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Commit 7a9934df authored by Zhichao Lu's avatar Zhichao Lu Committed by lzc5123016
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Merged commit includes the following changes: 

184048729  by Zhichao Lu:

    Modify target_assigner so that it creates regression targets taking keypoints into account.

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184027183  by Zhichao Lu:

    Resnet V1 FPN based feature extractors for SSD meta architecture in Object Detection V2 API.

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184004730  by Zhichao Lu:

    Expose a lever to override the configured mask_type.

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183933113  by Zhichao Lu:

    Weight shared convolutional box predictor as described in https://arxiv.org/abs/1708.02002

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183929669  by Zhichao Lu:

    Expanding box list operations for future data augmentations.

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183916792  by Zhichao Lu:

    Fix unrecognized assertion function in tests.

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183906851  by Zhichao Lu:

    - Change ssd meta architecture to use regression weights to compute loss normalizer.

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183871003  by Zhichao Lu:

    Fix config_util_test wrong dependency.

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183782120  by Zhichao Lu:

    Add __init__ file to third_party directories.

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183779109  by Zhichao Lu:

    Setup regular version sync.

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183768772  by Zhichao Lu:

    Make test compatible with numpy 1.12 and higher

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183767893  by Zhichao Lu:

    Make test compatible with numpy 1.12 and higher

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183719318  by Zhichao Lu:

    Use the new test interface in ssd feature extractor.

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183714671  by Zhichao Lu:

    Use the new test_case interface for all anchor generators.

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183708155  by Zhichao Lu:

    Change variable scopes in ConvolutionalBoxPredictor such that previously trained checkpoints are still compatible after the change in BoxPredictor interface

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183705798  by Zhichao Lu:

    Internal change.

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183636023  by Zhichao Lu:

    Fixing argument name for np_box_list_ops.concatenate() function.

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183490404  by Zhichao Lu:

    Make sure code that relies in SSD older code still works.

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175743440  by Zhichao Lu:

    Use Toggle instead of bool to make the layout optimizer name and usage consistent with other optimizers.

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174249903  by Zhichao Lu:

    Fix nasnet image classification and object detection by moving the option to turn ON or OFF batch norm training into it's own arg_scope used only by detection

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174048035  by Zhichao Lu:

    Fix the pointer for downloading the NAS Faster-RCNN model.

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172815204  by Zhichao Lu:

    Allow for label maps in tf.Example decoding.

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172509113  by Zhichao Lu:

    Allow for label maps in tf.Example decoding.

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172151758  by Zhichao Lu:

    Minor updates to some README files.

    As a result of these friendly issues:
    https://github.com/tensorflow/models/issues/2530
    https://github.com/tensorflow/models/issues/2534

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172147420  by Zhichao Lu:

    Fix illegal summary name and move from slim's get_or_create_global_step deprecated use of tf.contrib.framework* to tf.train*.

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169763373  by Zhichao Lu:

    Fix broken GitHub links in tensorflow and tensorflow_models resulting from The Great Models Move (a.k.a. the research subfolder)

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    Move display strings to below the bounding box if they would otherwise be outside the image.

