add swap cifar10 results property_metric path update

configs/config.yaml

@@ -2,20 +2,26 @@ general:
     name: 'graph_dit'
     wandb: 'disabled' 
     gpus: 1
-    gpu_number: 2
+    gpu_number: 0
     resume: null
     test_only: null
     sample_every_val: 2500
-    samples_to_generate: 512      
+    samples_to_generate: 1000
     samples_to_save: 3
     chains_to_save: 1
     log_every_steps: 50
     number_chain_steps: 8
-    final_model_samples_to_generate: 100
+    final_model_samples_to_generate: 1000
     final_model_samples_to_save: 20
     final_model_chains_to_save: 1
     enable_progress_bar: False
     save_model: True
+    log_dir: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT'
+    number_checkpoint_limit: 3
+    type: 'Trainer'
+    nas_201: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+    swap_result: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/swap_results.csv'
+    root: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/'
 model:
     type: 'discrete'
     transition: 'marginal'                  
@@ -32,7 +38,7 @@ model:
     ensure_connected: True
 train:
     # n_epochs: 5000
-    n_epochs: 500
+    n_epochs: 10
     batch_size: 1200
     lr: 0.0002
     clip_grad: null
@@ -41,8 +47,11 @@ train:
     seed: 0
     val_check_interval: null
     check_val_every_n_epoch: 1
+    gradient_accumulation_steps: 1
 dataset:
     datadir: 'data/'
     task_name: 'nasbench-201'
     guidance_target: 'nasbench-201'
     pin_memory: False
+ppo:
+    clip_param: 1

