def model_forward(model, data, sampler=None, is_train=True):
if sampler is None:
sampler = RandomSampler(data, FLAGS.batch_size, FLAGS.sample_induced)
if FLAGS.lower_level_layers and FLAGS.higher_level_layers:
if "model_init" in FLAGS.init_embds and is_train:
_get_initial_embd(data, model)
data.dataset.init_interaction_graph_embds(device=FLAGS.device)
model.init_x = data.dataset.interaction_combo_nxgraph.init_x.cpu(
).detach().numpy()
batch_gids, sampled_gids, subgraph = sampler.sample_next_training_batch(
)
batch_data = BatchData(
batch_gids,
data.dataset,
is_train=is_train,
sampled_gids=sampled_gids,
enforce_negative_sampling=FLAGS.enforce_negative_sampling,
unique_graphs=FLAGS.batch_unique_graphs,
subgraph=subgraph)
if FLAGS.pair_interaction:
model.use_layers = 'lower_layers'
model(batch_data)
model.use_layers = 'higher_layers'
else:
batch_gids, sampled_gids, subgraph = sampler.sample_next_training_batch(
)
batch_data = BatchData(
batch_gids,
data.dataset,
is_train=is_train,
sampled_gids=sampled_gids,
enforce_negative_sampling=FLAGS.enforce_negative_sampling,
unique_graphs=FLAGS.batch_unique_graphs,
subgraph=subgraph)
return batch_data