Source code for torch_geometric.datasets.particle

import os.path as osp
import glob

import torch
import numpy as np
from torch_scatter import scatter_add
from import Data, Dataset

class TrackingData(Data):
    def __inc__(self, key, value, *args, **kwargs):
        if key == 'y_index':
            return torch.tensor([value[0].max().item() + 1, self.num_nodes])
            return super().__inc__(key, value, *args, **kwargs)

[docs]class TrackMLParticleTrackingDataset(Dataset): r"""The `TrackML Particle Tracking Challenge <>`_ dataset to reconstruct particle tracks from 3D points left in the silicon detectors. Args: root (string): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an :obj:`` object and returns a transformed version. The data object will be transformed before every access. (default: :obj:`None`) """ url = '' def __init__(self, root, transform=None): super(TrackMLParticleTrackingDataset, self).__init__(root, transform) events = glob.glob(osp.join(self.raw_dir, 'event*-hits.csv')) events = [e.split(osp.sep)[-1].split('-')[0][5:] for e in events] = sorted(events) @property def raw_file_names(self): event_indices = ['000001000'] file_names = [] file_names += [f'event{idx}-cells.csv' for idx in event_indices] file_names += [f'event{idx}-hits.csv' for idx in event_indices] file_names += [f'event{idx}-particles.csv' for idx in event_indices] file_names += [f'event{idx}-truth.csv' for idx in event_indices] return file_names def download(self): raise RuntimeError( 'Dataset not found. Please download it from {} and move all ' '*.csv files to {}'.format(self.url, self.raw_dir))
[docs] def len(self): return len(glob.glob(osp.join(self.raw_dir, 'event*-hits.csv')))
def get(self, idx): import pandas as pd idx =[idx] # Get hit positions. hits_path = osp.join(self.raw_dir, f'event{idx}-hits.csv') pos = pd.read_csv(hits_path, usecols=['x', 'y', 'z'], dtype=np.float32) pos = torch.from_numpy(pos.values).div_(1000.) # Get hit features. cells_path = osp.join(self.raw_dir, f'event{idx}-cells.csv') cell = pd.read_csv(cells_path, usecols=['hit_id', 'value']) hit_id = torch.from_numpy(cell['hit_id'].values).to(torch.long).sub_(1) value = torch.from_numpy(cell['value'].values).to(torch.float) ones = torch.ones(hit_id.size(0)) num_cells = scatter_add(ones, hit_id, dim_size=pos.size(0)).div_(10.) value = scatter_add(value, hit_id, dim_size=pos.size(0)) x = torch.stack([num_cells, value], dim=-1) # Get ground-truth hit assignments. truth_path = osp.join(self.raw_dir, f'event{idx}-truth.csv') y = pd.read_csv(truth_path, usecols=['hit_id', 'particle_id', 'weight']) hit_id = torch.from_numpy(y['hit_id'].values).to(torch.long).sub_(1) particle_id = torch.from_numpy(y['particle_id'].values).to(torch.long) particle_id = particle_id.unique(return_inverse=True)[1].sub_(1) weight = torch.from_numpy(y['weight'].values).to(torch.float) # Sort. perm = (particle_id * hit_id.size(0) + hit_id).argsort() hit_id = hit_id[perm] particle_id = particle_id[perm] weight = weight[perm] # Remove invalid particle ids. mask = particle_id >= 0 hit_id = hit_id[mask] particle_id = particle_id[mask] weight = weight[mask] y_index = torch.stack([particle_id, hit_id], dim=0) return TrackingData(x=x, pos=pos, y_index=y_index, y_weight=weight)