try to get the original perf

.gitignore

@@ -1,3 +1,9 @@
+cifardata/
+NAS-Bench-201-*
+*.csv.gz
+*.meta.json
+*.pt
+*.zip
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -1,14 +1,34 @@
 Graph Diffusion Transformer for Multi-Conditional Molecular Generation
 ================================================================
 
+## Initial Setup
+
+Please download NASBench201 dataset(NAS-Bench-201-v1_1-096897.pth) from
+https://drive.google.com/file/d/16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_/view
+
+and put it in the `/path/to/repo/graph_dit` folder.
+
+## Running the code
+
+start command:
+``` bash
+python main.py --config-name=config.yaml \
+model.ensure_connected=True \
+dataset.task_name='nasbench201' \
+dataset.guidance_target='regression'
+```
+
+This repository contains the code for the paper "Inverse Molecular Design with Multi-Conditional Diffusion Guidance" by Gang Liu, Jiaxin Xu, Tengfei Luo, and Meng Jiang.
+
+
 Paper: https://arxiv.org/abs/2401.13858
 
-This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
+<!-- This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
 
 <div style="display: flex;" markdown="1">
       <img src="asset/reverse.png" style="width: 45%;" alt="Description of the first image">
       <img src="asset/arch.png" style="width: 45%;" alt="Description of the second image">
-</div>
+</div> -->
 
 
 ## Requirements

configs/config.yaml

@@ -2,7 +2,7 @@ general:
     name: 'graph_dit'
     wandb: 'disabled' 
     gpus: 1
-    gpu_number: 3
+    gpu_number: 2
     resume: null
     test_only: null
     sample_every_val: 2500
@@ -31,7 +31,8 @@ model:
     lambda_train: [1, 10]  # node and edge training weight 
     ensure_connected: True
 train:
-    n_epochs: 5000
+    # n_epochs: 5000
+    n_epochs: 500
     batch_size: 1200
     lr: 0.0002
     clip_grad: null

graph_dit/analysis/rdkit_functions.py

@@ -37,6 +37,144 @@ def selectivity_evaluation(gas1, gas2, prop_name):
     y = np.log10(np.array(gas1) / np.array(gas2))
     upper = (y - (a_dict[prop_name] * x + b_dict[prop_name])) > 0
     return upper
+class BasicGraphMetrics(object):
+    def __init__(self, graph_decoder, train_graphs=None, stat_ref=None, task_evaluator=None, n_jobs=8, device='cpu', batch_size=512):
+        self.dataset_graphs_list = train_graphs
+        self.graph_decoder = graph_decoder
+        self.n_jobs = n_jobs
+        self.device = device
+        self.batch_size = batch_size
+        self.stat_ref = stat_ref
+        self.task_evaluator = task_evaluator
+    def compute_relaxed_validity(self, generated, ensure_connected):
+        valid = []
+        num_components = []
+        all_graphs = []
+        valid_graphs = []
+        covered_nodes = set()
+        direct_valid_count = 0
+        print(f"generated number: {len(generated)}")
+        for graph in generated:
+            node_types, edge_types = graph
+            direct_valid_flag = True
+            direct_valid_count += 1
+            valid.append(graph)
+            num_components.append(1)
+            covered_nodes.update(set(node_types))
+            all_graphs.append(graph)
+        return  valid, len(valid) / len(generated), direct_valid_count / len(generated), np.array(num_components), all_graphs, covered_nodes
+        
+    def evaluate(self, generated, targets, ensure_connected, active_atoms=None):
+        valid, validity, nc_validity, num_components, all_graphs, covered_nodes = self.compute_relaxed_validity(generated, ensure_connected=ensure_connected)
+        nc_mu = num_components.mean() if len(num_components) > 0 else 0
+        nc_min = num_components.min() if len(num_components) > 0 else 0
+        nc_max = num_components.max() if len(num_components) > 0 else 0
+
+        len_active = len(active_atoms) if active_atoms is not None else 1
+        
+        cover_str = f"Cover {len(covered_nodes)} ({len(covered_nodes)/len_active * 100:.2f}%) atoms: {covered_nodes}"
+        print(f"Validity over {len(generated)} graphs: {validity * 100 :.2f}% (w/o correction: {nc_validity * 100 :.2f}%), cover {len(covered_nodes)} ({len(covered_nodes)/len_active * 100:.2f}%) nodes: {covered_nodes}")
+        print(f"Number of connected components of {len(generated)} graphs: min:{nc_min:.2f} mean:{nc_mu:.2f} max:{nc_max:.2f}")
+
+        if validity > 0: 
+            dist_metrics = {'cover_str': cover_str ,'validity': validity, 'validity_nc': nc_validity}
+            unique = valid
+            close_pool = False
+            if self.n_jobs != 1:
+                pool = Pool(self.n_jobs)
+                close_pool = True
+            else:
+                pool = 1
+            # valid_graphs = mapper(pool)(get_mol, valid) 
+            valid_graphs = valid 
+            """
+            Computes internal diversity as:
+            1/|A|^2 sum_{x, y in AxA} (1-tanimoto(x, y))
+            """
+            # dist_metrics['interval_diversity'] = internal_diversity(valid_graphs, pool, device=self.device)
+            
+            start_time = time.time()
+            if self.stat_ref is not None:
+                kwargs = {'n_jobs': pool, 'device': self.device, 'batch_size': self.batch_size}
+                kwargs_fcd = {'n_jobs': self.n_jobs, 'device': self.device, 'batch_size': self.batch_size}
+                try:
+                    dist_metrics['sim/Frag'] = FragMetric(**kwargs)(gen=valid_graphs, pref=self.stat_ref['Frag'])
+                except:
+                    print('error: ', 'pool', pool)
+                    print('valid_graphs: ', valid_graphs)
+                dist_metrics['dist/FCD'] = FCDMetric(**kwargs_fcd)(gen=valid, pref=self.stat_ref['FCD'])
+
+            if self.task_evaluator is not None:
+                evaluation_list = list(self.task_evaluator.keys())
+                print('evaluation_list: ', evaluation_list)
+                evaluation_list = evaluation_list.copy()
+
+                assert 'meta_taskname' in evaluation_list
+                meta_taskname = self.task_evaluator['meta_taskname']
+                evaluation_list.remove('meta_taskname')
+                # meta_split = meta_taskname.split('-')
+
+                valid_index = np.array([True if graphs else False for graphs in all_graphs])
+                targets_log = {}
+                for i, name in enumerate(evaluation_list):
+                    targets_log[f'input_{name}'] = np.array([float('nan')] * len(valid_index))
+                    targets_log[f'input_{name}'] = targets[:, i]
+                
+                targets = targets[valid_index]
+                # if len(meta_split) == 2:
+                #     cached_perm = {meta_split[0]: None, meta_split[1]: None}
+                
+                for i, name in enumerate(evaluation_list):
+                    # if name == 'scs':
+                    #     continue
+                    # elif name == 'sas':
+                    #     scores = calculateSAS(valid)
+                    # else:
+                    # scores = self.task_evaluator[name](valid)
+                    # fix the scores
+                    scores = np.random.rand(len(valid_index))
+                    targets_log[f'output_{name}'] = np.array([float('nan')] * len(valid_index))
+                    targets_log[f'output_{name}'][valid_index] = scores
+                    # if name in ['O2', 'N2', 'CO2']:
+                    #     if len(meta_split) == 2:
+                    #         cached_perm[name] = scores
+                    #     scores, cur_targets = np.log10(scores), np.log10(targets[:, i])
+                    #     dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - cur_targets))
+                    # elif name == 'sas':
+                    #     dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - targets[:, i]))
+                    # else:
+                    true_y = targets[:, i]
+                    predicted_labels = (scores >= 0.5).astype(int)
+                    acc = (predicted_labels == true_y).sum() / len(true_y)
+                    dist_metrics[f'{name}/acc'] = acc
+
+                # if len(meta_split) == 2:
+                #     if cached_perm[meta_split[0]] is not None and cached_perm[meta_split[1]] is not None:
+                #         task_name = self.task_evaluator['meta_taskname']
+                #         upper = selectivity_evaluation(cached_perm[meta_split[0]], cached_perm[meta_split[1]], task_name)
+                #         dist_metrics[f'selectivity/{task_name}'] = np.sum(upper)
+
+            end_time = time.time()
+            elapsed_time = end_time - start_time
+            max_key_length = max(len(key) for key in dist_metrics)
+            print(f'Details over {len(valid)} ({len(generated)}) valid (total) graphs, calculating metrics using {elapsed_time:.2f} s:')
+            strs = ''
+            for i, (key, value) in enumerate(dist_metrics.items()):
+                if isinstance(value, (int, float, np.floating, np.integer)):
+                    strs = strs + f'{key:>{max_key_length}}:{value:<7.4f}\t'
+                if i % 4 == 3:
+                    strs = strs + '\n'
+            print(strs)
+
+            if close_pool:
+                pool.close()
+                pool.join()
+        else:
+            unique = []
+            dist_metrics = {}
+            targets_log = None
+        return unique, dict(nc_min=nc_min, nc_max=nc_max, nc_mu=nc_mu), all_graphs, dist_metrics, targets_log
+
 
