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14 Commits
| Author | SHA1 | Date | |
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| d36e1d1077 | |||
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| a0473008a1 | |||
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| 1fa2d49c11 | |||
| 3c92e754d3 | |||
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| 4959c6c176 | |||
| b36ecd3ad0 | |||
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| 205f43291b |
Binary file not shown.
@@ -2,7 +2,7 @@ general:
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name: 'graph_dit'
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wandb: 'disabled'
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gpus: 1
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gpu_number: 0
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gpu_number: 2
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resume: null
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test_only: null
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sample_every_val: 2500
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@@ -31,6 +31,7 @@ model:
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lambda_train: [1, 10] # node and edge training weight
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ensure_connected: True
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train:
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# n_epochs: 5000
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n_epochs: 500
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batch_size: 1200
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lr: 0.0002
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@@ -1,20 +0,0 @@
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name: graphdit39
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dependencies:
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- python=3.9
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- fcd_torch==1.0.7
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- hydra-core==1.3.2
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- imageio==2.26.0
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- joblib==1.2.0
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- matplotlib==3.7.0
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- mini_moses==1.0
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- networkx==3.0
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- numpy==1.24.2
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- omegaconf==2.3.0
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- pandas==1.5.3
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- pytorch_lightning==2.0.1
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- rdkit==2023.9.4
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- scikit_learn==1.2.1
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- torch==2.0.0
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- torch_geometric==2.3.0
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- torchmetrics==0.11.4
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- tqdm==4.64.1
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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 +770,58 @@ 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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y = self.swap_scores[idx]
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print(y, idx)
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if y > 60000:
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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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self.swap_scores = []
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import csv
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# with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
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with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results_cifar100.csv', 'r') as f:
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reader = csv.reader(f)
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header = next(reader)
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data = [row for row in reader]
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self.swap_scores = [float(row[0]) for row in data]
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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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@@ -785,25 +831,17 @@ 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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# 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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print(f'iterate every graph in graph_list, here is {i}')
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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, 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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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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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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flex_graph_list.append({
