set y's points
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@@ -25,7 +25,9 @@ from sklearn.model_selection import train_test_split
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import utils as utils
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from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
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from diffusion.distributions import DistributionNodes
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# from naswot.score_networks import get_nasbench201_idx_score
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from naswot.score_networks import get_nasbench201_idx_score
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from naswot import nasspace
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from naswot import datasets as dt
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import networkx as nx
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@@ -682,7 +684,7 @@ class Dataset(InMemoryDataset):
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data_list = []
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# len_data = len(self.api)
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len_data = 1000
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len_data = 15625
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def check_valid_graph(nodes, edges):
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if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]:
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return False
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@@ -745,11 +747,9 @@ class Dataset(InMemoryDataset):
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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):
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def graph_to_graph_data(graph, idx, train_loader, searchspace, args, device):
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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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@@ -770,12 +770,49 @@ 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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data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
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# y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
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y = get_nasbench201_idx_score(idx, train_loader, searchspace, args, device)
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print(y, idx)
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if y > 1600:
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print(f'idx={idx}, y={y}')
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y = torch.tensor([1, 1], dtype=torch.float).view(1, -1)
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data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
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else:
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print(f'idx={idx}, y={y}')
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y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
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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 None
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return data
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graph_list = []
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class Args:
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pass
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args = Args()
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args.trainval = True
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args.augtype = 'none'
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args.repeat = 1
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args.score = 'hook_logdet'
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args.sigma = 0.05
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args.nasspace = 'nasbench201'
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args.batch_size = 128
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args.GPU = '0'
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args.dataset = 'cifar10'
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args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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args.data_loc = '../cifardata/'
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args.seed = 777
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args.init = ''
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args.save_loc = 'results'
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args.save_string = 'naswot'
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args.dropout = False
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args.maxofn = 1
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args.n_samples = 100
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args.n_runs = 500
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args.stem_out_channels = 16
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args.num_stacks = 3
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args.num_modules_per_stack = 3
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args.num_labels = 1
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searchspace = nasspace.get_search_space(args)
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train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
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device = torch.device('cuda:2')
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with tqdm(total = len_data) as pbar:
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active_nodes = set()
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file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
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@@ -785,6 +822,7 @@ class Dataset(InMemoryDataset):
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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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print(f'iterate every graph in graph_list, here is {i}')
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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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@@ -796,8 +834,11 @@ class Dataset(InMemoryDataset):
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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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data = graph_to_graph_data((adj_matrix, ops))
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data = graph_to_graph_data((adj_matrix, ops),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device)
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i += 1
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if data is None:
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pbar.update(1)
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continue
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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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@@ -816,18 +857,12 @@ class Dataset(InMemoryDataset):
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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=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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# 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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# 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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@@ -859,6 +894,7 @@ class Dataset(InMemoryDataset):
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# "seed": seed,
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# }for seed, result in results.items()]
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# })
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# i += 1
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pbar.update(1)
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for graph in graph_list:
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@@ -872,8 +908,8 @@ 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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# 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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@@ -1148,7 +1184,8 @@ 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'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-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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graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
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# check first five graphs
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for i in range(5):
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