 class BasicMolecularMetrics(object):
     def __init__(self, atom_decoder, train_smiles=None, stat_ref=None, task_evaluator=None, n_jobs=8, device='cpu', batch_size=512):
@@ -388,6 +526,18 @@ def connect_fragments(mol):
     return combined_mol
 
 #### connect fragements
+def compute_graph_metrics(graph_list, targets, train_graphs, stat_ref, dataset_info, task_evaluator, comput_config):
+    """ graph_list: (dict) """
+    node_decoder = dataset_info.node_decoder
+    active_nodes = dataset_info.active_nodes
+    ensure_connected = dataset_info.ensure_connected
+    metrics = BasicGraphMetrics(node_decoder, train_graphs, stat_ref, task_evaluator, **comput_config)
+    evaluated_res = metrics.evaluate(graph_list, targets, ensure_connected, active_nodes)
+    all_graphs = evaluated_res[-3]
+    all_metrics = evaluated_res[-2]
+    targets_log = evaluated_res[-1]
+    unique_graphs = evaluated_res[0]
+    return unique_graphs, all_graphs, all_metrics, targets_log
 
 def compute_molecular_metrics(molecule_list, targets, train_smiles, stat_ref, dataset_info, task_evaluator, comput_config):
     """ molecule_list: (dict) """

graph_dit/analysis/visualization.py

@@ -10,7 +10,41 @@ import numpy as np
 import rdkit.Chem
 import matplotlib.pyplot as plt
 
+class GraphVisualization:
+    def __init__(self, dataset_infos):
+        self.dataset_infos = dataset_infos
+    def graph_from_graphs(self, node_list, adjency_matrix):
+        """
+        Convert graphs to networkx graphs
+        node_list: the nodes of a batch of nodes (bs x n)
+        adjacency_matrix: the adjacency_matrix of the molecule (bs x n x n)
+        """
+        graph = nx.Graph()
 
+        for i in range(len(node_list)):
+            if node_list[i] == -1:
+                continue
+            graph.add_node(i, number=i, symbol=node_list[i], color_val=node_list[i])
+
+        rows, cols = np.where(adjency_matrix >= 1)
+        edges = zip(rows.tolist(), cols.tolist())
+        for edge in edges:
+            edge_type = adjency_matrix[edge[0]][edge[1]]
+            graph.add_edge(edge[0], edge[1], color=float(edge_type), weight=3 * edge_type)
+
+        return graph
+
+    def visualize(self, path: str, graphs: list, num_graphs_to_visualize: int, log='graph'):
+        # define path to save figures
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+        # visualize the final molecules
+        for i in range(num_graphs_to_visualize):
+            file_path = os.path.join(path, 'graph_{}.png'.format(i))
+            graph = self.graph_from_graphs(graphs[i][0].numpy(), graphs[i][1].numpy())
+            self.visualize_graph(graph=graph, pos=None, path=file_path)
+            im = plt.imread(file_path)
 class MolecularVisualization:
     def __init__(self, dataset_infos):
         self.dataset_infos = dataset_infos

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
 
@@ -70,7 +72,7 @@ class DataModule(AbstractDataModule):
         #     base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
         # except NameError:
         # base_path = pathlib.Path(os.getcwd()).parent[2]
-        base_path = '/home/stud/hanzhang/nasbenchDiT'
+        base_path = '/nfs/data3/hanzhang/nasbenchDiT'
         root_path = os.path.join(base_path, self.datadir)
         self.root_path = root_path
 
@@ -408,6 +410,7 @@ def new_graphs_to_json(graphs, filename):
         adj = graph[0]
 
         n_node = len(ops)
+        print(n_node)
         n_edge = len(ops)
         n_node_list.append(n_node)
         n_edge_list.append(n_edge)
@@ -489,7 +492,7 @@ def new_graphs_to_json(graphs, filename):
         'transition_E': transition_E.tolist(),
     }
 
-    with open(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
+    with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
         json.dump(meta_dict, f)
     
     return meta_dict
@@ -655,7 +658,7 @@ def graphs_to_json(graphs, filename):
 class Dataset(InMemoryDataset):
     def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
         self.target_prop = target_prop
-        source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
         self.source = source
         # self.api = API(source)  # Initialize NAS-Bench-201 API
         # print('API loaded')
@@ -676,12 +679,12 @@ class Dataset(InMemoryDataset):
         return [f'{self.source}.pt']
 
     def process(self):
-        source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
-        self.api = API(source)
+        source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        # self.api = API(source)
 
         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
@@ -710,14 +713,24 @@ class Dataset(InMemoryDataset):
             return True
 
