Compare commits
7 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| d800679b88 | |||
| b47e83330e | |||
| dd91da7921 | |||
| 586e354971 | |||
| e376f38dcb | |||
| 7149b49a39 | |||
| 83f9345028 |
6
.gitignore
vendored
6
.gitignore
vendored
@@ -1,3 +1,9 @@
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cifardata/
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NAS-Bench-201-*
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*.csv.gz
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*.meta.json
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*.pt
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*.zip
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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BIN
2401.13858v2 (2).pdf
Normal file
BIN
2401.13858v2 (2).pdf
Normal file
Binary file not shown.
@@ -2,7 +2,7 @@ general:
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name: 'graph_dit'
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wandb: 'disabled'
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gpus: 1
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gpu_number: 3
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gpu_number: 0
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resume: null
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test_only: null
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sample_every_val: 2500
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@@ -31,7 +31,7 @@ model:
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lambda_train: [1, 10] # node and edge training weight
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ensure_connected: True
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train:
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n_epochs: 5000
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n_epochs: 500
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batch_size: 1200
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lr: 0.0002
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clip_grad: null
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20
environment.yml
Normal file
20
environment.yml
Normal file
@@ -0,0 +1,20 @@
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name: graphdit39
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dependencies:
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- python=3.9
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- fcd_torch==1.0.7
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- hydra-core==1.3.2
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- imageio==2.26.0
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- joblib==1.2.0
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- matplotlib==3.7.0
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- mini_moses==1.0
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- networkx==3.0
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- numpy==1.24.2
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- omegaconf==2.3.0
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- pandas==1.5.3
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- pytorch_lightning==2.0.1
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- rdkit==2023.9.4
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- scikit_learn==1.2.1
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- torch==2.0.0
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- torch_geometric==2.3.0
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- torchmetrics==0.11.4
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- tqdm==4.64.1
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@@ -37,6 +37,144 @@ def selectivity_evaluation(gas1, gas2, prop_name):
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y = np.log10(np.array(gas1) / np.array(gas2))
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upper = (y - (a_dict[prop_name] * x + b_dict[prop_name])) > 0
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return upper
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class BasicGraphMetrics(object):
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def __init__(self, graph_decoder, train_graphs=None, stat_ref=None, task_evaluator=None, n_jobs=8, device='cpu', batch_size=512):
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self.dataset_graphs_list = train_graphs
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self.graph_decoder = graph_decoder
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self.n_jobs = n_jobs
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self.device = device
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self.batch_size = batch_size
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self.stat_ref = stat_ref
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self.task_evaluator = task_evaluator
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def compute_relaxed_validity(self, generated, ensure_connected):
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valid = []
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num_components = []
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all_graphs = []
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valid_graphs = []
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covered_nodes = set()
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direct_valid_count = 0
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print(f"generated number: {len(generated)}")
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for graph in generated:
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node_types, edge_types = graph
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direct_valid_flag = True
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direct_valid_count += 1
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valid.append(graph)
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num_components.append(1)
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covered_nodes.update(set(node_types))
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all_graphs.append(graph)
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return valid, len(valid) / len(generated), direct_valid_count / len(generated), np.array(num_components), all_graphs, covered_nodes
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def evaluate(self, generated, targets, ensure_connected, active_atoms=None):
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valid, validity, nc_validity, num_components, all_graphs, covered_nodes = self.compute_relaxed_validity(generated, ensure_connected=ensure_connected)
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nc_mu = num_components.mean() if len(num_components) > 0 else 0
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nc_min = num_components.min() if len(num_components) > 0 else 0
