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mhz
2024-08-20 21:57:47 +02:00
parent 3c92e754d3
commit 1fa2d49c11
1 changed files with 64 additions and 27 deletions
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graph_dit/datasets/dataset.py
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@@ -25,7 +25,9 @@ from sklearn.model_selection import train_test_split
import utils as utils import utils as utils
from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
from diffusion.distributions import DistributionNodes from diffusion.distributions import DistributionNodes
# from naswot.score_networks import get_nasbench201_idx_score from naswot.score_networks import get_nasbench201_idx_score
from naswot import nasspace
from naswot import datasets as dt
import networkx as nx import networkx as nx
@@ -682,7 +684,7 @@ class Dataset(InMemoryDataset):
data_list = [] data_list = []
# len_data = len(self.api) # len_data = len(self.api)
len_data = 1000 len_data = 15625
def check_valid_graph(nodes, edges): def check_valid_graph(nodes, edges):
if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]: if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]:
return False return False
@@ -745,11 +747,9 @@ class Dataset(InMemoryDataset):
print(f'edges size: {edges.shape}, nodes size: {len(nodes)}') print(f'edges size: {edges.shape}, nodes size: {len(nodes)}')
return edges,nodes return edges,nodes
def get_nasbench_201_val(idx):
pass
# def graph_to_graph_data(graph, idx): def graph_to_graph_data(graph, idx, train_loader, searchspace, args, device):
def graph_to_graph_data(graph): # def graph_to_graph_data(graph):
ops = graph[1] ops = graph[1]
adj = graph[0] adj = graph[0]
nodes = [] nodes = []
@@ -770,12 +770,49 @@ class Dataset(InMemoryDataset):
edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_index = torch.tensor(edges_list, dtype=torch.long).t()
edge_type = torch.tensor(edge_type, dtype=torch.long) edge_type = torch.tensor(edge_type, dtype=torch.long)
edge_attr = edge_type edge_attr = edge_type
y = torch.tensor([0, 0], dtype=torch.float).view(1, -1) # y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
# y = get_nasbench_201_val(idx) y = get_nasbench201_idx_score(idx, train_loader, searchspace, args, device)
print(y, idx)
if y > 1600:
print(f'idx={idx}, y={y}')
y = torch.tensor([1, 1], dtype=torch.float).view(1, -1)
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i) data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
else:
print(f'idx={idx}, y={y}')
y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
return None
return data return data
graph_list = [] graph_list = []
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'
args.api_loc = '/nfs/data3/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
searchspace = nasspace.get_search_space(args)
train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
device = torch.device('cuda:2')
with tqdm(total = len_data) as pbar: with tqdm(total = len_data) as pbar:
active_nodes = set() active_nodes = set()
file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json' file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
@@ -785,6 +822,7 @@ class Dataset(InMemoryDataset):
flex_graph_list = [] flex_graph_list = []
flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json' flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
for graph in graph_list: for graph in graph_list:
print(f'iterate every graph in graph_list, here is {i}')
# arch_info = self.api.query_meta_info_by_index(i) # arch_info = self.api.query_meta_info_by_index(i)
# results = self.api.query_by_index(i, 'cifar100') # results = self.api.query_by_index(i, 'cifar100')
arch_info = graph['arch_str'] arch_info = graph['arch_str']
@@ -796,8 +834,11 @@ class Dataset(InMemoryDataset):
for op in ops: for op in ops:
if op not in active_nodes: if op not in active_nodes:
active_nodes.add(op) active_nodes.add(op)
data = graph_to_graph_data((adj_matrix, ops),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device)
data = graph_to_graph_data((adj_matrix, ops)) i += 1
if data is None:
pbar.update(1)
continue
# with open(flex_graph_path, 'a') as f: # with open(flex_graph_path, 'a') as f:
# flex_graph = { # flex_graph = {
# 'adj_matrix': adj_matrix, # 'adj_matrix': adj_matrix,
@@ -816,18 +857,12 @@ class Dataset(InMemoryDataset):
f.write(str(data.edge_attr)) f.write(str(data.edge_attr))
data_list.append(data) data_list.append(data)
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) # 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)
flex_graph_list.append({ # flex_graph_list.append({
'adj_matrix':new_adj.tolist(),
'ops': new_ops,
})
# with open(flex_graph_path, 'w') as f:
# flex_graph = {
# 'adj_matrix':new_adj.tolist(), # 'adj_matrix':new_adj.tolist(),
# 'ops': new_ops, # 'ops': new_ops,
# } # })
# json.dump(flex_graph, f) # data_list.append(graph_to_graph_data((new_adj, new_ops)))
data_list.append(graph_to_graph_data((new_adj, new_ops)))
# graph_list.append({ # graph_list.append({
# "adj_matrix": adj_matrix, # "adj_matrix": adj_matrix,
@@ -859,6 +894,7 @@ class Dataset(InMemoryDataset):
# "seed": seed, # "seed": seed,
# }for seed, result in results.items()] # }for seed, result in results.items()]
# }) # })
# i += 1
pbar.update(1) pbar.update(1)
for graph in graph_list: for graph in graph_list:
@@ -872,8 +908,8 @@ class Dataset(InMemoryDataset):
graph['ops'] = ops graph['ops'] = ops
with open(f'nasbench-201-graph.json', 'w') as f: with open(f'nasbench-201-graph.json', 'w') as f:
json.dump(graph_list, f) json.dump(graph_list, f)
with open(flex_graph_path, 'w') as f: # with open(flex_graph_path, 'w') as f:
json.dump(flex_graph_list, f) # json.dump(flex_graph_list, f)
torch.save(self.collate(data_list), self.processed_paths[0]) torch.save(self.collate(data_list), self.processed_paths[0])
@@ -1148,7 +1184,8 @@ class DataInfos(AbstractDatasetInfos):
# ops_type[op] = len(ops_type) # ops_type[op] = len(ops_type)
# len_ops.add(len(ops)) # len_ops.add(len(ops))
# graphs.append((adj_matrix, ops)) # graphs.append((adj_matrix, ops))
graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json') # graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json')
graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
# check first five graphs # check first five graphs
for i in range(5): for i in range(5):