try to run the graph, commented sampling codes

configs/config.yaml

@@ -41,6 +41,6 @@ train:
     check_val_every_n_epoch: 1
 dataset:
     datadir: 'data/'
-    task_name: null
-    guidance_target: null
+    task_name: 'nasbench-201'
+    guidance_target: 'nasbench-201'
     pin_memory: False

graph_dit/datasets/abstract_dataset.py

@@ -116,7 +116,7 @@ class AbstractDatasetInfos:
     def compute_input_output_dims(self, datamodule):
         example_batch = datamodule.example_batch()
         example_batch_x = torch.nn.functional.one_hot(example_batch.x, num_classes=118).float()[:, self.active_index]
-        example_batch_edge_attr = torch.nn.functional.one_hot(example_batch.edge_attr, num_classes=5).float()
+        example_batch_edge_attr = torch.nn.functional.one_hot(example_batch.edge_attr, num_classes=10).float()
 
         self.input_dims = {'X': example_batch_x.size(1), 
                            'E': example_batch_edge_attr.size(1), 

graph_dit/datasets/dataset.py

@@ -13,6 +13,7 @@ import torch
 import torch.nn.functional as F
 from rdkit import Chem, RDLogger
 from rdkit.Chem.rdchem import BondType as BT
+from rdkit.Chem import rdchem
 from tqdm import tqdm
 import numpy as np
 import pandas as pd
@@ -24,6 +25,9 @@ import utils as utils
 from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
 from diffusion.distributions import DistributionNodes
 
+import networkx as nx
+
+
 bonds = {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
 
 op_to_atom = {
@@ -77,6 +81,7 @@ class DataModule(AbstractDataModule):
         
         train_dataset, val_dataset, test_dataset = dataset[train_index], dataset[val_index], dataset[test_index]
         self.train_dataset = train_dataset  
+        self.test_dataset = test_dataset
         print('train len', len(train_dataset), 'val len', len(val_dataset), 'test len', len(test_dataset))
         print('train len', len(train_index), 'val len', len(val_index), 'test len', len(test_index))
         print('dataset len', len(dataset), 'train len', len(train_dataset), 'val len', len(val_dataset), 'test len', len(test_dataset))
@@ -89,8 +94,9 @@ class DataModule(AbstractDataModule):
         self.training_iterations = training_iterations
     
     def random_data_split(self, dataset):
-        nan_count = torch.isnan(dataset.data.y[:, 0]).sum().item()
-        labeled_len = len(dataset) - nan_count
+        # nan_count = torch.isnan(dataset.data.y[:, 0]).sum().item()
+        # labeled_len = len(dataset) - nan_count
+        labeled_len = len(dataset) 
         full_idx = list(range(labeled_len))
         train_ratio, valid_ratio, test_ratio = 0.6, 0.2, 0.2
         train_index, test_index, _, _ = train_test_split(full_idx, full_idx, test_size=test_ratio, random_state=42)
@@ -111,8 +117,87 @@ class DataModule(AbstractDataModule):
         print(self.task, ' dataset len', len(dataset), 'train len', len(train_index), 'val len', len(val_index), 'test len', len(test_index))
         return train_index, val_index, test_index, []
 
