update the flex data code

graph_dit/datasets/dataset.py

@@ -70,7 +70,7 @@ 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 +408,7 @@ 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 +490,7 @@ 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 +656,7 @@ 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')
@@ -676,7 +677,7 @@ 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'
+        source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
         # self.api = API(source)
 
         data_list = []
@@ -712,6 +713,7 @@ class Dataset(InMemoryDataset):
         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
@@ -720,8 +722,13 @@ class Dataset(InMemoryDataset):
             # 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]
@@ -731,6 +738,11 @@ class Dataset(InMemoryDataset):
                     rand = random.random()
                     if rand < random_ratio:
                         edges[i, j] = 1
+            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):
@@ -766,10 +778,12 @@ class Dataset(InMemoryDataset):
 
         with tqdm(total = len_data) as pbar:
             active_nodes = set()
-            file_path = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+            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:
                 # arch_info = self.api.query_meta_info_by_index(i)
                 # results = self.api.query_by_index(i, 'cifar100')
@@ -784,6 +798,16 @@ class Dataset(InMemoryDataset):
                         active_nodes.add(op)
                 
                 data = graph_to_graph_data((adj_matrix, ops)) 
+                # with open(flex_graph_path, 'a') as f:
+                #     flex_graph = {
+                #         'adj_matrix': adj_matrix,
+                #         'ops': ops,
+                #     }
+                #     json.dump(flex_graph, f)
+                flex_graph_list.append({
+                    'adj_matrix':adj_matrix,
+                    'ops': ops,
+                })
                 if i < 3:
                     print(f"i={i}, data={data}")
                     with open(f'{i}.json', 'w') as f:
@@ -792,7 +816,17 @@ class Dataset(InMemoryDataset):
                         f.write(str(data.edge_attr))
                 data_list.append(data)
 
-                new_adj, new_ops = generate_flex_adj_mat(ori_nodes=ori_nodes, ori_edges=ori_adj, max_nodes=12, min_nodes=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,
+                })
+                # with open(flex_graph_path, 'w') as f:
+                #     flex_graph = {
+                #         'adj_matrix': new_adj.tolist(),
+                #         'ops': new_ops,
+                #     }
+                #     json.dump(flex_graph, f)
                 data_list.append(graph_to_graph_data((new_adj, new_ops)))
                
                 # graph_list.append({
@@ -838,6 +872,8 @@ 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])
 
@@ -1034,8 +1070,8 @@ def parse_architecture_string(arch_str, padding=0):
             assert idx == steps_coding[cont]
             cont += 1
             nodes.append(n)
-    ori_nodes = nodes.copy()
     nodes.append('output')  # Add output node
+    ori_nodes = nodes.copy()
     if padding > 0:
         for i in range(padding):
             nodes.append('none')
@@ -1048,7 +1084,7 @@ def parse_architecture_string(arch_str, padding=0):
     # 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):
@@ -1091,6 +1127,7 @@ 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]
@@ -1111,12 +1148,12 @@ 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')
 
         # 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
@@ -1325,11 +1362,11 @@ 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)
@@ -222,7 +222,7 @@ 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)
@@ -315,7 +315,7 @@ 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)
@@ -686,120 +686,120 @@ 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