# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.meta_architectures.ssd_meta_arch."""
import functools
import numpy as np
import tensorflow as tf
from tensorflow.python.training import saver as tf_saver
from object_detection.core import anchor_generator
from object_detection.core import box_list
from object_detection.core import losses
from object_detection.core import post_processing
from object_detection.core import region_similarity_calculator as sim_calc
from object_detection.meta_architectures import ssd_meta_arch
from object_detection.utils import test_utils
slim = tf.contrib.slim
class FakeSSDFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
def __init__(self):
super(FakeSSDFeatureExtractor, self).__init__(
depth_multiplier=0, min_depth=0, conv_hyperparams=None)
def preprocess(self, resized_inputs):
return tf.identity(resized_inputs)
def extract_features(self, preprocessed_inputs):
with tf.variable_scope('mock_model'):
features = slim.conv2d(inputs=preprocessed_inputs, num_outputs=32,
kernel_size=[1, 1], scope='layer1')
return [features]
class MockAnchorGenerator2x2(anchor_generator.AnchorGenerator):
"""Sets up a simple 2x2 anchor grid on the unit square."""
def name_scope(self):
return 'MockAnchorGenerator'
def num_anchors_per_location(self):
return [1]
def _generate(self, feature_map_shape_list):
return box_list.BoxList(
tf.constant([[0, 0, .5, .5],
[0, .5, .5, 1],
[.5, 0, 1, .5],
[.5, .5, 1, 1]], tf.float32))
class SsdMetaArchTest(tf.test.TestCase):
def setUp(self):
"""Set up mock SSD model.
Here we set up a simple mock SSD model that will always predict 4
detections that happen to always be exactly the anchors that are set up
in the above MockAnchorGenerator. Because we let max_detections=5,
we will also always end up with an extra padded row in the detection
results.
"""
is_training = False
self._num_classes = 1
mock_anchor_generator = MockAnchorGenerator2x2()
mock_box_predictor = test_utils.MockBoxPredictor(
is_training, self._num_classes)
mock_box_coder = test_utils.MockBoxCoder()
fake_feature_extractor = FakeSSDFeatureExtractor()
mock_matcher = test_utils.MockMatcher()
region_similarity_calculator = sim_calc.IouSimilarity()
def image_resizer_fn(image):
return tf.identity(image)
classification_loss = losses.WeightedSigmoidClassificationLoss(
anchorwise_output=True)
localization_loss = losses.WeightedSmoothL1LocalizationLoss(
anchorwise_output=True)
non_max_suppression_fn = functools.partial(
post_processing.batch_multiclass_non_max_suppression,
score_thresh=-20.0,
iou_thresh=1.0,
max_size_per_class=5,
max_total_size=5)
classification_loss_weight = 1.0
localization_loss_weight = 1.0
normalize_loss_by_num_matches = False
# This hard example miner is expected to be a no-op.
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=None,
iou_threshold=1.0)
self._num_anchors = 4
self._code_size = 4
self._model = ssd_meta_arch.SSDMetaArch(
is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
fake_feature_extractor, mock_matcher, region_similarity_calculator,
image_resizer_fn, non_max_suppression_fn, tf.identity,
classification_loss, localization_loss, classification_loss_weight,
localization_loss_weight, normalize_loss_by_num_matches,
hard_example_miner)
def test_preprocess_preserves_input_shapes(self):
image_shapes = [(3, None, None, 3),
(None, 10, 10, 3),
(None, None, None, 3)]
for image_shape in image_shapes:
image_placeholder = tf.placeholder(tf.float32, shape=image_shape)
preprocessed_inputs = self._model.preprocess(image_placeholder)
self.assertAllEqual(preprocessed_inputs.shape.as_list(), image_shape)
def test_predict_results_have_correct_keys_and_shapes(self):
batch_size = 3
image_size = 2
input_shapes = [(batch_size, image_size, image_size, 3),
(None, image_size, image_size, 3),
(batch_size, None, None, 3),
(None, None, None, 3)]
expected_box_encodings_shape_out = (
batch_size, self._num_anchors, self._code_size)
expected_class_predictions_with_background_shape_out = (
batch_size, self._num_anchors, self._num_classes+1)
for input_shape in input_shapes:
tf_graph = tf.Graph()
with tf_graph.as_default():
preprocessed_input_placeholder = tf.placeholder(tf.float32,
shape=input_shape)
prediction_dict = self._model.predict(preprocessed_input_placeholder)
self.assertTrue('box_encodings' in prediction_dict)
self.assertTrue('class_predictions_with_background' in prediction_dict)
self.assertTrue('feature_maps' in prediction_dict)
init_op = tf.global_variables_initializer()
with self.test_session(graph=tf_graph) as sess:
sess.run(init_op)
prediction_out = sess.run(prediction_dict,
feed_dict={
preprocessed_input_placeholder:
np.random.uniform(
size=(batch_size, 2, 2, 3))})
self.assertAllEqual(prediction_out['box_encodings'].shape,