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'adj_matrix':adj_matrix,
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'ops': ops,
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@@ -816,18 +854,12 @@ 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 +891,7 @@ 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 +905,8 @@ 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 +1181,8 @@ 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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@@ -356,7 +356,8 @@ class Graph_DiT(pl.LightningModule):
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to_generate = min(samples_left_to_generate, bs)
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to_save = min(samples_left_to_save, bs)
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chains_save = min(chains_left_to_save, bs)
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batch_y = test_y_collection[batch_id : batch_id + to_generate]
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# batch_y = test_y_collection[batch_id : batch_id + to_generate]
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batch_y = torch.ones(to_generate, self.ydim_output, device=self.device)
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cur_sample = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
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keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
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BIN
graph_dit/exp_201/barplog.png
Normal file
BIN
graph_dit/exp_201/barplog.png
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Binary file not shown.
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After Width: | Height: | Size: 30 KiB |
85
graph_dit/exp_201/main.py
Normal file
85
graph_dit/exp_201/main.py
Normal file
@@ -0,0 +1,85 @@
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import matplotlib.pyplot as plt
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import pandas as pd
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from nas_201_api import NASBench201API as API
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# from naswot.score_networks import get_nasbench201_idx_score
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# from naswot import datasets as dt
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# from naswot import nasspace
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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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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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api = API(source)
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# 示例百分数列表，精确到小数点后两位
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# percentages = [5.12, 15.78, 25.43, 35.22, 45.99, 55.34, 65.12, 75.68, 85.99, 95.25, 23.45, 12.34, 37.89, 58.67, 64.23, 72.15, 81.76, 99.99, 42.11, 61.58, 77.34, 14.56]
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percentages = []
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len_201 = 15625
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for i in range(len_201):
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# percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
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results = api.query_by_index(i, 'cifar10')
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result = results[111].get_eval('ori-test')
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percentages.append(result)
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# 定义10%区间
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bins = [i for i in range(0, 101, 10)]
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# 对数据进行分箱，计算每个区间的数据量
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hist, bin_edges = pd.cut(percentages, bins=bins, right=False, retbins=True, include_lowest=True)
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bin_counts = hist.value_counts().sort_index()
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total_counts = len(percentages)
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percentages_in_bins = (bin_counts / total_counts) * 100
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# 绘制条形图
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plt.figure(figsize=(10, 6))
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bars = plt.bar(bin_counts.index.astype(str), bin_counts.values, width=0.9, color='skyblue')
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for bar, percentage in zip(bars, percentages_in_bins):
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plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height(),
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f'{percentage:.2f}%', ha='center', va='bottom')
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# 添加标题和标签