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PiperOrigin-RevId: 184048729
parent 7ef602be
Hide whitespace changes
Inline Side-by-side
Showing with 2467 additions and 246 deletions
research/object_detection/meta_architectures/ssd_meta_arch_test.py
View file @ 7a9934df
...@@ -24,6 +24,7 @@ from object_detection.core import losses ...@@ -24,6 +24,7 @@ from object_detection.core import losses
from object_detection.core import post_processing from object_detection.core import post_processing
from object_detection.core import region_similarity_calculator as sim_calc from object_detection.core import region_similarity_calculator as sim_calc
from object_detection.meta_architectures import ssd_meta_arch from object_detection.meta_architectures import ssd_meta_arch
from object_detection.utils import test_case
from object_detection.utils import test_utils from object_detection.utils import test_utils
slim = tf.contrib.slim slim = tf.contrib.slim
...@@ -46,7 +47,7 @@ class FakeSSDFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor): ...@@ -46,7 +47,7 @@ class FakeSSDFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
def extract_features(self, preprocessed_inputs): def extract_features(self, preprocessed_inputs):
with tf.variable_scope('mock_model'): with tf.variable_scope('mock_model'):
features = slim.conv2d(inputs=preprocessed_inputs, num_outputs=32, features = slim.conv2d(inputs=preprocessed_inputs, num_outputs=32,
kernel_size=[1, 1], scope='layer1') kernel_size=1, scope='layer1')
return [features] return [features]
...@@ -64,37 +65,31 @@ class MockAnchorGenerator2x2(anchor_generator.AnchorGenerator): ...@@ -64,37 +65,31 @@ class MockAnchorGenerator2x2(anchor_generator.AnchorGenerator):
tf.constant([[0, 0, .5, .5], tf.constant([[0, 0, .5, .5],
[0, .5, .5, 1], [0, .5, .5, 1],
[.5, 0, 1, .5], [.5, 0, 1, .5],
[.5, .5, 1, 1]], tf.float32)) [1., 1., 1.5, 1.5] # Anchor that is outside clip_window.
], tf.float32))
def num_anchors(self):
return 4
class SsdMetaArchTest(tf.test.TestCase):
def setUp(self): class SsdMetaArchTest(test_case.TestCase):
"""Set up mock SSD model.
Here we set up a simple mock SSD model that will always predict 4 def _create_model(self, apply_hard_mining=True):
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 is_training = False
self._num_classes = 1 num_classes = 1
mock_anchor_generator = MockAnchorGenerator2x2() mock_anchor_generator = MockAnchorGenerator2x2()
mock_box_predictor = test_utils.MockBoxPredictor( mock_box_predictor = test_utils.MockBoxPredictor(
is_training, self._num_classes) is_training, num_classes)
mock_box_coder = test_utils.MockBoxCoder() mock_box_coder = test_utils.MockBoxCoder()
fake_feature_extractor = FakeSSDFeatureExtractor() fake_feature_extractor = FakeSSDFeatureExtractor()
mock_matcher = test_utils.MockMatcher() mock_matcher = test_utils.MockMatcher()
region_similarity_calculator = sim_calc.IouSimilarity() region_similarity_calculator = sim_calc.IouSimilarity()
def image_resizer_fn(image): def image_resizer_fn(image):
return tf.identity(image) return [tf.identity(image), tf.shape(image)]
classification_loss = losses.WeightedSigmoidClassificationLoss( classification_loss = losses.WeightedSigmoidClassificationLoss()
anchorwise_output=True) localization_loss = losses.WeightedSmoothL1LocalizationLoss()
localization_loss = losses.WeightedSmoothL1LocalizationLoss(
anchorwise_output=True)
non_max_suppression_fn = functools.partial( non_max_suppression_fn = functools.partial(
post_processing.batch_multiclass_non_max_suppression, post_processing.batch_multiclass_non_max_suppression,
score_thresh=-20.0, score_thresh=-20.0,
...@@ -105,48 +100,56 @@ class SsdMetaArchTest(tf.test.TestCase): ...@@ -105,48 +100,56 @@ class SsdMetaArchTest(tf.test.TestCase):
localization_loss_weight = 1.0 localization_loss_weight = 1.0
normalize_loss_by_num_matches = False normalize_loss_by_num_matches = False
# This hard example miner is expected to be a no-op. hard_example_miner = None
hard_example_miner = losses.HardExampleMiner( if apply_hard_mining:
num_hard_examples=None, # This hard example miner is expected to be a no-op.
iou_threshold=1.0) hard_example_miner = losses.HardExampleMiner(
num_hard_examples=None,
iou_threshold=1.0)
self._num_anchors = 4 code_size = 4
self._code_size = 4 model = ssd_meta_arch.SSDMetaArch(
self._model = ssd_meta_arch.SSDMetaArch(
is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder, is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
fake_feature_extractor, mock_matcher, region_similarity_calculator, fake_feature_extractor, mock_matcher, region_similarity_calculator,
image_resizer_fn, non_max_suppression_fn, tf.identity, image_resizer_fn, non_max_suppression_fn, tf.identity,
classification_loss, localization_loss, classification_loss_weight, classification_loss, localization_loss, classification_loss_weight,
localization_loss_weight, normalize_loss_by_num_matches, localization_loss_weight, normalize_loss_by_num_matches,
hard_example_miner) hard_example_miner, add_summaries=False)
return model, num_classes, mock_anchor_generator.num_anchors(), code_size
def test_preprocess_preserves_input_shapes(self): def test_preprocess_preserves_shapes_with_dynamic_input_image(self):
image_shapes = [(3, None, None, 3), image_shapes = [(3, None, None, 3),
(None, 10, 10, 3), (None, 10, 10, 3),
(None, None, None, 3)] (None, None, None, 3)]
model, _, _, _ = self._create_model()
for image_shape in image_shapes: for image_shape in image_shapes:
image_placeholder = tf.placeholder(tf.float32, shape=image_shape) image_placeholder = tf.placeholder(tf.float32, shape=image_shape)
preprocessed_inputs = self._model.preprocess(image_placeholder) preprocessed_inputs, _ = model.preprocess(image_placeholder)
self.assertAllEqual(preprocessed_inputs.shape.as_list(), image_shape) self.assertAllEqual(preprocessed_inputs.shape.as_list(), image_shape)
def test_predict_results_have_correct_keys_and_shapes(self): def test_preprocess_preserves_shape_with_static_input_image(self):
def graph_fn(input_image):
model, _, _, _ = self._create_model()
return model.preprocess(input_image)
input_image = np.random.rand(2, 3, 3, 3).astype(np.float32)
preprocessed_inputs, _ = self.execute(graph_fn, [input_image])
self.assertAllEqual(preprocessed_inputs.shape, [2, 3, 3, 3])
def test_predict_result_shapes_on_image_with_dynamic_shape(self):
batch_size = 3 batch_size = 3
image_size = 2 image_size = 2
input_shapes = [(batch_size, image_size, image_size, 3), input_shapes = [(None, image_size, image_size, 3),
(None, image_size, image_size, 3),
(batch_size, None, None, 3), (batch_size, None, None, 3),
(None, 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: for input_shape in input_shapes:
tf_graph = tf.Graph() tf_graph = tf.Graph()
with tf_graph.as_default(): with tf_graph.as_default():
model, num_classes, num_anchors, code_size = self._create_model()
preprocessed_input_placeholder = tf.placeholder(tf.float32, preprocessed_input_placeholder = tf.placeholder(tf.float32,
shape=input_shape) shape=input_shape)
prediction_dict = self._model.predict(preprocessed_input_placeholder) prediction_dict = model.predict(
preprocessed_input_placeholder, true_image_shapes=None)
self.assertTrue('box_encodings' in prediction_dict) self.assertTrue('box_encodings' in prediction_dict)
self.assertTrue('class_predictions_with_background' in prediction_dict) self.assertTrue('class_predictions_with_background' in prediction_dict)
...@@ -161,12 +164,42 @@ class SsdMetaArchTest(tf.test.TestCase): ...@@ -161,12 +164,42 @@ class SsdMetaArchTest(tf.test.TestCase):
preprocessed_input_placeholder: preprocessed_input_placeholder:
np.random.uniform( np.random.uniform(
size=(batch_size, 2, 2, 3))}) size=(batch_size, 2, 2, 3))})
expected_box_encodings_shape_out = (batch_size, num_anchors, code_size)
expected_class_predictions_with_background_shape_out = (batch_size,
num_anchors,
num_classes + 1)
self.assertAllEqual(prediction_out['box_encodings'].shape, self.assertAllEqual(prediction_out['box_encodings'].shape,
expected_box_encodings_shape_out) expected_box_encodings_shape_out)
self.assertAllEqual( self.assertAllEqual(
prediction_out['class_predictions_with_background'].shape, prediction_out['class_predictions_with_background'].shape,
expected_class_predictions_with_background_shape_out) expected_class_predictions_with_background_shape_out)
def test_predict_result_shapes_on_image_with_static_shape(self):
with tf.Graph().as_default():
_, num_classes, num_anchors, code_size = self._create_model()
def graph_fn(input_image):
model, _, _, _ = self._create_model()
predictions = model.predict(input_image, true_image_shapes=None)
return (predictions['box_encodings'],
predictions['class_predictions_with_background'],
predictions['feature_maps'],
predictions['anchors'])
batch_size = 3
image_size = 2
channels = 3
input_image = np.random.rand(batch_size, image_size, image_size,
channels).astype(np.float32)
expected_box_encodings_shape = (batch_size, num_anchors, code_size)
expected_class_predictions_shape = (batch_size, num_anchors, num_classes+1)
(box_encodings, class_predictions, _, _) = self.execute(graph_fn,
[input_image])
self.assertAllEqual(box_encodings.shape, expected_box_encodings_shape)
self.assertAllEqual(class_predictions.shape,
expected_class_predictions_shape)
def test_postprocess_results_are_correct(self): def test_postprocess_results_are_correct(self):
batch_size = 2 batch_size = 2
image_size = 2 image_size = 2
...@@ -178,26 +211,30 @@ class SsdMetaArchTest(tf.test.TestCase): ...@@ -178,26 +211,30 @@ class SsdMetaArchTest(tf.test.TestCase):
expected_boxes = np.array([[[0, 0, .5, .5], expected_boxes = np.array([[[0, 0, .5, .5],
[0, .5, .5, 1], [0, .5, .5, 1],
[.5, 0, 1, .5], [.5, 0, 1, .5],
[.5, .5, 1, 1], [0, 0, 0, 0], # pruned prediction
[0, 0, 0, 0]], [0, 0, 0, 0]], # padding
[[0, 0, .5, .5], [[0, 0, .5, .5],
[0, .5, .5, 1], [0, .5, .5, 1],
[.5, 0, 1, .5], [.5, 0, 1, .5],
[.5, .5, 1, 1], [0, 0, 0, 0], # pruned prediction
[0, 0, 0, 0]]]) [0, 0, 0, 0]] # padding
])
expected_scores = np.array([[0, 0, 0, 0, 0], expected_scores = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]) [0, 0, 0, 0, 0]])
expected_classes = np.array([[0, 0, 0, 0, 0], expected_classes = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]) [0, 0, 0, 0, 0]])
expected_num_detections = np.array([4, 4]) expected_num_detections = np.array([3, 3])
for input_shape in input_shapes: for input_shape in input_shapes:
tf_graph = tf.Graph() tf_graph = tf.Graph()
with tf_graph.as_default(): with tf_graph.as_default():
preprocessed_input_placeholder = tf.placeholder(tf.float32, model, _, _, _ = self._create_model()
shape=input_shape) input_placeholder = tf.placeholder(tf.float32, shape=input_shape)
prediction_dict = self._model.predict(preprocessed_input_placeholder) preprocessed_inputs, true_image_shapes = model.preprocess(
detections = self._model.postprocess(prediction_dict) input_placeholder)
prediction_dict = model.predict(preprocessed_inputs,
true_image_shapes)
detections = model.postprocess(prediction_dict, true_image_shapes)
self.assertTrue('detection_boxes' in detections) self.assertTrue('detection_boxes' in detections)
self.assertTrue('detection_scores' in detections) self.assertTrue('detection_scores' in detections)
self.assertTrue('detection_classes' in detections) self.assertTrue('detection_classes' in detections)
...@@ -207,7 +244,7 @@ class SsdMetaArchTest(tf.test.TestCase): ...@@ -207,7 +244,7 @@ class SsdMetaArchTest(tf.test.TestCase):
sess.run(init_op) sess.run(init_op)
detections_out = sess.run(detections, detections_out = sess.run(detections,
feed_dict={ feed_dict={
preprocessed_input_placeholder: input_placeholder:
np.random.uniform( np.random.uniform(
size=(batch_size, 2, 2, 3))}) size=(batch_size, 2, 2, 3))})
self.assertAllClose(detections_out['detection_boxes'], expected_boxes) self.assertAllClose(detections_out['detection_boxes'], expected_boxes)
...@@ -217,47 +254,91 @@ class SsdMetaArchTest(tf.test.TestCase): ...@@ -217,47 +254,91 @@ class SsdMetaArchTest(tf.test.TestCase):
expected_num_detections) expected_num_detections)
def test_loss_results_are_correct(self): 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)
with tf.Graph().as_default():
_, num_classes, num_anchors, _ = self._create_model()
def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
groundtruth_classes1, groundtruth_classes2):
groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2]
groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2]
model, _, _, _ = self._create_model(apply_hard_mining=False)
model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list)
prediction_dict = model.predict(preprocessed_tensor,
true_image_shapes=None)
loss_dict = model.loss(prediction_dict, true_image_shapes=None)
return (loss_dict['localization_loss'], loss_dict['classification_loss'])
batch_size = 2
preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
groundtruth_boxes1 = np.array([[0, 0, .5, .5]], dtype=np.float32)
groundtruth_boxes2 = np.array([[0, 0, .5, .5]], dtype=np.float32)
groundtruth_classes1 = np.array([[1]], dtype=np.float32)
groundtruth_classes2 = np.array([[1]], dtype=np.float32)
expected_localization_loss = 0.0 expected_localization_loss = 0.0
expected_classification_loss = (batch_size * self._num_anchors expected_classification_loss = (batch_size * num_anchors
* (self._num_classes+1) * np.log(2.0)) * (num_classes+1) * np.log(2.0))
init_op = tf.global_variables_initializer() (localization_loss,
with self.test_session() as sess: classification_loss) = self.execute(graph_fn, [preprocessed_input,
sess.run(init_op) groundtruth_boxes1,
losses_out = sess.run(loss_dict) groundtruth_boxes2,
groundtruth_classes1,
groundtruth_classes2])
self.assertAllClose(localization_loss, expected_localization_loss)
self.assertAllClose(classification_loss, expected_classification_loss)
def test_loss_results_are_correct_with_hard_example_mining(self):
with tf.Graph().as_default():
_, num_classes, num_anchors, _ = self._create_model()
def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
groundtruth_classes1, groundtruth_classes2):
groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2]
groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2]
model, _, _, _ = self._create_model()
model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list)
prediction_dict = model.predict(preprocessed_tensor,
true_image_shapes=None)
loss_dict = model.loss(prediction_dict, true_image_shapes=None)
return (loss_dict['localization_loss'], loss_dict['classification_loss'])
self.assertAllClose(losses_out['localization_loss'], batch_size = 2
expected_localization_loss) preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
