add paddings
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@ -677,7 +677,7 @@ class Dataset(InMemoryDataset):
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def process(self):
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source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.api = API(source)
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# self.api = API(source)
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data_list = []
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# len_data = len(self.api)
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@ -710,6 +710,10 @@ class Dataset(InMemoryDataset):
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return True
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def generate_flex_adj_mat(ori_nodes, ori_edges, max_nodes=12, min_nodes=8,random_ratio=0.5):
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# print(ori_nodes)
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# print(ori_edges)
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ori_edges = np.array(ori_edges)
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# ori_nodes = np.array(ori_nodes)
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nasbench_201_node_num = 8
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# random.seed(random_seed)
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nodes_num = random.randint(min_nodes, max_nodes)
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@ -727,12 +731,13 @@ class Dataset(InMemoryDataset):
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rand = random.random()
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if rand < random_ratio:
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edges[i, j] = 1
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return nodes, edges
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return edges,nodes
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def get_nasbench_201_val(idx):
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pass
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def graph_to_graph_data(graph, idx):
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# def graph_to_graph_data(graph, idx):
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def graph_to_graph_data(graph):
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ops = graph[1]
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adj = graph[0]
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nodes = []
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@ -753,58 +758,73 @@ class Dataset(InMemoryDataset):
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edge_index = torch.tensor(edges_list, dtype=torch.long).t()
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edge_type = torch.tensor(edge_type, dtype=torch.long)
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edge_attr = edge_type
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# y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
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y = get_nasbench_201_val(idx)
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y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
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# y = get_nasbench_201_val(idx)
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data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
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return data
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graph_list = []
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with tqdm(total = len_data) as pbar:
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active_nodes = set()
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for i in range(len_data):
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arch_info = self.api.query_meta_info_by_index(i)
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results = self.api.query_by_index(i, 'cifar100')
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file_path = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
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with open(file_path, 'r') as f:
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graph_list = json.load(f)
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i = 0
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for graph in graph_list:
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# arch_info = self.api.query_meta_info_by_index(i)
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# results = self.api.query_by_index(i, 'cifar100')
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arch_info = graph['arch_str']
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# results =
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# nodes, edges = parse_architecture_string(arch_info.arch_str)
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ops, adj_matrix = parse_architecture_string(arch_info.arch_str)
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# ops, adj_matrix = parse_architecture_string(arch_info.arch_str, padding=4)
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ops, adj_matrix, ori_nodes, ori_adj = parse_architecture_string(arch_info, padding=4)
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# adj_matrix, ops = create_adj_matrix_and_ops(nodes, edges)
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for op in ops:
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if op not in active_nodes:
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active_nodes.add(op)
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graph_list.append({
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"adj_matrix": adj_matrix,
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"ops": ops,
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"idx": i,
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"train": [{
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"iepoch": result.get_train()['iepoch'],
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"loss": result.get_train()['loss'],
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"accuracy": result.get_train()['accuracy'],
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"cur_time": result.get_train()['cur_time'],
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"all_time": result.get_train()['all_time'],
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"seed": seed,
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}for seed, result in results.items()],
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"valid": [{
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"iepoch": result.get_eval('x-valid')['iepoch'],
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"loss": result.get_eval('x-valid')['loss'],
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"accuracy": result.get_eval('x-valid')['accuracy'],
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"cur_time": result.get_eval('x-valid')['cur_time'],
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"all_time": result.get_eval('x-valid')['all_time'],
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"seed": seed,
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}for seed, result in results.items()],
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"test": [{
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"iepoch": result.get_eval('x-test')['iepoch'],
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"loss": result.get_eval('x-test')['loss'],
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"accuracy": result.get_eval('x-test')['accuracy'],
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"cur_time": result.get_eval('x-test')['cur_time'],
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"all_time": result.get_eval('x-test')['all_time'],
