def test_disable_path_drop(self):
# Test path drop is disabled when the probabilities
# are set to 1.0.
train_val_test = 'train'
# Overwrite path drop probabilities
model_config = config_builder.proto_to_obj(self.model_config)
model_config.path_drop_probabilities = [1.0, 1.0]
with tf.Graph().as_default():
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=self.dataset)
model.build()
# These variables are set during path drop only
# in the case of no path-drop, they should be non-existence
self.assertFalse(hasattr(model, 'img_path_drop_mask'))
self.assertFalse(hasattr(model, 'bev_path_drop_mask'))
def test_path_drop_weights(self):
# Tests the effect of path-drop on network's feature maps.
# It sets up a minimal-training process to check the
# feature before and after running the 'train_op' while
# path-drop is in effect.
train_val_test = 'train'
# overwrite the training iterations
self.train_config.max_iterations = 2
self.train_config.overwrite_checkpoints = True
# Overwrite path drop probabilities
model_config = config_builder.proto_to_obj(self.model_config)
model_config.path_drop_probabilities = [0.0, 0.8]
with tf.Graph().as_default():
# Set a graph-level seed
tf.set_random_seed(1245)
model = RpnModel(model_config,
train_val_test=train_val_test,
dataset=self.dataset)
prediction_dict = model.build()
losses_dict, total_loss = model.loss(prediction_dict)
global_summaries = set([])
# Optimizer
training_optimizer = optimizer_builder.build(
self.train_config.optimizer, global_summaries)
train_op = slim.learning.create_train_op(total_loss,
training_optimizer)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
for step in range(1, self.train_config.max_iterations):
feed_dict = model.create_feed_dict()
if step == 1:
current_feature_maps = sess.run(model.img_feature_maps,
feed_dict=feed_dict)
exp_feature_maps = current_feature_maps
train_op_loss = sess.run(train_op, feed_dict=feed_dict)
print('Step {}, Total Loss {:0.3f} '.format(
step, train_op_loss))
updated_feature_maps = sess.run(model.img_feature_maps,
feed_dict=feed_dict)
# The feature maps should have remained the same since
# the image path was dropped
np.testing.assert_array_almost_equal(updated_feature_maps,
exp_feature_maps,
decimal=4)
def test_rpn_loss(self):
# Use "val" so that the first sample is loaded each time
rpn_model = RpnModel(self.model_config,
train_val_test="val",
dataset=self.dataset)
predictions = rpn_model.build()
loss, total_loss = rpn_model.loss(predictions)
feed_dict = rpn_model.create_feed_dict()
with self.test_session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
loss_dict_out = sess.run(loss, feed_dict=feed_dict)
print('Losses ', loss_dict_out)
def test_create_path_drop_masks(self):
# Tests creating path drop choices
# based on the given probabilities
rpn_model = RpnModel(self.model_config,
train_val_test="val",
dataset=self.dataset)
rpn_model.build()
##################################
# Test-Case 1 : Keep img, Keep bev
##################################
p_img = tf.constant(0.6)
p_bev = tf.constant(0.85)
# Set the random numbers for testing purposes
rand_choice = [0.53, 0.83, 0.05]
rand_choice_tensor = tf.convert_to_tensor(rand_choice)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 1.0)
np.testing.assert_array_equal(bev_mask_out, 1.0)
##################################
# Test-Case 2 : Kill img, Keep bev
##################################
p_img = tf.constant(0.2)
p_bev = tf.constant(0.85)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 0.0)
np.testing.assert_array_equal(bev_mask_out, 1.0)
##################################
# Test-Case 3 : Keep img, Kill bev
##################################
p_img = tf.constant(0.9)
p_bev = tf.constant(0.1)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 1.0)
np.testing.assert_array_equal(bev_mask_out, 0.0)
##############################################
# Test-Case 4 : Kill img, Kill bev, third flip
##############################################
p_img = tf.constant(0.0)
p_bev = tf.constant(0.1)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
np.testing.assert_array_equal(img_mask_out, 0.0)
# Because of the third condition, we expect to be keeping bev
np.testing.assert_array_equal(bev_mask_out, 1.0)
##############################################
# Test-Case 5 : Kill img, Kill bev, third flip
##############################################
# Let's flip the third chance and keep img instead
rand_choice = [0.53, 0.83, 0.61]