self.assertAllClose(losses_out['classification_loss'], groundtruth_boxes1 = np.array([[0, 0, .5, .5]], dtype=np.float32)
expected_classification_loss) groundtruth_boxes2 = np.array([[0, 0, .5, .5]], dtype=np.float32)
groundtruth_classes1 = np.array([[1]], dtype=np.float32)
groundtruth_classes2 = np.array([[1]], dtype=np.float32)
expected_localization_loss = 0.0
expected_classification_loss = (batch_size * num_anchors
* (num_classes+1) * np.log(2.0))
(localization_loss, classification_loss) = self.execute_cpu(
graph_fn, [
preprocessed_input, groundtruth_boxes1, groundtruth_boxes2,
groundtruth_classes1, groundtruth_classes2
])
self.assertAllClose(localization_loss, expected_localization_loss)
self.assertAllClose(classification_loss, expected_classification_loss)
def test_restore_map_for_detection_ckpt(self): def test_restore_map_for_detection_ckpt(self):
model, _, _, _ = self._create_model()
model.predict(tf.constant(np.array([[[0, 0], [1, 1]], [[1, 0], [0, 1]]],
dtype=np.float32)),
true_image_shapes=None)
init_op = tf.global_variables_initializer() init_op = tf.global_variables_initializer()
saver = tf.train.Saver() saver = tf.train.Saver()
save_path = self.get_temp_dir() save_path = self.get_temp_dir()
with self.test_session() as sess: with self.test_session() as sess:
sess.run(init_op) sess.run(init_op)
saved_model_path = saver.save(sess, save_path) saved_model_path = saver.save(sess, save_path)
var_map = self._model.restore_map(from_detection_checkpoint=True) var_map = model.restore_map(
from_detection_checkpoint=True,
load_all_detection_checkpoint_vars=False)
self.assertIsInstance(var_map, dict) self.assertIsInstance(var_map, dict)
saver = tf.train.Saver(var_map) saver = tf.train.Saver(var_map)
saver.restore(sess, saved_model_path) saver.restore(sess, saved_model_path)
for var in sess.run(tf.report_uninitialized_variables()): for var in sess.run(tf.report_uninitialized_variables()):
self.assertNotIn('FeatureExtractor', var.name) self.assertNotIn('FeatureExtractor', var)
def test_restore_map_for_classification_ckpt(self): def test_restore_map_for_classification_ckpt(self):
# Define mock tensorflow classification graph and save variables. # Define mock tensorflow classification graph and save variables.
...@@ -271,7 +352,7 @@ class SsdMetaArchTest(tf.test.TestCase): ...@@ -271,7 +352,7 @@ class SsdMetaArchTest(tf.test.TestCase):
init_op = tf.global_variables_initializer() init_op = tf.global_variables_initializer()
saver = tf.train.Saver() saver = tf.train.Saver()
save_path = self.get_temp_dir() save_path = self.get_temp_dir()
with self.test_session() as sess: with self.test_session(graph=test_graph_classification) as sess:
sess.run(init_op) sess.run(init_op)
saved_model_path = saver.save(sess, save_path) saved_model_path = saver.save(sess, save_path)
...@@ -279,19 +360,39 @@ class SsdMetaArchTest(tf.test.TestCase): ...@@ -279,19 +360,39 @@ class SsdMetaArchTest(tf.test.TestCase):
# classification checkpoint. # classification checkpoint.
test_graph_detection = tf.Graph() test_graph_detection = tf.Graph()
with test_graph_detection.as_default(): with test_graph_detection.as_default():
model, _, _, _ = self._create_model()
inputs_shape = [2, 2, 2, 3] inputs_shape = [2, 2, 2, 3]
inputs = tf.to_float(tf.random_uniform( inputs = tf.to_float(tf.random_uniform(
inputs_shape, minval=0, maxval=255, dtype=tf.int32)) inputs_shape, minval=0, maxval=255, dtype=tf.int32))
preprocessed_inputs = self._model.preprocess(inputs) preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
prediction_dict = self._model.predict(preprocessed_inputs) prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
self._model.postprocess(prediction_dict) model.postprocess(prediction_dict, true_image_shapes)
var_map = self._model.restore_map(from_detection_checkpoint=False) another_variable = tf.Variable([17.0], name='another_variable') # pylint: disable=unused-variable
var_map = model.restore_map(from_detection_checkpoint=False)
self.assertNotIn('another_variable', var_map)
self.assertIsInstance(var_map, dict) self.assertIsInstance(var_map, dict)
saver = tf.train.Saver(var_map) saver = tf.train.Saver(var_map)
with self.test_session() as sess: with self.test_session(graph=test_graph_detection) as sess:
saver.restore(sess, saved_model_path) saver.restore(sess, saved_model_path)
for var in sess.run(tf.report_uninitialized_variables()): for var in sess.run(tf.report_uninitialized_variables()):
self.assertNotIn('FeatureExtractor', var.name) self.assertNotIn('FeatureExtractor', var)
def test_load_all_det_checkpoint_vars(self):
test_graph_detection = tf.Graph()
with test_graph_detection.as_default():
model, _, _, _ = self._create_model()
inputs_shape = [2, 2, 2, 3]
inputs = tf.to_float(
tf.random_uniform(inputs_shape, minval=0, maxval=255, dtype=tf.int32))
preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
model.postprocess(prediction_dict, true_image_shapes)
another_variable = tf.Variable([17.0], name='another_variable') # pylint: disable=unused-variable
var_map = model.restore_map(
from_detection_checkpoint=True,
load_all_detection_checkpoint_vars=True)
self.assertIsInstance(var_map, dict)
self.assertIn('another_variable', var_map)
if __name__ == '__main__': if __name__ == '__main__':
......
research/object_detection/metrics/BUILD
View file @ 7a9934df
...@@ -8,6 +8,57 @@ licenses(["notice"]) ...@@ -8,6 +8,57 @@ licenses(["notice"])
# Apache 2.0 # Apache 2.0
py_library(
name = "coco_tools",
srcs = [
"coco_tools.py",
],
deps = [
"//file/localfile",
"//file/placer",
"//pycocotools",
"//tensorflow",
"//tensorflow/models/research/object_detection/utils:json_utils",
],
)
py_test(
name = "coco_tools_test",
srcs = [
"coco_tools_test.py",
],
deps = [
":coco_tools",
"//testing/pybase",
"//numpy",
],
)
py_library(
name = "coco_evaluation",
srcs = [
"coco_evaluation.py",
],
deps = [
":coco_tools",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/utils:object_detection_evaluation",
],
)
py_test(
name = "coco_evaluation_test",
srcs = [
"coco_evaluation_test.py",
],
deps = [
":coco_evaluation",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:standard_fields",
],
)
py_binary( py_binary(
name = "offline_eval_map_corloc", name = "offline_eval_map_corloc",
srcs = [ srcs = [
...@@ -15,11 +66,11 @@ py_binary( ...@@ -15,11 +66,11 @@ py_binary(
], ],
deps = [ deps = [
":tf_example_parser", ":tf_example_parser",
"//tensorflow_models/object_detection:evaluator", "//tensorflow/models/research/object_detection:evaluator",
"//tensorflow_models/object_detection/builders:input_reader_builder", "//tensorflow/models/research/object_detection/builders:input_reader_builder",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow_models/object_detection/utils:config_util", "//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow_models/object_detection/utils:label_map_util", "//tensorflow/models/research/object_detection/utils:label_map_util",
], ],
) )
...@@ -39,8 +90,8 @@ py_library( ...@@ -39,8 +90,8 @@ py_library(
srcs = ["tf_example_parser.py"], srcs = ["tf_example_parser.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:data_parser", "//tensorflow/models/research/object_detection/core:data_parser",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
], ],
) )
...@@ -50,6 +101,6 @@ py_test( ...@@ -50,6 +101,6 @@ py_test(
deps = [ deps = [
":tf_example_parser", ":tf_example_parser",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
], ],
) )
research/object_detection/metrics/coco_evaluation.py 0 → 100644
View file @ 7a9934df
"""Class for evaluating object detections with COCO metrics."""
import numpy as np
import tensorflow as tf
from object_detection.core import standard_fields
from object_detection.metrics import coco_tools
from object_detection.utils import object_detection_evaluation
class CocoDetectionEvaluator(object_detection_evaluation.DetectionEvaluator):
"""Class to evaluate COCO detection metrics."""
def __init__(self, categories, all_metrics_per_category=False):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys -
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name e.g., 'cat', 'dog'.
all_metrics_per_category: Whether to include all the summary metrics for
each category in per_category_ap. Be careful with setting it to true if
you have more than handful of categories, because it will pollute
your mldash.
"""
super(CocoDetectionEvaluator, self).__init__(categories)
# _image_ids is a dictionary that maps unique image ids to Booleans which
# indicate whether a corresponding detection has been added.
self._image_ids = {}
self._groundtruth_list = []
self._detection_boxes_list = []
self._category_id_set = set([cat['id'] for cat in self._categories])
self._annotation_id = 1
self._metrics = None
self._all_metrics_per_category = all_metrics_per_category
def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
self._image_ids.clear()
self._groundtruth_list = []
self._detection_boxes_list = []
def add_single_ground_truth_image_info(self,
image_id,
groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
If the image has already been added, a warning is logged, and groundtruth is
ignored.
Args:
image_id: A unique string/integer identifier for the image.
groundtruth_dict: A dictionary containing -
InputDataFields.groundtruth_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
InputDataFields.groundtruth_classes: integer numpy array of shape
[num_boxes] containing 1-indexed groundtruth classes for the boxes.
"""
if image_id in self._image_ids:
tf.logging.warning('Ignoring ground truth with image id %s since it was '
'previously added', image_id)
return
self._groundtruth_list.extend(
coco_tools.
ExportSingleImageGroundtruthToCoco(
image_id=image_id,
next_annotation_id=self._annotation_id,
category_id_set=self._category_id_set,
groundtruth_boxes=groundtruth_dict[standard_fields.InputDataFields.
groundtruth_boxes],
groundtruth_classes=groundtruth_dict[standard_fields.
InputDataFields.
groundtruth_classes]))
self._annotation_id += groundtruth_dict[standard_fields.InputDataFields.
groundtruth_boxes].shape[0]
self._image_ids[image_id] = False
def add_single_detected_image_info(self,
image_id,
detections_dict):
"""Adds detections for a single image to be used for evaluation.
If a detection has already been added for this image id, a warning is
logged, and the detection is skipped.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing -
DetectionResultFields.detection_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` detection boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
DetectionResultFields.detection_scores: float32 numpy array of shape
[num_boxes] containing detection scores for the boxes.
DetectionResultFields.detection_classes: integer numpy array of shape
[num_boxes] containing 1-indexed detection classes for the boxes.
DetectionResultFields.detection_masks: optional uint8 numpy array of
shape [num_boxes, image_height, image_width] containing instance
masks for the boxes.
Raises:
ValueError: If groundtruth for the image_id is not available.
"""
if image_id not in self._image_ids:
raise ValueError('Missing groundtruth for image id: {}'.format(image_id))
if self._image_ids[image_id]:
tf.logging.warning('Ignoring detection with image id %s since it was '
'previously added', image_id)
return
self._detection_boxes_list.extend(
coco_tools.ExportSingleImageDetectionBoxesToCoco(
image_id=image_id,
category_id_set=self._category_id_set,
detection_boxes=detections_dict[standard_fields.
DetectionResultFields
.detection_boxes],
detection_scores=detections_dict[standard_fields.
DetectionResultFields.
detection_scores],
detection_classes=detections_dict[standard_fields.
DetectionResultFields.
detection_classes]))
self._image_ids[image_id] = True
def evaluate(self):
"""Evaluates the detection boxes and returns a dictionary of coco metrics.
Returns:
A dictionary holding -
1. summary_metrics:
'DetectionBoxes_Precision/mAP': mean average precision over classes
averaged over IOU thresholds ranging from .5 to .95 with .05
increments.
'DetectionBoxes_Precision/mAP@.50IOU': mean average precision at 50% IOU
'DetectionBoxes_Precision/mAP@.75IOU': mean average precision at 75% IOU
'DetectionBoxes_Precision/mAP (small)': mean average precision for small
objects (area < 32^2 pixels).
'DetectionBoxes_Precision/mAP (medium)': mean average precision for
medium sized objects (32^2 pixels < area < 96^2 pixels).
'DetectionBoxes_Precision/mAP (large)': mean average precision for large
objects (96^2 pixels < area < 10000^2 pixels).
'DetectionBoxes_Recall/AR@1': average recall with 1 detection.
'DetectionBoxes_Recall/AR@10': average recall with 10 detections.
'DetectionBoxes_Recall/AR@100': average recall with 100 detections.
'DetectionBoxes_Recall/AR@100 (small)': average recall for small objects
with 100.
'DetectionBoxes_Recall/AR@100 (medium)': average recall for medium objects
with 100.
'DetectionBoxes_Recall/AR@100 (large)': average recall for large objects
with 100 detections.
2. per_category_ap: category specific results with keys of the form:
'Precision mAP ByCategory/category' (without the supercategory part if
no supercategories exist). For backward compatibility
'PerformanceByCategory' is included in the output regardless of
all_metrics_per_category.
"""
groundtruth_dict = {
'annotations': self._groundtruth_list,
'images': [{'id': image_id} for image_id in self._image_ids],
'categories': self._categories
}
coco_wrapped_groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
coco_wrapped_detections = coco_wrapped_groundtruth.LoadAnnotations(
self._detection_boxes_list)
box_evaluator = coco_tools.COCOEvalWrapper(
coco_wrapped_groundtruth, coco_wrapped_detections, agnostic_mode=False)
box_metrics, box_per_category_ap = box_evaluator.ComputeMetrics(
all_metrics_per_category=self._all_metrics_per_category)
box_metrics.update(box_per_category_ap)
box_metrics = {'DetectionBoxes_'+ key: value
for key, value in box_metrics.iteritems()}
return box_metrics
def get_estimator_eval_metric_ops(self, image_id, groundtruth_boxes,
groundtruth_classes, detection_boxes,
detection_scores, detection_classes):
"""Returns a dictionary of eval metric ops to use with `tf.EstimatorSpec`.
Note that once value_op is called, the detections and groundtruth added via
update_op are cleared.
Args:
image_id: Unique string/integer identifier for the image.
groundtruth_boxes: float32 tensor of shape [num_boxes, 4] containing
`num_boxes` groundtruth boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
groundtruth_classes: int32 tensor of shape [num_boxes] containing
1-indexed groundtruth classes for the boxes.
detection_boxes: float32 tensor of shape [num_boxes, 4] containing
`num_boxes` detection boxes of the format [ymin, xmin, ymax, xmax]
