def test_get_simple_gaussian_embedder(self):
# Shape = [4, 2, 3].
input_features = tf.constant([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
[[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],
[[13.0, 14.0, 15.0], [16.0, 17.0, 18.0]],
[[19.0, 20.0, 21.0], [22.0, 23.0, 24.0]]])
embedder_fn = models.get_embedder(
base_model_type=common.BASE_MODEL_TYPE_SIMPLE,
embedding_type=common.EMBEDDING_TYPE_GAUSSIAN,
num_embedding_components=3,
embedding_size=16,
num_embedding_samples=32,
is_training=True,
weight_max_norm=0.0)
outputs, activations = embedder_fn(input_features)
self.assertCountEqual(outputs.keys(), [
common.KEY_EMBEDDING_MEANS,
common.KEY_EMBEDDING_STDDEVS,
common.KEY_EMBEDDING_SAMPLES,
])
self.assertAllEqual(outputs[common.KEY_EMBEDDING_MEANS].shape.as_list(),
[4, 2, 3, 16])
self.assertAllEqual(outputs[common.KEY_EMBEDDING_STDDEVS].shape.as_list(),
[4, 2, 3, 16])
self.assertAllEqual(outputs[common.KEY_EMBEDDING_SAMPLES].shape.as_list(),
[4, 2, 3, 32, 16])
self.assertCountEqual(activations.keys(), ['base_activations'])
self.assertAllEqual(activations['base_activations'].shape.as_list(),
[4, 2, 1024])
def main(_):
"""Runs inference."""
keypoint_profile_2d = (keypoint_profiles.create_keypoint_profile_or_die(
FLAGS.input_keypoint_profile_name_2d))
g = tf.Graph()
with g.as_default():
keypoints_2d, keypoint_masks_2d = read_inputs(keypoint_profile_2d)
model_inputs, _ = input_generator.create_model_input(
keypoints_2d,
keypoint_masks_2d=keypoint_masks_2d,
keypoints_3d=None,
model_input_keypoint_type=common.
MODEL_INPUT_KEYPOINT_TYPE_2D_INPUT,
model_input_keypoint_mask_type=FLAGS.
model_input_keypoint_mask_type,
keypoint_profile_2d=keypoint_profile_2d,
# Fix seed for determinism.
seed=1)
embedder_fn = models.get_embedder(
base_model_type=FLAGS.base_model_type,
embedding_type=FLAGS.embedding_type,
num_embedding_components=FLAGS.num_embedding_components,
embedding_size=FLAGS.embedding_size,
num_embedding_samples=FLAGS.num_embedding_samples,
is_training=False,
num_fc_blocks=FLAGS.num_fc_blocks,
num_fcs_per_block=FLAGS.num_fcs_per_block,
num_hidden_nodes=FLAGS.num_hidden_nodes,
num_bottleneck_nodes=FLAGS.num_bottleneck_nodes,
weight_max_norm=FLAGS.weight_max_norm)
outputs, _ = embedder_fn(model_inputs)
if FLAGS.use_moving_average:
variables_to_restore = (
pipeline_utils.get_moving_average_variables_to_restore())
saver = tf.train.Saver(variables_to_restore)
else:
saver = tf.train.Saver()
scaffold = tf.train.Scaffold(init_op=tf.global_variables_initializer(),
saver=saver)
session_creator = tf.train.ChiefSessionCreator(
scaffold=scaffold,
master=FLAGS.master,
checkpoint_filename_with_path=FLAGS.checkpoint_path)
with tf.train.MonitoredSession(session_creator=session_creator,
hooks=None) as sess:
outputs_result = sess.run(outputs)
tf.gfile.MakeDirs(FLAGS.output_dir)
for key in [
common.KEY_EMBEDDING_MEANS, common.KEY_EMBEDDING_STDDEVS,
common.KEY_EMBEDDING_SAMPLES
]:
if key in outputs_result:
output = outputs_result[key]
np.savetxt(os.path.join(FLAGS.output_dir, key + '.csv'),
output.reshape([output.shape[0], -1]),
delimiter=',')