rand_choice_tensor = tf.convert_to_tensor(rand_choice)
p_img = tf.constant(0.0)
p_bev = tf.constant(0.1)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
with self.test_session():
img_mask_out = img_mask.eval()
bev_mask_out = bev_mask.eval()
# Because of the third condition, we expect to be keeping img
np.testing.assert_array_equal(img_mask_out, 1.0)
np.testing.assert_array_equal(bev_mask_out, 0.0)
def test_path_drop_input_multiplication(self):
# Tests the result of final image/bev inputs
# based on the path drop decisions
rpn_model = RpnModel(self.model_config,
train_val_test="val",
dataset=self.dataset)
rpn_model.build()
# Shape of input feature map
dummy_img_feature_shape = [1, 30, 50, 2]
random_values = np.random.randint(low=1.0, high=256.0,
size=2).astype(np.float32)
dummy_img_feature_map = tf.fill(dummy_img_feature_shape,
random_values[0])
# Assume both features map are the same size, this is not
# the case inside the network
dummy_bev_feature_map = tf.fill(dummy_img_feature_shape,
random_values[1])
##################################
# Test-Case 1 : Keep img, Kill bev
##################################
exp_img_input = np.full(dummy_img_feature_shape, random_values[0])
exp_bev_input = np.full(dummy_img_feature_shape, 0.0)
p_img = tf.constant(0.6)
p_bev = tf.constant(0.4)
# Set the random numbers for testing purposes
rand_choice = [0.53, 0.83, 0.05]
rand_choice_tensor = tf.convert_to_tensor(rand_choice)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
final_img_input = tf.multiply(dummy_img_feature_map, img_mask)
final_bev_input = tf.multiply(dummy_bev_feature_map, bev_mask)
with self.test_session():
final_img_input_out = final_img_input.eval()
final_bev_input_out = final_bev_input.eval()
np.testing.assert_array_equal(final_img_input_out, exp_img_input)
np.testing.assert_array_equal(final_bev_input_out, exp_bev_input)
##################################
# Test-Case 2 : Kill img, Keep bev
##################################
exp_img_input = np.full(dummy_img_feature_shape, 0)
exp_bev_input = np.full(dummy_img_feature_shape, random_values[1])
p_img = tf.constant(0.4)
p_bev = tf.constant(0.9)
img_mask, bev_mask = rpn_model.create_path_drop_masks(
p_img, p_bev, rand_choice_tensor)
final_img_input = tf.multiply(dummy_img_feature_map, img_mask)
final_bev_input = tf.multiply(dummy_bev_feature_map, bev_mask)
with self.test_session():
final_img_input_out = final_img_input.eval()
final_bev_input_out = final_bev_input.eval()
np.testing.assert_array_equal(final_img_input_out, exp_img_input)
np.testing.assert_array_equal(final_bev_input_out, exp_bev_input)
def test_load_model_weights(self):
# Tests loading weights
train_val_test = 'train'
# Overwrite the training iterations
self.train_config.max_iterations = 1
self.train_config.overwrite_checkpoints = True
with tf.Graph().as_default():
model = RpnModel(self.model_config,
train_val_test=train_val_test,
dataset=self.dataset)
trainer.train(model, self.train_config)
paths_config = self.model_config.paths_config
rpn_checkpoint_dir = paths_config.checkpoint_dir
# load the weights back in
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
trainer_utils.load_checkpoints(rpn_checkpoint_dir, saver)
checkpoint_to_restore = saver.last_checkpoints[-1]
trainer_utils.load_model_weights(sess, checkpoint_to_restore)
rpn_vars = slim.get_model_variables()
rpn_weights = sess.run(rpn_vars)
self.assertGreater(len(rpn_weights),
0,
msg='Loaded RPN weights are empty')
with tf.Graph().as_default():
model = AvodModel(self.model_config,
train_val_test=train_val_test,
dataset=self.dataset)
model.build()
# load the weights back in
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
trainer_utils.load_checkpoints(rpn_checkpoint_dir, saver)
checkpoint_to_restore = saver.last_checkpoints[-1]
trainer_utils.load_model_weights(sess, checkpoint_to_restore)
avod_vars = slim.get_model_variables()
avod_weights = sess.run(avod_vars)
# AVOD weights should include both RPN + AVOD weights
self.assertGreater(len(avod_weights),
len(rpn_weights),
msg='Expected more weights for AVOD')
# grab weights corresponding to RPN by index
# since the model variables are ordered
rpn_len = len(rpn_weights)
loaded_rpn_vars = avod_vars[0:rpn_len]
rpn_weights_reload = sess.run(loaded_rpn_vars)
# Make sure the reloaded weights match the originally
# loaded weights
for i in range(rpn_len):
np.testing.assert_array_equal(rpn_weights_reload[i],
rpn_weights[i])