in absolute image coordinates.
detection_scores: float32 tensor of shape [num_boxes] containing
detection scores for the boxes.
detection_classes: int32 tensor of shape [num_boxes] containing
1-indexed detection classes for the boxes.
Returns:
a dictionary of metric names to tuple of value_op and update_op that can
be used as eval metric ops in tf.EstimatorSpec. Note that all update ops
must be run together and similarly all value ops must be run together to
guarantee correct behaviour.
"""
def update_op(
image_id,
groundtruth_boxes,
groundtruth_classes,
detection_boxes,
detection_scores,
detection_classes):
self.add_single_ground_truth_image_info(
image_id,
{'groundtruth_boxes': groundtruth_boxes,
'groundtruth_classes': groundtruth_classes})
self.add_single_detected_image_info(
image_id,
{'detection_boxes': detection_boxes,
'detection_scores': detection_scores,
'detection_classes': detection_classes})
update_op = tf.py_func(update_op, [image_id,
groundtruth_boxes,
groundtruth_classes,
detection_boxes,
detection_scores,
detection_classes], [])
metric_names = ['DetectionBoxes_Precision/mAP',
'DetectionBoxes_Precision/mAP@.50IOU',
'DetectionBoxes_Precision/mAP@.75IOU',
'DetectionBoxes_Precision/mAP (large)',
'DetectionBoxes_Precision/mAP (medium)',
'DetectionBoxes_Precision/mAP (small)',
'DetectionBoxes_Recall/AR@1',
'DetectionBoxes_Recall/AR@10',
'DetectionBoxes_Recall/AR@100',
'DetectionBoxes_Recall/AR@100 (large)',
'DetectionBoxes_Recall/AR@100 (medium)',
'DetectionBoxes_Recall/AR@100 (small)']
for category_dict in self._categories:
metric_names.append('DetectionBoxes_PerformanceByCategory/mAP/' +
category_dict['name'])
def first_value_func():
self._metrics = self.evaluate()
self.clear()
return np.float32(self._metrics[metric_names[0]])
def value_func_factory(metric_name):
def value_func():
return np.float32(self._metrics[metric_name])
return value_func
first_value_op = tf.py_func(first_value_func, [], tf.float32)
eval_metric_ops = {metric_names[0]: (first_value_op, update_op)}
with tf.control_dependencies([first_value_op]):
for metric_name in metric_names[1:]:
eval_metric_ops[metric_name] = (tf.py_func(
value_func_factory(metric_name), [], np.float32), update_op)
return eval_metric_ops
def _check_mask_type_and_value(array_name, masks):
"""Checks whether mask dtype is uint8 anf the values are either 0 or 1."""
if masks.dtype != np.uint8:
raise ValueError('{} must be of type np.uint8. Found {}.'.format(
array_name, masks.dtype))
if np.any(np.logical_and(masks != 0, masks != 1)):
raise ValueError('{} elements can only be either 0 or 1.'.format(
array_name))
class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
"""Class to evaluate COCO detection metrics."""
def __init__(self, categories):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys -
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name e.g., 'cat', 'dog'.
"""
super(CocoMaskEvaluator, self).__init__(categories)
self._image_id_to_mask_shape_map = {}
self._image_ids_with_detections = set([])
self._groundtruth_list = []
self._detection_masks_list = []
self._category_id_set = set([cat['id'] for cat in self._categories])
self._annotation_id = 1
def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
self._image_id_to_mask_shape_map.clear()
self._image_ids_with_detections.clear()
self._groundtruth_list = []
self._detection_masks_list = []
def add_single_ground_truth_image_info(self,
image_id,
groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
groundtruth_dict: A dictionary containing -
InputDataFields.groundtruth_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
InputDataFields.groundtruth_classes: integer numpy array of shape
[num_boxes] containing 1-indexed groundtruth classes for the boxes.
InputDataFields.groundtruth_instance_masks: uint8 numpy array of shape
[num_boxes, image_height, image_width] containing groundtruth masks
corresponding to the boxes. The elements of the array must be in
{0, 1}.
"""
if image_id in self._image_id_to_mask_shape_map:
tf.logging.warning('Ignoring ground truth with image id %s since it was '
'previously added', image_id)
return
groundtruth_instance_masks = groundtruth_dict[
standard_fields.InputDataFields.groundtruth_instance_masks]
_check_mask_type_and_value(standard_fields.InputDataFields.
groundtruth_instance_masks,
groundtruth_instance_masks)
self._groundtruth_list.extend(
coco_tools.
ExportSingleImageGroundtruthToCoco(
image_id=image_id,
next_annotation_id=self._annotation_id,
category_id_set=self._category_id_set,
groundtruth_boxes=groundtruth_dict[standard_fields.InputDataFields.
groundtruth_boxes],
groundtruth_classes=groundtruth_dict[standard_fields.
InputDataFields.
groundtruth_classes],
groundtruth_masks=groundtruth_instance_masks))
self._annotation_id += groundtruth_dict[standard_fields.InputDataFields.
groundtruth_boxes].shape[0]
self._image_id_to_mask_shape_map[image_id] = groundtruth_dict[
standard_fields.InputDataFields.groundtruth_instance_masks].shape
def add_single_detected_image_info(self,
image_id,
detections_dict):
"""Adds detections for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing -
DetectionResultFields.detection_scores: float32 numpy array of shape
[num_boxes] containing detection scores for the boxes.
DetectionResultFields.detection_classes: integer numpy array of shape
[num_boxes] containing 1-indexed detection classes for the boxes.
DetectionResultFields.detection_masks: optional uint8 numpy array of
shape [num_boxes, image_height, image_width] containing instance
masks corresponding to the boxes. The elements of the array must be
in {0, 1}.
Raises:
ValueError: If groundtruth for the image_id is not available or if
spatial shapes of groundtruth_instance_masks and detection_masks are
incompatible.
"""
if image_id not in self._image_id_to_mask_shape_map:
raise ValueError('Missing groundtruth for image id: {}'.format(image_id))
if image_id in self._image_ids_with_detections:
tf.logging.warning('Ignoring detection with image id %s since it was '
'previously added', image_id)
return
groundtruth_masks_shape = self._image_id_to_mask_shape_map[image_id]
detection_masks = detections_dict[standard_fields.DetectionResultFields.
detection_masks]
if groundtruth_masks_shape[1:] != detection_masks.shape[1:]:
raise ValueError('Spatial shape of groundtruth masks and detection masks '
'are incompatible: {} vs {}'.format(
groundtruth_masks_shape,
detection_masks.shape))
_check_mask_type_and_value(standard_fields.DetectionResultFields.
detection_masks,
detection_masks)
self._detection_masks_list.extend(
coco_tools.ExportSingleImageDetectionMasksToCoco(
image_id=image_id,
category_id_set=self._category_id_set,
detection_masks=detection_masks,
detection_scores=detections_dict[standard_fields.
DetectionResultFields.
detection_scores],
detection_classes=detections_dict[standard_fields.
DetectionResultFields.
detection_classes]))
self._image_ids_with_detections.update([image_id])
def evaluate(self):
"""Evaluates the detection masks and returns a dictionary of coco metrics.
Returns:
A dictionary holding -
1. summary_metrics:
'Precision/mAP': mean average precision over classes averaged over IOU
thresholds ranging from .5 to .95 with .05 increments
'Precision/mAP@.50IOU': mean average precision at 50% IOU
'Precision/mAP@.75IOU': mean average precision at 75% IOU
'Precision/mAP (small)': mean average precision for small objects
(area < 32^2 pixels)
'Precision/mAP (medium)': mean average precision for medium sized
objects (32^2 pixels < area < 96^2 pixels)
'Precision/mAP (large)': mean average precision for large objects
(96^2 pixels < area < 10000^2 pixels)
'Recall/AR@1': average recall with 1 detection
'Recall/AR@10': average recall with 10 detections
'Recall/AR@100': average recall with 100 detections
'Recall/AR@100 (small)': average recall for small objects with 100
detections
'Recall/AR@100 (medium)': average recall for medium objects with 100
detections
'Recall/AR@100 (large)': average recall for large objects with 100
detections
2. per_category_ap: category specific results with keys of the form:
'Precision mAP ByCategory/category' (without the supercategory part if
no supercategories exist). For backward compatibility
'PerformanceByCategory' is included in the output regardless of
all_metrics_per_category.
"""
groundtruth_dict = {
'annotations': self._groundtruth_list,
'images': [{'id': image_id, 'height': shape[1], 'width': shape[2]}
for image_id, shape in self._image_id_to_mask_shape_map.
iteritems()],
'categories': self._categories
}
coco_wrapped_groundtruth = coco_tools.COCOWrapper(
groundtruth_dict, detection_type='segmentation')
coco_wrapped_detection_masks = coco_wrapped_groundtruth.LoadAnnotations(
self._detection_masks_list)
mask_evaluator = coco_tools.COCOEvalWrapper(
coco_wrapped_groundtruth, coco_wrapped_detection_masks,
agnostic_mode=False, iou_type='segm')
mask_metrics, mask_per_category_ap = mask_evaluator.ComputeMetrics()
mask_metrics.update(mask_per_category_ap)
mask_metrics = {'DetectionMasks_'+ key: value
for key, value in mask_metrics.iteritems()}
return mask_metrics
research/object_detection/metrics/coco_evaluation_test.py 0 → 100644
View file @ 7a9934df
"""Tests for image.understanding.object_detection.metrics.coco_evaluation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import tensorflow as tf
from object_detection.core import standard_fields
from object_detection.metrics import coco_evaluation
class CocoDetectionEvaluationTest(tf.test.TestCase):
def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
"""Tests that mAP is calculated correctly on GT and Detections."""
category_list = [{'id': 0, 'name': 'person'},
{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'}]
coco_evaluator = coco_evaluation.CocoDetectionEvaluator(category_list)
coco_evaluator.add_single_ground_truth_image_info(
image_id='image1',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1])
})
coco_evaluator.add_single_detected_image_info(
image_id='image1',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1])
})
coco_evaluator.add_single_ground_truth_image_info(
image_id='image2',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[50., 50., 100., 100.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1])
})
coco_evaluator.add_single_detected_image_info(
image_id='image2',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[50., 50., 100., 100.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1])
})
coco_evaluator.add_single_ground_truth_image_info(
image_id='image3',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[25., 25., 50., 50.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1])
})
coco_evaluator.add_single_detected_image_info(
image_id='image3',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[25., 25., 50., 50.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1])
})
metrics = coco_evaluator.evaluate()
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
def testReturnAllMetricsPerCategory(self):
"""Tests that mAP is calculated correctly on GT and Detections."""
category_list = [{'id': 0, 'name': 'person'}]
coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
category_list, all_metrics_per_category=True)
coco_evaluator.add_single_ground_truth_image_info(
image_id='image1',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1])
})
coco_evaluator.add_single_detected_image_info(
image_id='image1',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1])
})
metrics = coco_evaluator.evaluate()
expected_metrics = [
'DetectionBoxes_Recall AR@10 ByCategory/person',
'DetectionBoxes_Precision mAP (medium) ByCategory/person',
'DetectionBoxes_Precision mAP ByCategory/person',
'DetectionBoxes_Precision mAP@.50IOU ByCategory/person',
'DetectionBoxes_Precision mAP (small) ByCategory/person',
'DetectionBoxes_Precision mAP (large) ByCategory/person',
'DetectionBoxes_Recall AR@1 ByCategory/person',
'DetectionBoxes_Precision mAP@.75IOU ByCategory/person',
'DetectionBoxes_Recall AR@100 ByCategory/person',
'DetectionBoxes_Recall AR@100 (medium) ByCategory/person',
'DetectionBoxes_Recall AR@100 (large) ByCategory/person']
self.assertTrue(set(expected_metrics).issubset(set(metrics)))
def testRejectionOnDuplicateGroundtruth(self):
"""Tests that groundtruth cannot be added more than once for an image."""
categories = [{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'},
{'id': 3, 'name': 'elephant'}]
# Add groundtruth
coco_evaluator = coco_evaluation.CocoDetectionEvaluator(categories)
image_key1 = 'img1'
groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
dtype=float)
groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
coco_evaluator.add_single_ground_truth_image_info(image_key1, {
standard_fields.InputDataFields.groundtruth_boxes:
groundtruth_boxes1,
standard_fields.InputDataFields.groundtruth_classes:
groundtruth_class_labels1
})
groundtruth_lists_len = len(coco_evaluator._groundtruth_list)
# Add groundtruth with the same image id.
coco_evaluator.add_single_ground_truth_image_info(image_key1, {
standard_fields.InputDataFields.groundtruth_boxes:
groundtruth_boxes1,
standard_fields.InputDataFields.groundtruth_classes:
groundtruth_class_labels1
})
self.assertEqual(groundtruth_lists_len,
len(coco_evaluator._groundtruth_list))
def testRejectionOnDuplicateDetections(self):
"""Tests that detections cannot be added more than once for an image."""
categories = [{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'},
{'id': 3, 'name': 'elephant'}]
# Add groundtruth
coco_evaluator = coco_evaluation.CocoDetectionEvaluator(categories)
coco_evaluator.add_single_ground_truth_image_info(
image_id='image1',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[99., 100., 200., 200.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1])
})
coco_evaluator.add_single_detected_image_info(
image_id='image1',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1])
})
detections_lists_len = len(coco_evaluator._detection_boxes_list)
coco_evaluator.add_single_detected_image_info(
image_id='image1', # Note that this image id was previously added.
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1])
})
self.assertEqual(detections_lists_len,
len(coco_evaluator._detection_boxes_list))
def testExceptionRaisedWithMissingGroundtruth(self):
"""Tests that exception is raised for detection with missing groundtruth."""
categories = [{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'},
{'id': 3, 'name': 'elephant'}]
coco_evaluator = coco_evaluation.CocoDetectionEvaluator(categories)
with self.assertRaises(ValueError):
coco_evaluator.add_single_detected_image_info(
image_id='image1',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1])
})
class CocoEvaluationPyFuncTest(tf.test.TestCase):
def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