environment.yaml

@@ -0,0 +1,228 @@
+name: graphdit
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - asttokens=2.4.1=pyhd8ed1ab_0
+  - blas=1.0=mkl
+  - brotli-python=1.0.9=py39h6a678d5_8
+  - bzip2=1.0.8=h5eee18b_6
+  - ca-certificates=2024.7.2=h06a4308_0
+  - comm=0.2.2=pyhd8ed1ab_0
+  - debugpy=1.6.7=py39h6a678d5_0
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - exceptiongroup=1.2.0=pyhd8ed1ab_2
+  - executing=2.0.1=pyhd8ed1ab_0
+  - ffmpeg=4.3=hf484d3e_0
+  - freetype=2.12.1=h4a9f257_0
+  - gmp=6.2.1=h295c915_3
+  - gmpy2=2.1.2=py39heeb90bb_0
+  - gnutls=3.6.15=he1e5248_0
+  - idna=3.7=py39h06a4308_0
+  - importlib-metadata=7.1.0=pyha770c72_0
+  - importlib_metadata=7.1.0=hd8ed1ab_0
+  - intel-openmp=2023.1.0=hdb19cb5_46306
+  - ipykernel=6.29.4=pyh3099207_0
+  - ipython=8.18.1=pyh707e725_3
+  - jedi=0.19.1=pyhd8ed1ab_0
+  - jinja2=3.1.4=py39h06a4308_0
+  - jpeg=9e=h5eee18b_1
+  - jupyter_client=8.6.2=pyhd8ed1ab_0
+  - jupyter_core=5.7.2=py39hf3d152e_0
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - lerc=3.0=h295c915_0
+  - libdeflate=1.17=h5eee18b_1
+  - libffi=3.4.4=h6a678d5_1
+  - libgcc-ng=13.2.0=h77fa898_7
+  - libgomp=13.2.0=h77fa898_7
+  - libiconv=1.16=h5eee18b_3
+  - libidn2=2.3.4=h5eee18b_0
+  - libpng=1.6.39=h5eee18b_0
+  - libsodium=1.0.18=h36c2ea0_1
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libtasn1=4.19.0=h5eee18b_0
+  - libtiff=4.5.1=h6a678d5_0
+  - libunistring=0.9.10=h27cfd23_0
+  - libwebp-base=1.3.2=h5eee18b_0
+  - lz4-c=1.9.4=h6a678d5_1
+  - matplotlib-inline=0.1.7=pyhd8ed1ab_0
+  - mkl=2023.1.0=h213fc3f_46344
+  - mkl-service=2.4.0=py39h5eee18b_1
+  - mkl_fft=1.3.8=py39h5eee18b_0
+  - mkl_random=1.2.4=py39hdb19cb5_0
+  - mpc=1.1.0=h10f8cd9_1
+  - mpfr=4.0.2=hb69a4c5_1
+  - mpmath=1.3.0=py39h06a4308_0
+  - ncurses=6.4=h6a678d5_0
+  - nest-asyncio=1.6.0=pyhd8ed1ab_0
+  - nettle=3.7.3=hbbd107a_1
+  - numpy-base=1.26.4=py39hb5e798b_0
+  - openh264=2.1.1=h4ff587b_0
+  - openjpeg=2.4.0=h9ca470c_2
+  - openssl=3.3.1=h4ab18f5_0
+  - packaging=24.0=pyhd8ed1ab_0
+  - parso=0.8.4=pyhd8ed1ab_0
+  - pexpect=4.9.0=pyhd8ed1ab_0
+  - pickleshare=0.7.5=py_1003
+  - pip=24.0=py39h06a4308_0
+  - platformdirs=4.2.2=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.46=pyha770c72_0
+  - psutil=5.9.8=py39hd1e30aa_0
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - pygments=2.18.0=pyhd8ed1ab_0
+  - pysocks=1.7.1=py39h06a4308_0
+  - python=3.9.19=h955ad1f_1
+  - python_abi=3.9=2_cp39
+  - pytorch-mutex=1.0=cpu
+  - pyzmq=25.1.2=py39h6a678d5_0
+  - readline=8.2=h5eee18b_0
+  - setuptools=69.5.1=py39h06a4308_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sqlite=3.45.3=h5eee18b_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - sympy=1.12=py39h06a4308_0
+  - tbb=2021.8.0=hdb19cb5_0
+  - tk=8.6.14=h39e8969_0
+  - tornado=6.4.1=py39hd3abc70_0
+  - traitlets=5.14.3=pyhd8ed1ab_0
+  - typing_extensions=4.12.2=pyha770c72_0
+  - wcwidth=0.2.13=pyhd8ed1ab_0
+  - wheel=0.43.0=py39h06a4308_0
+  - xz=5.4.6=h5eee18b_1
+  - zeromq=4.3.5=h6a678d5_0
+  - zlib=1.2.13=h5eee18b_1
+  - zstd=1.5.5=hc292b87_2
+  - pip:
+      - absl-py==2.1.0
+      - accelerate==0.34.2
+      - aiohttp==3.9.5
+      - aiosignal==1.3.1
+      - antlr4-python3-runtime==4.9.3
+      - astunparse==1.6.3
+      - async-timeout==4.0.3
+      - attrs==23.2.0
+      - beautifulsoup4==4.12.3
+      - bleach==6.1.0
+      - certifi==2024.2.2
+      - charset-normalizer==3.1.0
