explore the 201 space script

configs/config.yaml

@@ -2,7 +2,7 @@ general:
     name: 'graph_dit'
     wandb: 'disabled' 
     gpus: 1
-    gpu_number: 0
+    gpu_number: 2
     resume: null
     test_only: null
     sample_every_val: 2500
@@ -31,6 +31,7 @@ model:
     lambda_train: [1, 10]  # node and edge training weight 
     ensure_connected: True
 train:
+    # n_epochs: 5000
     n_epochs: 500
     batch_size: 1200
     lr: 0.0002

environment.yml

@@ -1,20 +0,0 @@
-name: graphdit39
-dependencies:
-  - python=3.9
-  - fcd_torch==1.0.7
-  - hydra-core==1.3.2
-  - imageio==2.26.0
-  - joblib==1.2.0
-  - matplotlib==3.7.0
-  - mini_moses==1.0
-  - networkx==3.0
-  - numpy==1.24.2
-  - omegaconf==2.3.0
-  - pandas==1.5.3
-  - pytorch_lightning==2.0.1
-  - rdkit==2023.9.4
-  - scikit_learn==1.2.1
-  - torch==2.0.0
-  - torch_geometric==2.3.0
-  - torchmetrics==0.11.4
-  - tqdm==4.64.1

graph_dit/datasets/dataset.py

@@ -25,7 +25,9 @@ from sklearn.model_selection import train_test_split
 import utils as utils
 from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
 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
 
@@ -682,7 +684,7 @@ class Dataset(InMemoryDataset):
 
         data_list = []
         # len_data = len(self.api)
-        len_data = 1000
+        len_data = 15625
         def check_valid_graph(nodes, edges):
             if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]:
                 return False
@@ -745,11 +747,9 @@ class Dataset(InMemoryDataset):
             print(f'edges size: {edges.shape}, nodes size: {len(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]
             adj = graph[0]
             nodes = []
@@ -770,12 +770,49 @@ class Dataset(InMemoryDataset):
             edge_index = torch.tensor(edges_list, dtype=torch.long).t()
             edge_type = torch.tensor(edge_type, dtype=torch.long)
             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)
-            data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
+            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)
+            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
         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:
             active_nodes = set()
             file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
@@ -785,6 +822,7 @@ class Dataset(InMemoryDataset):
             flex_graph_list = []
             flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
             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)
                 # results = self.api.query_by_index(i, 'cifar100')
                 arch_info = graph['arch_str']
@@ -796,8 +834,11 @@ class Dataset(InMemoryDataset):
                 for op in ops:
                     if op not in active_nodes:
                         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:
                 #     flex_graph = {
                 #         'adj_matrix': adj_matrix,
@@ -816,18 +857,12 @@ class Dataset(InMemoryDataset):
                         f.write(str(data.edge_attr))
                 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(),
+                #     'adj_matrix':new_adj.tolist(),
-                    'ops': new_ops,
+                #     'ops': new_ops,
-                })
+                # })
-                # with open(flex_graph_path, 'w') as f:
+                # data_list.append(graph_to_graph_data((new_adj, new_ops)))
-                #     flex_graph = {
-                #         'adj_matrix': new_adj.tolist(),
-                #         'ops': new_ops,
-                #     }
-                #     json.dump(flex_graph, f)
-                data_list.append(graph_to_graph_data((new_adj, new_ops)))
                
                 # graph_list.append({
                 #     "adj_matrix": adj_matrix,
@@ -859,6 +894,7 @@ class Dataset(InMemoryDataset):
                 #         "seed": seed,
                 #     }for seed, result in results.items()]
                 # })
+                # i += 1
                 pbar.update(1)
         
         for graph in graph_list:
@@ -872,8 +908,8 @@ class Dataset(InMemoryDataset):
                 graph['ops'] = ops
         with open(f'nasbench-201-graph.json', 'w') as 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])
 