category_list = [{'id': 0, 'name': 'person'},
{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'}]
coco_evaluator = coco_evaluation.CocoDetectionEvaluator(category_list)
image_id = tf.placeholder(tf.string, shape=())
groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
detection_boxes = tf.placeholder(tf.float32, shape=(None, 4))
detection_scores = tf.placeholder(tf.float32, shape=(None))
detection_classes = tf.placeholder(tf.float32, shape=(None))
eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(
image_id, groundtruth_boxes,
groundtruth_classes,
detection_boxes,
detection_scores,
detection_classes)
_, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']
with self.test_session() as sess:
sess.run(update_op,
feed_dict={
image_id: 'image1',
groundtruth_boxes: np.array([[100., 100., 200., 200.]]),
groundtruth_classes: np.array([1]),
detection_boxes: np.array([[100., 100., 200., 200.]]),
detection_scores: np.array([.8]),
detection_classes: np.array([1])
})
sess.run(update_op,
feed_dict={
image_id: 'image2',
groundtruth_boxes: np.array([[50., 50., 100., 100.]]),
groundtruth_classes: np.array([3]),
detection_boxes: np.array([[50., 50., 100., 100.]]),
detection_scores: np.array([.7]),
detection_classes: np.array([3])
})
sess.run(update_op,
feed_dict={
image_id: 'image3',
groundtruth_boxes: np.array([[25., 25., 50., 50.]]),
groundtruth_classes: np.array([2]),
detection_boxes: np.array([[25., 25., 50., 50.]]),
detection_scores: np.array([.9]),
detection_classes: np.array([2])
})
metrics = {}
for key, (value_op, _) in eval_metric_ops.iteritems():
metrics[key] = value_op
metrics = sess.run(metrics)
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
-1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
-1.0)
self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
self.assertAlmostEqual(metrics[
'DetectionBoxes_PerformanceByCategory/mAP/dog'], 1.0)
self.assertAlmostEqual(metrics[
'DetectionBoxes_PerformanceByCategory/mAP/cat'], 1.0)
self.assertTrue(math.isnan(metrics[
'DetectionBoxes_PerformanceByCategory/mAP/person']))
self.assertFalse(coco_evaluator._groundtruth_list)
self.assertFalse(coco_evaluator._detection_boxes_list)
self.assertFalse(coco_evaluator._image_ids)
class CocoMaskEvaluationTest(tf.test.TestCase):
def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
category_list = [{'id': 0, 'name': 'person'},
{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'}]
coco_evaluator = coco_evaluation.CocoMaskEvaluator(category_list)
coco_evaluator.add_single_ground_truth_image_info(
image_id='image1',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
standard_fields.InputDataFields.groundtruth_instance_masks:
np.pad(np.ones([1, 100, 100], dtype=np.uint8),
((0, 0), (10, 10), (10, 10)), mode='constant')
})
coco_evaluator.add_single_detected_image_info(
image_id='image1',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[100., 100., 200., 200.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1]),
standard_fields.DetectionResultFields.detection_masks:
np.pad(np.ones([1, 100, 100], dtype=np.uint8),
((0, 0), (10, 10), (10, 10)), mode='constant')
})
coco_evaluator.add_single_ground_truth_image_info(
image_id='image2',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[50., 50., 100., 100.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
standard_fields.InputDataFields.groundtruth_instance_masks:
np.pad(np.ones([1, 50, 50], dtype=np.uint8),
((0, 0), (10, 10), (10, 10)), mode='constant')
})
coco_evaluator.add_single_detected_image_info(
image_id='image2',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[50., 50., 100., 100.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1]),
standard_fields.DetectionResultFields.detection_masks:
np.pad(np.ones([1, 50, 50], dtype=np.uint8),
((0, 0), (10, 10), (10, 10)), mode='constant')
})
coco_evaluator.add_single_ground_truth_image_info(
image_id='image3',
groundtruth_dict={
standard_fields.InputDataFields.groundtruth_boxes:
np.array([[25., 25., 50., 50.]]),
standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
standard_fields.InputDataFields.groundtruth_instance_masks:
np.pad(np.ones([1, 25, 25], dtype=np.uint8),
((0, 0), (10, 10), (10, 10)), mode='constant')
})
coco_evaluator.add_single_detected_image_info(
image_id='image3',
detections_dict={
standard_fields.DetectionResultFields.detection_boxes:
np.array([[25., 25., 50., 50.]]),
standard_fields.DetectionResultFields.detection_scores:
np.array([.8]),
standard_fields.DetectionResultFields.detection_classes:
np.array([1]),
standard_fields.DetectionResultFields.detection_masks:
np.pad(np.ones([1, 25, 25], dtype=np.uint8),
((0, 0), (10, 10), (10, 10)), mode='constant')
})
metrics = coco_evaluator.evaluate()
self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP'], 1.0)
coco_evaluator.clear()
self.assertFalse(coco_evaluator._image_id_to_mask_shape_map)
self.assertFalse(coco_evaluator._image_ids_with_detections)
self.assertFalse(coco_evaluator._groundtruth_list)
self.assertFalse(coco_evaluator._detection_masks_list)
if __name__ == '__main__':
tf.test.main()
research/object_detection/metrics/coco_tools.py 0 → 100644
View file @ 7a9934df
"""Wrappers for third party pycocotools to be used within i/u/object_detection.
Note that nothing in this file is tensorflow related and thus cannot
be called directly as a slim metric, for example.
TODO: wrap as a slim metric in metrics.py
Usage example: given a set of images with ids in the list image_ids
and corresponding lists of numpy arrays encoding groundtruth (boxes and classes)
and detections (boxes, scores and classes), where elements of each list
correspond to detections/annotations of a single image,
then evaluation (in multi-class mode) can be invoked as follows:
groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
image_ids, groundtruth_boxes_list, groundtruth_classes_list,
max_num_classes, output_path=None)
detections_list = coco_tools.ExportDetectionsToCOCO(
image_ids, detection_boxes_list, detection_scores_list,
detection_classes_list, output_path=None)
groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
detections = groundtruth.LoadAnnotations(detections_list)
evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
agnostic_mode=False)
metrics = evaluator.ComputeMetrics()
"""
from collections import OrderedDict
import copy
import time
import numpy as np
from pycocotools import coco
from pycocotools import cocoeval
from pycocotools import mask
import tensorflow as tf
from object_detection.utils import json_utils
class COCOWrapper(coco.COCO):
"""Wrapper for the pycocotools COCO class."""
def __init__(self, dataset, detection_type='bbox'):
"""COCOWrapper constructor.
See http://mscoco.org/dataset/#format for a description of the format.
By default, the coco.COCO class constructor reads from a JSON file.
This function duplicates the same behavior but loads from a dictionary,
allowing us to perform evaluation without writing to external storage.
Args:
dataset: a dictionary holding bounding box annotations in the COCO format.
detection_type: type of detections being wrapped. Can be one of ['bbox',
'segmentation']
Raises:
ValueError: if detection_type is unsupported.
"""
supported_detection_types = ['bbox', 'segmentation']
if detection_type not in supported_detection_types:
raise ValueError('Unsupported detection type: {}. '
'Supported values are: {}'.format(
detection_type, supported_detection_types))
self._detection_type = detection_type
coco.COCO.__init__(self)
self.dataset = dataset
self.createIndex()
def LoadAnnotations(self, annotations):
"""Load annotations dictionary into COCO datastructure.
See http://mscoco.org/dataset/#format for a description of the annotations
format. As above, this function replicates the default behavior of the API
but does not require writing to external storage.
Args:
annotations: python list holding object detection results where each
detection is encoded as a dict with required keys ['image_id',
'category_id', 'score'] and one of ['bbox', 'segmentation'] based on
`detection_type`.
Returns:
a coco.COCO datastructure holding object detection annotations results
Raises:
ValueError: if annotations is not a list
ValueError: if annotations do not correspond to the images contained
in self.
"""
results = coco.COCO()
results.dataset['images'] = [img for img in self.dataset['images']]
tf.logging.info('Loading and preparing annotation results...')
tic = time.time()
if not isinstance(annotations, list):
raise ValueError('annotations is not a list of objects')
annotation_img_ids = [ann['image_id'] for ann in annotations]
if (set(annotation_img_ids) != (set(annotation_img_ids)
& set(self.getImgIds()))):
raise ValueError('Results do not correspond to current coco set')
results.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
if self._detection_type == 'bbox':
for idx, ann in enumerate(annotations):
bb = ann['bbox']
ann['area'] = bb[2] * bb[3]
ann['id'] = idx + 1
ann['iscrowd'] = 0
elif self._detection_type == 'segmentation':
for idx, ann in enumerate(annotations):
ann['area'] = mask.area(ann['segmentation'])
ann['bbox'] = mask.toBbox(ann['segmentation'])
ann['id'] = idx + 1
ann['iscrowd'] = 0
tf.logging.info('DONE (t=%0.2fs)', (time.time() - tic))
results.dataset['annotations'] = annotations
results.createIndex()
return results
class COCOEvalWrapper(cocoeval.COCOeval):
"""Wrapper for the pycocotools COCOeval class.
To evaluate, create two objects (groundtruth_dict and detections_list)
using the conventions listed at http://mscoco.org/dataset/#format.
Then call evaluation as follows:
groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
detections = groundtruth.LoadAnnotations(detections_list)
evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
agnostic_mode=False)
metrics = evaluator.ComputeMetrics()
"""
def __init__(self, groundtruth=None, detections=None, agnostic_mode=False,
iou_type='bbox'):
"""COCOEvalWrapper constructor.
Note that for the area-based metrics to be meaningful, detection and
groundtruth boxes must be in image coordinates measured in pixels.
Args:
groundtruth: a coco.COCO (or coco_tools.COCOWrapper) object holding
groundtruth annotations
detections: a coco.COCO (or coco_tools.COCOWrapper) object holding
detections
agnostic_mode: boolean (default: False). If True, evaluation ignores
class labels, treating all detections as proposals.
iou_type: IOU type to use for evaluation. Supports `bbox` or `segm`.
"""
cocoeval.COCOeval.__init__(self, groundtruth, detections,
iouType=iou_type)
if agnostic_mode:
self.params.useCats = 0
def GetCategory(self, category_id):
"""Fetches dictionary holding category information given category id.
Args:
category_id: integer id
Returns:
dictionary holding 'id', 'name'.
"""
return self.cocoGt.cats[category_id]
def GetAgnosticMode(self):
"""Returns true if COCO Eval is configured to evaluate in agnostic mode."""
return self.params.useCats == 0
def GetCategoryIdList(self):
"""Returns list of valid category ids."""
return self.params.catIds
def ComputeMetrics(self, all_metrics_per_category=False):
"""Computes detection metrics.
Args:
all_metrics_per_category: If true, include all the summery metrics for
each category in per_category_ap. Be careful with setting it to true if
you have more than handful of categories, because it will pollute
your mldash.
Returns:
1. summary_metrics: a dictionary holding:
'Precision/mAP': mean average precision over classes averaged over IOU
thresholds ranging from .5 to .95 with .05 increments
'Precision/mAP@.50IOU': mean average precision at 50% IOU
'Precision/mAP@.75IOU': mean average precision at 75% IOU
'Precision/mAP (small)': mean average precision for small objects
(area < 32^2 pixels)
'Precision/mAP (medium)': mean average precision for medium sized
objects (32^2 pixels < area < 96^2 pixels)
'Precision/mAP (large)': mean average precision for large objects
(96^2 pixels < area < 10000^2 pixels)
'Recall/AR@1': average recall with 1 detection
'Recall/AR@10': average recall with 10 detections
'Recall/AR@100': average recall with 100 detections
'Recall/AR@100 (small)': average recall for small objects with 100
detections
'Recall/AR@100 (medium)': average recall for medium objects with 100
detections
'Recall/AR@100 (large)': average recall for large objects with 100
detections
2. per_category_ap: a dictionary holding category specific results with
keys of the form: 'Precision mAP ByCategory/category'
(without the supercategory part if no supercategories exist).
For backward compatibility 'PerformanceByCategory' is included in the
output regardless of all_metrics_per_category.
If evaluating class-agnostic mode, per_category_ap is an empty
dictionary.
"""
self.evaluate()
self.accumulate()
self.summarize()
summary_metrics = OrderedDict([
('Precision/mAP', self.stats[0]),
('Precision/mAP@.50IOU', self.stats[1]),
('Precision/mAP@.75IOU', self.stats[2]),
('Precision/mAP (small)', self.stats[3]),
('Precision/mAP (medium)', self.stats[4]),
('Precision/mAP (large)', self.stats[5]),
('Recall/AR@1', self.stats[6]),
('Recall/AR@10', self.stats[7]),
('Recall/AR@100', self.stats[8]),
('Recall/AR@100 (small)', self.stats[9]),
('Recall/AR@100 (medium)', self.stats[10]),
('Recall/AR@100 (large)', self.stats[11])
])
per_category_ap = OrderedDict([])
if self.GetAgnosticMode():
return summary_metrics, per_category_ap
for category_index, category_id in enumerate(self.GetCategoryIdList()):
category = self.GetCategory(category_id)['name']
# Kept for backward compatilbility
per_category_ap['PerformanceByCategory/mAP/{}'.format(
category)] = self.category_stats[0][category_index]
if all_metrics_per_category:
per_category_ap['Precision mAP ByCategory/{}'.format(
category)] = self.category_stats[0][category_index]
per_category_ap['Precision mAP@.50IOU ByCategory/{}'.format(
category)] = self.category_stats[1][category_index]
per_category_ap['Precision mAP@.75IOU ByCategory/{}'.format(
category)] = self.category_stats[2][category_index]
per_category_ap['Precision mAP (small) ByCategory/{}'.format(
category)] = self.category_stats[3][category_index]
per_category_ap['Precision mAP (medium) ByCategory/{}'.format(
category)] = self.category_stats[4][category_index]
per_category_ap['Precision mAP (large) ByCategory/{}'.format(
category)] = self.category_stats[5][category_index]
per_category_ap['Recall AR@1 ByCategory/{}'.format(
category)] = self.category_stats[6][category_index]
per_category_ap['Recall AR@10 ByCategory/{}'.format(
category)] = self.category_stats[7][category_index]
per_category_ap['Recall AR@100 ByCategory/{}'.format(
category)] = self.category_stats[8][category_index]
per_category_ap['Recall AR@100 (small) ByCategory/{}'.format(
category)] = self.category_stats[9][category_index]
