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egnn_qm9.py
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from qm9 import dataset
from qm9.models import EGNN
import torch
from torch import nn, optim
import argparse
from qm9 import utils as qm9_utils
import utils
import json
import neptune.new as neptune
parser = argparse.ArgumentParser(description='QM9 Example')
parser.add_argument('--exp_name', type=str, default='exp_1', metavar='N',
help='experiment_name')
parser.add_argument('--batch_size', type=int, default=96, metavar='N',
help='input batch size for training (default: 128)')
parser.add_argument('--epochs', type=int, default=1000, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log_interval', type=int, default=20, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--test_interval', type=int, default=1, metavar='N',
help='how many epochs to wait before logging test')
parser.add_argument('--outf', type=str, default='qm9/logs', metavar='N',
help='folder to output vae')
parser.add_argument('--lr', type=float, default=1e-3, metavar='N',
help='learning rate')
parser.add_argument('--nf', type=int, default=128, metavar='N',
help='learning rate')
parser.add_argument('--attention', type=int, default=1, metavar='N',
help='attention in the ae model')
parser.add_argument('--n_layers', type=int, default=7, metavar='N',
help='number of layers for the autoencoder')
parser.add_argument('--property', type=str, default='homo', metavar='N',
help='label to predict: alpha | gap | homo | lumo | mu | Cv | G | H | r2 | U | U0 | zpve')
parser.add_argument('--num_workers', type=int, default=0, metavar='N',
help='number of workers for the dataloader')
parser.add_argument('--charge_power', type=int, default=2, metavar='N',
help='maximum power to take into one-hot features')
parser.add_argument('--dataset_paper', type=str, default="cormorant", metavar='N',
help='cormorant, lie_conv')
parser.add_argument('--node_attr', type=int, default=0, metavar='N',
help='node_attr or not')
parser.add_argument('--weight_decay', type=float, default=1e-16, metavar='N',
help='weight decay')
## neptune setting
run = neptune.init_run(
project="ahn-group/egnn-3dpe",
api_token="eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiJiZGIwM2Y1ZC1jODdiLTRkMDItYWUxNy0yZjRiMmEzMDJjY2MifQ==",
) # your credentials
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
dtype = torch.float32
print(args)
utils.makedir(args.outf)
utils.makedir(args.outf + "/" + args.exp_name)
dataloaders, charge_scale = dataset.retrieve_dataloaders(args.batch_size, args.num_workers)
# compute mean and mean absolute deviation
meann, mad = qm9_utils.compute_mean_mad(dataloaders, args.property)
model = EGNN(in_node_nf=15, in_edge_nf=0, hidden_nf=args.nf, device=device, n_layers=args.n_layers, coords_weight=1.0,
attention=args.attention, node_attr=args.node_attr)
print(model)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs)
loss_l1 = nn.L1Loss()
def train(epoch, loader, partition='train'):
lr_scheduler.step()
res = {'loss': 0, 'counter': 0, 'loss_arr':[]}
for i, data in enumerate(loader):
if partition == 'train':
model.train()
optimizer.zero_grad()
else:
model.eval()
batch_size, n_nodes, _ = data['positions'].size()
atom_positions = data['positions'].view(batch_size * n_nodes, -1).to(device, dtype)
atom_mask = data['atom_mask'].view(batch_size * n_nodes, -1).to(device, dtype)
edge_mask = data['edge_mask'].to(device, dtype)
one_hot = data['one_hot'].to(device, dtype)
charges = data['charges'].to(device, dtype)
nodes = qm9_utils.preprocess_input(one_hot, charges, args.charge_power, charge_scale, device)
nodes = nodes.view(batch_size * n_nodes, -1)
# nodes = torch.cat([one_hot, charges], dim=1)
edges = qm9_utils.get_adj_matrix(n_nodes, batch_size, device)
label = data[args.property].to(device, dtype)
pred = model(h0=nodes, x=atom_positions, edges=edges, edge_attr=None, node_mask=atom_mask, edge_mask=edge_mask,
n_nodes=n_nodes)
if partition == 'train':
loss = loss_l1(pred, (label - meann) / mad)
loss.backward()
optimizer.step()
else:
loss = loss_l1(mad * pred + meann, label)
res['loss'] += loss.item() * batch_size
res['counter'] += batch_size
res['loss_arr'].append(loss.item())
prefix = ""
if partition != 'train':
prefix = ">> %s \t" % partition
if i % args.log_interval == 0:
print(prefix + "Epoch %d \t Iteration %d \t loss %.4f" % (epoch, i, sum(res['loss_arr'][-10:])/len(res['loss_arr'][-10:])))
if i==1040 and partition=='train':
run[args.property+'/train_mae'].log(sum(res['loss_arr'][-10:])/len(res['loss_arr'][-10:]))
return res['loss'] / res['counter']
if __name__ == "__main__":
res = {'epochs': [], 'losess': [], 'best_val': 1e10, 'best_test': 1e10, 'best_epoch': 0}
for epoch in range(0, args.epochs):
train(epoch, dataloaders['train'], partition='train')
if epoch % args.test_interval == 0:
val_loss = train(epoch, dataloaders['valid'], partition='valid')
test_loss = train(epoch, dataloaders['test'], partition='test')
res['epochs'].append(epoch)
res['losess'].append(test_loss)
if val_loss < res['best_val']:
res['best_val'] = val_loss
res['best_test'] = test_loss
res['best_epoch'] = epoch
print("Val loss: %.4f \t test loss: %.4f \t epoch %d" % (val_loss, test_loss, epoch))
run[args.property+"/valid_mae"].log(val_loss)
run[args.property+"/test_mae"].log(test_loss)
print("Best: val loss: %.4f \t test loss: %.4f \t epoch %d" % (res['best_val'], res['best_test'], res['best_epoch']))
run[args.property+"/best_val"].log(res['best_val'])
json_object = json.dumps(res, indent=4)
with open(args.outf + "/" + args.exp_name + "/losess.json", "w") as outfile:
outfile.write(json_object)