@@ -1148,7 +1184,8 @@ class DataInfos(AbstractDatasetInfos):
             #         ops_type[op] = len(ops_type)
             # len_ops.add(len(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
         for i in range(5):

graph_dit/diffusion_model.py

@@ -356,7 +356,8 @@ class Graph_DiT(pl.LightningModule):
             to_generate = min(samples_left_to_generate, bs)
             to_save = min(samples_left_to_save, bs)
             chains_save = min(chains_left_to_save, bs)
-            batch_y = test_y_collection[batch_id : batch_id + to_generate]
+            # batch_y = test_y_collection[batch_id : batch_id + to_generate]
+            batch_y = torch.ones(to_generate, self.ydim_output, device=self.device)
 
             cur_sample = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
                                             keep_chain=chains_save, number_chain_steps=self.number_chain_steps)

graph_dit/exp_201/main.py

@@ -0,0 +1,82 @@
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from nas_201_api import NASBench201API as API
+from naswot.score_networks import get_nasbench201_idx_score
+from naswot import datasets as dt
+from naswot import nasspace
+
+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')
+
+
+         
+# source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+# api = API(source)
+
+
+
+# 示例百分数列表，精确到小数点后两位
+# 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]
+percentages = []
+
+len_201 = 15625
+
+for i in range(len_201):
+    percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
+    percentages.append(percentage)
+
+# 定义10%区间
+bins = [i for i in range(0, 101, 10)]
+
+# 对数据进行分箱，计算每个区间的数据量
+hist, bin_edges = pd.cut(percentages, bins=bins, right=False, retbins=True, include_lowest=True)
+bin_counts = hist.value_counts().sort_index()
+
+total_counts = len(percentages)
+percentages_in_bins = (bin_counts / total_counts) * 100
+
+# 绘制条形图
+plt.figure(figsize=(10, 6))
+bars = plt.bar(bin_counts.index.astype(str), bin_counts.values, width=0.9, color='skyblue')
+
+for bar, percentage in zip(bars, percentages_in_bins):
+    plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height(),
+            f'{percentage:.2f}%', ha='center', va='bottom')
+
+# 添加标题和标签
+plt.title('Distribution of Percentages in 10% Intervals')
+plt.xlabel('Percentage Interval')
+plt.ylabel('Count')
+
+# 显示图表
+plt.xticks(rotation=45)
+plt.savefig('barplog.png')
+

graph_dit/models/transformer.py

@@ -86,7 +86,7 @@ class Denoiser(nn.Module):
     """
     def forward(self, x, e, node_mask, y, t, unconditioned):
         
-        print("Denoiser Forward")
+        # print("Denoiser Forward")
         # print(x.shape, e.shape, y.shape, t.shape, unconditioned)
         force_drop_id = torch.zeros_like(y.sum(-1))
         # drop the nan values

graph_dit/nasbench-201-meta.json

@@ -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]]]}

graph_dit/naswot/naswot/datasets/data.py

@@ -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,

graph_dit/naswot/naswot/score_networks.py

@@ -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 = argparse.ArgumentParser(description='NAS Without Training')
-parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder')
+# 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',
+# parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth',
-                    type=str, help='path to API')
+#                     type=str, help='path to API')
-parser.add_argument('--save_loc', default='results', type=str, help='folder to save results')
+# 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('--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('--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('--nasspace', default='nasbench201', type=str, help='the nas search space to use')
-parser.add_argument('--batch_size', default=128, type=int)
+# 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('--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('--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('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"')
-parser.add_argument('--GPU', default='0', type=str)
+# parser.add_argument('--GPU', default='0', type=str)
-parser.add_argument('--seed', default=1, type=int)
+# parser.add_argument('--seed', default=1, type=int)
-parser.add_argument('--init', default='', type=str)
+# parser.add_argument('--init', default='', type=str)
-parser.add_argument('--trainval', action='store_true')
+# parser.add_argument('--trainval', action='store_true')
-parser.add_argument('--dropout', action='store_true')
+# parser.add_argument('--dropout', action='store_true')
-parser.add_argument('--dataset', default='cifar10', type=str)
+# 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('--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_samples', default=100, type=int)
-parser.add_argument('--n_runs', default=500, 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('--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_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_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.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)')
 
-args = parser.parse_args()
+# args = parser.parse_args()
-os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
+# os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
 