per_category_ap['Recall AR@100 (medium) ByCategory/{}'.format(
category)] = self.category_stats[10][category_index]
per_category_ap['Recall AR@100 (large) ByCategory/{}'.format(
category)] = self.category_stats[11][category_index]
return summary_metrics, per_category_ap
def _ConvertBoxToCOCOFormat(box):
"""Converts a box in [ymin, xmin, ymax, xmax] format to COCO format.
This is a utility function for converting from our internal
[ymin, xmin, ymax, xmax] convention to the convention used by the COCO API
i.e., [xmin, ymin, width, height].
Args:
box: a [ymin, xmin, ymax, xmax] numpy array
Returns:
a list of floats representing [xmin, ymin, width, height]
"""
return [float(box[1]), float(box[0]), float(box[3] - box[1]),
float(box[2] - box[0])]
def _RleCompress(masks):
"""Compresses mask using Run-length encoding provided by pycocotools.
Args:
masks: uint8 numpy array of shape [mask_height, mask_width] with values in
{0, 1}.
Returns:
A pycocotools Run-length encoding of the mask.
"""
return mask.encode(np.asfortranarray(masks))
def ExportSingleImageGroundtruthToCoco(image_id,
next_annotation_id,
category_id_set,
groundtruth_boxes,
groundtruth_classes,
groundtruth_masks=None):
"""Export groundtruth of a single image to COCO format.
This function converts groundtruth detection annotations represented as numpy
arrays to dictionaries that can be ingested by the COCO evaluation API. Note
that the image_ids provided here must match the ones given to
ExportSingleImageDetectionsToCoco. We assume that boxes and classes are in
correspondence - that is: groundtruth_boxes[i, :], and
groundtruth_classes[i] are associated with the same groundtruth annotation.
In the exported result, "area" fields are always set to the area of the
groundtruth bounding box and "iscrowd" fields are always set to 0.
TODO: pass in "iscrowd" array for evaluating on COCO dataset.
Args:
image_id: a unique image identifier either of type integer or string.
next_annotation_id: integer specifying the first id to use for the
groundtruth annotations. All annotations are assigned a continuous integer
id starting from this value.
category_id_set: A set of valid class ids. Groundtruth with classes not in
category_id_set are dropped.
groundtruth_boxes: numpy array (float32) with shape [num_gt_boxes, 4]
groundtruth_classes: numpy array (int) with shape [num_gt_boxes]
groundtruth_masks: optional uint8 numpy array of shape [num_detections,
image_height, image_width] containing detection_masks.
Returns:
a list of groundtruth annotations for a single image in the COCO format.
Raises:
ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
right lengths or (2) if each of the elements inside these lists do not
have the correct shapes or (3) if image_ids are not integers
"""
if len(groundtruth_classes.shape) != 1:
raise ValueError('groundtruth_classes is '
'expected to be of rank 1.')
if len(groundtruth_boxes.shape) != 2:
raise ValueError('groundtruth_boxes is expected to be of '
'rank 2.')
if groundtruth_boxes.shape[1] != 4:
raise ValueError('groundtruth_boxes should have '
'shape[1] == 4.')
num_boxes = groundtruth_classes.shape[0]
if num_boxes != groundtruth_boxes.shape[0]:
raise ValueError('Corresponding entries in groundtruth_classes, '
'and groundtruth_boxes should have '
'compatible shapes (i.e., agree on the 0th dimension).'
'Classes shape: %d. Boxes shape: %d. Image ID: %s' % (
groundtruth_classes.shape[0],
groundtruth_boxes.shape[0], image_id))
groundtruth_list = []
for i in range(num_boxes):
if groundtruth_classes[i] in category_id_set:
export_dict = {
'id': next_annotation_id + i,
'image_id': image_id,
'category_id': int(groundtruth_classes[i]),
'bbox': list(_ConvertBoxToCOCOFormat(groundtruth_boxes[i, :])),
'area': float((groundtruth_boxes[i, 2] - groundtruth_boxes[i, 0]) *
(groundtruth_boxes[i, 3] - groundtruth_boxes[i, 1])),
'iscrowd': 0
}
if groundtruth_masks is not None:
export_dict['segmentation'] = _RleCompress(groundtruth_masks[i])
groundtruth_list.append(export_dict)
return groundtruth_list
def ExportGroundtruthToCOCO(image_ids,
groundtruth_boxes,
groundtruth_classes,
categories,
output_path=None):
"""Export groundtruth detection annotations in numpy arrays to COCO API.
This function converts a set of groundtruth detection annotations represented
as numpy arrays to dictionaries that can be ingested by the COCO API.
Inputs to this function are three lists: image ids for each groundtruth image,
groundtruth boxes for each image and groundtruth classes respectively.
Note that the image_ids provided here must match the ones given to the
ExportDetectionsToCOCO function in order for evaluation to work properly.
We assume that for each image, boxes, scores and classes are in
correspondence --- that is: image_id[i], groundtruth_boxes[i, :] and
groundtruth_classes[i] are associated with the same groundtruth annotation.
In the exported result, "area" fields are always set to the area of the
groundtruth bounding box and "iscrowd" fields are always set to 0.
TODO: pass in "iscrowd" array for evaluating on COCO dataset.
Args:
image_ids: a list of unique image identifier either of type integer or
string.
groundtruth_boxes: list of numpy arrays with shape [num_gt_boxes, 4]
(note that num_gt_boxes can be different for each entry in the list)
groundtruth_classes: list of numpy arrays (int) with shape [num_gt_boxes]
(note that num_gt_boxes can be different for each entry in the list)
categories: a list of dictionaries representing all possible categories.
Each dict in this list has the following keys:
'id': (required) an integer id uniquely identifying this category
'name': (required) string representing category name
e.g., 'cat', 'dog', 'pizza'
'supercategory': (optional) string representing the supercategory
e.g., 'animal', 'vehicle', 'food', etc
output_path: (optional) path for exporting result to JSON
Returns:
dictionary that can be read by COCO API
Raises:
ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
right lengths or (2) if each of the elements inside these lists do not
have the correct shapes or (3) if image_ids are not integers
"""
category_id_set = set([cat['id'] for cat in categories])
groundtruth_export_list = []
image_export_list = []
if not len(image_ids) == len(groundtruth_boxes) == len(groundtruth_classes):
raise ValueError('Input lists must have the same length')
# For reasons internal to the COCO API, it is important that annotation ids
# are not equal to zero; we thus start counting from 1.
annotation_id = 1
for image_id, boxes, classes in zip(image_ids, groundtruth_boxes,
groundtruth_classes):
image_export_list.append({'id': image_id})
groundtruth_export_list.extend(ExportSingleImageGroundtruthToCoco(
image_id,
annotation_id,
category_id_set,
boxes,
classes))
num_boxes = classes.shape[0]
annotation_id += num_boxes
groundtruth_dict = {
'annotations': groundtruth_export_list,
'images': image_export_list,
'categories': categories
}
if output_path:
with tf.gfile.GFile(output_path, 'w') as fid:
json_utils.Dump(groundtruth_dict, fid, float_digits=4, indent=2)
return groundtruth_dict
def ExportSingleImageDetectionBoxesToCoco(image_id,
category_id_set,
detection_boxes,
detection_scores,
detection_classes):
"""Export detections of a single image to COCO format.
This function converts detections represented as numpy arrays to dictionaries
that can be ingested by the COCO evaluation API. Note that the image_ids
provided here must match the ones given to the
ExporSingleImageDetectionBoxesToCoco. We assume that boxes, and classes are in
correspondence - that is: boxes[i, :], and classes[i]
are associated with the same groundtruth annotation.
Args:
image_id: unique image identifier either of type integer or string.
category_id_set: A set of valid class ids. Detections with classes not in
category_id_set are dropped.
detection_boxes: float numpy array of shape [num_detections, 4] containing
detection boxes.
detection_scores: float numpy array of shape [num_detections] containing
scored for the detection boxes.
detection_classes: integer numpy array of shape [num_detections] containing
the classes for detection boxes.
Returns:
a list of detection annotations for a single image in the COCO format.
Raises:
ValueError: if (1) detection_boxes, detection_scores and detection_classes
do not have the right lengths or (2) if each of the elements inside these
lists do not have the correct shapes or (3) if image_ids are not integers.
"""
if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
raise ValueError('All entries in detection_classes and detection_scores'
'expected to be of rank 1.')
if len(detection_boxes.shape) != 2:
raise ValueError('All entries in detection_boxes expected to be of '
'rank 2.')
if detection_boxes.shape[1] != 4:
raise ValueError('All entries in detection_boxes should have '
'shape[1] == 4.')
num_boxes = detection_classes.shape[0]
if not num_boxes == detection_boxes.shape[0] == detection_scores.shape[0]:
raise ValueError('Corresponding entries in detection_classes, '
'detection_scores and detection_boxes should have '
'compatible shapes (i.e., agree on the 0th dimension). '
'Classes shape: %d. Boxes shape: %d. '
'Scores shape: %d' % (
detection_classes.shape[0], detection_boxes.shape[0],
detection_scores.shape[0]
))
detections_list = []
for i in range(num_boxes):
if detection_classes[i] in category_id_set:
detections_list.append({
'image_id': image_id,
'category_id': int(detection_classes[i]),
'bbox': list(_ConvertBoxToCOCOFormat(detection_boxes[i, :])),
'score': float(detection_scores[i])
})
return detections_list
def ExportSingleImageDetectionMasksToCoco(image_id,
category_id_set,
detection_masks,
detection_scores,
detection_classes):
"""Export detection masks of a single image to COCO format.
This function converts detections represented as numpy arrays to dictionaries
that can be ingested by the COCO evaluation API. We assume that
detection_masks, detection_scores, and detection_classes are in correspondence
- that is: detection_masks[i, :], detection_classes[i] and detection_scores[i]
are associated with the same annotation.
Args:
image_id: unique image identifier either of type integer or string.
category_id_set: A set of valid class ids. Detections with classes not in
category_id_set are dropped.
detection_masks: uint8 numpy array of shape [num_detections, image_height,
image_width] containing detection_masks.
detection_scores: float numpy array of shape [num_detections] containing
scores for detection masks.
detection_classes: integer numpy array of shape [num_detections] containing
the classes for detection masks.
Returns:
a list of detection mask annotations for a single image in the COCO format.
Raises:
ValueError: if (1) detection_masks, detection_scores and detection_classes
do not have the right lengths or (2) if each of the elements inside these
lists do not have the correct shapes or (3) if image_ids are not integers.
"""
if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
raise ValueError('All entries in detection_classes and detection_scores'
'expected to be of rank 1.')
num_boxes = detection_classes.shape[0]
if not num_boxes == len(detection_masks) == detection_scores.shape[0]:
raise ValueError('Corresponding entries in detection_classes, '
'detection_scores and detection_masks should have '
'compatible lengths and shapes '
'Classes length: %d. Masks length: %d. '
'Scores length: %d' % (
detection_classes.shape[0], len(detection_masks),
detection_scores.shape[0]
))
detections_list = []
for i in range(num_boxes):
if detection_classes[i] in category_id_set:
detections_list.append({
'image_id': image_id,
'category_id': int(detection_classes[i]),
'segmentation': _RleCompress(detection_masks[i]),
'score': float(detection_scores[i])
})
return detections_list
def ExportDetectionsToCOCO(image_ids,
detection_boxes,
detection_scores,
detection_classes,
categories,
output_path=None):
"""Export detection annotations in numpy arrays to COCO API.
This function converts a set of predicted detections represented
as numpy arrays to dictionaries that can be ingested by the COCO API.
Inputs to this function are lists, consisting of boxes, scores and
classes, respectively, corresponding to each image for which detections
have been produced. Note that the image_ids provided here must
match the ones given to the ExportGroundtruthToCOCO function in order
for evaluation to work properly.
We assume that for each image, boxes, scores and classes are in
correspondence --- that is: detection_boxes[i, :], detection_scores[i] and
detection_classes[i] are associated with the same detection.
Args:
image_ids: a list of unique image identifier either of type integer or
string.
detection_boxes: list of numpy arrays with shape [num_detection_boxes, 4]
detection_scores: list of numpy arrays (float) with shape
[num_detection_boxes]. Note that num_detection_boxes can be different
for each entry in the list.
detection_classes: list of numpy arrays (int) with shape
[num_detection_boxes]. Note that num_detection_boxes can be different
for each entry in the list.
categories: a list of dictionaries representing all possible categories.
Each dict in this list must have an integer 'id' key uniquely identifying
this category.
output_path: (optional) path for exporting result to JSON
Returns:
list of dictionaries that can be read by COCO API, where each entry
corresponds to a single detection and has keys from:
['image_id', 'category_id', 'bbox', 'score'].
Raises:
ValueError: if (1) detection_boxes and detection_classes do not have the
right lengths or (2) if each of the elements inside these lists do not
have the correct shapes or (3) if image_ids are not integers.
"""
category_id_set = set([cat['id'] for cat in categories])
detections_export_list = []
if not (len(image_ids) == len(detection_boxes) == len(detection_scores) ==
len(detection_classes)):
raise ValueError('Input lists must have the same length')
for image_id, boxes, scores, classes in zip(image_ids, detection_boxes,
detection_scores,
detection_classes):
detections_export_list.extend(ExportSingleImageDetectionBoxesToCoco(
image_id,
category_id_set,
boxes,
scores,
classes))
if output_path:
with tf.gfile.GFile(output_path, 'w') as fid:
json_utils.Dump(detections_export_list, fid, float_digits=4, indent=2)
return detections_export_list
def ExportSegmentsToCOCO(image_ids,
detection_masks,
detection_scores,
detection_classes,
categories,
output_path=None):
"""Export segmentation masks in numpy arrays to COCO API.
This function converts a set of predicted instance masks represented
as numpy arrays to dictionaries that can be ingested by the COCO API.
Inputs to this function are lists, consisting of segments, scores and
