train.py

import argparse, time, os, pickle
import numpy as np

import dgl
import torch
import torch.optim as optim

from models import LANDER
from dataset import LanderDataset


###########
# ArgParser
parser = argparse.ArgumentParser()

# Dataset
parser.add_argument('--data_path', type=str, required=True)
parser.add_argument('--test_data_path', type=str, required=True)
parser.add_argument('--levels', type=str, default='1')
parser.add_argument('--faiss_gpu', action='store_true')
parser.add_argument('--model_filename', type=str, default='lander.pth')

# KNN
parser.add_argument('--knn_k', type=str, default='10')

# Model
parser.add_argument('--hidden', type=int, default=512)
parser.add_argument('--num_conv', type=int, default=4)
parser.add_argument('--dropout', type=float, default=0.)
parser.add_argument('--gat', action='store_true')
parser.add_argument('--gat_k', type=int, default=1)
parser.add_argument('--balance', action='store_true')
parser.add_argument('--use_cluster_feat', action='store_true')
parser.add_argument('--use_focal_loss', action='store_true')

# Training
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight_decay', type=float, default=1e-5)

args = parser.parse_args()

###########################
# Environment Configuration
if torch.cuda.is_available():
    device = torch.device('cuda')
else:
    device = torch.device('cpu')

##################
# Data Preparation
def prepare_dataset_graphs(data_path, k_list, lvl_list):
    with open(data_path, 'rb') as f:
        features, labels = pickle.load(f)
    gs = []
    for k, l in zip(k_list, lvl_list):
        dataset = LanderDataset(features=features, labels=labels, k=k,
                                levels=l, faiss_gpu=args.faiss_gpu)
        gs += [g.to(device) for g in dataset.gs]
    return gs

k_list = [int(k) for k in args.knn_k.split(',')]
lvl_list = [int(l) for l in args.levels.split(',')]
gs = prepare_dataset_graphs(args.data_path, k_list, lvl_list)
test_gs = prepare_dataset_graphs(args.test_data_path, k_list, lvl_list)

##################
# Model Definition
feature_dim = gs[0].ndata['features'].shape[1]
model = LANDER(feature_dim=feature_dim, nhid=args.hidden,
               num_conv=args.num_conv, dropout=args.dropout,
               use_GAT=args.gat, K=args.gat_k,
               balance=args.balance,
               use_cluster_feat=args.use_cluster_feat,
               use_focal_loss=args.use_focal_loss)
model = model.to(device)
model.train()
best_model = None
best_loss = np.Inf

#################
# Hyperparameters
opt = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum,
                weight_decay=args.weight_decay)
scheduler = optim.lr_scheduler.CosineAnnealingLR(opt, T_max=args.epochs, eta_min=1e-5)

###############
# Training Loop
for epoch in range(args.epochs):
    all_loss_den_val = 0
    all_loss_conn_val = 0
    for g in gs:
        opt.zero_grad()
        g = model(g)
        loss, loss_den_val, loss_conn_val = model.compute_loss(g)
        all_loss_den_val += loss_den_val
        all_loss_conn_val += loss_conn_val
        loss.backward()
        opt.step()
    scheduler.step()
    print('Training, epoch: %d, loss_den: %.6f, loss_conn: %.6f'%
          (epoch, all_loss_den_val, all_loss_conn_val))
    # Report test
    all_test_loss_den_val = 0
    all_test_loss_conn_val = 0
    with torch.no_grad():
        for g in test_gs:
            g = model(g)
            loss, loss_den_val, loss_conn_val = model.compute_loss(g)
            all_test_loss_den_val += loss_den_val
            all_test_loss_conn_val += loss_conn_val
    print('Testing, epoch: %d, loss_den: %.6f, loss_conn: %.6f'%
          (epoch, all_test_loss_den_val, all_test_loss_conn_val))
    if all_test_loss_conn_val + all_test_loss_den_val < best_loss:
        best_loss = all_test_loss_conn_val + all_test_loss_den_val
        print ('New best epoch', epoch)
        torch.save(model.state_dict(), args.model_filename+'_best')
    torch.save(model.state_dict(), args.model_filename)

torch.save(model.state_dict(), args.model_filename)