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plt.title('Distribution of Percentages in 10% Intervals')
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plt.xlabel('Percentage Interval')
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plt.ylabel('Count')
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# 显示图表
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plt.xticks(rotation=45)
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plt.savefig('barplog.png')
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File diff suppressed because one or more lines are too long
@@ -86,7 +86,7 @@ class Denoiser(nn.Module):
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"""
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def forward(self, x, e, node_mask, y, t, unconditioned):
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print("Denoiser Forward")
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# print("Denoiser Forward")
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# print(x.shape, e.shape, y.shape, t.shape, unconditioned)
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force_drop_id = torch.zeros_like(y.sum(-1))
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# drop the nan values
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@@ -1 +1 @@
|
||||
{"source": "nasbench-201", "num_graph": 31250, "n_nodes_per_graph": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "max_n_nodes": 12, "max_n_edges": 12, "node_type_list": [0.08333333333333333, 0.12076, 0.121096, 0.12054933333333333, 0.120808, 0.35012, 0.08333333333333333, 0.0], "edge_type_list": [0.7757650537496, 0.22423494625039994], "valencies": [0.08333333333333333, 0.12076, 0.121096, 0.12054933333333333, 0.120808, 0.35012, 0.08333333333333333, 0.0], "active_nodes": ["*", "input", "nor_conv_1x1", "nor_conv_3x3", "avg_pool_3x3", "skip_connect", "none"], "num_active_nodes": 7, "transition_E": [[[1.0, 0.0], [0.4991939935961135, 0.5008060064038865], [0.5003633480874677, 0.4996366519125322], [0.49935849223554396, 0.500641507764456], [0.5018652186389422, 0.49813478136105777], [0.8275181842415934, 0.17248181575840665], [0.752416, 0.247584], [1.0, 0.0]], [[0.4991939935961135, 0.5008060064038865], [0.6929218744736044, 0.3070781255263956], [0.6891703482219348, 0.3108296517780652], [0.6909309288988885, 0.3090690711011114], [0.6876641745807234, 0.3123358254192766], [0.8913831706500085, 0.10861682934999148], [0.3948327260682345, 0.6051672739317655], [1.0, 0.0]], [[0.5003633480874677, 0.4996366519125322], [0.6891703482219348, 0.3108296517780652], [0.6877141129844832, 0.3122858870155169], [0.6899900524354673, 0.3100099475645327], [0.6869198878799577, 0.3130801121200423], [0.8910209102091021, 0.1089790897908979], [0.39503644491422785, 0.6049635550857722], [1.0, 0.0]], [[0.49935849223554396, 0.500641507764456], [0.6909309288988885, 0.3090690711011114], [0.6899900524354673, 0.3100099475645327], [0.6918940854215279, 0.30810591457847214], [0.6933245431647987, 0.30667545683520125], [0.8933821584543675, 0.10661784154563249], [0.3977348139627483, 0.6022651860372517], [1.0, 0.0]], [[0.5018652186389422, 0.49813478136105777], [0.6876641745807234, 0.3123358254192766], [0.6869198878799577, 0.3130801121200423], [0.6933245431647987, 0.30667545683520125], [0.6879391891891892, 0.31206081081081083], [0.8921497860953153, 0.10785021390468477], [0.39730260689137586, 0.6026973931086241], [1.0, 0.0]], [[0.8275181842415934, 0.17248181575840665], [0.8913831706500085, 0.10861682934999148], [0.8910209102091021, 0.1089790897908979], [0.8933821584543675, 0.10661784154563249], [0.8921497860953153, 0.10785021390468477], [0.9634043948311156, 0.03659560516888434], [0.79138581057923, 0.20861418942077004], [1.0, 0.0]], [[0.752416, 0.247584], [0.3948327260682345, 0.6051672739317655], [0.39503644491422785, 0.6049635550857722], [0.3977348139627483, 0.6022651860372517], [0.39730260689137586, 0.6026973931086241], [0.79138581057923, 0.20861418942077004], [1.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]]}
|
||||
{"source": "nasbench-201", "num_graph": 15625, "n_nodes_per_graph": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "max_n_nodes": 8, "max_n_edges": 8, "node_type_list": [0.125, 0.15, 0.15, 0.15, 0.15, 0.15, 0.125, 0.0], "edge_type_list": [0.6666666666666666, 0.3333333333333333], "valencies": [0.125, 0.15, 0.15, 0.15, 0.15, 0.15, 0.125, 0.0], "active_nodes": ["*", "input", "nor_conv_1x1", "nor_conv_3x3", "avg_pool_3x3", "skip_connect", "none"], "num_active_nodes": 7, "transition_E": [[[1.0, 0.0], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [1.0, 0.0], [1.0, 0.0]], [[0.5, 0.5], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.5, 0.5], [1.0, 0.0]], [[0.5, 0.5], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.5, 0.5], [1.0, 0.0]], [[0.5, 0.5], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.5, 0.5], [1.0, 0.0]], [[0.5, 0.5], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.5, 0.5], [1.0, 0.0]], [[0.5, 0.5], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.7333333333333333, 0.26666666666666666], [0.5, 0.5], [1.0, 0.0]], [[1.0, 0.0], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [1.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]]}