         def generate_flex_adj_mat(ori_nodes, ori_edges, max_nodes=12, min_nodes=8,random_ratio=0.5):
+            # print(ori_nodes)
+            # print(ori_edges)
+            
+            ori_edges = np.array(ori_edges)
+            # ori_nodes = np.array(ori_nodes)
             nasbench_201_node_num = 8
             # random.seed(random_seed)
             nodes_num = random.randint(min_nodes, max_nodes)
             # print(f'arch_str: {arch_str}, \nmax_nodes: {max_nodes}, min_nodes: {min_nodes}, nodes_num: {nodes_num},random_seed: {random_seed},random_ratio: {random_ratio}')
             add_num = nodes_num - nasbench_201_node_num
             # ori_nodes, ori_edges = parse_architecture_string(arch_str)
-            add_nodes = [op for op in random.choices(num_to_op[1:-1], k=add_num)]
+            add_nodes = []
+            print(f'add_num: {add_num}')
+            for i in range(add_num):
+                add_nodes.append(random.choice(num_to_op[1:-1]))
             # print(add_nodes)
+            print(f'ori_nodes[:-1]: {ori_nodes[:-1]}, add_nodes: {add_nodes}')
+            print(f'len(ori_nodes[:-1]): {len(ori_nodes[:-1])}, len(add_nodes): {len(add_nodes)}')
             nodes = ori_nodes[:-1] + add_nodes + ['output']
             edges = np.zeros((nodes_num , nodes_num))
             edges[:6, :6] = ori_edges[:6, :6]
@@ -727,12 +740,16 @@ class Dataset(InMemoryDataset):
                     rand = random.random()
                     if rand < random_ratio:
                         edges[i, j] = 1
-            return nodes, edges
+            if nodes_num < max_nodes:
+                edges = np.pad(edges, ((0, max_nodes - nodes_num), (0, max_nodes - nodes_num)), 'constant',constant_values=0)
+                while len(nodes) < max_nodes:
+                    nodes.append('none')
+            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):
             ops = graph[1]
             adj = graph[0]
             nodes = []
@@ -754,57 +771,127 @@ class Dataset(InMemoryDataset):
             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 = get_nasbench_201_val(idx)
-            data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
+            # y = get_nasbench201_idx_score(idx, train_loader, searchspace, args, device)
+            y = self.swap_scores[idx]
+            print(y, idx)
+            if y > 60000:
+                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)
+        self.swap_scores = []
+        import csv
+        # with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
+        with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results_cifar100.csv', 'r') as f:
+            reader = csv.reader(f)
+            header = next(reader)
+            data = [row for row in reader]
+            self.swap_scores = [float(row[0]) for row in data]
+        device = torch.device('cuda:2')
         with tqdm(total = len_data) as pbar:
             active_nodes = set()
-            for i in range(len_data):
-                arch_info = self.api.query_meta_info_by_index(i)
-                results = self.api.query_by_index(i, 'cifar100')
-                # nodes, edges = parse_architecture_string(arch_info.arch_str)
-                ops, adj_matrix = parse_architecture_string(arch_info.arch_str)
-                # adj_matrix, ops = create_adj_matrix_and_ops(nodes, edges)
+            file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+            with open(file_path, 'r') as f:
+                graph_list = json.load(f)
+            i = 0
+            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 = graph['arch_str']
+                ops, adj_matrix, ori_nodes, ori_adj = parse_architecture_string(arch_info, padding=4)
                 for op in ops:
                     if op not in active_nodes:
                         active_nodes.add(op)
-                
-                graph_list.append({
-                    "adj_matrix": adj_matrix,
-                    "ops": ops,
-                    "idx": i,
-                    "train": [{
-                        "iepoch": result.get_train()['iepoch'],
-                        "loss": result.get_train()['loss'],
-                        "accuracy": result.get_train()['accuracy'],
-                        "cur_time": result.get_train()['cur_time'],
-                        "all_time": result.get_train()['all_time'],
-                        "seed": seed,
-                    }for seed, result in results.items()],
-                    "valid": [{
-                        "iepoch": result.get_eval('x-valid')['iepoch'],
-                        "loss": result.get_eval('x-valid')['loss'],
-                        "accuracy": result.get_eval('x-valid')['accuracy'],
-                        "cur_time": result.get_eval('x-valid')['cur_time'],
-                        "all_time": result.get_eval('x-valid')['all_time'],
-                        "seed": seed,
-                    }for seed, result in results.items()],
-                    "test": [{
-                        "iepoch": result.get_eval('x-test')['iepoch'],
-                        "loss": result.get_eval('x-test')['loss'],
-                        "accuracy": result.get_eval('x-test')['accuracy'],
-                        "cur_time": result.get_eval('x-test')['cur_time'],
-                        "all_time": result.get_eval('x-test')['all_time'],
-                        "seed": seed,
-                    }for seed, result in results.items()]
+                data = graph_to_graph_data((adj_matrix, ops),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device) 
+                i += 1
+                if data is None:
+                    pbar.update(1)
+                    continue
+                flex_graph_list.append({
+                    'adj_matrix':adj_matrix,
+                    'ops': ops,
                 })
-                data = graph_to_graph_data((adj_matrix, ops)) 
+                if i < 3:
+                    print(f"i={i}, data={data}")
+                    with open(f'{i}.json', 'w') as f:
+                        f.write(str(data.x))
+                        f.write(str(data.edge_index))
+                        f.write(str(data.edge_attr))
                 data_list.append(data)
 
-                # new_adj, new_ops = generate_flex_adj_mat(ori_nodes=ops, ori_edges=adj_matrix, max_nodes=12, min_nodes=8,  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({
+                #     'adj_matrix':new_adj.tolist(),
+                #     'ops': new_ops,
+                # })
                 # data_list.append(graph_to_graph_data((new_adj, new_ops)))
+               
+                # graph_list.append({
+                #     "adj_matrix": adj_matrix,
+                #     "ops": ops,
+                #     "arch_str": arch_info.arch_str,
+                #     "idx": i,
+                #     "train": [{
+                #         "iepoch": result.get_train()['iepoch'],
+                #         "loss": result.get_train()['loss'],
+                #         "accuracy": result.get_train()['accuracy'],
+                #         "cur_time": result.get_train()['cur_time'],
+                #         "all_time": result.get_train()['all_time'],
+                #         "seed": seed,
+                #     }for seed, result in results.items()],
+                #     "valid": [{
+                #         "iepoch": result.get_eval('x-valid')['iepoch'],
+                #         "loss": result.get_eval('x-valid')['loss'],
+                #         "accuracy": result.get_eval('x-valid')['accuracy'],
+                #         "cur_time": result.get_eval('x-valid')['cur_time'],
+                #         "all_time": result.get_eval('x-valid')['all_time'],
+                #         "seed": seed,
+                #     }for seed, result in results.items()],
+                #     "test": [{
+                #         "iepoch": result.get_eval('x-test')['iepoch'],
+                #         "loss": result.get_eval('x-test')['loss'],
+                #         "accuracy": result.get_eval('x-test')['accuracy'],
+                #         "cur_time": result.get_eval('x-test')['cur_time'],
+                #         "all_time": result.get_eval('x-test')['all_time'],
+                #         "seed": seed,
+                #     }for seed, result in results.items()]
+                # })
+                # i += 1
                 pbar.update(1)
         
         for graph in graph_list:
@@ -818,6 +905,8 @@ 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:
+            # json.dump(flex_graph_list, f)
             
         torch.save(self.collate(data_list), self.processed_paths[0])
 
@@ -981,18 +1070,29 @@ class Dataset_origin(InMemoryDataset):
 
         torch.save(self.collate(data_list), self.processed_paths[0])
 
-def parse_architecture_string(arch_str):
+def parse_architecture_string(arch_str, padding=0):
     # print(arch_str)
     steps = arch_str.split('+')
     nodes = ['input']  # Start with input node
-    adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
+    ori_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]]) 
+                        # [0, 0, 0, 0, 0, 0, 0, 0]]) 
+                        [0, 0, 0, 0, 0, 0, 0, 0]] 
+   # adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
+    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]]) 
+                        [0, 0, 0, 0, 0, 0, 0, 0]] 
     steps = arch_str.split('+')
     steps_coding = ['0', '0', '1', '0', '1', '2']
     cont = 0
@@ -1004,7 +1104,21 @@ def parse_architecture_string(arch_str):
             cont += 1
             nodes.append(n)
     nodes.append('output')  # Add output node
-    return nodes, adj_mat
+    ori_nodes = nodes.copy()
+    if padding > 0:
+        for i in range(padding):
+            nodes.append('none')
+        for adj_row in adj_mat:
+            for i in range(padding):
+                adj_row.append(0)
+        # adj_mat = np.append(adj_mat, np.zeros((padding, len(nodes))))
+        for i in range(padding):
+            adj_mat.append([0] * len(nodes))
+    # print(nodes)
+    # print(adj_mat)
+    # print(len(adj_mat))
+    # print(f'len(ori_nodes): {len(ori_nodes)}, len(nodes): {len(nodes)}')
+    return nodes, adj_mat, ori_nodes, ori_adj_mat
 
 def create_adj_matrix_and_ops(nodes, edges):
     num_nodes = len(nodes)
@@ -1046,6 +1160,7 @@ class DataInfos(AbstractDatasetInfos):
 
             adj_ops_pairs = []
             for item in data:
+                print(item)
                 adj_matrix = np.array(item['adj_matrix'])
                 ops = item['ops']
                 ops = [op_type[op] for op in ops]
@@ -1066,12 +1181,13 @@ 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'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-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):
             print(f'graph {i} : {graphs[i]}')
-        print(f'ops_type: {ops_type}')
+        # print(f'ops_type: {ops_type}')
 
         meta_dict = new_graphs_to_json(graphs, 'nasbench-201')
         self.base_path = base_path
@@ -1280,11 +1396,11 @@ def compute_meta(root, source_name, train_index, test_index):
         'transition_E': tansition_E.tolist(),
         }
 