classes, respectively, corresponding to each image for which detections
have been produced.
Note this function is recommended to use for small dataset.
For large dataset, it should be used with a merge function
(e.g. in map reduce), otherwise the memory consumption is large.
We assume that for each image, masks, scores and classes are in
correspondence --- that is: detection_masks[i, :, :, :], detection_scores[i]
and detection_classes[i] are associated with the same detection.
Args:
image_ids: list of image ids (typically ints or strings)
detection_masks: list of numpy arrays with shape [num_detection, h, w, 1]
and type uint8. The height and width should match the shape of
corresponding image.
detection_scores: list of numpy arrays (float) with shape
[num_detection]. Note that num_detection can be different
for each entry in the list.
detection_classes: list of numpy arrays (int) with shape
[num_detection]. Note that num_detection can be different
for each entry in the list.
categories: a list of dictionaries representing all possible categories.
Each dict in this list must have an integer 'id' key uniquely identifying
this category.
output_path: (optional) path for exporting result to JSON
Returns:
list of dictionaries that can be read by COCO API, where each entry
corresponds to a single detection and has keys from:
['image_id', 'category_id', 'segmentation', 'score'].
Raises:
ValueError: if detection_masks and detection_classes do not have the
right lengths or if each of the elements inside these lists do not
have the correct shapes.
"""
if not (len(image_ids) == len(detection_masks) == len(detection_scores) ==
len(detection_classes)):
raise ValueError('Input lists must have the same length')
segment_export_list = []
for image_id, masks, scores, classes in zip(image_ids, detection_masks,
detection_scores,
detection_classes):
if len(classes.shape) != 1 or len(scores.shape) != 1:
raise ValueError('All entries in detection_classes and detection_scores'
'expected to be of rank 1.')
if len(masks.shape) != 4:
raise ValueError('All entries in masks expected to be of '
'rank 4. Given {}'.format(masks.shape))
num_boxes = classes.shape[0]
if not num_boxes == masks.shape[0] == scores.shape[0]:
raise ValueError('Corresponding entries in segment_classes, '
'detection_scores and detection_boxes should have '
'compatible shapes (i.e., agree on the 0th dimension).')
category_id_set = set([cat['id'] for cat in categories])
segment_export_list.extend(ExportSingleImageDetectionMasksToCoco(
image_id, category_id_set, np.squeeze(masks, axis=3), scores, classes))
if output_path:
with tf.gfile.GFile(output_path, 'w') as fid:
json_utils.Dump(segment_export_list, fid, float_digits=4, indent=2)
return segment_export_list
def ExportKeypointsToCOCO(image_ids,
detection_keypoints,
detection_scores,
detection_classes,
categories,
output_path=None):
"""Exports keypoints in numpy arrays to COCO API.
This function converts a set of predicted keypoints represented
as numpy arrays to dictionaries that can be ingested by the COCO API.
Inputs to this function are lists, consisting of keypoints, scores and
classes, respectively, corresponding to each image for which detections
have been produced.
We assume that for each image, keypoints, scores and classes are in
correspondence --- that is: detection_keypoints[i, :, :, :],
detection_scores[i] and detection_classes[i] are associated with the same
detection.
Args:
image_ids: list of image ids (typically ints or strings)
detection_keypoints: list of numpy arrays with shape
[num_detection, num_keypoints, 2] and type float32 in absolute
x-y coordinates.
detection_scores: list of numpy arrays (float) with shape
[num_detection]. Note that num_detection can be different
for each entry in the list.
detection_classes: list of numpy arrays (int) with shape
[num_detection]. Note that num_detection can be different
for each entry in the list.
categories: a list of dictionaries representing all possible categories.
Each dict in this list must have an integer 'id' key uniquely identifying
this category and an integer 'num_keypoints' key specifying the number of
keypoints the category has.
output_path: (optional) path for exporting result to JSON
Returns:
list of dictionaries that can be read by COCO API, where each entry
corresponds to a single detection and has keys from:
['image_id', 'category_id', 'keypoints', 'score'].
Raises:
ValueError: if detection_keypoints and detection_classes do not have the
right lengths or if each of the elements inside these lists do not
have the correct shapes.
"""
if not (len(image_ids) == len(detection_keypoints) ==
len(detection_scores) == len(detection_classes)):
raise ValueError('Input lists must have the same length')
keypoints_export_list = []
for image_id, keypoints, scores, classes in zip(
image_ids, detection_keypoints, detection_scores, detection_classes):
if len(classes.shape) != 1 or len(scores.shape) != 1:
raise ValueError('All entries in detection_classes and detection_scores'
'expected to be of rank 1.')
if len(keypoints.shape) != 3:
raise ValueError('All entries in keypoints expected to be of '
'rank 3. Given {}'.format(keypoints.shape))
num_boxes = classes.shape[0]
if not num_boxes == keypoints.shape[0] == scores.shape[0]:
raise ValueError('Corresponding entries in detection_classes, '
'detection_keypoints, and detection_scores should have '
'compatible shapes (i.e., agree on the 0th dimension).')
category_id_set = set([cat['id'] for cat in categories])
category_id_to_num_keypoints_map = {
cat['id']: cat['num_keypoints'] for cat in categories
if 'num_keypoints' in cat}
for i in range(num_boxes):
if classes[i] not in category_id_set:
raise ValueError('class id should be in category_id_set\n')
if classes[i] in category_id_to_num_keypoints_map:
num_keypoints = category_id_to_num_keypoints_map[classes[i]]
# Adds extra ones to indicate the visibility for each keypoint as is
# recommended by MSCOCO.
instance_keypoints = np.concatenate(
[keypoints[i, 0:num_keypoints, :],
np.expand_dims(np.ones(num_keypoints), axis=1)],
axis=1).astype(int)
instance_keypoints = instance_keypoints.flatten().tolist()
keypoints_export_list.append({
'image_id': image_id,
'category_id': int(classes[i]),
'keypoints': instance_keypoints,
'score': float(scores[i])
})
if output_path:
with tf.gfile.GFile(output_path, 'w') as fid:
json_utils.Dump(keypoints_export_list, fid, float_digits=4, indent=2)
return keypoints_export_list
research/object_detection/metrics/coco_tools_test.py 0 → 100644
View file @ 7a9934df
"""Tests for google3.image.understanding.object_detection.metrics.coco_tools."""
import json
import os
import re
import numpy as np
from pycocotools import mask
import tensorflow as tf
from object_detection.metrics import coco_tools
class CocoToolsTest(tf.test.TestCase):
def setUp(self):
groundtruth_annotations_list = [
{
'id': 1,
'image_id': 'first',
'category_id': 1,
'bbox': [100., 100., 100., 100.],
'area': 100.**2,
'iscrowd': 0
},
{
'id': 2,
'image_id': 'second',
'category_id': 1,
'bbox': [50., 50., 50., 50.],
'area': 50.**2,
'iscrowd': 0
},
]
image_list = [{'id': 'first'}, {'id': 'second'}]
category_list = [{'id': 0, 'name': 'person'},
{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'}]
self._groundtruth_dict = {
'annotations': groundtruth_annotations_list,
'images': image_list,
'categories': category_list
}
self._detections_list = [
{
'image_id': 'first',
'category_id': 1,
'bbox': [100., 100., 100., 100.],
'score': .8
},
{
'image_id': 'second',
'category_id': 1,
'bbox': [50., 50., 50., 50.],
'score': .7
},
]
def testCocoWrappers(self):
groundtruth = coco_tools.COCOWrapper(self._groundtruth_dict)
detections = groundtruth.LoadAnnotations(self._detections_list)
evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections)
summary_metrics, _ = evaluator.ComputeMetrics()
self.assertAlmostEqual(1.0, summary_metrics['Precision/mAP'])
def testExportGroundtruthToCOCO(self):
image_ids = ['first', 'second']
groundtruth_boxes = [np.array([[100, 100, 200, 200]], np.float),
np.array([[50, 50, 100, 100]], np.float)]
groundtruth_classes = [np.array([1], np.int32), np.array([1], np.int32)]
categories = [{'id': 0, 'name': 'person'},
{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'}]
output_path = os.path.join(tf.test.get_temp_dir(), 'groundtruth.json')
result = coco_tools.ExportGroundtruthToCOCO(
image_ids,
groundtruth_boxes,
groundtruth_classes,
categories,
output_path=output_path)
self.assertDictEqual(result, self._groundtruth_dict)
with tf.gfile.GFile(output_path, 'r') as f:
written_result = f.read()
# The json output should have floats written to 4 digits of precision.
matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE)
self.assertTrue(matcher.findall(written_result))
written_result = json.loads(written_result)
self.assertAlmostEqual(result, written_result)
def testExportDetectionsToCOCO(self):
image_ids = ['first', 'second']
detections_boxes = [np.array([[100, 100, 200, 200]], np.float),
np.array([[50, 50, 100, 100]], np.float)]
detections_scores = [np.array([.8], np.float), np.array([.7], np.float)]
detections_classes = [np.array([1], np.int32), np.array([1], np.int32)]
categories = [{'id': 0, 'name': 'person'},
{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'}]
output_path = os.path.join(tf.test.get_temp_dir(), 'detections.json')
result = coco_tools.ExportDetectionsToCOCO(
image_ids,
detections_boxes,
detections_scores,
detections_classes,
categories,
output_path=output_path)
self.assertListEqual(result, self._detections_list)
with tf.gfile.GFile(output_path, 'r') as f:
written_result = f.read()
# The json output should have floats written to 4 digits of precision.
matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE)
self.assertTrue(matcher.findall(written_result))
written_result = json.loads(written_result)
self.assertAlmostEqual(result, written_result)
def testExportSegmentsToCOCO(self):
image_ids = ['first', 'second']
detection_masks = [np.array(
[[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]],
dtype=np.uint8), np.array(
[[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]],
dtype=np.uint8)]
for i, detection_mask in enumerate(detection_masks):
detection_masks[i] = detection_mask[:, :, :, None]
detection_scores = [np.array([.8], np.float), np.array([.7], np.float)]
detection_classes = [np.array([1], np.int32), np.array([1], np.int32)]
categories = [{'id': 0, 'name': 'person'},
{'id': 1, 'name': 'cat'},
{'id': 2, 'name': 'dog'}]
output_path = os.path.join(tf.test.get_temp_dir(), 'segments.json')
result = coco_tools.ExportSegmentsToCOCO(
image_ids,
detection_masks,
detection_scores,
detection_classes,
categories,
output_path=output_path)
with tf.gfile.GFile(output_path, 'r') as f:
written_result = f.read()
written_result = json.loads(written_result)
mask_load = mask.decode([written_result[0]['segmentation']])
self.assertTrue(np.allclose(mask_load, detection_masks[0]))
self.assertAlmostEqual(result, written_result)
def testExportKeypointsToCOCO(self):
image_ids = ['first', 'second']
detection_keypoints = [
np.array(
[[[100, 200], [300, 400], [500, 600]],
[[50, 150], [250, 350], [450, 550]]], dtype=np.int32),
np.array(
[[[110, 210], [310, 410], [510, 610]],
[[60, 160], [260, 360], [460, 560]]], dtype=np.int32)]
detection_scores = [np.array([.8, 0.2], np.float),
np.array([.7, 0.3], np.float)]
detection_classes = [np.array([1, 1], np.int32), np.array([1, 1], np.int32)]
categories = [{'id': 1, 'name': 'person', 'num_keypoints': 3},
{'id': 2, 'name': 'cat'},
{'id': 3, 'name': 'dog'}]
output_path = os.path.join(tf.test.get_temp_dir(), 'keypoints.json')
result = coco_tools.ExportKeypointsToCOCO(
image_ids,
detection_keypoints,
detection_scores,
detection_classes,
categories,
output_path=output_path)
with tf.gfile.GFile(output_path, 'r') as f:
written_result = f.read()
written_result = json.loads(written_result)
self.assertAlmostEqual(result, written_result)
def testSingleImageDetectionBoxesExport(self):
boxes = np.array([[0, 0, 1, 1],
[0, 0, .5, .5],
[.5, .5, 1, 1]], dtype=np.float32)
classes = np.array([1, 2, 3], dtype=np.int32)
scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
coco_boxes = np.array([[0, 0, 1, 1],
[0, 0, .5, .5],
[.5, .5, .5, .5]], dtype=np.float32)
coco_annotations = coco_tools.ExportSingleImageDetectionBoxesToCoco(
image_id='first_image',
category_id_set=set([1, 2, 3]),
detection_boxes=boxes,
detection_classes=classes,
detection_scores=scores)
for i, annotation in enumerate(coco_annotations):
self.assertEqual(annotation['image_id'], 'first_image')
self.assertEqual(annotation['category_id'], classes[i])
self.assertAlmostEqual(annotation['score'], scores[i])
self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
def testSingleImageDetectionMaskExport(self):
masks = np.array(
[[[1, 1,], [1, 1]],
[[0, 0], [0, 1]],
[[0, 0], [0, 0]]], dtype=np.uint8)
classes = np.array([1, 2, 3], dtype=np.int32)
scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
coco_annotations = coco_tools.ExportSingleImageDetectionMasksToCoco(
image_id='first_image',
category_id_set=set([1, 2, 3]),
detection_classes=classes,
detection_scores=scores,
detection_masks=masks)
expected_counts = ['04', '31', '4']
for i, mask_annotation in enumerate(coco_annotations):
self.assertEqual(mask_annotation['segmentation']['counts'],
expected_counts[i])
self.assertTrue(np.all(np.equal(mask.decode(
mask_annotation['segmentation']), masks[i])))
self.assertEqual(mask_annotation['image_id'], 'first_image')
self.assertEqual(mask_annotation['category_id'], classes[i])
self.assertAlmostEqual(mask_annotation['score'], scores[i])
def testSingleImageGroundtruthExport(self):
masks = np.array(
[[[1, 1,], [1, 1]],
[[0, 0], [0, 1]],
[[0, 0], [0, 0]]], dtype=np.uint8)
boxes = np.array([[0, 0, 1, 1],
[0, 0, .5, .5],
[.5, .5, 1, 1]], dtype=np.float32)
coco_boxes = np.array([[0, 0, 1, 1],
[0, 0, .5, .5],
[.5, .5, .5, .5]], dtype=np.float32)
classes = np.array([1, 2, 3], dtype=np.int32)
next_annotation_id = 1
coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
image_id='first_image',
category_id_set=set([1, 2, 3]),
next_annotation_id=next_annotation_id,
groundtruth_boxes=boxes,
groundtruth_classes=classes,
groundtruth_masks=masks)
expected_counts = ['04', '31', '4']
for i, annotation in enumerate(coco_annotations):
self.assertEqual(annotation['segmentation']['counts'],
expected_counts[i])
self.assertTrue(np.all(np.equal(mask.decode(
annotation['segmentation']), masks[i])))
self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
self.assertEqual(annotation['image_id'], 'first_image')
self.assertEqual(annotation['category_id'], classes[i])
self.assertEqual(annotation['id'], i + next_annotation_id)
if __name__ == '__main__':
tf.test.main()