+    def parse_architecture_string(self, arch_str):
+            stages = arch_str.split('+')
+            nodes = ['input']
+            edges = []
+            
+            for stage in stages:
+                operations = stage.strip('|').split('|')
+                for op in operations:
+                    operation, idx = op.split('~')
+                    idx = int(idx)
+                    edges.append((idx, len(nodes)))  # Add edge from idx to the new node
+                    nodes.append(operation)
+            nodes.append('output')  # Add the output node
+            return nodes, edges
+
+    # def create_molecule_from_graph(nodes, edges):
+    def create_molecule_from_graph(self, graph):
+        nodes = graph.x
+        edges = graph.edge_index
+        mol = Chem.RWMol()  # RWMol allows for building the molecule step by step
+        atom_indices = {}
+        num_to_op = {
+            1 :'nor_conv_1x1',
+            2 :'nor_conv_3x3',
+            3 :'avg_pool_3x3',
+            4 :'skip_connect',
+            5 :'output',
+            6 :'none',
+            7 :'input'
+        } 
+
+        # Extract node operations from the data object
+
+        # Add atoms to the molecule
+        for i, op_tensor in enumerate(nodes):
+            op = op_tensor.item()
+            if op == 0: continue
+            op = num_to_op[op]
+            atom_symbol = op_to_atom[op]
+            atom = Chem.Atom(atom_symbol)
+            atom_idx = mol.AddAtom(atom)
+            atom_indices[i] = atom_idx
+        
+        # Add bonds to the molecule
+        edge_number = edges.shape[1]
+        for i in range(edge_number):
+            start = edges[0, i].item()
+            end = edges[1, i].item()
+            mol.AddBond(atom_indices[start], atom_indices[end], rdchem.BondType.SINGLE)
+        
+        return mol
+
+    def arch_str_to_smiles(self, arch_str):
+        nodes, edges = self.parse_architecture_string(arch_str)
+        mol = self.create_molecule_from_graph(nodes, edges)
+        smiles = Chem.MolToSmiles(mol)
+        return smiles
+
     def get_train_smiles(self):
-        raise NotImplementedError("This method is not applicable for NAS-Bench-201 data.")
+        train_smiles = []   
+        test_smiles = []
+
+        for graph in self.train_dataset:
+            # print(f'idx={idx}')
+            # graph = self.train_dataset[idx]
+            print(graph.x)
+            print(graph.edge_index)
+            print(f'class of graph.x: {graph.x.__class__}, class of graph.edge_index: {graph.edge_index.__class__}')
+            mol = self.create_molecule_from_graph(graph)
+            train_smiles.append(Chem.MolToSmiles(mol))
+        
+        # for idx in self.test_index:
+        for graph in self.test_dataset:
+            # graph = self.dataset[idx]
+            # mol = self.create_molecule_from_graph(graph.x, graph.edge_index)
+            mol = self.create_molecule_from_graph(graph)
+            test_smiles.append(Chem.MolToSmiles(mol))
+        
+        # train_smiles = [self.arch_str_to_smiles(arch_str) for arch_str in train_arch_strs]
+        # test_smiles = [self.arch_str_to_smiles(arch_str) for arch_str in test_arch_strs]
+        return train_smiles, test_smiles
 
     def get_data_split(self):
         raise NotImplementedError("This method is not applicable for NAS-Bench-201 data.")
@@ -129,161 +214,8 @@ class DataModule(AbstractDataModule):
     def test_dataloader(self):
         return self.test_loader
 
-def graphs_to_json(graphs, filename):
-    bonds = {
-        'nor_conv_1x1': 1,
-        'nor_conv_3x3': 2,
-        'avg_pool_3x3': 3,
-        'skip_connect': 4,
-        'input': 7,
-        'output': 5,
-        'none': 6
-    }
 
-    source_name = "nas-bench-201"
-    num_graph = len(graphs)
-    pt = Chem.GetPeriodicTable()
-    atom_name_list = []
-    atom_count_list = []
-    for i in range(2, 119):
-        atom_name_list.append(pt.GetElementSymbol(i))
-        atom_count_list.append(0)
-    atom_name_list.append('*')
-    atom_count_list.append(0)
-    n_atoms_per_mol = [0] * 500
-    bond_count_list = [0, 0, 0, 0, 0, 0, 0, 0]
-    bond_type_to_index =  {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
-    valencies = [0] * 500
-    transition_E = np.zeros((118, 118, 5))
 