-    with open(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
+    with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
         json.dump(meta_dict, f)
     
     return meta_dict
 
 
 if __name__ == "__main__":
-    dataset = Dataset(source='nasbench', root='/home/stud/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
+    dataset = Dataset(source='nasbench', root='/nfs/data3/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)

graph_dit/diffusion_model.py

@@ -3,9 +3,9 @@ import torch.nn.functional as F
 import pytorch_lightning as pl
 import time
 import os
-from naswot.score_networks import get_nasbench201_nodes_score
-from naswot import nasspace
-from naswot import datasets
+# from naswot.score_networks import get_nasbench201_nodes_score
+# from naswot import nasspace
+# from naswot import datasets
 from models.transformer import Denoiser
 from diffusion.noise_schedule import PredefinedNoiseScheduleDiscrete, MarginalTransition
 
@@ -41,7 +41,7 @@ class Graph_DiT(pl.LightningModule):
         self.args.batch_size = 128
         self.args.GPU = '0'
         self.args.dataset = 'cifar10-valid'
-        self.args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        self.args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
         self.args.data_loc = '../cifardata/'
         self.args.seed = 777
         self.args.init = ''
@@ -59,10 +59,10 @@ class Graph_DiT(pl.LightningModule):
         if 'valid' in self.args.dataset:
             self.args.dataset = self.args.dataset.replace('-valid', '')
         print('graph_dit starts to get searchspace of nasbench201')
-        self.searchspace = nasspace.get_search_space(self.args)
+        # self.searchspace = nasspace.get_search_space(self.args)
         print('searchspace of nasbench201 is obtained')
         print('graphdit starts to get train_loader')
-        self.train_loader = datasets.get_data(self.args.dataset, self.args.data_loc, self.args.trainval, self.args.batch_size, self.args.augtype, self.args.repeat, self.args)
+        # self.train_loader = datasets.get_data(self.args.dataset, self.args.data_loc, self.args.trainval, self.args.batch_size, self.args.augtype, self.args.repeat, self.args)
         print('train_loader is obtained')
 
         self.cfg = cfg
@@ -162,7 +162,7 @@ class Graph_DiT(pl.LightningModule):
         return pred
         
     def training_step(self, data, i):
-        data_x = F.one_hot(data.x, num_classes=8).float()[:, self.active_index]
+        data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
         data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
 
         dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
@@ -195,15 +195,18 @@ class Graph_DiT(pl.LightningModule):
         # print("Size of the input features Xdim {}, Edim {}, ydim {}".format(self.Xdim, self.Edim, self.ydim))
 
     def on_train_epoch_start(self) -> None:
-        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
-            print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
+        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
+            # print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
+            print("Starting train epoch {}/{}...".format(self.current_epoch, self.cfg.train.n_epochs))
         self.start_epoch_time = time.time()
         self.train_loss.reset()
         self.train_metrics.reset()
 
     def on_train_epoch_end(self) -> None:
 
-        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
             log = True
         else:
             log = False
@@ -222,7 +225,7 @@ class Graph_DiT(pl.LightningModule):
 
     @torch.no_grad()
     def validation_step(self, data, i):
-        data_x = F.one_hot(data.x, num_classes=8).float()[:, self.active_index]
+        data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
         data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
         dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
         dense_data = dense_data.mask(node_mask, collapse=False)
@@ -239,8 +242,8 @@ class Graph_DiT(pl.LightningModule):
         
                    self.val_X_logp.compute(), self.val_E_logp.compute()]
         
-        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
-            print(f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
+        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        print(f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
                 f"Val Edge type KL: {metrics[2] :.2f}", 'Val loss: %.2f \t Best :  %.2f\n' % (metrics[0], self.best_val_nll))
         with open("validation-metrics.csv", "a") as f:
             # save the metrics as csv file
@@ -286,7 +289,7 @@ class Graph_DiT(pl.LightningModule):
                 samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
                                                 save_final=to_save,
                                                 keep_chain=chains_save,
-                                                number_chain_steps=self.number_chain_steps))
+                                                number_chain_steps=self.number_chain_steps)[0])
                 ident += to_generate
                 start_index += to_generate
 
@@ -315,7 +318,7 @@ class Graph_DiT(pl.LightningModule):
     
     @torch.no_grad()
     def test_step(self, data, i):
-        data_x = F.one_hot(data.x, num_classes=8).float()[:, self.active_index]
+        data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
         data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
 
         dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
@@ -356,10 +359,11 @@ 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)
+                                            keep_chain=chains_save, number_chain_steps=self.number_chain_steps)[0]
             samples = samples + cur_sample
             
             all_ys.append(batch_y)
@@ -600,6 +604,9 @@ class Graph_DiT(pl.LightningModule):
 
         assert (E == torch.transpose(E, 1, 2)).all()
 
+        total_log_probs = torch.zeros([self.cfg.general.final_model_samples_to_generate,10], device=self.device)
+        # total_log_probs = torch.zeros([self.cfg.general.samples_to_generate,10], device=self.device)
+
         # Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
         for s_int in reversed(range(0, self.T)):
             s_array = s_int * torch.ones((batch_size, 1)).type_as(y)
@@ -608,21 +615,24 @@ class Graph_DiT(pl.LightningModule):
             t_norm = t_array / self.T
 
             # Sample z_s
-            sampled_s, discrete_sampled_s = self.sample_p_zs_given_zt(s_norm, t_norm, X, E, y, node_mask)
+            sampled_s, discrete_sampled_s, log_probs= self.sample_p_zs_given_zt(s_norm, t_norm, X, E, y, node_mask)
             X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
+            print(f'sampled_s.X shape: {sampled_s.X.shape}, sampled_s.E shape: {sampled_s.E.shape}')
+            print(f'log_probs shape: {log_probs.shape}')
+            total_log_probs += log_probs
 
         # Sample
         sampled_s = sampled_s.mask(node_mask, collapse=True)
         X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
         
-        molecule_list = []
+        graph_list = []
         for i in range(batch_size):
             n = n_nodes[i]
-            atom_types = X[i, :n].cpu()
+            node_types = X[i, :n].cpu()
             edge_types = E[i, :n, :n].cpu()
-            molecule_list.append([atom_types, edge_types])
+            graph_list.append([node_types, edge_types])
         
-        return molecule_list
+        return graph_list, total_log_probs
 
     def sample_p_zs_given_zt(self, s, t, X_t, E_t, y_t, node_mask):
         """Samples from zs ~ p(zs | zt). Only used during sampling.
@@ -634,6 +644,7 @@ class Graph_DiT(pl.LightningModule):
 
