Comments (1)
Yes, you may have to get the tensor that does that operation. For example, you may do something similar to the following:
You may restore the session and get the placeholders like this :
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.import_meta_graph(META_GRAPH)
saver.restore(sess, tf.train.latest_checkpoint(LOG_DIR))
# We can now access the default graph where all our metadata has been loaded
graph = tf.get_default_graph()
# tmp = graph.get_operations()
# tmp = [n.name for n in graph.as_graph_def().node]
# print tmp
# placeholders we need
farthest_points_pl = graph.get_tensor_by_name('layer1/FarthestPointSample:0')
pointclouds_pl = graph.get_tensor_by_name('Placeholder:0')
pred_pl = graph.get_tensor_by_name('fc2/BiasAdd:0')
is_training_pl = graph.get_tensor_by_name('Placeholder_3:0')
META_GRAPH and LOG_DIR are directory paths you define.
Then run a forward pass:
TEST_DATASET = scannet_dataset.ScannetDatasetWholeScene(...)
batch_data, batch_label, batch_smpw = TEST_DATASET[scene_idx]
feed_dict = {pointclouds_pl: batch_data,
is_training_pl: False}
farthest_points, pred_val = sess.run([farthest_points_pl,pred_pl], feed_dict=feed_dict)
farthest_points should give you something close to what you're looking for. Please see
pointnet2/utils/pointnet_util.py
Line 87 in 7961e26
and it's containing file for more info.
Hopefully that helps.
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