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nc_max = num_components.max() if len(num_components) > 0 else 0
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len_active = len(active_atoms) if active_atoms is not None else 1
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cover_str = f"Cover {len(covered_nodes)} ({len(covered_nodes)/len_active * 100:.2f}%) atoms: {covered_nodes}"
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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}")
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print(f"Number of connected components of {len(generated)} graphs: min:{nc_min:.2f} mean:{nc_mu:.2f} max:{nc_max:.2f}")
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if validity > 0:
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dist_metrics = {'cover_str': cover_str ,'validity': validity, 'validity_nc': nc_validity}
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unique = valid
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close_pool = False
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if self.n_jobs != 1:
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pool = Pool(self.n_jobs)
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close_pool = True
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else:
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pool = 1
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# valid_graphs = mapper(pool)(get_mol, valid)
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valid_graphs = valid
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"""
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Computes internal diversity as:
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1/|A|^2 sum_{x, y in AxA} (1-tanimoto(x, y))
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"""
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# dist_metrics['interval_diversity'] = internal_diversity(valid_graphs, pool, device=self.device)
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start_time = time.time()
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if self.stat_ref is not None:
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kwargs = {'n_jobs': pool, 'device': self.device, 'batch_size': self.batch_size}
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kwargs_fcd = {'n_jobs': self.n_jobs, 'device': self.device, 'batch_size': self.batch_size}
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try:
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dist_metrics['sim/Frag'] = FragMetric(**kwargs)(gen=valid_graphs, pref=self.stat_ref['Frag'])
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except:
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print('error: ', 'pool', pool)
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print('valid_graphs: ', valid_graphs)
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dist_metrics['dist/FCD'] = FCDMetric(**kwargs_fcd)(gen=valid, pref=self.stat_ref['FCD'])
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if self.task_evaluator is not None:
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evaluation_list = list(self.task_evaluator.keys())
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print('evaluation_list: ', evaluation_list)
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evaluation_list = evaluation_list.copy()
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assert 'meta_taskname' in evaluation_list
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meta_taskname = self.task_evaluator['meta_taskname']
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evaluation_list.remove('meta_taskname')
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# meta_split = meta_taskname.split('-')
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valid_index = np.array([True if graphs else False for graphs in all_graphs])
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targets_log = {}
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for i, name in enumerate(evaluation_list):
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targets_log[f'input_{name}'] = np.array([float('nan')] * len(valid_index))
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targets_log[f'input_{name}'] = targets[:, i]
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targets = targets[valid_index]
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# if len(meta_split) == 2:
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# cached_perm = {meta_split[0]: None, meta_split[1]: None}
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for i, name in enumerate(evaluation_list):
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# if name == 'scs':
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# continue
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# elif name == 'sas':
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# scores = calculateSAS(valid)
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# else:
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# scores = self.task_evaluator[name](valid)
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# fix the scores
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scores = np.random.rand(len(valid_index))
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targets_log[f'output_{name}'] = np.array([float('nan')] * len(valid_index))
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targets_log[f'output_{name}'][valid_index] = scores
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# if name in ['O2', 'N2', 'CO2']:
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# if len(meta_split) == 2:
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# cached_perm[name] = scores
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# scores, cur_targets = np.log10(scores), np.log10(targets[:, i])
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# dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - cur_targets))
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# elif name == 'sas':
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# dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - targets[:, i]))
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# else:
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true_y = targets[:, i]
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predicted_labels = (scores >= 0.5).astype(int)
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acc = (predicted_labels == true_y).sum() / len(true_y)
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dist_metrics[f'{name}/acc'] = acc
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# if len(meta_split) == 2:
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# if cached_perm[meta_split[0]] is not None and cached_perm[meta_split[1]] is not None:
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# task_name = self.task_evaluator['meta_taskname']