|
||||
File diff suppressed because one or more lines are too long
@@ -49,7 +49,7 @@ def get_data(dataset, data_loc, trainval, batch_size, augtype, repeat, args, pin
|
||||
val_acc_type = 'x-valid'
|
||||
|
||||
if trainval and 'cifar10' in dataset:
|
||||
cifar_split = load_config('config_utils/cifar-split.txt', None, None)
|
||||
cifar_split = load_config('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/naswot/naswot/config_utils/cifardata/cifar-split.txt', None, None)
|
||||
train_split, valid_split = cifar_split.train, cifar_split.valid
|
||||
if repeat > 0:
|
||||
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
|
||||
|
||||
1
graph_dit/naswot/naswot/nasbench
Submodule
1
graph_dit/naswot/naswot/nasbench
Submodule
Submodule graph_dit/naswot/naswot/nasbench added at b94247037e
@@ -12,41 +12,41 @@ import time
|
||||
from naswot.models import get_cell_based_tiny_net
|
||||
from naswot.utils import add_dropout, init_network
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS Without Training')
|
||||
parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder')
|
||||
parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth',
|
||||
type=str, help='path to API')
|
||||
parser.add_argument('--save_loc', default='results', type=str, help='folder to save results')
|
||||
parser.add_argument('--save_string', default='naswot', type=str, help='prefix of results file')
|
||||
parser.add_argument('--score', default='hook_logdet', type=str, help='the score to evaluate')
|
||||
parser.add_argument('--nasspace', default='nasbench201', type=str, help='the nas search space to use')
|
||||
parser.add_argument('--batch_size', default=128, type=int)
|
||||
parser.add_argument('--repeat', default=1, type=int, help='how often to repeat a single image with a batch')
|
||||
parser.add_argument('--augtype', default='none', type=str, help='which perturbations to use')
|
||||
parser.add_argument('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"')
|
||||
parser.add_argument('--GPU', default='0', type=str)
|
||||
parser.add_argument('--seed', default=1, type=int)
|
||||
parser.add_argument('--init', default='', type=str)
|
||||
parser.add_argument('--trainval', action='store_true')
|
||||
parser.add_argument('--dropout', action='store_true')
|
||||
parser.add_argument('--dataset', default='cifar10', type=str)
|
||||
parser.add_argument('--maxofn', default=1, type=int, help='score is the max of this many evaluations of the network')
|
||||
parser.add_argument('--n_samples', default=100, type=int)
|
||||
parser.add_argument('--n_runs', default=500, type=int)
|
||||
parser.add_argument('--stem_out_channels', default=16, type=int, help='output channels of stem convolution (nasbench101)')
|
||||
parser.add_argument('--num_stacks', default=3, type=int, help='#stacks of modules (nasbench101)')
|
||||
parser.add_argument('--num_modules_per_stack', default=3, type=int, help='#modules per stack (nasbench101)')
|
||||
parser.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)')
|
||||
# parser = argparse.ArgumentParser(description='NAS Without Training')
|
||||
# parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder')
|
||||
# parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth',
|
||||
# type=str, help='path to API')
|
||||
# parser.add_argument('--save_loc', default='results', type=str, help='folder to save results')
|
||||
# parser.add_argument('--save_string', default='naswot', type=str, help='prefix of results file')
|
||||
# parser.add_argument('--score', default='hook_logdet', type=str, help='the score to evaluate')
|
||||
# parser.add_argument('--nasspace', default='nasbench201', type=str, help='the nas search space to use')
|
||||
# parser.add_argument('--batch_size', default=128, type=int)
|
||||
# parser.add_argument('--repeat', default=1, type=int, help='how often to repeat a single image with a batch')
|
||||
# parser.add_argument('--augtype', default='none', type=str, help='which perturbations to use')
|
||||
# parser.add_argument('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"')
|
||||
# parser.add_argument('--GPU', default='0', type=str)
|
||||
# parser.add_argument('--seed', default=1, type=int)
|
||||
# parser.add_argument('--init', default='', type=str)
|
||||
# parser.add_argument('--trainval', action='store_true')
|
||||
# parser.add_argument('--dropout', action='store_true')
|
||||
# parser.add_argument('--dataset', default='cifar10', type=str)
|
||||
# parser.add_argument('--maxofn', default=1, type=int, help='score is the max of this many evaluations of the network')
|
||||
# parser.add_argument('--n_samples', default=100, type=int)
|
||||
# parser.add_argument('--n_runs', default=500, type=int)
|
||||
# parser.add_argument('--stem_out_channels', default=16, type=int, help='output channels of stem convolution (nasbench101)')
|
||||
# parser.add_argument('--num_stacks', default=3, type=int, help='#stacks of modules (nasbench101)')
|
||||
