add config path
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xmuhanma
parent
f75657ac3b
commit
9360839a35
@ -16,9 +16,12 @@ general:
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final_model_chains_to_save: 1
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enable_progress_bar: False
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save_model: True
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log_dir: '/nfs/data3/hanzhang/nasbenchDiT'
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log_dir: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT'
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number_checkpoint_limit: 3
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type: 'Trainer'
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nas_201: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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swap_result: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/swap_results.csv'
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root: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/'
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model:
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type: 'discrete'
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transition: 'marginal'
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@ -25,7 +25,6 @@ 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 import nasspace
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from naswot import datasets as dt
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@ -72,7 +71,9 @@ class DataModule(AbstractDataModule):
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# base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
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# except NameError:
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# base_path = pathlib.Path(os.getcwd()).parent[2]
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base_path = '/nfs/data3/hanzhang/nasbenchDiT'
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# base_path = '/nfs/data3/hanzhang/nasbenchDiT'
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base_path = os.path.join(self.cfg.general.root, "..")
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root_path = os.path.join(base_path, self.datadir)
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self.root_path = root_path
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@ -84,7 +85,7 @@ class DataModule(AbstractDataModule):
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# Load the dataset to the memory
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# Dataset has target property, root path, and transform
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source = './NAS-Bench-201-v1_1-096897.pth'
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dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None)
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dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None, cfg=self.cfg)
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self.dataset = dataset
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# self.api = dataset.api
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@ -384,7 +385,7 @@ class DataModule_original(AbstractDataModule):
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def test_dataloader(self):
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return self.test_loader
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def new_graphs_to_json(graphs, filename):
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def new_graphs_to_json(graphs, filename, cfg):
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source_name = "nasbench-201"
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num_graph = len(graphs)
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@ -491,8 +492,9 @@ def new_graphs_to_json(graphs, filename):
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'num_active_nodes': len(active_nodes),
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'transition_E': transition_E.tolist(),
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}
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with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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import os
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# with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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with open(os.path.join(cfg.general.root,'nasbench-201-meta.json'), 'w') as f:
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json.dump(meta_dict, f)
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return meta_dict
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@ -656,9 +658,11 @@ def graphs_to_json(graphs, filename):
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json.dump(meta_dict, f)
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return meta_dict
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class Dataset(InMemoryDataset):
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def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
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def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None, cfg=None):
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self.target_prop = target_prop
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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.cfg = cfg
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# source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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source = os.path.join(self.cfg.general.root, 'NAS-Bench-201-v1_1-096897.pth')
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self.source = source
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# self.api = API(source) # Initialize NAS-Bench-201 API
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# print('API loaded')
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@ -679,7 +683,8 @@ class Dataset(InMemoryDataset):
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return [f'{self.source}.pt']
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def process(self):
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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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# source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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source = self.cfg.general.nas_201
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# self.api = API(source)
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data_list = []
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@ -748,7 +753,8 @@ class Dataset(InMemoryDataset):
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return edges,nodes
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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, idx, train_loader, searchspace, args, device):
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def graph_to_graph_data(graph, idx, 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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@ -797,7 +803,7 @@ class Dataset(InMemoryDataset):
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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.api_loc = self.cfg.general.nas_201
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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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@ -812,10 +818,11 @@ class Dataset(InMemoryDataset):
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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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# 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.csv', 'r') as f:
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with open(self.cfg.general.swap_result, '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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@ -824,12 +831,15 @@ class Dataset(InMemoryDataset):
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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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import os
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# file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
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file_path = os.path.join(self.cfg.general.root, 'nasbench-201-graph.json')
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with open(file_path, 'r') as f:
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graph_list = json.load(f)
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i = 0
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flex_graph_list = []
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flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
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# flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
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flex_graph_path = os.path.join(self.cfg.general.root,'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 = graph['arch_str']
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@ -837,7 +847,8 @@ 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),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device)
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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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data = graph_to_graph_data((adj_matrix, ops),idx=i, 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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@ -1140,6 +1151,7 @@ class DataInfos(AbstractDatasetInfos):
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self.task = task_name
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self.task_type = tasktype_dict.get(task_name, "regression")
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self.ensure_connected = cfg.model.ensure_connected
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self.cfg = cfg
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# self.api = dataset.api
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datadir = cfg.dataset.datadir
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@ -1182,14 +1194,15 @@ class DataInfos(AbstractDatasetInfos):
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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/nasbench-201-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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graphs = read_adj_ops_from_json(os.path.join(self.cfg.general.root, '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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print(f'graph {i} : {graphs[i]}')
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# print(f'ops_type: {ops_type}')
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meta_dict = new_graphs_to_json(graphs, 'nasbench-201')
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meta_dict = new_graphs_to_json(graphs, 'nasbench-201', self.cfg)
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self.base_path = base_path
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self.active_nodes = meta_dict['active_nodes']
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self.max_n_nodes = meta_dict['max_n_nodes']
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@ -1396,11 +1409,12 @@ def compute_meta(root, source_name, train_index, test_index):
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'transition_E': tansition_E.tolist(),
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}
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with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
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# with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
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with open(os.path.join(self.cfg.general.root, 'nasbench201.meta.json'), "w") as f:
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json.dump(meta_dict, f)
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return meta_dict
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if __name__ == "__main__":
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dataset = Dataset(source='nasbench', root='/nfs/data3/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
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dataset = Dataset(source='nasbench', root='/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/', target_prop='Class', transform=None)
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@ -24,7 +24,7 @@ class Graph_DiT(pl.LightningModule):
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self.guidance_target = getattr(cfg.dataset, 'guidance_target', None)
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from nas_201_api import NASBench201API as API
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self.api = API('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
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self.api = API(cfg.general.nas_201)
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input_dims = dataset_infos.input_dims
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output_dims = dataset_infos.output_dims
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@ -44,7 +44,7 @@ class Graph_DiT(pl.LightningModule):
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self.args.batch_size = 128
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self.args.GPU = '0'
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self.args.dataset = 'cifar10-valid'
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self.args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.args.api_loc = cfg.general.nas_201
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self.args.data_loc = '../cifardata/'
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self.args.seed = 777
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self.args.init = ''
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@ -177,7 +177,7 @@ class Graph_DiT(pl.LightningModule):
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rewards = []
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if reward_model == 'swap':
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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(self.cfg.general.swap_result, '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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@ -345,10 +345,15 @@ class Graph_DiT(pl.LightningModule):
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num_examples = self.val_y_collection.size(0)
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batch_y = self.val_y_collection[start_index:start_index + to_generate]
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all_ys.append(batch_y)
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samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
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cur_sample, logprobs = self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
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save_final=to_save,
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keep_chain=chains_save,
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number_chain_steps=self.number_chain_steps))
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number_chain_steps=self.number_chain_steps)
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samples.extend(cur_sample)
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# samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
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# save_final=to_save,
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# keep_chain=chains_save,
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# number_chain_steps=self.number_chain_steps))
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ident += to_generate
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start_index += to_generate
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@ -423,7 +428,7 @@ class Graph_DiT(pl.LightningModule):
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cur_sample, log_probs = 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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samples.append(cur_sample)
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samples.extend(cur_sample)
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all_ys.append(batch_y)
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batch_id += to_generate
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