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# upper = selectivity_evaluation(cached_perm[meta_split[0]], cached_perm[meta_split[1]], task_name)
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# dist_metrics[f'selectivity/{task_name}'] = np.sum(upper)
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end_time = time.time()
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elapsed_time = end_time - start_time
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max_key_length = max(len(key) for key in dist_metrics)
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print(f'Details over {len(valid)} ({len(generated)}) valid (total) graphs, calculating metrics using {elapsed_time:.2f} s:')
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strs = ''
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for i, (key, value) in enumerate(dist_metrics.items()):
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if isinstance(value, (int, float, np.floating, np.integer)):
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strs = strs + f'{key:>{max_key_length}}:{value:<7.4f}\t'
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if i % 4 == 3:
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strs = strs + '\n'
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print(strs)
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if close_pool:
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pool.close()
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pool.join()
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else:
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unique = []
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dist_metrics = {}
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targets_log = None
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return unique, dict(nc_min=nc_min, nc_max=nc_max, nc_mu=nc_mu), all_graphs, dist_metrics, targets_log
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class BasicMolecularMetrics(object):
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def __init__(self, atom_decoder, train_smiles=None, stat_ref=None, task_evaluator=None, n_jobs=8, device='cpu', batch_size=512):
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@@ -388,6 +526,18 @@ def connect_fragments(mol):
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return combined_mol
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#### connect fragements
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def compute_graph_metrics(graph_list, targets, train_graphs, stat_ref, dataset_info, task_evaluator, comput_config):
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""" graph_list: (dict) """
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node_decoder = dataset_info.node_decoder
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active_nodes = dataset_info.active_nodes
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ensure_connected = dataset_info.ensure_connected
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metrics = BasicGraphMetrics(node_decoder, train_graphs, stat_ref, task_evaluator, **comput_config)
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evaluated_res = metrics.evaluate(graph_list, targets, ensure_connected, active_nodes)
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all_graphs = evaluated_res[-3]
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all_metrics = evaluated_res[-2]
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targets_log = evaluated_res[-1]
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unique_graphs = evaluated_res[0]
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return unique_graphs, all_graphs, all_metrics, targets_log
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def compute_molecular_metrics(molecule_list, targets, train_smiles, stat_ref, dataset_info, task_evaluator, comput_config):
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""" molecule_list: (dict) """
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@@ -10,7 +10,41 @@ import numpy as np
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import rdkit.Chem
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import matplotlib.pyplot as plt
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class GraphVisualization:
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def __init__(self, dataset_infos):
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self.dataset_infos = dataset_infos
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def graph_from_graphs(self, node_list, adjency_matrix):
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"""
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Convert graphs to networkx graphs
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node_list: the nodes of a batch of nodes (bs x n)
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adjacency_matrix: the adjacency_matrix of the molecule (bs x n x n)
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"""
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graph = nx.Graph()
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for i in range(len(node_list)):
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if node_list[i] == -1:
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continue
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graph.add_node(i, number=i, symbol=node_list[i], color_val=node_list[i])
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rows, cols = np.where(adjency_matrix >= 1)
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edges = zip(rows.tolist(), cols.tolist())
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for edge in edges:
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edge_type = adjency_matrix[edge[0]][edge[1]]
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graph.add_edge(edge[0], edge[1], color=float(edge_type), weight=3 * edge_type)
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return graph
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def visualize(self, path: str, graphs: list, num_graphs_to_visualize: int, log='graph'):
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# define path to save figures
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if not os.path.exists(path):
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os.makedirs(path)
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# visualize the final molecules
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for i in range(num_graphs_to_visualize):
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file_path = os.path.join(path, 'graph_{}.png'.format(i))
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graph = self.graph_from_graphs(graphs[i][0].numpy(), graphs[i][1].numpy())
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self.visualize_graph(graph=graph, pos=None, path=file_path)