# parser.add_argument('--num_modules_per_stack', default=3, type=int, help='#modules per stack (nasbench101)')
|
||||
# parser.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)')
|
||||
|
||||
args = parser.parse_args()
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
|
||||
# args = parser.parse_args()
|
||||
# os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
|
||||
|
||||
# Reproducibility
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
random.seed(args.seed)
|
||||
np.random.seed(args.seed)
|
||||
torch.manual_seed(args.seed)
|
||||
# # Reproducibility
|
||||
# torch.backends.cudnn.deterministic = True
|
||||
# torch.backends.cudnn.benchmark = False
|
||||
# random.seed(args.seed)
|
||||
# np.random.seed(args.seed)
|
||||
# torch.manual_seed(args.seed)
|
||||
|
||||
|
||||
def get_batch_jacobian(net, x, target, device, args=None):
|
||||
@@ -58,10 +58,16 @@ def get_batch_jacobian(net, x, target, device, args=None):
|
||||
return jacob, target.detach(), y.detach(), out.detach()
|
||||
|
||||
def get_config_by_nodes(nodes):
|
||||
num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
|
||||
arch_str = '|' + num_to_op[nodes[1]] + '~0|+|' + \
|
||||
# check if the nodes[0] is a number
|
||||
if isinstance(nodes[0], int):
|
||||
num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
|
||||
arch_str = '|' + num_to_op[nodes[1]] + '~0|+|' + \
|
||||
num_to_op[nodes[2]] + '~0|' + num_to_op[nodes[3]] + '~1|+|' + \
|
||||
num_to_op[nodes[4]] + '~0|' + num_to_op[nodes[5]] + '~1|' + num_to_op[nodes[6]] + '~2|'
|
||||
else:
|
||||
arch_str = '|' + nodes[1] + '~0|+|' + \
|
||||
nodes[2] + '~0|' + nodes[3] + '~1|+|' + \
|
||||
nodes[4] + '~0|' + nodes[5] + '~1|' + nodes[6] + '~2|'
|
||||
config = {
|
||||
'name': 'infer.tiny',
|
||||
'C': 16,
|
||||
@@ -234,64 +240,64 @@ def get_nasbench201_idx_score(idx, train_loader, searchspace, args, device):
|
||||
print('final result')
|
||||
return np.nan
|
||||
|
||||
class Args:
|
||||
pass
|
||||
args = Args()
|
||||
args.trainval = True
|
||||
args.augtype = 'none'
|
||||
args.repeat = 1
|
||||
args.score = 'hook_logdet'
|
||||
args.sigma = 0.05
|
||||
args.nasspace = 'nasbench201'
|
||||
args.batch_size = 128
|
||||
args.GPU = '0'
|
||||
args.dataset = 'cifar10-valid'
|
||||
args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
|
||||
args.data_loc = '../cifardata/'
|
||||
args.seed = 777
|
||||
args.init = ''
|
||||
args.save_loc = 'results'
|
||||
args.save_string = 'naswot'
|
||||
args.dropout = False
|
||||
args.maxofn = 1
|
||||
args.n_samples = 100
|
||||
args.n_runs = 500
|
||||
args.stem_out_channels = 16
|
||||
args.num_stacks = 3
|
||||
args.num_modules_per_stack = 3
|
||||
args.num_labels = 1
|
||||
# class Args:
|
||||
# pass
|
||||
# args = Args()
|
||||
# args.trainval = True
|
||||
# args.augtype = 'none'
|
||||
# args.repeat = 1
|
||||
# args.score = 'hook_logdet'
|
||||
# args.sigma = 0.05
|
||||
# args.nasspace = 'nasbench201'
|
||||
# args.batch_size = 128
|
||||
# args.GPU = '0'
|
||||
# args.dataset = 'cifar10-valid'
|
||||
# args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
|
||||
# args.data_loc = '../cifardata/'
|
||||
# args.seed = 777
|
||||
# args.init = ''
|
||||
# args.save_loc = 'results'
|
||||
# args.save_string = 'naswot'
|
||||
# args.dropout = False
|
||||
# args.maxofn = 1
|
||||
# args.n_samples = 100
|
||||
# args.n_runs = 500
|
||||
# args.stem_out_channels = 16
|
||||
# args.num_stacks = 3
|
||||
# args.num_modules_per_stack = 3
|
||||
# args.num_labels = 1
|
||||
|
||||
if 'valid' in args.dataset:
|
||||
args.dataset = args.dataset.replace('-valid', '')
|
||||
print('start to get search space')
|
||||
start_time = time.time()
|
||||
print(get_config_by_nodes(nodes=[0,2,2,3,4,2,4,6]))
|
||||
end_time = time.time()
|
||||
start_time = time.time()
|
||||
searchspace = nasspace.get_search_space(args)
|
||||
end_time = time.time()
|
||||
print(f'search space time: {end_time - start_time}')
|
||||
train_loader = datasets.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
|
||||
print('start to get score')
|
||||
print('5374')
|
||||
num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
|
||||
start_time = time.time()
|
||||
print(get_nasbench201_nodes_score(nodes=[0,2,2,3,4,2,4,6],train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
end_time = time.time()
|
||||
start_time = time.time()
|
||||
print(get_nasbench201_idx_score(5374,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
end_time = time.time()
|
||||
print(f'5374 time: {end_time - start_time}')
|
||||
print('5375')
|
||||
start_time = time.time()
|
||||
print(get_nasbench201_idx_score(5375,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
end_time = time.time()
|
||||
print(f'5375 time: {end_time - start_time}')
|
||||
print('5376')
|
||||
start_time = time.time()
|
||||
print(get_nasbench201_idx_score(5376,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
end_time = time.time()
|
||||
print(f'5376 time: {end_time - start_time}')
|
||||
# if 'valid' in args.dataset:
|
||||
# args.dataset = args.dataset.replace('-valid', '')
|
||||
