torch_gnn/main_enkarate.py at master · mtiezzi/torch_gnn · GitHub

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"""
main Karate
===========

This is the main file for the PyTorch Karate club example
"""

import torch
import torchvision
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import argparse
import utils
import dataloader

from gnn_wrapper import GNNWrapper, SemiSupGNNWrapper


#
# # fix random seeds for reproducibility
# SEED = 123
# torch.manual_seed(SEED)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# np.random.seed(SEED)


def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=100, metavar='N',
                        help='input batch size for testing (default: 100)')
    parser.add_argument('--epochs', type=int, default=100000, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.0001, metavar='LR',
                        help='learning rate (default: 0.0001)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--cuda_dev', type=int, default=0,
                        help='select specific CUDA device for training')
    parser.add_argument('--n_gpu_use', type=int, default=1,
                        help='select number of CUDA device for training')
    # parser.add_argument('--seed', type=int, default=1, metavar='S',
    #                     help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=50, metavar='N',
                        help='logging training status cadency')
    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    parser.add_argument('--tensorboard', action='store_true', default=True,
                        help='For logging the model in tensorboard')

    args = parser.parse_args()

    use_cuda = not args.no_cuda and torch.cuda.is_available()
    if not use_cuda:
        args.n_gpu_use = 0

    device = utils.prepare_device(n_gpu_use=args.n_gpu_use, gpu_id=args.cuda_dev)
    # kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}

    # torch.manual_seed(args.seed)
    # # fix random seeds for reproducibility
    # SEED = 123
    # torch.manual_seed(SEED)
    # torch.backends.cudnn.deterministic = True
    # torch.backends.cudnn.benchmark = False
    # np.random.seed(SEED)

    # configugations
    cfg = GNNWrapper.Config()
    cfg.use_cuda = use_cuda
    cfg.device = device

    cfg.log_interval = args.log_interval
    cfg.tensorboard = args.tensorboard

    # cfg.batch_size = args.batch_size
    # cfg.test_batch_size = args.test_batch_size
    # cfg.momentum = args.momentum

    cfg.dataset_path = './data'
    cfg.epochs = args.epochs
    cfg.lrw = args.lr
    cfg.activation = nn.Tanh()
    cfg.state_transition_hidden_dims = [5,]
    cfg.output_function_hidden_dims = [5]
    cfg.state_dim = 5
    cfg.max_iterations = 50
    cfg.convergence_threshold = 0.1
    cfg.graph_based = False
    cfg.log_interval = 10
    cfg.task_type = "semisupervised"

    cfg.lrw = 0.01

    # model creation
    model = SemiSupGNNWrapper(cfg)
    # dataset creation
    E, N, targets, mask_train, mask_test = dataloader.old_load_karate()
    dset = dataloader.from_EN_to_GNN(E, N, targets, aggregation_type="sum", sparse_matrix=True)  # generate the dataset
    dset.idx_train = mask_train
    dset.idx_test = mask_test
    model(dset)  # dataset initalization into the GNN

    # training code
    for epoch in range(1, args.epochs + 1):
        model.train_step(epoch)

        if epoch % 10 == 0:
            model.test_step(epoch)
    # model.test_step()

    # if args.save_model:
    #     torch.save(model.gnn.state_dict(), "mnist_cnn.pt")


if __name__ == '__main__':
    main()