-# Reproducibility
+# # Reproducibility
-torch.backends.cudnn.deterministic = True
+# torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
+# torch.backends.cudnn.benchmark = False
-random.seed(args.seed)
+# random.seed(args.seed)
-np.random.seed(args.seed)
+# np.random.seed(args.seed)
-torch.manual_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']
+    # check if the nodes[0] is a number
-    arch_str = '|' + num_to_op[nodes[1]] + '~0|+|' + \
+    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:
+# class Args:
-    pass
+#     pass
-args = Args()
+# args = Args()
-args.trainval = True
+# args.trainval = True
-args.augtype = 'none'
+# args.augtype = 'none'
-args.repeat = 1
+# args.repeat = 1
-args.score = 'hook_logdet'
+# args.score = 'hook_logdet'
-args.sigma = 0.05
+# args.sigma = 0.05
-args.nasspace = 'nasbench201'
+# args.nasspace = 'nasbench201'
-args.batch_size = 128
+# args.batch_size = 128
-args.GPU = '0'
+# args.GPU = '0'
-args.dataset = 'cifar10-valid'
+# args.dataset = 'cifar10-valid'
-args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+# args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
-args.data_loc = '../cifardata/'
+# args.data_loc = '../cifardata/'
-args.seed = 777
+# args.seed = 777
-args.init = ''
+# args.init = ''
-args.save_loc = 'results'
+# args.save_loc = 'results'
-args.save_string = 'naswot'
+# args.save_string = 'naswot'
-args.dropout = False
+# args.dropout = False
-args.maxofn = 1
+# args.maxofn = 1
-args.n_samples = 100
+# args.n_samples = 100
-args.n_runs = 500
+# args.n_runs = 500
-args.stem_out_channels = 16
+# args.stem_out_channels = 16
-args.num_stacks = 3
+# args.num_stacks = 3
-args.num_modules_per_stack = 3
+# args.num_modules_per_stack = 3
-args.num_labels = 1
+# args.num_labels = 1
 
-if 'valid' in args.dataset:
+# if 'valid' in args.dataset:
-    args.dataset = args.dataset.replace('-valid', '')
+#     args.dataset = args.dataset.replace('-valid', '')
-print('start to get search space')
+# print('start to get search space')
-start_time = time.time()
+# start_time = time.time()
-print(get_config_by_nodes(nodes=[0,2,2,3,4,2,4,6]))
+# print(get_config_by_nodes(nodes=[0,2,2,3,4,2,4,6]))
-end_time = time.time()
+# end_time = time.time()
-start_time = time.time()
+# start_time = time.time()
-searchspace = nasspace.get_search_space(args)
+# searchspace = nasspace.get_search_space(args)
-end_time = time.time()
+# end_time = time.time()
-print(f'search space time: {end_time - start_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)
+# 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('start to get score')
-print('5374')
+# print('5374')
-num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
+# num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
-start_time = time.time()
+# 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")))
+# 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()
+# end_time = time.time()
-start_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")))
+# 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()
+# end_time = time.time()
-print(f'5374 time: {end_time - start_time}')
+# print(f'5374 time: {end_time - start_time}')
-print('5375')
+# print('5375')
-start_time = time.time()
+# 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")))
+# 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()
+# end_time = time.time()
-print(f'5375 time: {end_time - start_time}')
+# print(f'5375 time: {end_time - start_time}')
-print('5376')
+# print('5376')
-start_time = time.time()
+# 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")))
+# 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()
+# end_time = time.time()
-print(f'5376 time: {end_time - start_time}')
+# print(f'5376 time: {end_time - start_time}')
 
 # device = "cuda:0"
 # dataset = dataset

graph_dit/naswot/setup.py

@@ -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/*']
+    }
 )

graph_dit/output_graphs/151042.txt

@@ -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
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+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
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graph_dit/output_graphs/151042.txt.json

@@ -0,0 +1 @@
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graph_dit/output_graphs/151613.txt

@@ -0,0 +1,101 @@
+graph, input_nasbench201, output_nasbench201
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graph_dit/output_graphs/151613.txt.json

@@ -0,0 +1 @@
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graph_dit/output_graphs/161959.txt

@@ -0,0 +1,101 @@
+graph, input_nasbench201, output_nasbench201
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graph_dit/output_graphs/161959.txt.json

@@ -0,0 +1,2 @@
+1600分以上
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graph_dit/output_graphs/170944-1000>1600.txt

@@ -0,0 +1,101 @@
+graph, input_nasbench201, output_nasbench201
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graph_dit/output_graphs/170944.txt.json

@@ -0,0 +1,2 @@
+1600 分以上
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graph_dit/output_graphs/171452-15625>1600.txt

@@ -0,0 +1,101 @@
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+nodes: tensor(0) tensor(4) tensor(1) tensor(5) tensor(2) 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, 1690.9168701171875, 0.1041212420812444
+nodes: tensor(0) tensor(5) tensor(5) 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, 1663.3424072265625, 0.41158586912698036
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graph_dit/output_graphs/171452.txt.json

@@ -0,0 +1 @@
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graph_dit/output_graphs/final_graphs1212.txt

@@ -1,101 +0,0 @@
-graph, input_nasbench201, output_nasbench201
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graph_dit/output_graphs/final_graphs122318.txt

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graph_dit/output_graphs/final_graphs130942.txt

@@ -1,101 +0,0 @@
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