expected_box_encodings_shape_out)
self.assertAllEqual(
prediction_out['class_predictions_with_background'].shape,
expected_class_predictions_with_background_shape_out)
def test_postprocess_results_are_correct(self):
batch_size = 2
image_size = 2
input_shapes = [(batch_size, image_size, image_size, 3),
(None, image_size, image_size, 3),
(batch_size, None, None, 3),
(None, None, None, 3)]
expected_boxes = np.array([[[0, 0, .5, .5],
[0, .5, .5, 1],
[.5, 0, 1, .5],
[.5, .5, 1, 1],
[0, 0, 0, 0]],
[[0, 0, .5, .5],
[0, .5, .5, 1],
[.5, 0, 1, .5],
[.5, .5, 1, 1],
[0, 0, 0, 0]]])
expected_scores = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
expected_classes = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
expected_num_detections = np.array([4, 4])
for input_shape in input_shapes:
tf_graph = tf.Graph()
with tf_graph.as_default():
preprocessed_input_placeholder = tf.placeholder(tf.float32,
shape=input_shape)
prediction_dict = self._model.predict(preprocessed_input_placeholder)
detections = self._model.postprocess(prediction_dict)
self.assertTrue('detection_boxes' in detections)
self.assertTrue('detection_scores' in detections)
self.assertTrue('detection_classes' in detections)
self.assertTrue('num_detections' in detections)
init_op = tf.global_variables_initializer()
with self.test_session(graph=tf_graph) as sess:
sess.run(init_op)
detections_out = sess.run(detections,
feed_dict={
preprocessed_input_placeholder:
np.random.uniform(
size=(batch_size, 2, 2, 3))})
self.assertAllClose(detections_out['detection_boxes'], expected_boxes)
self.assertAllClose(detections_out['detection_scores'], expected_scores)
self.assertAllClose(detections_out['detection_classes'], expected_classes)
self.assertAllClose(detections_out['num_detections'],
expected_num_detections)
def test_loss_results_are_correct(self):
batch_size = 2
preprocessed_input = tf.random_uniform((batch_size, 2, 2, 3),
dtype=tf.float32)
groundtruth_boxes_list = [tf.constant([[0, 0, .5, .5]], dtype=tf.float32),
tf.constant([[0, 0, .5, .5]], dtype=tf.float32)]
groundtruth_classes_list = [tf.constant([[1]], dtype=tf.float32),
tf.constant([[1]], dtype=tf.float32)]
self._model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list)
prediction_dict = self._model.predict(preprocessed_input)
loss_dict = self._model.loss(prediction_dict)
self.assertTrue('localization_loss' in loss_dict)
self.assertTrue('classification_loss' in loss_dict)
expected_localization_loss = 0.0
expected_classification_loss = (batch_size * self._num_anchors
* (self._num_classes+1) * np.log(2.0))
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
losses_out = sess.run(loss_dict)
self.assertAllClose(losses_out['localization_loss'],
expected_localization_loss)
self.assertAllClose(losses_out['classification_loss'],
expected_classification_loss)
def test_restore_map_for_detection_ckpt(self):
init_op = tf.global_variables_initializer()
saver = tf_saver.Saver()
save_path = self.get_temp_dir()
with self.test_session() as sess:
sess.run(init_op)
saved_model_path = saver.save(sess, save_path)
var_map = self._model.restore_map(from_detection_checkpoint=True)
self.assertIsInstance(var_map, dict)
saver = tf.train.Saver(var_map)
saver.restore(sess, saved_model_path)
for var in sess.run(tf.report_uninitialized_variables()):
self.assertNotIn('FeatureExtractor', var.name)
def test_restore_map_for_classification_ckpt(self):
# Define mock tensorflow classification graph and save variables.
test_graph_classification = tf.Graph()
with test_graph_classification.as_default():
image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
with tf.variable_scope('mock_model'):
net = slim.conv2d(image, num_outputs=32, kernel_size=1, scope='layer1')
slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
save_path = self.get_temp_dir()
with self.test_session() as sess:
sess.run(init_op)
saved_model_path = saver.save(sess, save_path)
# Create tensorflow detection graph and load variables from
# classification checkpoint.
test_graph_detection = tf.Graph()
with test_graph_detection.as_default():
inputs_shape = [2, 2, 2, 3]
inputs = tf.to_float(tf.random_uniform(
inputs_shape, minval=0, maxval=255, dtype=tf.int32))
preprocessed_inputs = self._model.preprocess(inputs)
prediction_dict = self._model.predict(preprocessed_inputs)
self._model.postprocess(prediction_dict)
var_map = self._model.restore_map(from_detection_checkpoint=False)
self.assertIsInstance(var_map, dict)
saver = tf.train.Saver(var_map)
with self.test_session() as sess:
saver.restore(sess, saved_model_path)
for var in sess.run(tf.report_uninitialized_variables()):
self.assertNotIn('FeatureExtractor', var.name)
if __name__ == '__main__':
tf.test.main()
