def main(args, path_to_candidate_bonds):
if args['test_path'] is None:
test_set = USPTORank(
subset='test',
candidate_bond_path=path_to_candidate_bonds['test'],
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_eval'],
num_processes=args['num_processes'])
else:
test_set = WLNRankDataset(
path_to_reaction_file=args['test_path'],
candidate_bond_path=path_to_candidate_bonds['test'],
mode='test',
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_eval'],
num_processes=args['num_processes'])
test_loader = DataLoader(test_set,
batch_size=1,
collate_fn=collate_rank_eval,
shuffle=False,
num_workers=args['num_workers'])
if args['num_workers'] > 1:
torch.multiprocessing.set_sharing_strategy('file_system')
if args['model_path'] is None:
model = load_pretrained('wln_rank_uspto')
else:
model = WLNReactionRanking(
node_in_feats=args['node_in_feats'],
edge_in_feats=args['edge_in_feats'],
node_hidden_feats=args['hidden_size'],
num_encode_gnn_layers=args['num_encode_gnn_layers'])
model.load_state_dict(
torch.load(args['model_path'],
map_location='cpu')['model_state_dict'])
model = model.to(args['device'])
prediction_summary = candidate_ranking_eval(args, model, test_loader)
with open(args['result_path'] + '/test_eval.txt', 'w') as f:
f.write(prediction_summary)
def main(args, path_to_candidate_bonds):
if args['train_path'] is None:
train_set = USPTORank(
subset='train',
candidate_bond_path=path_to_candidate_bonds['train'],
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_train'],
num_processes=args['num_processes'])
else:
train_set = WLNRankDataset(
path_to_reaction_file=args['train_path'],
candidate_bond_path=path_to_candidate_bonds['train'],
mode='train',
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_train'],
num_processes=args['num_processes'])
train_set.ignore_large()
if args['val_path'] is None:
val_set = USPTORank(subset='val',
candidate_bond_path=path_to_candidate_bonds['val'],
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_eval'],
num_processes=args['num_processes'])
else:
val_set = WLNRankDataset(
path_to_reaction_file=args['val_path'],
candidate_bond_path=path_to_candidate_bonds['val'],
mode='val',
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_eval'],
num_processes=args['num_processes'])
if args['num_workers'] > 1:
torch.multiprocessing.set_sharing_strategy('file_system')
train_loader = DataLoader(train_set,
batch_size=args['batch_size'],
collate_fn=collate_rank_train,
shuffle=True,
num_workers=args['num_workers'])
val_loader = DataLoader(val_set,
batch_size=args['batch_size'],
collate_fn=collate_rank_eval,
shuffle=False,
num_workers=args['num_workers'])
model = WLNReactionRanking(
node_in_feats=args['node_in_feats'],
edge_in_feats=args['edge_in_feats'],
node_hidden_feats=args['hidden_size'],
num_encode_gnn_layers=args['num_encode_gnn_layers']).to(args['device'])
criterion = CrossEntropyLoss(reduction='sum')
optimizer = Adam(model.parameters(), lr=args['lr'])
from utils import Optimizer
optimizer = Optimizer(model,
args['lr'],
optimizer,
max_grad_norm=args['max_norm'])
acc_sum = 0
grad_norm_sum = 0
dur = []
total_samples = 0
for epoch in range(args['num_epochs']):
t0 = time.time()
model.train()
for batch_id, batch_data in enumerate(train_loader):
batch_reactant_graphs, batch_product_graphs, \
batch_combo_scores, batch_labels, batch_num_candidate_products = batch_data
batch_reactant_graphs = batch_reactant_graphs.to(args['device'])
batch_product_graphs = batch_product_graphs.to(args['device'])
batch_combo_scores = batch_combo_scores.to(args['device'])
batch_labels = batch_labels.to(args['device'])
reactant_node_feats = batch_reactant_graphs.ndata.pop('hv').to(
args['device'])
reactant_edge_feats = batch_reactant_graphs.edata.pop('he').to(
args['device'])
product_node_feats = batch_product_graphs.ndata.pop('hv').to(
args['device'])
product_edge_feats = batch_product_graphs.edata.pop('he').to(
args['device'])
pred = model(
reactant_graph=batch_reactant_graphs,
reactant_node_feats=reactant_node_feats,
reactant_edge_feats=reactant_edge_feats,
product_graphs=batch_product_graphs,
product_node_feats=product_node_feats,
product_edge_feats=product_edge_feats,
candidate_scores=batch_combo_scores,
batch_num_candidate_products=batch_num_candidate_products)
# Check if the ground truth candidate has the highest score
batch_loss = 0
product_graph_start = 0
for i in range(len(batch_num_candidate_products)):
product_graph_end = product_graph_start + batch_num_candidate_products[
i]
reaction_pred = pred[product_graph_start:product_graph_end, :]
acc_sum += float(
reaction_pred.max(
dim=0)[1].detach().cpu().data.item() == 0)
batch_loss += criterion(reaction_pred.reshape(1, -1),
batch_labels[i, :])
product_graph_start = product_graph_end
grad_norm_sum += optimizer.backward_and_step(batch_loss)
total_samples += args['batch_size']
if total_samples % args['print_every'] == 0:
progress = 'Epoch {:d}/{:d}, iter {:d}/{:d} | time {:.4f} | ' \
'accuracy {:.4f} | grad norm {:.4f}'.format(
epoch + 1, args['num_epochs'],
(batch_id + 1) * args['batch_size'] // args['print_every'],
len(train_set) // args['print_every'],
(sum(dur) + time.time() - t0) / total_samples * args['print_every'],
acc_sum / args['print_every'],
grad_norm_sum / args['print_every'])
print(progress)
acc_sum = 0
grad_norm_sum = 0
if total_samples % args['decay_every'] == 0:
dur.append(time.time() - t0)
old_lr = optimizer.lr
optimizer.decay_lr(args['lr_decay_factor'])
new_lr = optimizer.lr
print('Learning rate decayed from {:.4f} to {:.4f}'.format(
old_lr, new_lr))
torch.save({'model_state_dict': model.state_dict()},
args['result_path'] +
'/model_{:d}.pkl'.format(total_samples))
prediction_summary = 'total samples {:d}, (epoch {:d}/{:d}, iter {:d}/{:d})\n'.format(
total_samples, epoch + 1, args['num_epochs'],
(batch_id + 1) * args['batch_size'] // args['print_every'],
len(train_set) //
args['print_every']) + candidate_ranking_eval(
args, model, val_loader)
print(prediction_summary)
with open(args['result_path'] + '/val_eval.txt', 'a') as f:
f.write(prediction_summary)
t0 = time.time()
model.train()