+      - cmake==3.29.3
+      - contourpy==1.2.1
+      - cycler==0.12.1
+      - defusedxml==0.7.1
+      - fastjsonschema==2.19.1
+      - fcd-torch==1.0.7
+      - filelock==3.14.0
+      - flatbuffers==24.3.25
+      - fonttools==4.52.4
+      - frozenlist==1.4.1
+      - fsspec==2024.5.0
+      - gast==0.5.4
+      - google-pasta==0.2.0
+      - grpcio==1.64.1
+      - h5py==3.11.0
+      - huggingface-hub==0.24.6
+      - hydra-core==1.3.2
+      - imageio==2.26.0
+      - importlib-resources==6.4.0
+      - joblib==1.2.0
+      - jsonschema==4.22.0
+      - jsonschema-specifications==2023.12.1
+      - jupyterlab-pygments==0.3.0
+      - keras==3.3.3
+      - kiwisolver==1.4.5
+      - libclang==18.1.1
+      - lightning-utilities==0.11.2
+      - lit==18.1.6
+      - markdown==3.6
+      - markdown-it-py==3.0.0
+      - markupsafe==2.1.5
+      - matplotlib==3.7.0
+      - mdurl==0.1.2
+      - mini-moses==1.0
+      - mistune==3.0.2
+      - ml-dtypes==0.3.2
+      - multidict==6.0.5
+      - namex==0.0.8
+      - nas-bench-201==2.1
+      - nasbench==1.0
+      - nbclient==0.10.0
+      - nbconvert==7.16.4
+      - nbformat==5.10.4
+      - networkx==3.0
+      - numpy==1.24.2
+      - nvidia-cublas-cu11==11.10.3.66
+      - nvidia-cublas-cu12==12.1.3.1
+      - nvidia-cuda-cupti-cu11==11.7.101
+      - nvidia-cuda-cupti-cu12==12.1.105
+      - nvidia-cuda-nvrtc-cu11==11.7.99
+      - nvidia-cuda-nvrtc-cu12==12.1.105
+      - nvidia-cuda-runtime-cu11==11.7.99
+      - nvidia-cuda-runtime-cu12==12.1.105
+      - nvidia-cudnn-cu11==8.5.0.96
+      - nvidia-cudnn-cu12==8.9.2.26
+      - nvidia-cufft-cu11==10.9.0.58
+      - nvidia-cufft-cu12==11.0.2.54
+      - nvidia-curand-cu11==10.2.10.91
+      - nvidia-curand-cu12==10.3.2.106
+      - nvidia-cusolver-cu11==11.4.0.1
+      - nvidia-cusolver-cu12==11.4.5.107
+      - nvidia-cusparse-cu11==11.7.4.91
+      - nvidia-cusparse-cu12==12.1.0.106
+      - nvidia-nccl-cu11==2.14.3
+      - nvidia-nccl-cu12==2.20.5
+      - nvidia-nvjitlink-cu12==12.5.40
+      - nvidia-nvtx-cu11==11.7.91
+      - nvidia-nvtx-cu12==12.1.105
+      - omegaconf==2.3.0
+      - opt-einsum==3.3.0
+      - optree==0.11.0
+      - pandas==1.5.3
+      - pandocfilters==1.5.1
+      - pillow==10.3.0
+      - protobuf==3.20.3
+      - pyparsing==3.1.2
+      - python-dateutil==2.9.0.post0
+      - pytorch-lightning==2.0.1
+      - pytz==2024.1
+      - pyyaml==6.0.1
+      - rdkit==2023.9.4
+      - referencing==0.35.1
+      - requests==2.32.2
+      - rich==13.7.1
+      - rpds-py==0.18.1
+      - safetensors==0.4.5
+      - scikit-learn==1.2.1
+      - scipy==1.13.1
+      - seaborn==0.13.2
+      - simplejson==3.19.2
+      - soupsieve==2.5
+      - tensorboard==2.16.2
+      - tensorboard-data-server==0.7.2
+      - tensorflow==2.16.1
+      - tensorflow-io-gcs-filesystem==0.37.0
+      - termcolor==2.4.0
+      - threadpoolctl==3.5.0
+      - tinycss2==1.3.0
+      - torch==2.0.0
+      - torch-geometric==2.3.0
+      - torchaudio==2.0.1+rocm5.4.2
+      - torchmetrics==0.11.4
+      - torchvision==0.15.1
+      - tqdm==4.64.1
+      - triton==2.0.0
+      - typing-extensions==4.12.0
+      - tzdata==2024.1
+      - urllib3==2.2.1
+      - webencodings==0.5.1
+      - werkzeug==3.0.3
+      - wrapt==1.16.0
+      - yacs==0.1.8
+      - yarl==1.9.4
+      - zipp==3.19.0
+prefix: /home/stud/hanzhang/anaconda3/envs/graphdit

