr=[36132], y=[553, 5], idx=[553]),
self.slices: defaultdict(<class 'dict'>, {'x': tensor([ 0, 9, 32, 56, 78, 113, 138, 157, 173, 203,
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'edge_index': tensor([ 0, 18, 70, 124, 172, 250, 308, 350, 384, 454,
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'edge_attr': tensor([ 0, 18, 70, 124, 172, 250, 308, 350, 384, 454,
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'y': tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
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532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,
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'idx': tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
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504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517,
518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531,
532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,
546, 547, 548, 549, 550, 551, 552, 553])})
The different keys in the dictionary are:
self.dataedge_attr: In nasbenchDiT, we get (edge_number, 1) size, but in DiT, we get (edge_number) dimension.idx: we do not have this key in nasbenchDiT.
self.slices: same withself.data
So I try to modify the self.data and self.slices to be compatible with the DiT dataset.
DataInfos
now need a dataset to init the datainfo, because I need the nasbench api in the dataset
self.api = dataset.api
get_train_smiles() vs. get_train_graphs()
We want to remove molecules from the dataset, so we need to rerwrite the get_train_smiles() function to get_train_graphs() function.
20240628
change the dataset ready for DiT
Changed the process function in the dataset class to be compatible with the DiT dataset.
def process(self):
data_list = []
len_data = len(self.api)
def graph_to_graph_data(graph):
ops = graph[1]
adj = graph[0]
nodes = []
for op in ops:
nodes.append(op_type[op])
x = torch.LongTensor(nodes)
edges_list = []
edge_type = []
for start in range(len(ops)):
for end in range(len(ops)):
if adj[start][end] == 1:
edges_list.append((start, end))
edge_type.append(1)
edges_list.append((end, start))
edge_type.append(1)
edge_index = torch.tensor(edges_list, dtype=torch.long).t()
edge_type = torch.tensor(edge_type, dtype=torch.long)
edge_attr = edge_type
y = torch.tensor([0], dtype=torch.float).view(1, -1)
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
return data
with tqdm(total = len_data) as pbar:
active_nodes = set()
for i in range(len_data):
arch_info = self.api.query_meta_info_by_index(i)
nodes, edges = parse_architecture_string(arch_info.arch_str)
adj_matrix, ops = create_adj_matrix_and_ops(nodes, edges)
for op in ops:
if op not in active_nodes:
active_nodes.add(op)
data = graph_to_graph_data((adj_matrix, ops))
data_list.append(data)
pbar.update(1)
torch.save(self.collate(data_list), self.processed_paths[0])
20240629