utils.py

import numpy as np
import random
import torch
from models import MWE_GCN, MWE_DGCN
import torch.nn.functional as F

def set_random_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
    print ('random seed set to be ' + str(seed))

def load_model(args):
    if args['model'] == 'MWE-GCN':
        model = MWE_GCN(
                n_input=args['in_feats'],
                n_hidden=args['hidden_feats'],
                n_output=args['out_feats'],
                n_layers=args['n_layers'],
                activation=torch.nn.Tanh(),
                dropout=args['dropout'],
                aggr_mode=args['aggr_mode'],
                device=args['device'])
    elif args['model'] == 'MWE-DGCN':
        model = MWE_DGCN(
                n_input=args['in_feats'],
                n_hidden=args['hidden_feats'],
                n_output=args['out_feats'],
                n_layers=args['n_layers'],
                activation=torch.nn.ReLU(),
                dropout=args['dropout'],
                aggr_mode=args['aggr_mode'],
                residual=args['residual'],
                device=args['device'])
    else:
        raise ValueError('Unexpected model {}'.format(args['model']))

    return model

class Logger(object):
    def __init__(self, runs, info=None):
        self.info = info
        self.results = [[] for _ in range(runs)]

    def add_result(self, run, result):
        assert len(result) == 3
        assert run >= 0 and run < len(self.results)
        self.results[run].append(result)

    def print_statistics(self, run=None):
        if run is not None:
            result = 100 * torch.tensor(self.results[run])
            argmax = result[:, 1].argmax().item()
            print(f'Run {run + 1:02d}:')
            print(f'Highest Train: {result[:, 0].max():.2f}')
            print(f'Highest Valid: {result[:, 1].max():.2f}')
            print(f'  Final Train: {result[argmax, 0]:.2f}')
            print(f'   Final Test: {result[argmax, 2]:.2f}')
        else:
            result = 100 * torch.tensor(self.results)

            best_results = []
            for r in result:
                train1 = r[:, 0].max().item()
                valid = r[:, 1].max().item()
                train2 = r[r[:, 1].argmax(), 0].item()
                test = r[r[:, 1].argmax(), 2].item()
                best_results.append((train1, valid, train2, test))

            best_result = torch.tensor(best_results)

            print(f'All runs:')
            r = best_result[:, 0]
            print(f'Highest Train: {r.mean():.2f} ± {r.std():.2f}')
            r = best_result[:, 1]
            print(f'Highest Valid: {r.mean():.2f} ± {r.std():.2f}')
            r = best_result[:, 2]
            print(f'  Final Train: {r.mean():.2f} ± {r.std():.2f}')
            r = best_result[:, 3]
            print(f'   Final Test: {r.mean():.2f} ± {r.std():.2f}')