graph_dit/analysis/rdkit_functions.py

@@ -54,7 +54,9 @@ class BasicGraphMetrics(object):
         covered_nodes = set()
         direct_valid_count = 0
         print(f"generated number: {len(generated)}")
+        print(f"generated: {generated}")
         for graph in generated:
+            print(f"graph: {graph}")
             node_types, edge_types = graph
             direct_valid_flag = True
             direct_valid_count += 1

graph_dit/datasets/dataset.py

@@ -25,7 +25,6 @@ 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 import nasspace
 from naswot import datasets as dt
 
@@ -72,7 +71,9 @@ 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 = '/nfs/data3/hanzhang/nasbenchDiT'
+        # base_path = '/nfs/data3/hanzhang/nasbenchDiT'
+        base_path = os.path.join(self.cfg.general.root, "..")
+
         root_path = os.path.join(base_path, self.datadir)
         self.root_path = root_path
 
@@ -84,7 +85,7 @@ class DataModule(AbstractDataModule):
         # Load the dataset to the memory
         # Dataset has target property, root path, and transform
         source = './NAS-Bench-201-v1_1-096897.pth'
-        dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None)
+        dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None, cfg=self.cfg)
         self.dataset = dataset
         # self.api = dataset.api
 
@@ -384,7 +385,7 @@ class DataModule_original(AbstractDataModule):
     def test_dataloader(self):
         return self.test_loader
 
-def new_graphs_to_json(graphs, filename):
+def new_graphs_to_json(graphs, filename, cfg):
     source_name = "nasbench-201"
     num_graph = len(graphs)
 
@@ -491,8 +492,9 @@ def new_graphs_to_json(graphs, filename):
         'num_active_nodes': len(active_nodes),
         'transition_E': transition_E.tolist(),
     }
-
+    import os
-    with open(f'/nfs/data3/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:
+    with open(os.path.join(cfg.general.root,'nasbench-201-meta.json'), 'w') as f:
         json.dump(meta_dict, f)
     
     return meta_dict
@@ -656,9 +658,11 @@ def graphs_to_json(graphs, filename):
         json.dump(meta_dict, f)
     return meta_dict
 class Dataset(InMemoryDataset):
-    def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
+    def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None, cfg=None):
         self.target_prop = target_prop
-        source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        self.cfg = cfg
+        # source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        source = os.path.join(self.cfg.general.root, 'NAS-Bench-201-v1_1-096897.pth')
         self.source = source
         # self.api = API(source)  # Initialize NAS-Bench-201 API
         # print('API loaded')
@@ -679,7 +683,8 @@ class Dataset(InMemoryDataset):
         return [f'{self.source}.pt']
 
     def process(self):
-        source = '/nfs/data3/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'
+        source = self.cfg.general.nas_201
         # self.api = API(source)
 
         data_list = []
@@ -748,7 +753,8 @@ class Dataset(InMemoryDataset):
             return  edges,nodes
         
 
-        def graph_to_graph_data(graph, idx, train_loader, searchspace, args, device):
+        # def graph_to_graph_data(graph, idx, train_loader, searchspace, args, device):
+        def graph_to_graph_data(graph, idx,  args, device):
         # def graph_to_graph_data(graph):
             ops = graph[1]
             adj = graph[0]
@@ -771,9 +777,10 @@ 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_nasbench201_idx_score(idx, train_loader, searchspace, args, device)
+            # y = get_nasbench201_idx_score(idx, train_loader, searchspace, args, device)
+            y = self.swap_scores[idx]
             print(y, idx)
-            if y > 1600:
+            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)
@@ -796,7 +803,7 @@ class Dataset(InMemoryDataset):
         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.api_loc = self.cfg.general.nas_201 
         args.data_loc = '../cifardata/'
         args.seed = 777
         args.init = ''
@@ -811,40 +818,41 @@ class Dataset(InMemoryDataset):
         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)
+        # 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(self.cfg.general.swap_result, '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()
-            file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+            import os
+            # file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+            file_path = os.path.join(self.cfg.general.root, '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'
+            # flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
+            flex_graph_path = os.path.join(self.cfg.general.root,'flex-nasbench201-graph.json')
             for graph in graph_list:
                 print(f'iterate every graph in graph_list, here is {i}')
-                # arch_info = self.api.query_meta_info_by_index(i)
-                # results = self.api.query_by_index(i, 'cifar100')
                 arch_info = graph['arch_str']
-                # results = 
-                # nodes, edges = parse_architecture_string(arch_info.arch_str)
-                # ops, adj_matrix = parse_architecture_string(arch_info.arch_str, padding=4)
                 ops, adj_matrix, ori_nodes, ori_adj = parse_architecture_string(arch_info, padding=4)
-                # adj_matrix, ops = create_adj_matrix_and_ops(nodes, edges)
                 for op in ops:
                     if op not in active_nodes:
                         active_nodes.add(op)
-                data = graph_to_graph_data((adj_matrix, ops),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device) 
+                # data = graph_to_graph_data((adj_matrix, ops),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device) 
+                data = graph_to_graph_data((adj_matrix, ops),idx=i, args=args, device=device) 
                 i += 1
                 if data is None:
                     pbar.update(1)
                     continue
-                # 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,
@@ -1143,6 +1151,7 @@ class DataInfos(AbstractDatasetInfos):
         self.task = task_name
         self.task_type = tasktype_dict.get(task_name, "regression")
         self.ensure_connected = cfg.model.ensure_connected
+        self.cfg = cfg
         # self.api = dataset.api
 