# print('start to get search space')
|
||||
# start_time = time.time()
|
||||
# print(get_config_by_nodes(nodes=[0,2,2,3,4,2,4,6]))
|
||||
# end_time = time.time()
|
||||
# start_time = time.time()
|
||||
# searchspace = nasspace.get_search_space(args)
|
||||
# end_time = time.time()
|
||||
# print(f'search space time: {end_time - start_time}')
|
||||
# train_loader = datasets.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
|
||||
# print('start to get score')
|
||||
# print('5374')
|
||||
# num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
|
||||
# start_time = time.time()
|
||||
# print(get_nasbench201_nodes_score(nodes=[0,2,2,3,4,2,4,6],train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
# end_time = time.time()
|
||||
# start_time = time.time()
|
||||
# print(get_nasbench201_idx_score(5374,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
# end_time = time.time()
|
||||
# print(f'5374 time: {end_time - start_time}')
|
||||
# print('5375')
|
||||
# start_time = time.time()
|
||||
# print(get_nasbench201_idx_score(5375,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
# end_time = time.time()
|
||||
# print(f'5375 time: {end_time - start_time}')
|
||||
# print('5376')
|
||||
# start_time = time.time()
|
||||
# print(get_nasbench201_idx_score(5376,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
|
||||
# end_time = time.time()
|
||||
# print(f'5376 time: {end_time - start_time}')
|
||||
|
||||
# device = "cuda:0"
|
||||
# dataset = dataset
|
||||
|
||||
@@ -3,5 +3,8 @@ from setuptools import setup, find_packages
|
||||
setup(
|
||||
name='naswot',
|
||||
version='0.1',
|
||||
packages=find_packages()
|
||||
packages=find_packages(),
|
||||
package_data={
|
||||
'naswot': ['config_utils/cifardata/*']
|
||||
}
|
||||
)
|
||||
101
graph_dit/output_graphs/151042.txt
Normal file
101
graph_dit/output_graphs/151042.txt
Normal file
@@ -0,0 +1,101 @@
|
||||
graph, input_nasbench201, output_nasbench201
|
||||
nodes: tensor(0) tensor(4) tensor(5) tensor(3) tensor(3) tensor(1) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.152663734901322
|
||||
nodes: tensor(0) tensor(1) tensor(5) tensor(4) tensor(3) tensor(4) tensor(2) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.3023566097075212
|
||||
nodes: tensor(0) tensor(1) tensor(5) tensor(4) tensor(3) tensor(5) tensor(3) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.062036414714562005
|
||||
nodes: tensor(0) tensor(5) tensor(4) tensor(3) tensor(5) tensor(5) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.4598603416089688
|
||||
nodes: tensor(0) tensor(3) tensor(5) tensor(3) tensor(5) tensor(5) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.8352533844376283
|
||||
nodes: tensor(0) tensor(3) tensor(4) tensor(3) tensor(2) tensor(4) tensor(2) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.9269970477498947
|
||||
nodes: tensor(0) tensor(1) tensor(5) tensor(5) tensor(3) tensor(4) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.7269889843190701
|
||||
nodes: tensor(0) tensor(2) tensor(1) tensor(1) tensor(4) tensor(3) tensor(4) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.7684962219206448
|
||||
nodes: tensor(0) tensor(1) tensor(2) tensor(1) tensor(4) tensor(3) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.2692050664721932
|
||||
nodes: tensor(0) tensor(5) tensor(1) tensor(2) tensor(4) tensor(3) tensor(2) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.6440292920709894
|
||||
nodes: tensor(0) tensor(5) tensor(5) tensor(1) tensor(1) tensor(5) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.09337325682922837
|
||||
nodes: tensor(0) tensor(1) tensor(2) tensor(5) tensor(3) tensor(2) tensor(3) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.07968588561148293
|
||||
nodes: tensor(0) tensor(4) tensor(2) tensor(2) tensor(5) tensor(3) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.5896137534558643
|
||||
nodes: tensor(0) tensor(4) tensor(1) tensor(5) tensor(3) tensor(5) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.3433405382219368
|
||||
nodes: tensor(0) tensor(4) tensor(4) tensor(4) tensor(5) tensor(3) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.9888761485727554
|
||||
nodes: tensor(0) tensor(3) tensor(4) tensor(4) tensor(5) tensor(2) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.626473206229759
|
||||
nodes: tensor(0) tensor(3) tensor(4) tensor(2) tensor(5) tensor(1) tensor(2) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.6817792797809532
|
||||
nodes: tensor(0) tensor(5) tensor(5) tensor(3) tensor(3) tensor(5) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.5522568137371598
|
||||
nodes: tensor(0) tensor(5) tensor(1) tensor(3) tensor(5) tensor(2) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.2688600578882592
|
||||
nodes: tensor(0) tensor(1) tensor(1) tensor(2) tensor(2) tensor(1) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.37325938643684664
|
||||
nodes: tensor(0) tensor(3) tensor(4) tensor(5) tensor(3) tensor(2) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.2229280989962561
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1