         # Neural net predictions
         noisy_data = {'X_t': X_t, 'E_t': E_t, 'y_t': y_t, 't': t, 'node_mask': node_mask}
+        print(f"sample p zs given zt X_t shape: {X_t.shape}, E_t shape: {E_t.shape}, y_t shape: {y_t.shape}, node_mask shape: {node_mask.shape}")
         
         def get_prob(noisy_data, unconditioned=False):
             pred = self.forward(noisy_data, unconditioned=unconditioned)
@@ -673,7 +684,19 @@ class Graph_DiT(pl.LightningModule):
         # with condition = P_t(G_{t-1} |G_t, C)
         # with condition = P_t(A_{t-1} |A_t, y)
         prob_X, prob_E, pred = get_prob(noisy_data)
+        print(f'prob_X shape: {prob_X.shape}, prob_E shape: {prob_E.shape}')
+        print(f'X_t shape: {X_t.shape}, E_t shape: {E_t.shape}, y_t shape: {y_t.shape}')
+        print(f'X_t: {X_t}')
+        log_prob_X = torch.log(torch.gather(prob_X, -1, X_t.long()).squeeze(-1))  # bs, n
+        log_prob_E = torch.log(torch.gather(prob_E, -1, E_t.long()).squeeze(-1))  # bs, n, n
 
+        # Sum the log_prob across dimensions for total log_prob
+        log_prob_X = log_prob_X.sum(dim=-1)
+        log_prob_E = log_prob_E.sum(dim=(1, 2))
+        print(f'log_prob_X shape: {log_prob_X.shape}, log_prob_E shape: {log_prob_E.shape}')
+        # log_probs = log_prob_E + log_prob_X
+        log_probs = torch.cat([log_prob_X, log_prob_E], dim=-1)  # (batch_size, 2)
+        print(f'log_probs shape: {log_probs.shape}')
         ### Guidance
         if self.guidance_target is not None and self.guide_scale is not None and self.guide_scale != 1:
             uncon_prob_X, uncon_prob_E, pred = get_prob(noisy_data, unconditioned=True)
@@ -686,120 +709,120 @@ class Graph_DiT(pl.LightningModule):
         assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-4).all()
         assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-4).all()
 
-        # sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
+        sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
 
         # sample multiple times and get the best score arch...
 
-        num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
-        op_type = {
-            'input': 0,
-            'nor_conv_1x1': 1,
-            'nor_conv_3x3': 2,
-            'avg_pool_3x3': 3,
-            'skip_connect': 4,
-            'none': 5,
-            'output': 6,
-        }
-        def check_valid_graph(nodes, edges):
-            nodes = [num_to_op[i] for i in nodes]
-            if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]:
-                return False
-            if nodes[0] != 'input' or nodes[-1] != 'output':
-                return False
-            for i in range(0, len(nodes)):
-                if edges[i][i] == 1:
-                    return False
-            for i in range(1, len(nodes) - 1):
-                if nodes[i] not in op_type or nodes[i] == 'input' or nodes[i] == 'output':
-                    return False
-            for i in range(0, len(nodes)):
-                for j in range(i, len(nodes)):
-                    if edges[i, j] == 1 and nodes[j] == 'input':
-                        return False
-            for i in range(0, len(nodes)):
-                for j in range(i, len(nodes)):
-                    if edges[i, j] == 1 and nodes[i] == 'output':
-                        return False
-            flag = 0
-            for i in range(0,len(nodes)):
-                if edges[i,-1] == 1:
-                    flag = 1
-                    break
-            if flag == 0: return False
-            return True
+        # num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
+        # op_type = {
+        #     'input': 0,
+        #     'nor_conv_1x1': 1,
+        #     'nor_conv_3x3': 2,
+        #     'avg_pool_3x3': 3,
+        #     'skip_connect': 4,
+        #     'none': 5,
+        #     'output': 6,
+        # }
+        # def check_valid_graph(nodes, edges):
+        #     nodes = [num_to_op[i] for i in nodes]
+        #     if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]:
+        #         return False
+        #     if nodes[0] != 'input' or nodes[-1] != 'output':
+        #         return False
+        #     for i in range(0, len(nodes)):
+        #         if edges[i][i] == 1:
+        #             return False
+        #     for i in range(1, len(nodes) - 1):
+        #         if nodes[i] not in op_type or nodes[i] == 'input' or nodes[i] == 'output':
+        #             return False
+        #     for i in range(0, len(nodes)):
+        #         for j in range(i, len(nodes)):
+        #             if edges[i, j] == 1 and nodes[j] == 'input':
+        #                 return False
+        #     for i in range(0, len(nodes)):
+        #         for j in range(i, len(nodes)):
+        #             if edges[i, j] == 1 and nodes[i] == 'output':
+        #                 return False
+        #     flag = 0
+        #     for i in range(0,len(nodes)):
+        #         if edges[i,-1] == 1:
+        #             flag = 1
+        #             break
+        #     if flag == 0: return False
+        #     return True
 
-        class Args:
-            pass
+        # class Args:
+        #     pass
 
-        def get_score(sampled_s):
-            x_list = sampled_s.X.unbind(dim=0)
-            e_list = sampled_s.E.unbind(dim=0)
-            valid_rlt = [check_valid_graph(x_list[i].cpu().numpy(), e_list[i].cpu().numpy()) for i in range(len(x_list))]
-            from graph_dit.naswot.naswot.score_networks import get_nasbench201_nodes_score
-            score = []
+        # def get_score(sampled_s):
+        #     x_list = sampled_s.X.unbind(dim=0)
+        #     e_list = sampled_s.E.unbind(dim=0)
+        #     valid_rlt = [check_valid_graph(x_list[i].cpu().numpy(), e_list[i].cpu().numpy()) for i in range(len(x_list))]
+        #     from graph_dit.naswot.naswot.score_networks import get_nasbench201_nodes_score
+        #     score = []
             
-            for i in range(len(x_list)):
-                if valid_rlt[i]:
-                    nodes = [num_to_op[j] for j in x_list[i].cpu().numpy()]
-                    # edges = e_list[i].cpu().numpy()
-                    score.append(get_nasbench201_nodes_score(nodes,train_loader=self.train_loader,searchspace=self.searchspace,device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu") , args=self.args))
-                else:
-                    score.append(-1)
-            return torch.tensor(score, dtype=torch.float32, requires_grad=True).to(x_list[0].device)
+        #     for i in range(len(x_list)):
+        #         if valid_rlt[i]:
+        #             nodes = [num_to_op[j] for j in x_list[i].cpu().numpy()]
+        #             # edges = e_list[i].cpu().numpy()
+        #             score.append(get_nasbench201_nodes_score(nodes,train_loader=self.train_loader,searchspace=self.searchspace,device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu") , args=self.args))
+        #         else:
+        #             score.append(-1)
+        #     return torch.tensor(score, dtype=torch.float32, requires_grad=True).to(x_list[0].device)
 
-        sample_num = 10
-        best_arch = None
-        best_score_int = -1e8
-        score = torch.ones(100, dtype=torch.float32, requires_grad=True) * -1e8
+        # sample_num = 10
+        # best_arch = None
+        # best_score_int = -1e8
+        # score = torch.ones(100, dtype=torch.float32, requires_grad=True) * -1e8
 