         datadir = cfg.dataset.datadir
@@ -1185,14 +1194,15 @@ class DataInfos(AbstractDatasetInfos):
             # len_ops.add(len(ops))
             # graphs.append((adj_matrix, ops))
         # graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json')
-        graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
+        # graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
+        graphs = read_adj_ops_from_json(os.path.join(self.cfg.general.root, '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}')
 
-        meta_dict = new_graphs_to_json(graphs, 'nasbench-201')
+        meta_dict = new_graphs_to_json(graphs, 'nasbench-201', self.cfg)
         self.base_path = base_path
         self.active_nodes = meta_dict['active_nodes']
         self.max_n_nodes = meta_dict['max_n_nodes']
@@ -1399,11 +1409,12 @@ def compute_meta(root, source_name, train_index, test_index):
         'transition_E': tansition_E.tolist(),
         }
 
-    with open(f'/nfs/data3/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:
+    with open(os.path.join(self.cfg.general.root, 'nasbench201.meta.json'), "w") as f:
         json.dump(meta_dict, f)
     
     return meta_dict
 
 
 if __name__ == "__main__":
-    dataset = Dataset(source='nasbench', root='/nfs/data3/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
+    dataset = Dataset(source='nasbench', root='/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/', target_prop='Class', transform=None)

graph_dit/diffusion_model.py

@@ -23,6 +23,9 @@ class Graph_DiT(pl.LightningModule):
         self.test_only = cfg.general.test_only
         self.guidance_target = getattr(cfg.dataset, 'guidance_target', None)
 
+        from nas_201_api import NASBench201API as API
+        self.api = API(cfg.general.nas_201)
+
         input_dims = dataset_infos.input_dims
         output_dims = dataset_infos.output_dims
         nodes_dist = dataset_infos.nodes_dist
@@ -41,7 +44,7 @@ class Graph_DiT(pl.LightningModule):
         self.args.batch_size = 128
         self.args.GPU = '0'
         self.args.dataset = 'cifar10-valid'
-        self.args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        self.args.api_loc = cfg.general.nas_201
         self.args.data_loc = '../cifardata/'
         self.args.seed = 777
         self.args.init = ''
@@ -79,6 +82,7 @@ class Graph_DiT(pl.LightningModule):
         self.node_dist = nodes_dist
         self.active_index = active_index
         self.dataset_info = dataset_infos
+        self.cur_epoch = 0
 
         self.train_loss = TrainLossDiscrete(self.cfg.model.lambda_train)
 
@@ -162,6 +166,62 @@ class Graph_DiT(pl.LightningModule):
         return pred
         
     def training_step(self, data, i):
+        if self.cfg.general.type != 'accelerator' and self.current_epoch > self.cfg.train.n_epochs / 5 * 4:
+            samples_left_to_generate = self.cfg.general.samples_to_generate
+            samples_left_to_save = self.cfg.general.samples_to_save
+            chains_left_to_save = self.cfg.general.chains_to_save
+
+            samples, all_ys, batch_id = [], [], 0
+
+            def graph_reward_fn(graphs, true_graphs=None, device=None, reward_model='swap'):
+                rewards = []
+                if reward_model == 'swap':
+                    import csv
+                    with open(self.cfg.general.swap_result, '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[self.api.query_index_by_arch(arch_str)]
+                            rewards.append(reward)
+                return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
+            old_log_probs = None
+
+            bs = 1 * self.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, self.ydim_output, device=self.device)
+
+            cur_sample, log_probs = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
+                                            keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
+            # samples = samples + cur_sample
+            samples.append(cur_sample) 
+            reward = graph_reward_fn(cur_sample, device=self.device)
+            advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6) # 
+            if old_log_probs is None:
+                old_log_probs = log_probs.clone()
+            ratio = torch.exp(log_probs - old_log_probs)
+            print(f"ratio: {ratio.shape}, advantages: {advantages.shape}")
+            unclipped_loss = -advantages * ratio
+            clipped_loss = -advantages * torch.clamp(ratio, 1.0 - self.cfg.ppo.clip_param, 1.0 + self.cfg.ppo.clip_param)
+            loss = torch.mean(torch.max(unclipped_loss, clipped_loss))
+            return {'loss': loss}
+        else:
             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()
 
