Value = value.narrow(cat_dim or 0, start, end - start) Self.len_dict = self.get(i).verts.size(0)įile '/path/to/env/lib/python3.8/site-packages/torch_geometric/data/in_memory_dataset.py ', line 84, in getįile '/path/to/env/lib/python3.8/site-packages/torch_geometric/data/separate.py ', line 37, in separateĭata_store = _separate(attr, batch_store, idx, slices,įile '/path/to/env/lib/python3.8/site-packages/torch_geometric/data/separate.py ', line 65, in _separate ain_datasets.append(dataset.get_dataset( 'train '))įile 'path/to/M圜ode/datasets/in_memory_shape_dataset.py ', line 82, in get_datasetįile 'path/to/M圜ode/datasets/my_custom_dataset_in_memory.py ', line 114, in do_subset_updateįile 'path/to/M圜ode/datasets/my_custom_dataset_in_memory.py ', line 130, in load_corres_files

Self._call_setup_hook() # allow user to setup lightning_module in accelerator environmentįile '/path/to/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py ', line 1438, in _call_setup_hookįile '/path/to/env/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py ', line 474, in wrapped_fnįile 'path/to/M圜ode/datasets/my_custom_datamodule.py ', line 44, in setup Trainer.fit(model, shape_matching_datamodule)įile '/path/to/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py ', line 740, in fitįile '/path/to/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py ', line 685, in _call_and_handle_interruptįile '/path/to/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py ', line 777, in _fit_implįile '/path/to/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py ', line 1138, in _run Train_dataset = PathGraph( transform = LocalDegreeProfile()),īatch = next( iter( data_module. path_graph( i) for i in range( 3, 6))įor g, p in zip( graphs, self. gml' for i in range( 3, property def processed_file_names( self): processed_paths[ property def raw_file_names( self):

_init_( root, transform, pre_transform, pre_filter) utils import from_networkx class PathGraph( InMemoryDataset): transforms import LocalDegreeProfile from torch_geometric. data import InMemoryDataset, LightningDataset from torch_geometric. From pprint import pprint import networkx as nx import torch as th from torch_geometric.

Amadeus pro 2.0.5