-    n_atom_list = []
-    n_bond_list = []
-    # graphs = [(adj_matrix, ops), ...]
-    for graph in graphs:
-        ops = graph[1]
-        adj = graph[0]
-        n_atom = len(ops)
-        n_bond = len(ops)
-        n_atom_list.append(n_atom)
-        n_bond_list.append(n_bond)
-
-        n_atoms_per_mol[n_atom] += 1
-        cur_atom_count_arr = np.zeros(118)
-
-        for op in ops:
-            symbol = op_to_atom[op]
-            if symbol == 'H':
-                continue
-            elif symbol == '*':
-                atom_count_list[-1] += 1
-                cur_atom_count_arr[-1] += 1
-            else:
-                atom_count_list[pt.GetAtomicNumber(symbol)-2] += 1
-                cur_atom_count_arr[pt.GetAtomicNumber(symbol)-2] += 1
-                # print('symbol', symbol)
-                # print('pt.GetDefaultValence(symbol)', pt.GetDefaultValence(symbol))
-                # print(f'cur_atom_count_arr[{pt.GetAtomicNumber(symbol)-2}], {cur_atom_count_arr[pt.GetAtomicNumber(symbol)-2]}')
-                try:
-                    valencies[int(pt.GetDefaultValence(symbol))] += 1
-                except:
-                    print('int(pt.GetDefaultValence(symbol))', int(pt.GetDefaultValence(symbol)))
-        transition_E_temp = np.zeros((118, 118, 5))
-        # print(n_atom)
-        for i in range(n_atom):
-            for j in range(n_atom):
-                if i == j or adj[i][j] == 0:
-                    continue
-                start_atom, end_atom = i, j
-                if ops[start_atom] == 'input' or ops[end_atom] == 'input':
-                    continue
-                if ops[start_atom] == 'output' or ops[end_atom] == 'output':
-                    continue
-                if ops[start_atom] == 'none' or ops[end_atom] == 'none':
-                    continue
-                
-                start_index = pt.GetAtomicNumber(op_to_atom[ops[start_atom]]) - 2
-                end_index = pt.GetAtomicNumber(op_to_atom[ops[end_atom]]) - 2
-                bond_index = bonds[ops[end_atom]]
-                bond_count_list[bond_index] += 2
-
-                # print(start_index, end_index, bond_index)
-                
-                transition_E[start_index, end_index, bond_index] += 2
-                transition_E[end_index, start_index, bond_index] += 2
-                transition_E_temp[start_index, end_index, bond_index] += 2
-                transition_E_temp[end_index, start_index, bond_index] += 2
-
-        bond_count_list[0] += n_atom * (n_atom - 1) - n_bond * 2
-        print(bond_count_list)
-        cur_tot_bond = cur_atom_count_arr.reshape(-1,1) * cur_atom_count_arr.reshape(1,-1) * 2
-        # print(f'cur_tot_bond={cur_tot_bond}')   
-        # find non-zero element in cur_tot_bond
-        # for i in range(118):
-        #     for j in range(118):
-        #         if cur_tot_bond[i][j] != 0:
-        #             print(f'i={i}, j={j}, cur_tot_bond[i][j]={cur_tot_bond[i][j]}')