@@ -196,14 +256,15 @@ class Graph_DiT(pl.LightningModule):
 
     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.cur_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
+            print("Starting train epoch {}/{}...".format(self.cur_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.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
             log = True
         else:
             log = False
@@ -240,6 +301,7 @@ 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]:
+        # if self.cur_epoch / self.cfg.train.n_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:
@@ -283,10 +345,15 @@ class Graph_DiT(pl.LightningModule):
                     num_examples = self.val_y_collection.size(0)
                 batch_y = self.val_y_collection[start_index:start_index + to_generate]                
                 all_ys.append(batch_y)
-                samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
+                cur_sample, logprobs = 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)
+                samples.extend(cur_sample)
+                # 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))
                 ident += to_generate
                 start_index += to_generate
 
@@ -336,7 +403,7 @@ class Graph_DiT(pl.LightningModule):
         print(f"Epoch {self.current_epoch}: Test NLL {metrics[0] :.2f} -- Test Atom type KL {metrics[1] :.2f} -- ",
               f"Test Edge type KL: {metrics[2] :.2f}")
 
-        ## final epcoh
+        ## final epoch
         samples_left_to_generate = self.cfg.general.final_model_samples_to_generate
         samples_left_to_save = self.cfg.general.final_model_samples_to_save
         chains_left_to_save = self.cfg.general.final_model_chains_to_save
@@ -359,9 +426,9 @@ class Graph_DiT(pl.LightningModule):
             # 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,
+            cur_sample, log_probs = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
                                             keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
-            samples = samples + cur_sample
+            samples.extend(cur_sample)  
             
             all_ys.append(batch_y)
             batch_id += to_generate
@@ -601,6 +668,12 @@ class Graph_DiT(pl.LightningModule):
 
         assert (E == torch.transpose(E, 1, 2)).all()
 
+        if self.cfg.general.type != 'accelerator':
+            if self.trainer.training or self.trainer.validating:
+                total_log_probs = torch.zeros([self.cfg.general.samples_to_generate, 10], device=self.device)
+            elif self.trainer.testing:
+                total_log_probs = torch.zeros([self.cfg.general.final_model_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)
@@ -609,21 +682,24 @@ 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
+            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
+        total_log_probs = torch.sum(total_log_probs, dim=-1)
+        
+        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.
@@ -675,6 +751,14 @@ class Graph_DiT(pl.LightningModule):
         # with condition = P_t(A_{t-1} |A_t, y)
         prob_X, prob_E, pred = get_prob(noisy_data)
 
+        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))
+
+        log_probs = torch.cat([log_prob_X, log_prob_E], dim=-1)
         ### 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)
@@ -810,4 +894,4 @@ 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

@@ -2,44 +2,45 @@
 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.score_networks import get_nasbench201_idx_score
-from naswot import datasets as dt
+# from naswot import datasets as dt
-from naswot import nasspace
+# from naswot import nasspace
 