graph_dit/output_graphs/151042.txt.json
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1
graph_dit/output_graphs/151042.txt.json
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101
graph_dit/output_graphs/151613.txt
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101
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graph, input_nasbench201, output_nasbench201
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1
graph_dit/output_graphs/151613.txt.json
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1
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101
graph_dit/output_graphs/161959.txt
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graph, input_nasbench201, output_nasbench201
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|
||||
2
graph_dit/output_graphs/161959.txt.json
Normal file
2
graph_dit/output_graphs/161959.txt.json
Normal file
@@ -0,0 +1,2 @@
|
||||
1600分以上
|
||||
[[3701, 81.88], [8730, 85.06], [8421, 10.0], [3578, 85.18], [7253, 73.97], [9307, 30.88], [11161, 78.98], [9354, 85.67], [11323, 79.28], [12638, 84.54], [6117, 84.47], [6876, 83.63], [4574, 73.42], [11272, 85.13], [2165, 85.24], [10798, 77.08], [5094, 70.43], [8793, 79.3], [3191, 74.31], [13138, 68.1], [6745, 80.82], [5207, 84.35], [7356, 74.59], [7561, 80.96], [351, 84.33], [2202, 79.85], [316, 79.86], [1509, 10.0], [8891, 81.05], [13653, 83.0], [7552, 78.67], [8050, 84.72], [220, 71.79], [4147, 78.07], [15300, 72.31], [2242, 78.95], [9631, 85.88], [9281, 85.86], [10855, 82.28], [12599, 86.94], [7269, 83.93], [8521, 60.16], [9991, 65.21], [7770, 83.5], [6995, 69.2], [14804, 76.73], [7737, 83.23], [7129, 85.89], [12183, 74.68], [12557, 77.58], [11349, 77.49], [10257, 79.58], [10405, 81.27], [14604, 84.12], [7439, 78.68], [46, 77.81], [4728, 85.11], [14286, 76.99], [5330, 76.07], [12556, 79.12], [4959, 85.47], [14090, 84.16], [13507, 85.03], [383, 68.91], [13362, 80.76], [4626, 70.49], [13617, 81.27], [5843, 10.0], [12256, 84.86], [10789, 78.03], [15455, 61.81], [11898, 76.73], [11073, 78.84], [10907, 80.0], [462, 78.04], [5293, 83.08], [6750, 79.18], [8082, 87.11], [11453, 66.1], [6565, 71.23], [4446, 70.95], [12301, 77.23], [12986, 81.49], [6208, 86.24], [1692, 84.16], [11177, 79.32], [418, 83.87], [14323, 83.36], [4856, 84.65], [14604, 84.12], [15064, 83.22], [14268, 85.39], [835, 80.18], [1668, 84.41], [6431, 85.32], [5191, 77.79], [1713, 73.72]]
|
||||
101
graph_dit/output_graphs/170944-1000>1600.txt
Normal file
101
graph_dit/output_graphs/170944-1000>1600.txt
Normal file
@@ -0,0 +1,101 @@
|
||||
graph, input_nasbench201, output_nasbench201
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2
graph_dit/output_graphs/170944.txt.json
Normal file
2
graph_dit/output_graphs/170944.txt.json
Normal file
@@ -0,0 +1,2 @@
|
||||
1600 分以上
|
||||
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||||
101
graph_dit/output_graphs/171452-15625>1600.txt
Normal file
101
graph_dit/output_graphs/171452-15625>1600.txt
Normal file
@@ -0,0 +1,101 @@
|
||||
graph, input_nasbench201, output_nasbench201
|
||||
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1
graph_dit/output_graphs/171452.txt.json
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1
graph_dit/output_graphs/171452.txt.json
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File diff suppressed because it is too large
Load Diff
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graph, input_nasbench201, output_nasbench201
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|
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nodes: tensor(0) tensor(3) tensor(4) tensor(4) tensor(2) tensor(1) tensor(5) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.9023312033135338
|
||||
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|
||||
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|
||||
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|
||||
nodes: tensor(0) tensor(5) tensor(3) tensor(3) tensor(5) tensor(4) tensor(5) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.10025992551975493
|
||||
nodes: tensor(0) tensor(4) tensor(5) tensor(3) tensor(5) tensor(2) tensor(1) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.2838407593739203
|
||||
nodes: tensor(0) tensor(5) tensor(5) tensor(5) tensor(2) tensor(2) tensor(5) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.24227967740337342
|
||||
nodes: tensor(0) tensor(1) tensor(2) tensor(5) tensor(3) tensor(3) tensor(1) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.5962977148254313
|
||||
nodes: tensor(0) tensor(4) tensor(5) tensor(3) tensor(2) tensor(4) tensor(3) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.6426282401919712
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
144
graph_dit/test_perf.py
Normal file
144
graph_dit/test_perf.py
Normal file
@@ -0,0 +1,144 @@
|
||||
from nas_201_api import NASBench201API as API
|
||||
import re
|
||||
import pandas as pd
|
||||
import json
|
||||
import numpy as np
|
||||
import argparse
|
||||
|
||||
api = API('./NAS-Bench-201-v1_1-096897.pth')
|
||||
|
||||
parser = argparse.ArgumentParser(description='Process some integers.')