-        # n_atoms_per_mol = np.array(n_atoms_per_mol) / np.sum(n_atoms_per_mol)
-        cur_tot_bond = cur_tot_bond - np.diag(cur_atom_count_arr) * 2
-        # print(f"transition_E[:,:,0]={cur_tot_bond - transition_E_temp.sum(axis=-1)}")
-        transition_E[:, :, 0] += cur_tot_bond - transition_E_temp.sum(axis=-1)
-        # find non-zero element in transition_E
-        # for i in range(118):
-        #     for j in range(118):
-        #         if transition_E[i][j][0] != 0:
-        #             print(f'i={i}, j={j}, transition_E[i][j][0]={transition_E[i][j][0]}')
-        assert (cur_tot_bond > transition_E_temp.sum(axis=-1)).sum() >= 0, f'i:{i}, sms:{sms}'
-    
-    n_atoms_per_mol = np.array(n_atoms_per_mol) / np.sum(n_atoms_per_mol)
-    n_atoms_per_mol = n_atoms_per_mol.tolist()[:51]
-
-    atom_count_list = np.array(atom_count_list) / np.sum(atom_count_list)
-    print('processed meta info: ------', filename, '------')
-    print('len atom_count_list', len(atom_count_list))
-    print('len atom_name_list', len(atom_name_list))
-    active_atoms = np.array(atom_name_list)[atom_count_list > 0]
-    active_atoms = active_atoms.tolist()
-    atom_count_list = atom_count_list.tolist()
-
-    bond_count_list = np.array(bond_count_list) / np.sum(bond_count_list)
-    bond_count_list = bond_count_list.tolist()
-    valencies = np.array(valencies) / np.sum(valencies)
-    valencies = valencies.tolist()
-
-    no_edge = np.sum(transition_E, axis=-1) == 0
-    for i in range(118):
-        for j in range(118):
-            if no_edge[i][j] == False:
-                print(f'have an edge at i={i} , j={j}, transition_E[i][j]={transition_E[i][j]}')
-    # print(f'no_edge: {no_edge}')
-    first_elt = transition_E[:, :, 0]
-    first_elt[no_edge] = 1
-    transition_E[:, :, 0] = first_elt
-
-    transition_E = transition_E / np.sum(transition_E, axis=-1, keepdims=True)
-
-    # find non-zero element in transition_E again
-    for i in range(118):
-        for j in range(118):
-            if transition_E[i][j][0] != 0 and transition_E[i][j][0] != 1 and transition_E[i][j][0] != -1:
-                print(f'i={i}, j={j}, 2_transition_E[i][j][0]={transition_E[i][j][0]}')
-
-    meta_dict = {
-        'source': 'nasbench-201',
-        'num_graph': num_graph,
-        'n_atoms_per_mol_dist': n_atoms_per_mol[:51],
-        'max_node': max(n_atom_list),
-        'max_bond': max(n_bond_list),
-        'atom_type_dist': atom_count_list,
-        'bond_type_dist': bond_count_list,
-        'valencies': valencies,
-        'active_atoms': [atom_name_list[i] for i in range(118) if atom_count_list[i] > 0],
-        'num_atom_type': len([atom_name_list[i] for i in range(118) if atom_count_list[i] > 0]),
-        'transition_E': transition_E.tolist(),
-    }
-
-    with open(f'{filename}.meta.json', 'w') as f:
-        json.dump(meta_dict, f)
-    return meta_dict
 