-class Args():
+# class Args():
-    pass
+#     pass
-args = Args()
+# args = Args()
-args.trainval = True
+# args.trainval = True
-args.augtype = 'none'
+# args.augtype = 'none'
-args.repeat = 1
+# args.repeat = 1
-args.score = 'hook_logdet'
+# args.score = 'hook_logdet'
-args.sigma = 0.05
+# args.sigma = 0.05
-args.nasspace = 'nasbench201'
+# args.nasspace = 'nasbench201'
-args.batch_size = 128
+# args.batch_size = 128
-args.GPU = '0'
+# args.GPU = '0'
-args.dataset = 'cifar10'
+# args.dataset = 'cifar10'
-args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+# args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
-args.data_loc = '../cifardata/'
+# args.data_loc = '../cifardata/'
-args.seed = 777
+# args.seed = 777
-args.init = ''
+# args.init = ''
-args.save_loc = 'results'
+# args.save_loc = 'results'
-args.save_string = 'naswot'
+# args.save_string = 'naswot'
-args.dropout = False
+# args.dropout = False
-args.maxofn = 1
+# args.maxofn = 1
-args.n_samples = 100
+# args.n_samples = 100
-args.n_runs = 500
+# args.n_runs = 500
-args.stem_out_channels = 16
+# args.stem_out_channels = 16
-args.num_stacks = 3
+# args.num_stacks = 3
-args.num_modules_per_stack = 3
+# args.num_modules_per_stack = 3
-args.num_labels = 1
+# args.num_labels = 1
-searchspace = nasspace.get_search_space(args)
+# 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)
+# 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')
+# device = torch.device('cuda:2')
 
 
+source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+api = API(source)
+
 
-# source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
-# api = API(source)
 
 
 
@@ -50,8 +51,10 @@ percentages = []
 len_201 = 15625
 
 for i in range(len_201):
-    percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
+    # percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
-    percentages.append(percentage)
+    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)]

graph_dit/main.py

@@ -177,6 +177,66 @@ def test(cfg: DictConfig):
         os.chdir(cfg.general.resume.split("checkpoints")[0])
     # os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
     model = Graph_DiT(cfg=cfg, **model_kwargs)
+
+    if cfg.general.type == "accelerator":
+        graph_dit_model = model
+
+        from accelerate import Accelerator
+        from accelerate.utils import set_seed, ProjectConfiguration
+
+        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, 
+        )
+
+        optimizer = graph_dit_model.configure_optimizers()
+
+        train_dataloader = datamodule.train_dataloader()
+        train_dataloader = accelerator.prepare(train_dataloader)
+        val_dataloader = datamodule.val_dataloader()
+        val_dataloader = accelerator.prepare(val_dataloader)
+        test_dataloader = datamodule.test_dataloader()
+        test_dataloader = accelerator.prepare(test_dataloader)
+
+        optimizer, graph_dit_model = accelerator.prepare(optimizer, graph_dit_model)
+
+        # train_epoch
+        from pytorch_lightning import seed_everything
+        seed_everything(cfg.train.seed)
+        for epoch in range(cfg.train.n_epochs):
+            print(f"Epoch {epoch}")
+            graph_dit_model.train()
+            graph_dit_model.cur_epoch = epoch
+            graph_dit_model.on_train_epoch_start()
+            for batch in train_dataloader:
+                optimizer.zero_grad()
+                loss = graph_dit_model.training_step(batch, epoch)['loss']
+                accelerator.backward(loss)
+                optimizer.step()
+            graph_dit_model.on_train_epoch_end()
+            for batch in val_dataloader:
+                if epoch % cfg.train.check_val_every_n_epoch == 0:
+                    graph_dit_model.eval()
+                    graph_dit_model.on_validation_epoch_start()
+                    graph_dit_model.validation_step(batch, epoch)
+                    graph_dit_model.on_validation_epoch_end()
+        
+        # test_epoch
+
+        graph_dit_model.test()
+        graph_dit_model.on_test_epoch_start()
+        for batch in test_dataloader:
+            graph_dit_model.test_step(batch, epoch)
+        graph_dit_model.on_test_epoch_end()
+    
+    elif cfg.general.type == "Trainer": 
         trainer = Trainer(
             gradient_clip_val=cfg.train.clip_grad,
             # accelerator="cpu",

graph_dit/metrics/property_metric.py

@@ -83,7 +83,8 @@ class TaskModel():
             return adj_ops_pairs
         def feature_from_adj_and_ops(adj, ops):
             return np.concatenate([adj.flatten(), ops])
-        filename = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+        # filename = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+        filename = '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/nasbench-201-graph.json'
         graphs = read_adj_ops_from_json(filename)
         adjs = []
         opss = []

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]))
+
+