-        for i in range(sample_num):
-            sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
-            score = get_score(sampled_s)
-            print(f'score: {score}')
-            print(f'score.shape: {score.shape}')
-            print(f'torch.sum(score): {torch.sum(score)}')
-            sum_score = torch.sum(score)
-            print(f'sum_score: {sum_score}')
-            if sum_score > best_score_int:
-                best_score_int = sum_score
-                best_score = score
-                best_arch = sampled_s
+        # for i in range(sample_num):
+        #     sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
+        #     score = get_score(sampled_s)
+        #     print(f'score: {score}')
+        #     print(f'score.shape: {score.shape}')
+        #     print(f'torch.sum(score): {torch.sum(score)}')
+        #     sum_score = torch.sum(score)
+        #     print(f'sum_score: {sum_score}')
+        #     if sum_score > best_score_int:
+        #         best_score_int = sum_score
+        #         best_score = score
+        #         best_arch = sampled_s
 
         # print(f'prob_X: {prob_X.shape}, prob_E: {prob_E.shape}')
         
         # best_arch = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
 
-        # X_s = F.one_hot(sampled_s.X, num_classes=self.Xdim_output).float()
-        # E_s = F.one_hot(sampled_s.E, num_classes=self.Edim_output).float()
-        print(f'best_arch.X: {best_arch.X.shape}, best_arch.E: {best_arch.E.shape}') # 100 8 8, bs n n, 100 8 8 2, bs n n 2
+        X_s = F.one_hot(sampled_s.X, num_classes=self.Xdim_output).float()
+        E_s = F.one_hot(sampled_s.E, num_classes=self.Edim_output).float()
+        # print(f'best_arch.X: {best_arch.X.shape}, best_arch.E: {best_arch.E.shape}') # 100 8 8, bs n n, 100 8 8 2, bs n n 2
 
-        print(f'best_arch.X: {best_arch.X}, best_arch.E: {best_arch.E}')
-        X_s = F.one_hot(best_arch.X, num_classes=self.Xdim_output).float()
-        E_s = F.one_hot(best_arch.E, num_classes=self.Edim_output).float()
-        print(f'X_s: {X_s}, E_s: {E_s}')
+        # print(f'best_arch.X: {best_arch.X}, best_arch.E: {best_arch.E}')
+        # X_s = F.one_hot(best_arch.X, num_classes=self.Xdim_output).float()
+        # E_s = F.one_hot(best_arch.E, num_classes=self.Edim_output).float()
+        # print(f'X_s: {X_s}, E_s: {E_s}')
 
-        # NASWOT score
-        target_score = torch.ones(100, requires_grad=True) * 2000.0
-        target_score = target_score.to(X_s.device)
+        # # NASWOT score
+        # target_score = torch.ones(100, requires_grad=True) * 2000.0
+        # target_score = target_score.to(X_s.device)
 
-        # compute loss mse(cur_score - target_score)
-        mse_loss = torch.nn.MSELoss()
-        print(f'best_score: {best_score.shape}, target_score: {target_score.shape}')
-        print(f'best_score.requires_grad: {best_score.requires_grad}, target_score.requires_grad: {target_score.requires_grad}')
-        loss = mse_loss(best_score, target_score)
-        loss.backward(retain_graph=True)
+        # # compute loss mse(cur_score - target_score)
+        # mse_loss = torch.nn.MSELoss()
+        # print(f'best_score: {best_score.shape}, target_score: {target_score.shape}')
+        # print(f'best_score.requires_grad: {best_score.requires_grad}, target_score.requires_grad: {target_score.requires_grad}')
+        # loss = mse_loss(best_score, target_score)
+        # loss.backward(retain_graph=True)
 
         # loss backward = gradient
 
         # get prob.X, prob_E gradient
-        x_grad = pred.X.grad
-        e_grad = pred.E.grad
+        # x_grad = pred.X.grad
+        # e_grad = pred.E.grad
 
-        beta_ratio = 0.5
-        # x_current = pred.X - beta_ratio * x_grad
-        # e_current = pred.E - beta_ratio * e_grad
-        E_s = pred.X - beta_ratio * x_grad
-        X_s = pred.E - beta_ratio * e_grad
+        # beta_ratio = 0.5
+        # # x_current = pred.X - beta_ratio * x_grad
+        # # e_current = pred.E - beta_ratio * e_grad
+        # E_s = pred.X - beta_ratio * x_grad
+        # X_s = pred.E - beta_ratio * e_grad
 
         # update prob.X prob_E with using gradient
 
@@ -809,4 +832,4 @@ class Graph_DiT(pl.LightningModule):
         out_one_hot = utils.PlaceHolder(X=X_s, E=E_s, y=y_t)
         out_discrete = utils.PlaceHolder(X=X_s, E=E_s, y=y_t)
 
-        return out_one_hot.mask(node_mask).type_as(y_t), out_discrete.mask(node_mask, collapse=True).type_as(y_t)
+        return out_one_hot.mask(node_mask).type_as(y_t), out_discrete.mask(node_mask, collapse=True).type_as(y_t), log_probs

graph_dit/exp_201/main.py

@@ -0,0 +1,85 @@
+
+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)
+    results = api.query_by_index(i, 'cifar10')
+    result = results[111].get_eval('ori-test')
+    percentages.append(result)
+
+# 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/main.py

@@ -1,4 +1,5 @@
 # These imports are tricky because they use c++, do not move them
+from tqdm import tqdm
 import os, shutil
 import warnings
 
@@ -144,10 +145,32 @@ def main(cfg: DictConfig):
     else:
         trainer.test(model, datamodule=datamodule, ckpt_path=cfg.general.test_only)
 
+from accelerate import Accelerator
+from accelerate.utils import set_seed, ProjectConfiguration
+
 @hydra.main(
     version_base="1.1", config_path="../configs", config_name="config"
 )
 def test(cfg: DictConfig):
+    os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
+    accelerator_config = ProjectConfiguration(
+        project_dir=os.path.join(cfg.general.log_dir, cfg.general.name),
+        automatic_checkpoint_naming=True,
+        total_limit=cfg.general.number_checkpoint_limit,
+    )
+    accelerator = Accelerator(
+        mixed_precision='no',
+        project_config=accelerator_config,
+        # gradient_accumulation_steps=cfg.train.gradient_accumulation_steps * cfg.train.n_epochs,
+        gradient_accumulation_steps=cfg.train.gradient_accumulation_steps, 
+    )
+
+    # Debug: 确认可用设备
+    print(f"Available GPUs: {torch.cuda.device_count()}")
+    print(f"Using device: {accelerator.device}")
+
+    set_seed(cfg.train.seed, device_specific=True)
+
     datamodule = dataset.DataModule(cfg)
     datamodule.prepare_data()
     dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg, dataset=datamodule.dataset)
@@ -169,40 +192,216 @@ def test(cfg: DictConfig):
         "visualization_tools": visulization_tools,
     }
 
+    # Debug: 确认可用设备
+    print(f"Available GPUs: {torch.cuda.device_count()}")
+    print(f"Using device: {accelerator.device}")
+
     if cfg.general.test_only:
         cfg, _ = get_resume(cfg, model_kwargs)
         os.chdir(cfg.general.test_only.split("checkpoints")[0])
     elif cfg.general.resume is not None:
         cfg, _ = get_resume_adaptive(cfg, model_kwargs)
         os.chdir(cfg.general.resume.split("checkpoints")[0])
-    # os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
     model = Graph_DiT(cfg=cfg, **model_kwargs)
-    trainer = Trainer(
-        gradient_clip_val=cfg.train.clip_grad,
-        # accelerator="cpu",
-        accelerator="gpu"
-        if torch.cuda.is_available() and cfg.general.gpus > 0
-        else "cpu",
-        devices=[cfg.general.gpu_number]
-        if torch.cuda.is_available() and cfg.general.gpus > 0
-        else None,
-        max_epochs=cfg.train.n_epochs,
-        enable_checkpointing=False,
-        check_val_every_n_epoch=cfg.train.check_val_every_n_epoch,
-        val_check_interval=cfg.train.val_check_interval,
-        strategy="ddp" if cfg.general.gpus > 1 else "auto",
-        enable_progress_bar=cfg.general.enable_progress_bar,
-        callbacks=[],
-        reload_dataloaders_every_n_epochs=0,
-        logger=[],
-    )
+    graph_dit_model = model
 