 class DataModule_original(AbstractDataModule):
     def __init__(self, cfg):
@@ -412,7 +344,7 @@ def graphs_to_json(graphs, filename):
     bond_count_list = [0, 0, 0, 0, 0, 0, 0, 0]
     bond_type_to_index =  {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
     valencies = [0] * 500
-    transition_E = np.zeros((118, 118, 5))
+    transition_E = np.zeros((118, 118, 8))
 
     n_atom_list = []
     n_bond_list = []
@@ -445,7 +377,7 @@ def graphs_to_json(graphs, filename):
                     valencies[int(pt.GetDefaultValence(symbol))] += 1
                 except:
                     print('int(pt.GetDefaultValence(symbol))', int(pt.GetDefaultValence(symbol)))
-        transition_E_temp = np.zeros((118, 118, 5))
+        transition_E_temp = np.zeros((118, 118, 8))
         # print(n_atom)
         for i in range(n_atom):
             for j in range(n_atom):
@@ -542,6 +474,102 @@ def graphs_to_json(graphs, filename):
     with open(f'{filename}.meta.json', 'w') as f:
         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):
+        self.target_prop = target_prop
+        source = '/home/stud/hanzhang/Graph-DiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        self.source = source
+        self.api = API(source)  # Initialize NAS-Bench-201 API
+        print('API loaded')
+        super().__init__(root, transform, pre_transform, pre_filter)
+        print('Dataset initialized')
+        print(self.processed_paths[0])
+        self.data, self.slices = torch.load(self.processed_paths[0])
+
+    @property
+    def raw_file_names(self):
+        return []  # NAS-Bench-201 data is loaded via the API, no raw files needed
+    
+    @property
+    def processed_file_names(self):
+        return [f'{self.source}.pt']
+
+    def process(self):
+        def parse_architecture_string(arch_str):
+            stages = arch_str.split('+')
+            nodes = ['input']
+            edges = []
+            
+            for stage in stages:
+                operations = stage.strip('|').split('|')
+                for op in operations:
+                    operation, idx = op.split('~')
+                    idx = int(idx)
+                    edges.append((idx, len(nodes)))  # Add edge from idx to the new node
+                    nodes.append(operation)
+            nodes.append('output')  # Add the output node
+            return nodes, edges
+
+        def create_graph(nodes, edges):
+            G = nx.DiGraph()
+            for i, node in enumerate(nodes):
+                G.add_node(i, label=node)
+            G.add_edges_from(edges)
+            return G
+
+        def arch_to_graph(arch_str, sa, sc, target, target2=None, target3=None):
+            nodes, edges = parse_architecture_string(arch_str)
+
+            node_labels = [bonds[node] for node in nodes]  # Replace with appropriate encoding if necessary
+            assert 0 not in node_labels, f'Invalid node label: {node_labels}'
+            x = torch.LongTensor(node_labels)
+            print(f'in initialize Dataset, arch_to_Graph x={x}')
+
+            edges_list = [(start, end) for start, end in edges]
+            edge_type = [bonds[nodes[end]] for start, end in edges]  # Example: using end node type as edge type
+            edge_index = torch.tensor(edges_list, dtype=torch.long).t().contiguous()
+            edge_type = torch.tensor(edge_type, dtype=torch.long)
+            edge_attr = edge_type.view(-1, 1)
+
+            if target3 is not None:
+                y = torch.tensor([sa, sc, target, target2, target3], dtype=torch.float).view(1, -1)
+            elif target2 is not None:
+                y = torch.tensor([sa, sc, target, target2], dtype=torch.float).view(1, -1)
+            else:
+                y = torch.tensor([sa, sc, target], dtype=torch.float).view(1, -1)
+
+            print(f'in initialize Dataset, Data_init, x={x}, y={y}, edge_index={edge_index}, edge_attr={edge_attr}')
+            data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y)
+            return data, nodes
+
+        bonds = {
+            'nor_conv_1x1': 1,
+            'nor_conv_3x3': 2,
+            'avg_pool_3x3': 3,
+            'skip_connect': 4,
+            'output': 5,
+            'none': 6,
+            'input': 7
+        }
+
+        # Prepare to process NAS-Bench-201 data
+        data_list = []
+        len_data = len(self.api)  # Number of architectures
+        with tqdm(total=len_data) as pbar:
+            for arch_index in range(len_data):
+                arch_info = self.api.query_meta_info_by_index(arch_index)
+                arch_str = arch_info.arch_str
+                sa = np.random.rand()  # Placeholder for synthetic accessibility
+                sc = np.random.rand()  # Placeholder for substructure count
+                target = np.random.rand()  # Placeholder for target value
+                target2 = np.random.rand()  # Placeholder for second target value
+                target3 = np.random.rand()  # Placeholder for third target value
+
+                data, active_nodes = arch_to_graph(arch_str, sa, sc, target, target2, target3)
+                data_list.append(data)
+                pbar.update(1)
+
+        torch.save(self.collate(data_list), self.processed_paths[0])
 
 class Dataset_origin(InMemoryDataset):
     def __init__(self, source, root, target_prop=None,
@@ -671,7 +699,7 @@ class DataInfos(AbstractDatasetInfos):
         length = 15625
         ops_type = {}
         len_ops = set()
-        api = API('../NAS-Bench-201-v1_0-e61699.pth')
+        api = API('/home/stud/hanzhang/Graph-DiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
         for i in range(length):
             arch_info = api.query_meta_info_by_index(i)
             nodes, edges = parse_architecture_string(arch_info.arch_str)

graph_dit/diffusion_model.py

@@ -13,9 +13,11 @@ from metrics.abstract_metrics import SumExceptBatchMetric, SumExceptBatchKL, NLL
 import utils
 
 class Graph_DiT(pl.LightningModule):
-    def __init__(self, cfg, dataset_infos, train_metrics, sampling_metrics, visualization_tools):
+    # def __init__(self, cfg, dataset_infos, train_metrics, sampling_metrics, visualization_tools):
+    def __init__(self, cfg, dataset_infos, visualization_tools):
+
         super().__init__()
-        self.save_hyperparameters(ignore=['train_metrics', 'sampling_metrics'])
+        # self.save_hyperparameters(ignore=['train_metrics', 'sampling_metrics'])
         self.test_only = cfg.general.test_only
         self.guidance_target = getattr(cfg.dataset, 'guidance_target', None)
 
@@ -55,8 +57,8 @@ class Graph_DiT(pl.LightningModule):
         self.test_E_logp = SumExceptBatchMetric()
         self.test_y_collection = []
 