|
||||
|
||||
parser.add_argument('--file_path', type=str, default='211035.txt',)
|
||||
parser.add_argument('--datasets', type=str, default='cifar10',)
|
||||
args = parser.parse_args()
|
||||
|
||||
def process_graph_data(text):
|
||||
# Split the input text into sections for each graph
|
||||
graph_sections = text.strip().split('nodes:')
|
||||
|
||||
# Prepare lists to store data
|
||||
nodes_list = []
|
||||
edges_list = []
|
||||
results_list = []
|
||||
|
||||
for section in graph_sections[1:]:
|
||||
# Extract nodes
|
||||
nodes_section = section.split('edges:')[0]
|
||||
nodes_match = re.search(r'(tensor\(\d+\) ?)+', section)
|
||||
if nodes_match:
|
||||
nodes = re.findall(r'tensor\((\d+)\)', nodes_match.group(0))
|
||||
nodes_list.append(nodes)
|
||||
|
||||
# Extract edges
|
||||
edge_section = section.split('edges:')[1]
|
||||
edges_match = re.search(r'edges:', section)
|
||||
if edges_match:
|
||||
edges = re.findall(r'tensor\((\d+)\)', edge_section)
|
||||
edges_list.append(edges)
|
||||
|
||||
# Extract the last floating point number as a result
|
||||
|
||||
# Create a DataFrame to store the extracted data
|
||||
data = {
|
||||
'nodes': nodes_list,
|
||||
'edges': edges_list,
|
||||
}
|
||||
data['nodes'] = [[int(x) for x in node] for node in data['nodes']]
|
||||
data['edges'] = [[int(x) for x in edge] for edge in data['edges']]
|
||||
def split_list(input_list, chunk_size):
|
||||
return [input_list[i:i + chunk_size] for i in range(0, len(input_list), chunk_size)]
|
||||
data['edges'] = [split_list(edge, 8) for edge in data['edges']]
|
||||
|
||||
print(data)
|
||||
df = pd.DataFrame(data)
|
||||
print('df')
|
||||
print(df['nodes'][0], df['edges'][0])
|
||||
return df
|
||||
|
||||
def is_valid_nasbench201(adj, ops):
|
||||
print(ops)
|
||||
if ops[0] != 0 or ops[-1] != 6:
|
||||
return False
|
||||
for i in range(2, len(ops) - 1):
|
||||
if ops[i] not in [1, 2, 3, 4, 5]:
|
||||
return False
|
||||
adj_mat = [ [0, 1, 1, 0, 1, 0, 0, 0],
|
||||
[0, 0, 0, 1, 0, 1 ,0 ,0],
|
||||
[0, 0, 0, 0, 0, 0, 1, 0],
|
||||
[0, 0, 0, 0, 0, 0, 1, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||||
|
||||
for i in range(len(adj)):
|
||||
for j in range(len(adj[i])):
|
||||
if adj[i][j] not in [0, 1]:
|
||||
return False
|
||||
if j > i:
|
||||
if adj[i][j] != adj_mat[i][j]:
|
||||
return False
|
||||
return True
|
||||
|
||||
num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
|
||||
def nodes_to_arch_str(nodes):
|
||||
nodes_str = [num_to_op[node] for node in nodes]
|
||||
arch_str = '|' + nodes_str[1] + '~0|+' + \
|
||||
'|' + nodes_str[2] + '~0|' + nodes_str[3] + '~1|+' +\
|
||||
'|' + nodes_str[4] + '~0|' + nodes_str[5] + '~1|' + nodes_str[6] + '~2|'
|
||||
return arch_str
|
||||
|
||||
filename = args.file_path
|
||||
datasets_name = args.datasets
|
||||
|
||||
with open('./output_graphs/' + filename, 'r') as f:
|
||||
texts = f.read()
|
||||
df = process_graph_data(texts)
|
||||
valid = 0
|
||||
not_valid = 0
|
||||
scores = []
|
||||
|
||||
# 定义分类标准和分布字典的映射
|
||||
thresholds = {
|
||||
'cifar10': [90, 91, 92, 93, 94],
|
||||
'cifar100': [68,69,70, 71, 72, 73]
|
||||
}
|
||||
dist = {f'<{threshold}': 0 for threshold in thresholds[datasets_name]}
|
||||
dist[f'>{thresholds[datasets_name][-1]}'] = 0
|
||||
|
||||
for i in range(len(df)):
|
||||
nodes = df['nodes'][i]
|
||||
edges = df['edges'][i]
|
||||
result = is_valid_nasbench201(edges, nodes)
|
||||
if result:
|
||||
valid += 1
|
||||
arch_str = nodes_to_arch_str(nodes)
|
||||
index = api.query_index_by_arch(arch_str)
|
||||
res = api.get_more_info(index, datasets_name, None, hp=200, is_random=False)
|
||||
acc = res['test-accuracy']
|
||||
scores.append((index, acc))
|
||||
|
||||
# 根据阈值更新分布
|
||||
updated = False
|
||||
for threshold in thresholds[datasets_name]:
|
||||
if acc < threshold:
|
||||
dist[f'<{threshold}'] += 1
|
||||
updated = True
|
||||
break
|
||||
if not updated:
|
||||
dist[f'>{thresholds[datasets_name][-1]}'] += 1
|
||||
else:
|
||||
not_valid += 1
|
||||
|
||||
with open('./output_graphs/' + filename + '_' + datasets_name +'.json', 'w') as f:
|
||||
json.dump(scores, f)
|
||||
|
||||
print(scores)
|
||||
print(valid, not_valid)
|
||||
print(dist)
|
||||
print("mean: ", np.mean([x[1] for x in scores]))
|
||||
print("max: ", np.max([x[1] for x in scores]))
|
||||
print("min: ", np.min([x[1] for x in scores]))
|
||||
|
||||
|
||||
Reference in New Issue
Block a user