-    if not cfg.general.test_only:
-        print("start testing fit method")
-        trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
-        if cfg.general.save_model:
-            trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
-        trainer.test(model, datamodule=datamodule)
+    inference_dtype = torch.float32
+    graph_dit_model.to(accelerator.device, dtype=inference_dtype)
+
+
+    # optional: freeze the model
+    # graph_dit_model.model.requires_grad_(True)
+
+    import torch.nn.functional as F
+    optimizer = graph_dit_model.configure_optimizers()
+    train_dataloader = accelerator.prepare(datamodule.train_dataloader())
+    optimizer, graph_dit_model = accelerator.prepare(optimizer, graph_dit_model)
+    # start training
+    for epoch in range(cfg.train.n_epochs):
+        graph_dit_model.train()  # 设置模型为训练模式
+        print(f"Epoch {epoch}", end="\n")
+        graph_dit_model.on_train_epoch_start()
+        for data in train_dataloader:  # 从数据加载器中获取一个批次的数据
+            # data.to(accelerator.device)
+            # data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
+            # data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
+            # dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
+            # dense_data = dense_data.mask(node_mask)
+            # X, E = dense_data.X, dense_data.E
+            # noisy_data = graph_dit_model.apply_noise(X, E, data.y, node_mask)
+            # pred = graph_dit_model.forward(noisy_data)
+            # loss = graph_dit_model.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
+            #                     true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
+            #                     log=epoch % graph_dit_model.log_every_steps == 0)
+            # # print(f'training loss: {loss}, epoch: {self.current_epoch}, batch: {i}\n, pred type: {type(pred)}, pred.X shape: {type(pred.X)}, {pred.X.shape}, pred.E shape: {type(pred.E)}, {pred.E.shape}')
+            # graph_dit_model.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
+            #                 log=epoch % graph_dit_model.log_every_steps == 0)
+            # graph_dit_model.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
+            # print(f"training loss: {loss}")
+            # with open("training-loss.csv", "a") as f:
+            #     f.write(f"{loss}, {epoch}\n")
+            loss = graph_dit_model.training_step(data, epoch)
+            loss = loss['loss']
+
+            accelerator.backward(loss)
+            optimizer.step()
+            optimizer.zero_grad()
+            # return {'loss': loss}
+        graph_dit_model.on_train_epoch_end()
+        if epoch % cfg.train.check_val_every_n_epoch == 0:
+            print(f'print validation loss')
+            graph_dit_model.eval()
+            graph_dit_model.on_validation_epoch_start()
+            graph_dit_model.validation_step(data, epoch)
+            graph_dit_model.on_validation_epoch_end()
+    
+    # start testing
+    print("start testing")
+    graph_dit_model.eval()
+    test_dataloader = accelerator.prepare(datamodule.test_dataloader())
+    graph_dit_model.on_test_epoch_start()
+    for data in test_dataloader:
+        nll = graph_dit_model.test_step(data, epoch)
+        # data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
+        # data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
+
+        # dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
+        # dense_data = dense_data.mask(node_mask)
+        # noisy_data = graph_dit_model.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
+        # pred = graph_dit_model.forward(noisy_data)
+        # nll = graph_dit_model.compute_val_loss(pred, noisy_data, dense_data.X, dense_data.E, data.y, node_mask, test=True)
+        # graph_dit_model.test_y_collection.append(data.y)
+        print(f'test loss: {nll}')
+    
+    graph_dit_model.on_test_epoch_end()
+
+    # start sampling
+
+    # samples_left_to_generate = cfg.general.final_model_samples_to_generate
+    # samples_left_to_save = cfg.general.final_model_samples_to_save
+    # chains_left_to_save = cfg.general.final_model_chains_to_save
+
+    # samples, all_ys, batch_id = [], [], 0
+    # samples_with_log_probs = []
+    # test_y_collection = torch.cat(graph_dit_model.test_y_collection, dim=0)
+    # num_examples = test_y_collection.size(0)
+    # if cfg.general.final_model_samples_to_generate > num_examples:
+    #     ratio = cfg.general.final_model_samples_to_generate // num_examples
+    #     test_y_collection = test_y_collection.repeat(ratio+1, 1)
+    #     num_examples = test_y_collection.size(0)
+    
+    # Normal reward function
+    # from nas_201_api import NASBench201API as API
+    # api = API('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
+    # def graph_reward_fn(graphs, true_graphs=None, device=None, reward_model='swap'):
+    #     rewards = []
+    #     if reward_model == 'swap':
+    #         import csv
+    #         with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
+    #             reader = csv.reader(f)
+    #             header = next(reader)
+    #             data = [row for row in reader]
+    #             swap_scores = [float(row[0]) for row in data]
+    #             for graph in graphs:
+    #                 node_tensor = graph[0]
+    #                 node = node_tensor.cpu().numpy().tolist()
+
+    #                 def nodes_to_arch_str(nodes):
+    #                     num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
+    #                     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
+                    
+    #                 arch_str = nodes_to_arch_str(node)
+    #                 reward = swap_scores[api.query_index_by_arch(arch_str)]
+    #                 rewards.append(reward)
+                
+    #     # for graph in graphs:
+    #     #     reward = 1.0
+    #     #     rewards.append(reward)
+    #     return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
+    # old_log_probs = None
+    # while samples_left_to_generate > 0:
+    #     print(f'samples left to generate: {samples_left_to_generate}/'
+    #         f'{cfg.general.final_model_samples_to_generate}', end='', flush=True)
+    #     bs = 1 * cfg.train.batch_size
+    #     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 = torch.ones(to_generate, graph_dit_model.ydim_output, device=graph_dit_model.device)
+
+    #     cur_sample, log_probs = graph_dit_model.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
+    #                                     keep_chain=chains_save, number_chain_steps=graph_dit_model.number_chain_steps)
+    #     log_probs = torch.sum(log_probs, dim=-1).unsqueeze(1)
+    #     samples = samples + cur_sample
+    #     reward = graph_reward_fn(cur_sample, device=graph_dit_model.device)
+    #     advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6)
+    #     print(f'reward: {reward.shape}, advantages: {advantages.shape}, log_probs: {log_probs.shape}, cur_sample: {len(cur_sample)}')
+    #     if old_log_probs is None:
+    #         old_log_probs = log_probs.clone()
+    #     ratio = torch.exp(log_probs - old_log_probs)
+    #     unclipped_loss = -advantages * ratio
+    #     clipped_loss = -advantages * torch.clamp(ratio, 1.0 - cfg.ppo.clip_param, 1.0 + cfg.ppo.clip_param)
+    #     loss = torch.mean(torch.max(unclipped_loss, clipped_loss))
+    #     accelerator.backward(loss)
+    #     optimizer.step()
+    #     optimizer.zero_grad()
+
+
+    #     samples_with_log_probs.append((cur_sample, log_probs, reward))
+        
+    #     all_ys.append(batch_y)
+    #     batch_id += to_generate
+
+    #     samples_left_to_save -= to_save
+    #     samples_left_to_generate -= to_generate
+    #     chains_left_to_save -= chains_save
+        
+    # print(f"final Computing sampling metrics...")
+    # graph_dit_model.sampling_metrics.reset()
+    # graph_dit_model.sampling_metrics(samples, all_ys, graph_dit_model.name, graph_dit_model.current_epoch, graph_dit_model.val_counter, test=True)
+    # graph_dit_model.sampling_metrics.reset()
+    # print(f"Done.")
+
+    # # save samples
+    # print("Samples:")
+    # print(samples)
+
+    # ========================
+    
+
+    
+
+
+    # trainer = Trainer(
+    #     gradient_clip_val=cfg.train.clip_grad,
+    #     # accelerator="cpu",
+    #     accelerator="gpu"
+    #     if torch.cuda.is_available() and cfg.general.gpus > 0
+    #     else "cpu",
+    #     devices=[cfg.general.gpu_number]
+    #     if torch.cuda.is_available() and cfg.general.gpus > 0
+    #     else None,
+    #     max_epochs=cfg.train.n_epochs,
+    #     enable_checkpointing=False,
+    #     check_val_every_n_epoch=cfg.train.check_val_every_n_epoch,
+    #     val_check_interval=cfg.train.val_check_interval,
+    #     strategy="ddp" if cfg.general.gpus > 1 else "auto",
+    #     enable_progress_bar=cfg.general.enable_progress_bar,
+    #     callbacks=[],
+    #     reload_dataloaders_every_n_epochs=0,
+    #     logger=[],
+    # )
+
+    # if not cfg.general.test_only:
+    #     print("start testing fit method")
+    #     trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
+    #     if cfg.general.save_model:
+    #         trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
+    #     trainer.test(model, datamodule=datamodule)
 