-        self.train_metrics = train_metrics
-        self.sampling_metrics = sampling_metrics
+        # self.train_metrics = train_metrics
+        # self.sampling_metrics = sampling_metrics
 
         self.visualization_tools = visualization_tools
         self.max_n_nodes = dataset_infos.max_n_nodes
@@ -171,7 +173,7 @@ class Graph_DiT(pl.LightningModule):
         self.val_E_kl.reset()
         self.val_X_logp.reset()
         self.val_E_logp.reset()
-        self.sampling_metrics.reset()
+        # self.sampling_metrics.reset()
         self.val_y_collection = []
 
     @torch.no_grad()
@@ -239,14 +241,15 @@ class Graph_DiT(pl.LightningModule):
                 samples_left_to_generate -= to_generate
                 chains_left_to_save -= chains_save
 
-            print(f"Computing sampling metrics", ' ...')
-            valid_smiles = self.sampling_metrics(samples, all_ys, self.name, self.current_epoch, val_counter=-1, test=False)
-            print(f'Done. Sampling took {time.time() - start:.2f} seconds\n')
+            # print(f"Computing sampling metrics", ' ...')
+            # valid_smiles = self.sampling_metrics(samples, all_ys, self.name, self.current_epoch, val_counter=-1, test=False)
+            # print(f'Done. Sampling took {time.time() - start:.2f} seconds\n')
+
             current_path = os.getcwd()
             result_path = os.path.join(current_path,
                                        f'graphs/{self.name}/epoch{self.current_epoch}_b0/')
-            self.visualization_tools.visualize_by_smiles(result_path, valid_smiles, self.cfg.general.samples_to_save)
-            self.sampling_metrics.reset()
+            # self.visualization_tools.visualize_by_smiles(result_path, valid_smiles, self.cfg.general.samples_to_save)
+            # self.sampling_metrics.reset()
 
     def on_test_epoch_start(self) -> None:
         print("Starting test...")

graph_dit/main.py

@@ -50,7 +50,6 @@ def get_resume_adaptive(cfg, model_kwargs):
     # Fetch path to this file to get base path
     current_path = os.path.dirname(os.path.realpath(__file__))
     root_dir = current_path.split("outputs")[0]
-
     resume_path = os.path.join(root_dir, cfg.general.resume)
 
     if cfg.model.type == "discrete":
@@ -80,21 +79,21 @@ def main(cfg: DictConfig):
     datamodule = dataset.DataModule(cfg)
     datamodule.prepare_data()
     dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg)
-    train_smiles, reference_smiles = datamodule.get_train_smiles()
+    # train_smiles, reference_smiles = datamodule.get_train_smiles()
 
     # get input output dimensions
     dataset_infos.compute_input_output_dims(datamodule=datamodule)
-    train_metrics = TrainMolecularMetricsDiscrete(dataset_infos)
+    # train_metrics = TrainMolecularMetricsDiscrete(dataset_infos)
 
-    sampling_metrics = SamplingMolecularMetrics(
-        dataset_infos, train_smiles, reference_smiles
-    )
+    # sampling_metrics = SamplingMolecularMetrics(
+    #     dataset_infos, train_smiles, reference_smiles
+    # )
     visualization_tools = MolecularVisualization(dataset_infos)
 
     model_kwargs = {
         "dataset_infos": dataset_infos,
-        "train_metrics": train_metrics,
-        "sampling_metrics": sampling_metrics,
+        # "train_metrics": train_metrics,
+        # "sampling_metrics": sampling_metrics,
         "visualization_tools": visualization_tools,
     }
 
@@ -110,9 +109,10 @@ def main(cfg: DictConfig):
     model = Graph_DiT(cfg=cfg, **model_kwargs)
     trainer = Trainer(
         gradient_clip_val=cfg.train.clip_grad,
-        accelerator="gpu"
-        if torch.cuda.is_available() and cfg.general.gpus > 0
-        else "cpu",
+        # accelerator="gpu"
+        # if torch.cuda.is_available() and cfg.general.gpus > 0
+        # else "cpu",
+        accelerator="cpu",
         devices=cfg.general.gpus
         if torch.cuda.is_available() and cfg.general.gpus > 0
         else None,