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im = plt.imread(file_path)
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class MolecularVisualization:
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def __init__(self, dataset_infos):
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self.dataset_infos = dataset_infos
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@@ -70,7 +70,7 @@ class DataModule(AbstractDataModule):
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# base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
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# except NameError:
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# base_path = pathlib.Path(os.getcwd()).parent[2]
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base_path = '/home/stud/hanzhang/nasbenchDiT'
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base_path = '/nfs/data3/hanzhang/nasbenchDiT'
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root_path = os.path.join(base_path, self.datadir)
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self.root_path = root_path
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@@ -408,6 +408,7 @@ def new_graphs_to_json(graphs, filename):
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adj = graph[0]
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n_node = len(ops)
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print(n_node)
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n_edge = len(ops)
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n_node_list.append(n_node)
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n_edge_list.append(n_edge)
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@@ -489,7 +490,7 @@ def new_graphs_to_json(graphs, filename):
|
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'transition_E': transition_E.tolist(),
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}
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with open(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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json.dump(meta_dict, f)
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return meta_dict
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@@ -655,7 +656,7 @@ def graphs_to_json(graphs, filename):
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class Dataset(InMemoryDataset):
|
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def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
|
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self.target_prop = target_prop
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source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
|
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self.source = source
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# self.api = API(source) # Initialize NAS-Bench-201 API
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# print('API loaded')
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@@ -676,8 +677,8 @@ class Dataset(InMemoryDataset):
|
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return [f'{self.source}.pt']
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|
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def process(self):
|
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source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
|
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self.api = API(source)
|
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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
|
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# self.api = API(source)
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|
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data_list = []
|
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# len_data = len(self.api)
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@@ -710,14 +711,24 @@ class Dataset(InMemoryDataset):
|
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return True
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|
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def generate_flex_adj_mat(ori_nodes, ori_edges, max_nodes=12, min_nodes=8,random_ratio=0.5):
|
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# print(ori_nodes)
|
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# print(ori_edges)
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|
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ori_edges = np.array(ori_edges)
|
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# ori_nodes = np.array(ori_nodes)
|
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nasbench_201_node_num = 8
|
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# random.seed(random_seed)
|
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nodes_num = random.randint(min_nodes, max_nodes)
|
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# 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}')
|
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add_num = nodes_num - nasbench_201_node_num
|
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# ori_nodes, ori_edges = parse_architecture_string(arch_str)
|
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add_nodes = [op for op in random.choices(num_to_op[1:-1], k=add_num)]
|
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add_nodes = []
|
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print(f'add_num: {add_num}')
|
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for i in range(add_num):
|
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add_nodes.append(random.choice(num_to_op[1:-1]))
|
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# print(add_nodes)
|
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print(f'ori_nodes[:-1]: {ori_nodes[:-1]}, add_nodes: {add_nodes}')
|
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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 +738,18 @@ class Dataset(InMemoryDataset):
|
||||
rand = random.random()
|
||||
if rand < random_ratio:
|
||||
edges[i, j] = 1
|
||||
return nodes, edges
|
||||
if nodes_num < max_nodes:
|
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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)}')
|
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return edges,nodes
|
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|
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def get_nasbench_201_val(idx):
|
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pass
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|
||||
def graph_to_graph_data(graph, idx):
|
||||
# def graph_to_graph_data(graph, idx):
|
||||
def graph_to_graph_data(graph):
|
||||
ops = graph[1]
|
||||
adj = graph[0]
|
||||
nodes = []
|
||||
@@ -753,58 +770,95 @@ class Dataset(InMemoryDataset):
|
||||
edge_index = torch.tensor(edges_list, dtype=torch.long).t()
|
||||
edge_type = torch.tensor(edge_type, dtype=torch.long)