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"seed": seed,
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}for seed, result in results.items()]
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})
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data = graph_to_graph_data((adj_matrix, ops))
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if i < 3:
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print(f"i={i}, data={data}")
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with open(f'{i}.json', 'w') as f:
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f.write(str(data.x))
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f.write(str(data.edge_index))
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f.write(str(data.edge_attr))
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data_list.append(data)
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# new_adj, new_ops = generate_flex_adj_mat(ori_nodes=ops, ori_edges=adj_matrix, max_nodes=12, min_nodes=8, random_ratio=0.5)
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# data_list.append(graph_to_graph_data((new_adj, new_ops)))
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new_adj, new_ops = generate_flex_adj_mat(ori_nodes=ori_nodes, ori_edges=ori_adj, max_nodes=12, min_nodes=8, random_ratio=0.5)
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data_list.append(graph_to_graph_data((new_adj, new_ops)))
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# graph_list.append({
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# "adj_matrix": adj_matrix,
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# "ops": ops,
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# "arch_str": arch_info.arch_str,
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# "idx": i,
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# "train": [{
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# "iepoch": result.get_train()['iepoch'],
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# "loss": result.get_train()['loss'],
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# "accuracy": result.get_train()['accuracy'],
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# "cur_time": result.get_train()['cur_time'],
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# "all_time": result.get_train()['all_time'],
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# "seed": seed,
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# }for seed, result in results.items()],
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# "valid": [{
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# "iepoch": result.get_eval('x-valid')['iepoch'],
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# "loss": result.get_eval('x-valid')['loss'],
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# "accuracy": result.get_eval('x-valid')['accuracy'],
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# "cur_time": result.get_eval('x-valid')['cur_time'],
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# "all_time": result.get_eval('x-valid')['all_time'],
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# "seed": seed,
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# }for seed, result in results.items()],
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# "test": [{
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# "iepoch": result.get_eval('x-test')['iepoch'],
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# "loss": result.get_eval('x-test')['loss'],
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# "accuracy": result.get_eval('x-test')['accuracy'],
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# "cur_time": result.get_eval('x-test')['cur_time'],
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# "all_time": result.get_eval('x-test')['all_time'],
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# "seed": seed,
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# }for seed, result in results.items()]
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# })
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pbar.update(1)
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for graph in graph_list:
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@ -981,18 +1001,29 @@ class Dataset_origin(InMemoryDataset):
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torch.save(self.collate(data_list), self.processed_paths[0])
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def parse_architecture_string(arch_str):
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def parse_architecture_string(arch_str, padding=0):
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# print(arch_str)
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steps = arch_str.split('+')
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nodes = ['input'] # Start with input node
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adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
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ori_adj_mat = [[0, 1, 1, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 1 ,0 ,0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 0]])
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# [0, 0, 0, 0, 0, 0, 0, 0]])
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[0, 0, 0, 0, 0, 0, 0, 0]]
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# adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
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adj_mat = [[0, 1, 1, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 1 ,0 ,0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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# [0, 0, 0, 0, 0, 0, 0, 0]])
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[0, 0, 0, 0, 0, 0, 0, 0]]
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steps = arch_str.split('+')
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steps_coding = ['0', '0', '1', '0', '1', '2']
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cont = 0
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@ -1003,8 +1034,22 @@ def parse_architecture_string(arch_str):
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assert idx == steps_coding[cont]
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cont += 1
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nodes.append(n)
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ori_nodes = nodes.copy()
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nodes.append('output') # Add output node
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return nodes, adj_mat
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if padding > 0:
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for i in range(padding):
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nodes.append('none')
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for adj_row in adj_mat:
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for i in range(padding):
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adj_row.append(0)
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# adj_mat = np.append(adj_mat, np.zeros((padding, len(nodes))))
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for i in range(padding):
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adj_mat.append([0] * len(nodes))
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# print(nodes)
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# print(adj_mat)
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# print(len(adj_mat))
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return nodes, adj_mat, ori_nodes, ori_adj_mat
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def create_adj_matrix_and_ops(nodes, edges):
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num_nodes = len(nodes)
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