add the results
This commit is contained in:
@@ -70,7 +70,7 @@ class DataModule(AbstractDataModule):
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# base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
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# except NameError:
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# base_path = pathlib.Path(os.getcwd()).parent[2]
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base_path = '/home/stud/hanzhang/nasbenchDiT'
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base_path = '/nfs/data3/hanzhang/nasbenchDiT'
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root_path = os.path.join(base_path, self.datadir)
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self.root_path = root_path
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@@ -408,6 +408,7 @@ def new_graphs_to_json(graphs, filename):
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adj = graph[0]
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n_node = len(ops)
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print(n_node)
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n_edge = len(ops)
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n_node_list.append(n_node)
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n_edge_list.append(n_edge)
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@@ -489,7 +490,7 @@ def new_graphs_to_json(graphs, filename):
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'transition_E': transition_E.tolist(),
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}
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with open(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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json.dump(meta_dict, f)
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return meta_dict
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@@ -655,7 +656,7 @@ def graphs_to_json(graphs, filename):
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class Dataset(InMemoryDataset):
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def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
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self.target_prop = target_prop
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source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.source = source
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# self.api = API(source) # Initialize NAS-Bench-201 API
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# print('API loaded')
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@@ -676,8 +677,8 @@ class Dataset(InMemoryDataset):
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return [f'{self.source}.pt']
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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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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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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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@@ -710,14 +711,24 @@ 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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# print(f'arch_str: {arch_str}, \nmax_nodes: {max_nodes}, min_nodes: {min_nodes}, nodes_num: {nodes_num},random_seed: {random_seed},random_ratio: {random_ratio}')
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add_num = nodes_num - nasbench_201_node_num
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# ori_nodes, ori_edges = parse_architecture_string(arch_str)
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add_nodes = [op for op in random.choices(num_to_op[1:-1], k=add_num)]
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add_nodes = []
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print(f'add_num: {add_num}')
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for i in range(add_num):
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add_nodes.append(random.choice(num_to_op[1:-1]))
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# print(add_nodes)
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print(f'ori_nodes[:-1]: {ori_nodes[:-1]}, add_nodes: {add_nodes}')
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print(f'len(ori_nodes[:-1]): {len(ori_nodes[:-1])}, len(add_nodes): {len(add_nodes)}')
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nodes = ori_nodes[:-1] + add_nodes + ['output']
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edges = np.zeros((nodes_num , nodes_num))
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edges[:6, :6] = ori_edges[:6, :6]
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@@ -727,12 +738,18 @@ 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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if nodes_num < max_nodes:
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edges = np.pad(edges, ((0, max_nodes - nodes_num), (0, max_nodes - nodes_num)), 'constant',constant_values=0)
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while len(nodes) < max_nodes:
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nodes.append('none')
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print(f'edges size: {edges.shape}, nodes size: {len(nodes)}')
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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 +770,95 @@ 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 = '/nfs/data3/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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flex_graph_list = []
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flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
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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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# with open(flex_graph_path, 'a') as f:
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# flex_graph = {
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# 'adj_matrix': adj_matrix,
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# 'ops': ops,
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# }
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# json.dump(flex_graph, f)
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flex_graph_list.append({
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'adj_matrix':adj_matrix,
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'ops': ops,
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})
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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=9, random_ratio=0.5)
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flex_graph_list.append({
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'adj_matrix':new_adj.tolist(),
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'ops': new_ops,
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})
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# with open(flex_graph_path, 'w') as f:
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# flex_graph = {
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# 'adj_matrix': new_adj.tolist(),
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# 'ops': new_ops,
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# }
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# json.dump(flex_graph, f)
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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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@@ -818,6 +872,8 @@ class Dataset(InMemoryDataset):
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graph['ops'] = ops
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with open(f'nasbench-201-graph.json', 'w') as f:
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json.dump(graph_list, f)
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with open(flex_graph_path, 'w') as f:
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json.dump(flex_graph_list, f)
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torch.save(self.collate(data_list), self.processed_paths[0])
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@@ -981,18 +1037,29 @@ 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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@@ -1004,7 +1071,21 @@ def parse_architecture_string(arch_str):
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cont += 1
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nodes.append(n)
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nodes.append('output') # Add output node
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return nodes, adj_mat
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ori_nodes = nodes.copy()
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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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# print(f'len(ori_nodes): {len(ori_nodes)}, len(nodes): {len(nodes)}')
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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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@@ -1046,6 +1127,7 @@ class DataInfos(AbstractDatasetInfos):
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adj_ops_pairs = []
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for item in data:
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print(item)
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adj_matrix = np.array(item['adj_matrix'])
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ops = item['ops']
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ops = [op_type[op] for op in ops]
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@@ -1066,12 +1148,12 @@ class DataInfos(AbstractDatasetInfos):
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# ops_type[op] = len(ops_type)
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# len_ops.add(len(ops))
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# graphs.append((adj_matrix, ops))
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graphs = read_adj_ops_from_json(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
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graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json')
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# check first five graphs
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for i in range(5):
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print(f'graph {i} : {graphs[i]}')
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print(f'ops_type: {ops_type}')
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# print(f'ops_type: {ops_type}')
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meta_dict = new_graphs_to_json(graphs, 'nasbench-201')
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self.base_path = base_path
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@@ -1280,11 +1362,11 @@ def compute_meta(root, source_name, train_index, test_index):
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'transition_E': tansition_E.tolist(),
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}
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with open(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
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with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
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json.dump(meta_dict, f)
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return meta_dict
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if __name__ == "__main__":
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dataset = Dataset(source='nasbench', root='/home/stud/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
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dataset = Dataset(source='nasbench', root='/nfs/data3/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
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Block a user