|
||||
edge_attr = edge_type
|
||||
# y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
|
||||
y = get_nasbench_201_val(idx)
|
||||
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)
|
||||
return data
|
||||
graph_list = []
|
||||
|
||||
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')
|
||||
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')
|
||||
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)
|
||||
# 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)
|
||||
|
||||
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))
|
||||
# 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:
|
||||
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)
|
||||
# data_list.append(graph_to_graph_data((new_adj, new_ops)))
|
||||
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({
|
||||
# "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()]
|
||||
# })
|
||||
pbar.update(1)
|
||||
|
||||
for graph in graph_list:
|
||||
@@ -818,6 +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])
|
||||
|
||||
@@ -981,18 +1037,29 @@ 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 +1071,21 @@ 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 +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]
|
||||
@@ -1066,12 +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
|
||||
@@ -1280,11 +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)
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
1
graph_dit/flex-nasbench201-graph.json
Normal file
1
graph_dit/flex-nasbench201-graph.json
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
1
graph_dit/nasbench-201-meta.json
Normal file
1
graph_dit/nasbench-201-meta.json
Normal file
@@ -0,0 +1 @@
|
||||
{"source": "nasbench-201", "num_graph": 31250, "n_nodes_per_graph": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "max_n_nodes": 12, "max_n_edges": 12, "node_type_list": [0.08333333333333333, 0.12076, 0.121096, 0.12054933333333333, 0.120808, 0.35012, 0.08333333333333333, 0.0], "edge_type_list": [0.7757650537496, 0.22423494625039994], "valencies": [0.08333333333333333, 0.12076, 0.121096, 0.12054933333333333, 0.120808, 0.35012, 0.08333333333333333, 0.0], "active_nodes": ["*", "input", "nor_conv_1x1", "nor_conv_3x3", "avg_pool_3x3", "skip_connect", "none"], "num_active_nodes": 7, "transition_E": [[[1.0, 0.0], [0.4991939935961135, 0.5008060064038865], [0.5003633480874677, 0.4996366519125322], [0.49935849223554396, 0.500641507764456], [0.5018652186389422, 0.49813478136105777], [0.8275181842415934, 0.17248181575840665], [0.752416, 0.247584], [1.0, 0.0]], [[0.4991939935961135, 0.5008060064038865], [0.6929218744736044, 0.3070781255263956], [0.6891703482219348, 0.3108296517780652], [0.6909309288988885, 0.3090690711011114], [0.6876641745807234, 0.3123358254192766], [0.8913831706500085, 0.10861682934999148], [0.3948327260682345, 0.6051672739317655], [1.0, 0.0]], [[0.5003633480874677, 0.4996366519125322], [0.6891703482219348, 0.3108296517780652], [0.6877141129844832, 0.3122858870155169], [0.6899900524354673, 0.3100099475645327], [0.6869198878799577, 0.3130801121200423], [0.8910209102091021, 0.1089790897908979], [0.39503644491422785, 0.6049635550857722], [1.0, 0.0]], [[0.49935849223554396, 0.500641507764456], [0.6909309288988885, 0.3090690711011114], [0.6899900524354673, 0.3100099475645327], [0.6918940854215279, 0.30810591457847214], [0.6933245431647987, 0.30667545683520125], [0.8933821584543675, 0.10661784154563249], [0.3977348139627483, 0.6022651860372517], [1.0, 0.0]], [[0.5018652186389422, 0.49813478136105777], [0.6876641745807234, 0.3123358254192766], [0.6869198878799577, 0.3130801121200423], [0.6933245431647987, 0.30667545683520125], [0.6879391891891892, 0.31206081081081083], [0.8921497860953153, 0.10785021390468477], [0.39730260689137586, 0.6026973931086241], [1.0, 0.0]], [[0.8275181842415934, 0.17248181575840665], [0.8913831706500085, 0.10861682934999148], [0.8910209102091021, 0.1089790897908979], [0.8933821584543675, 0.10661784154563249], [0.8921497860953153, 0.10785021390468477], [0.9634043948311156, 0.03659560516888434], [0.79138581057923, 0.20861418942077004], [1.0, 0.0]], [[0.752416, 0.247584], [0.3948327260682345, 0.6051672739317655], [0.39503644491422785, 0.6049635550857722], [0.3977348139627483, 0.6022651860372517], [0.39730260689137586, 0.6026973931086241], [0.79138581057923, 0.20861418942077004], [1.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]]}
|
||||
1100
graph_dit/output_graphs/final_graphs.txt
Normal file
1100
graph_dit/output_graphs/final_graphs.txt
Normal file
File diff suppressed because it is too large
Load Diff
101
graph_dit/output_graphs/final_graphs1212.txt
Normal file
101
graph_dit/output_graphs/final_graphs1212.txt
Normal file
@@ -0,0 +1,101 @@
|
||||
graph, input_nasbench201, output_nasbench201
|
||||
nodes: tensor(0) tensor(3) tensor(1) tensor(4) tensor(2) tensor(1) tensor(1) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.2488862962923356
|
||||
nodes: tensor(0) tensor(3) tensor(5) tensor(1) tensor(4) tensor(1) tensor(5) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.046388876844218
|
||||
nodes: tensor(0) tensor(4) tensor(4) tensor(5) tensor(4) tensor(2) tensor(4) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.08731827688338656
|
||||
nodes: tensor(0) tensor(2) tensor(4) tensor(1) tensor(4) tensor(2) tensor(5) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.6730363016633114
|
||||
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101
graph_dit/output_graphs/final_graphs122318.txt
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101
graph_dit/output_graphs/final_graphs122318.txt
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101
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101
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graph, input_nasbench201, output_nasbench201
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nodes: tensor(0) tensor(4) tensor(5) tensor(3) tensor(5) tensor(2) tensor(1) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.2838407593739203
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nodes: tensor(0) tensor(5) tensor(5) tensor(5) tensor(2) tensor(2) tensor(5) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.24227967740337342
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nodes: tensor(0) tensor(1) tensor(2) tensor(5) tensor(3) tensor(3) tensor(1) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.5962977148254313
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nodes: tensor(0) tensor(4) tensor(5) tensor(3) tensor(2) tensor(4) tensor(3) tensor(6) tensor(5) tensor(5) tensor(5) tensor(5) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) /n, 0.0, 0.6426282401919712
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1100
graph_dit/output_graphs/final_graphs1423.txt
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graph_dit/output_graphs/final_graphs1423.txt
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