def test_no_dangling_outputs(self):
image_features = tf.placeholder(dtype=tf.float32,
shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self.
_build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
dropout_keep_prob=0.8,
kernel_size=3,
box_code_size=4,
use_dropout=True,
use_depthwise=True))
box_predictions = conv_box_predictor.predict(
[image_features],
num_predictions_per_location=[5],
scope='BoxPredictor')
tf.concat(box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
bad_dangling_ops = []
types_safe_to_dangle = set(['Assign', 'Mul', 'Const'])
for op in tf.get_default_graph().get_operations():
if (not op.outputs) or (not op.outputs[0].consumers()):
if 'BoxPredictor' in op.name:
if op.type not in types_safe_to_dangle:
bad_dangling_ops.append(op)
self.assertEqual(bad_dangling_ops, [])
def test_no_dangling_outputs(self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4,
use_dropout=True,
use_depthwise=True))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
bad_dangling_ops = []
types_safe_to_dangle = set(['Assign', 'Mul', 'Const'])
for op in tf.get_default_graph().get_operations():
if (not op.outputs) or (not op.outputs[0].consumers()):
if 'BoxPredictor' in op.name:
if op.type not in types_safe_to_dangle:
bad_dangling_ops.append(op)
self.assertEqual(bad_dangling_ops, [])
def test_use_depthwise_convolution(self):
image_features = tf.compat.v1.placeholder(dtype=tf.float32,
shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self.
_build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4,
use_dropout=True,
use_depthwise=True))
box_predictions = conv_box_predictor.predict(
[image_features],
num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(box_predictions[box_predictor.BOX_ENCODINGS],
axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.compat.v1.global_variables_initializer()
resolution = 32
expected_num_anchors = resolution * resolution * 5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape, objectness_predictions_shape) = sess.run(
[
tf.shape(input=box_encodings),
tf.shape(input=objectness_predictions)
],
feed_dict={
image_features: np.random.rand(4, resolution, resolution,
64)
})
actual_variable_set = set(
[var.op.name for var in tf.compat.v1.trainable_variables()])
self.assertAllEqual(box_encodings_shape,
[4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
expected_variable_set = set([
'BoxPredictor/Conv2d_0_1x1_32/biases',
'BoxPredictor/Conv2d_0_1x1_32/weights',
'BoxPredictor/BoxEncodingPredictor_depthwise/biases',
'BoxPredictor/BoxEncodingPredictor_depthwise/depthwise_weights',
'BoxPredictor/BoxEncodingPredictor/biases',
'BoxPredictor/BoxEncodingPredictor/weights',
'BoxPredictor/ClassPredictor_depthwise/biases',
'BoxPredictor/ClassPredictor_depthwise/depthwise_weights',
'BoxPredictor/ClassPredictor/biases',
'BoxPredictor/ClassPredictor/weights'
])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_use_depthwise_convolution(self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4,
use_dropout=True,
use_depthwise=True))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.global_variables_initializer()
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
expected_variable_set = set([
'BoxPredictor/Conv2d_0_1x1_32/biases',
'BoxPredictor/Conv2d_0_1x1_32/weights',
'BoxPredictor/BoxEncodingPredictor_depthwise/biases',
'BoxPredictor/BoxEncodingPredictor_depthwise/depthwise_weights',
'BoxPredictor/BoxEncodingPredictor/biases',
'BoxPredictor/BoxEncodingPredictor/weights',
'BoxPredictor/ClassPredictor_depthwise/biases',
'BoxPredictor/ClassPredictor_depthwise/depthwise_weights',
'BoxPredictor/ClassPredictor/biases',
'BoxPredictor/ClassPredictor/weights'])
self.assertEqual(expected_variable_set, actual_variable_set)
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (box_encodings, objectness_predictions)
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (box_encodings, objectness_predictions)