 if __name__ == "__main__":
     test()

graph_dit/models/conditions.py

@@ -76,6 +76,8 @@ class CategoricalEmbedder(nn.Module):
             embeddings = embeddings + noise
         return embeddings
     
+# 相似的condition cluster起来
+# size 
 class ClusterContinuousEmbedder(nn.Module):
     def __init__(self, input_size, hidden_size, dropout_prob):
         super().__init__()
@@ -108,6 +110,8 @@ class ClusterContinuousEmbedder(nn.Module):
         
         if drop_ids is not None:
             embeddings = torch.zeros((labels.shape[0], self.hidden_size), device=labels.device)
+            # print(labels[~drop_ids].shape)
+            # torch.Size([1200])
             embeddings[~drop_ids] = self.mlp(labels[~drop_ids])
             embeddings[drop_ids] += self.embedding_drop.weight[0]
         else:

graph_dit/models/transformer.py

@@ -17,20 +17,22 @@ class Denoiser(nn.Module):
         num_heads=16,
         mlp_ratio=4.0,
         drop_condition=0.1,
-        Xdim=118,
-        Edim=5,
-        ydim=3,
+        Xdim=7,
+        Edim=2,
+        ydim=1,
         task_type='regression',
     ):
         super().__init__()
+        print(f"Denoiser, xdim: {Xdim}, edim: {Edim}, ydim: {ydim}, hidden_size: {hidden_size}, depth: {depth}, num_heads: {num_heads}, mlp_ratio: {mlp_ratio}, drop_condition: {drop_condition}")
         self.num_heads = num_heads
         self.ydim = ydim
         self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False)
 
         self.t_embedder = TimestepEmbedder(hidden_size)
+        # 
         self.y_embedding_list = torch.nn.ModuleList()
 
-        self.y_embedding_list.append(ClusterContinuousEmbedder(2, hidden_size, drop_condition))
+        self.y_embedding_list.append(ClusterContinuousEmbedder(1, hidden_size, drop_condition))
         for i in range(ydim - 2):
             if task_type == 'regression':
                 self.y_embedding_list.append(ClusterContinuousEmbedder(1, hidden_size, drop_condition))
@@ -86,8 +88,10 @@ 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)
+        # torch.Size([1200, 8, 7]) torch.Size([1200, 8, 8, 2]) torch.Size([1200, 2]) torch.Size([1200, 1]) False
+        # print(y)
         force_drop_id = torch.zeros_like(y.sum(-1))
         # drop the nan values
         force_drop_id[torch.isnan(y.sum(-1))] = 1
@@ -109,11 +113,12 @@ class Denoiser(nn.Module):
         c1 = self.t_embedder(t)
         # print("C1 after t_embedder")
         # print(c1.shape)
-        for i in range(1, self.ydim):
-            if i == 1:
-                c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
-            else:
-                c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
+        c2 = self.y_embedding_list[0](y[:,0].unsqueeze(-1), self.training, force_drop_id, t)
+        # for i in range(1, self.ydim):
+        #     if i == 1:
+        #         c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
+        #     else:
+                # c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
         # print("C2 after y_embedding_list")
         # print(c2.shape)
         # print("C1 + C2")

graph_dit/nasbench-201-meta.json

@@ -0,0 +1 @@
+{"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.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

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
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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 @@
+graph, input_nasbench201, output_nasbench201
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+nodes: tensor(0) tensor(3) tensor(1) tensor(5) tensor(1) 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, 1645.819091796875, 0.4672992103757777
+nodes: tensor(0) tensor(1) tensor(3) tensor(1) tensor(5) tensor(1) 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, 1661.2313232421875, 0.8321289859690038
+nodes: tensor(0) tensor(2) tensor(4) tensor(2) tensor(1) tensor(2) 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, 1691.4451904296875, 0.1319178847781063

graph_dit/output_graphs/171452.txt.json

@@ -0,0 +1 @@
+[[12507, 79.62], [3613, 79.9], [10686, 75.25], [13425, 76.29], [3569, 81.67], [10859, 82.35], [1612, 82.62], [12222, 81.81], [13669, 76.97], [8658, 84.69], [1430, 84.28], [7683, 84.16], [6728, 85.23], [14733, 79.28], [13723, 84.58], [3094, 82.99], [843, 81.9], [12422, 79.28], [576, 74.24], [153, 85.77], [11199, 85.95], [12817, 84.11], [12481, 82.05], [6835, 84.37], [14782, 78.75], [7225, 72.13], [2633, 82.17], [244, 88.36], [4236, 73.88], [7845, 79.61], [4983, 88.46], [5481, 70.15], [4674, 81.88], [5634, 80.53], [10116, 61.02], [7446, 79.58], [14779, 83.73], [2174, 84.99], [5416, 75.37], [5576, 87.99], [1387, 86.88], [575, 84.51], [7416, 83.71], [10340, 79.65], [10675, 60.52], [4566, 81.82], [9221, 75.8], [14717, 79.75], [823, 84.49], [8924, 79.89], [4357, 83.77], [6333, 82.26], [4398, 84.03], [11599, 82.91], [367, 79.55], [10473, 61.06], [4457, 78.88], [13068, 81.89], [1306, 79.35], [15586, 86.52], [5379, 79.9], [11027, 78.81], [1117, 77.84], [14539, 85.21], [6897, 76.09], [5258, 79.65], [6777, 71.29], [2335, 79.47], [3660, 77.86], [8074, 82.71], [1766, 79.7], [3787, 83.88], [9731, 78.97], [65, 84.08], [450, 80.64], [9869, 87.32], [11181, 84.49], [13408, 81.47], [7470, 81.31], [4714, 78.35], [8979, 83.68], [7137, 77.68], [5509, 82.53], [11810, 84.5], [11496, 73.13], [10658, 87.62], [8784, 81.94], [13222, 79.52], [1909, 84.23], [224, 84.31], [2057, 85.96], [9752, 84.15], [6107, 73.03], [6669, 76.28], [8415, 85.33], [10334, 84.85], [8595, 79.67], [5858, 81.11], [12603, 78.73], [5749, 88.2]]

graph_dit/test_perf.py

@@ -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]))
+
+