research/object_detection/metrics/offline_eval_map_corloc.py
View file @ 7a9934df
...@@ -22,7 +22,7 @@ The evaluation metrics set is supplied in object_detection.protos.EvalConfig ...@@ -22,7 +22,7 @@ The evaluation metrics set is supplied in object_detection.protos.EvalConfig
in metrics_set field. in metrics_set field.
Currently two set of metrics are supported: Currently two set of metrics are supported:
- pascal_voc_metrics: standard PASCAL VOC 2007 metric - pascal_voc_metrics: standard PASCAL VOC 2007 metric
- open_images_metrics: Open Image V2 metric - open_images_detection_metrics: Open Image V2 metric
All other field of object_detection.protos.EvalConfig are ignored. All other field of object_detection.protos.EvalConfig are ignored.
Example usage: Example usage:
......
research/object_detection/models/BUILD
View file @ 7a9934df
...@@ -15,6 +15,7 @@ py_library( ...@@ -15,6 +15,7 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow/models/research/object_detection/utils:ops",
], ],
) )
...@@ -36,6 +37,7 @@ py_library( ...@@ -36,6 +37,7 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow/models/research/object_detection/utils:test_case",
], ],
) )
...@@ -47,9 +49,10 @@ py_library( ...@@ -47,9 +49,10 @@ py_library(
deps = [ deps = [
":feature_map_generators", ":feature_map_generators",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:ssd_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow_models/object_detection/utils:ops", "//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow_models/slim:inception_v2", "//tensorflow/models/research/object_detection/utils:shape_utils",
"//third_party/tensorflow_models/slim:inception_v2",
], ],
) )
...@@ -61,9 +64,10 @@ py_library( ...@@ -61,9 +64,10 @@ py_library(
deps = [ deps = [
":feature_map_generators", ":feature_map_generators",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:ssd_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow_models/object_detection/utils:ops", "//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow_models/slim:inception_v3", "//tensorflow/models/research/object_detection/utils:shape_utils",
"//third_party/tensorflow_models/slim:inception_v3",
], ],
) )
...@@ -73,9 +77,10 @@ py_library( ...@@ -73,9 +77,10 @@ py_library(
deps = [ deps = [
":feature_map_generators", ":feature_map_generators",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:ssd_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow_models/object_detection/utils:ops", "//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow_models/slim:mobilenet_v1", "//tensorflow/models/research/object_detection/utils:shape_utils",
"//third_party/tensorflow_models/slim:mobilenet_v1",
], ],
) )
...@@ -86,8 +91,39 @@ py_library( ...@@ -86,8 +91,39 @@ py_library(
":feature_map_generators", ":feature_map_generators",
":ssd_mobilenet_v1_feature_extractor", ":ssd_mobilenet_v1_feature_extractor",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/utils:ops", "//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow_models/slim:mobilenet_v1", "//third_party/tensorflow_models/slim:mobilenet_v1",
],
)
py_library(
name = "ssd_resnet_v1_fpn_feature_extractor",
srcs = ["ssd_resnet_v1_fpn_feature_extractor.py"],
deps = [
":feature_map_generators",
"//tensorflow",
"//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow/models/research/object_detection/utils:shape_utils",
"//third_party/tensorflow_models/slim:resnet_v1",
],
)
py_library(
name = "ssd_resnet_v1_fpn_feature_extractor_testbase",
srcs = ["ssd_resnet_v1_fpn_feature_extractor_testbase.py"],
deps = [
"//tensorflow/models/research/object_detection/models:ssd_feature_extractor_test",
],
)
py_test(
name = "ssd_resnet_v1_fpn_feature_extractor_test",
srcs = ["ssd_resnet_v1_fpn_feature_extractor_test.py"],
deps = [
":ssd_resnet_v1_fpn_feature_extractor",
":ssd_resnet_v1_fpn_feature_extractor_testbase",
"//tensorflow",
], ],
) )
...@@ -153,8 +189,8 @@ py_library( ...@@ -153,8 +189,8 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:faster_rcnn_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow_models/slim:nasnet", "//third_party/tensorflow_models/slim:nasnet",
], ],
) )
...@@ -165,8 +201,8 @@ py_library( ...@@ -165,8 +201,8 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:faster_rcnn_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow_models/slim:inception_resnet_v2", "//third_party/tensorflow_models/slim:inception_resnet_v2",
], ],
) )
...@@ -188,8 +224,8 @@ py_library( ...@@ -188,8 +224,8 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:faster_rcnn_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow_models/slim:inception_v2", "//third_party/tensorflow_models/slim:inception_v2",
], ],
) )
...@@ -211,9 +247,9 @@ py_library( ...@@ -211,9 +247,9 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:faster_rcnn_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow_models/slim:resnet_utils", "//third_party/tensorflow_models/slim:resnet_utils",
"//tensorflow_models/slim:resnet_v1", "//third_party/tensorflow_models/slim:resnet_v1",
], ],
) )
......
research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor.py
View file @ 7a9934df
...@@ -51,7 +51,8 @@ class EmbeddedSSDMobileNetV1FeatureExtractor( ...@@ -51,7 +51,8 @@ class EmbeddedSSDMobileNetV1FeatureExtractor(
pad_to_multiple, pad_to_multiple,
conv_hyperparams, conv_hyperparams,
batch_norm_trainable=True, batch_norm_trainable=True,
reuse_weights=None): reuse_weights=None,
use_explicit_padding=False):
"""MobileNetV1 Feature Extractor for Embedded-friendly SSD Models. """MobileNetV1 Feature Extractor for Embedded-friendly SSD Models.
Args: Args:
...@@ -66,6 +67,8 @@ class EmbeddedSSDMobileNetV1FeatureExtractor( ...@@ -66,6 +67,8 @@ class EmbeddedSSDMobileNetV1FeatureExtractor(
(e.g. 1), it is desirable to disable batch norm update and use (e.g. 1), it is desirable to disable batch norm update and use
pretrained batch norm params. pretrained batch norm params.
reuse_weights: Whether to reuse variables. Default is None. reuse_weights: Whether to reuse variables. Default is None.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
Raises: Raises:
ValueError: upon invalid `pad_to_multiple` values. ValueError: upon invalid `pad_to_multiple` values.
...@@ -76,7 +79,8 @@ class EmbeddedSSDMobileNetV1FeatureExtractor( ...@@ -76,7 +79,8 @@ class EmbeddedSSDMobileNetV1FeatureExtractor(
super(EmbeddedSSDMobileNetV1FeatureExtractor, self).__init__( super(EmbeddedSSDMobileNetV1FeatureExtractor, self).__init__(
is_training, depth_multiplier, min_depth, pad_to_multiple, is_training, depth_multiplier, min_depth, pad_to_multiple,
conv_hyperparams, batch_norm_trainable, reuse_weights) conv_hyperparams, batch_norm_trainable, reuse_weights,
use_explicit_padding)
def extract_features(self, preprocessed_inputs): def extract_features(self, preprocessed_inputs):
"""Extract features from preprocessed inputs. """Extract features from preprocessed inputs.
...@@ -88,13 +92,25 @@ class EmbeddedSSDMobileNetV1FeatureExtractor( ...@@ -88,13 +92,25 @@ class EmbeddedSSDMobileNetV1FeatureExtractor(
Returns: Returns:
feature_maps: a list of tensors where the ith tensor has shape feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i] [batch, height_i, width_i, depth_i]
Raises:
ValueError: if image height or width are not 256 pixels.
""" """
preprocessed_inputs.get_shape().assert_has_rank(4) image_shape = preprocessed_inputs.get_shape()
shape_assert = tf.Assert( image_shape.assert_has_rank(4)
tf.logical_and( image_height = image_shape[1].value
tf.equal(tf.shape(preprocessed_inputs)[1], 256), image_width = image_shape[2].value
tf.equal(tf.shape(preprocessed_inputs)[2], 256)),
['image size must be 256 in both height and width.']) if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.equal(tf.shape(preprocessed_inputs)[1], 256),
tf.equal(tf.shape(preprocessed_inputs)[2], 256)),
['image size must be 256 in both height and width.'])
with tf.control_dependencies([shape_assert]):
preprocessed_inputs = tf.identity(preprocessed_inputs)
elif image_height != 256 or image_width != 256:
raise ValueError('image size must be = 256 in both height and width;'
' image dim = %d,%d' % (image_height, image_width))
feature_map_layout = { feature_map_layout = {
'from_layer': [ 'from_layer': [
...@@ -102,10 +118,11 @@ class EmbeddedSSDMobileNetV1FeatureExtractor( ...@@ -102,10 +118,11 @@ class EmbeddedSSDMobileNetV1FeatureExtractor(
], ],
'layer_depth': [-1, -1, 512, 256, 256], 'layer_depth': [-1, -1, 512, 256, 256],
'conv_kernel_size': [-1, -1, 3, 3, 2], 'conv_kernel_size': [-1, -1, 3, 3, 2],
'use_explicit_padding': self._use_explicit_padding,
} }
with tf.control_dependencies([shape_assert]): with slim.arg_scope(self._conv_hyperparams):
with slim.arg_scope(self._conv_hyperparams): with slim.arg_scope([slim.batch_norm], fused=False):
with tf.variable_scope('MobilenetV1', with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope: reuse=self._reuse_weights) as scope:
_, image_features = mobilenet_v1.mobilenet_v1_base( _, image_features = mobilenet_v1.mobilenet_v1_base(
......
research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor_test.py
View file @ 7a9934df
...@@ -22,7 +22,7 @@ from object_detection.models import ssd_feature_extractor_test ...@@ -22,7 +22,7 @@ from object_detection.models import ssd_feature_extractor_test
class EmbeddedSSDMobileNetV1FeatureExtractorTest( class EmbeddedSSDMobileNetV1FeatureExtractorTest(
ssd_feature_extractor_test.SsdFeatureExtractorTestBase, tf.test.TestCase): ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
is_training=True, batch_norm_trainable=True): is_training=True, batch_norm_trainable=True):
...@@ -51,11 +51,23 @@ class EmbeddedSSDMobileNetV1FeatureExtractorTest( ...@@ -51,11 +51,23 @@ class EmbeddedSSDMobileNetV1FeatureExtractorTest(
image_width = 256 image_width = 256
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 16, 16, 512), (4, 8, 8, 1024), expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
(4, 4, 4, 512), (4, 2, 2, 256), (2, 4, 4, 512), (2, 2, 2, 256),
(4, 1, 1, 256)] (2, 1, 1, 256)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
image_height = 256
image_width = 256
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
(2, 4, 4, 512), (2, 2, 2, 256),
(2, 1, 1, 256)]
self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_enforcing_min_depth(self): def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
...@@ -63,10 +75,10 @@ class EmbeddedSSDMobileNetV1FeatureExtractorTest( ...@@ -63,10 +75,10 @@ class EmbeddedSSDMobileNetV1FeatureExtractorTest(
image_width = 256 image_width = 256
depth_multiplier = 0.5**12 depth_multiplier = 0.5**12
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 16, 16, 32), (4, 8, 8, 32), (4, 4, 4, 32), expected_feature_map_shape = [(2, 16, 16, 32), (2, 8, 8, 32), (2, 4, 4, 32),
(4, 2, 2, 32), (4, 1, 1, 32)] (2, 2, 2, 32), (2, 1, 1, 32)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_with_pad_to_multiple_of_1( def test_extract_features_returns_correct_shapes_with_pad_to_multiple_of_1(
...@@ -75,11 +87,11 @@ class EmbeddedSSDMobileNetV1FeatureExtractorTest( ...@@ -75,11 +87,11 @@ class EmbeddedSSDMobileNetV1FeatureExtractorTest(
image_width = 256 image_width = 256
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 16, 16, 512), (4, 8, 8, 1024), expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
(4, 4, 4, 512), (4, 2, 2, 256), (2, 4, 4, 512), (2, 2, 2, 256),
(4, 1, 1, 256)] (2, 1, 1, 256)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_raises_error_with_pad_to_multiple_not_1(self): def test_extract_features_raises_error_with_pad_to_multiple_not_1(self):
......
research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py
View file @ 7a9934df
...@@ -180,7 +180,7 @@ class FasterRCNNInceptionResnetV2FeatureExtractor( ...@@ -180,7 +180,7 @@ class FasterRCNNInceptionResnetV2FeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
InceptionResnetV2 checkpoints. InceptionResnetV2 checkpoints.
TODO: revisit whether it's possible to force the TODO(jonathanhuang,rathodv): revisit whether it's possible to force the
`Repeat` namescope as created in `_extract_box_classifier_features` to `Repeat` namescope as created in `_extract_box_classifier_features` to
start counting at 2 (e.g. `Repeat_2`) so that the default restore_fn can start counting at 2 (e.g. `Repeat_2`) so that the default restore_fn can
be used. be used.
......
research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py
View file @ 7a9934df
...@@ -111,7 +111,8 @@ class FasterRCNNResnetV1FeatureExtractor( ...@@ -111,7 +111,8 @@ class FasterRCNNResnetV1FeatureExtractor(
with tf.control_dependencies([shape_assert]): with tf.control_dependencies([shape_assert]):
# Disables batchnorm for fine-tuning with smaller batch sizes. # Disables batchnorm for fine-tuning with smaller batch sizes.
# TODO: Figure out if it is needed when image batch size is bigger. # TODO: Figure out if it is needed when image
# batch size is bigger.
with slim.arg_scope( with slim.arg_scope(
resnet_utils.resnet_arg_scope( resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5, batch_norm_epsilon=1e-5,
......
research/object_detection/models/feature_map_generators.py
View file @ 7a9934df
...@@ -25,6 +25,7 @@ of final feature maps. ...@@ -25,6 +25,7 @@ of final feature maps.
""" """
import collections import collections
import tensorflow as tf import tensorflow as tf
from object_detection.utils import ops
slim = tf.contrib.slim slim = tf.contrib.slim
...@@ -115,6 +116,9 @@ def multi_resolution_feature_maps(feature_map_layout, depth_multiplier, ...@@ -115,6 +116,9 @@ def multi_resolution_feature_maps(feature_map_layout, depth_multiplier,
feature_map_keys = [] feature_map_keys = []
feature_maps = [] feature_maps = []
base_from_layer = '' base_from_layer = ''
use_explicit_padding = False
if 'use_explicit_padding' in feature_map_layout:
use_explicit_padding = feature_map_layout['use_explicit_padding']
use_depthwise = False use_depthwise = False
if 'use_depthwise' in feature_map_layout: if 'use_depthwise' in feature_map_layout:
use_depthwise = feature_map_layout['use_depthwise'] use_depthwise = feature_map_layout['use_depthwise']
...@@ -139,16 +143,21 @@ def multi_resolution_feature_maps(feature_map_layout, depth_multiplier, ...@@ -139,16 +143,21 @@ def multi_resolution_feature_maps(feature_map_layout, depth_multiplier,
padding='SAME', padding='SAME',
stride=1, stride=1,
scope=layer_name) scope=layer_name)
stride = 2
layer_name = '{}_2_Conv2d_{}_{}x{}_s2_{}'.format( layer_name = '{}_2_Conv2d_{}_{}x{}_s2_{}'.format(
base_from_layer, index, conv_kernel_size, conv_kernel_size, base_from_layer, index, conv_kernel_size, conv_kernel_size,
depth_fn(layer_depth)) depth_fn(layer_depth))
stride = 2
padding = 'SAME'
if use_explicit_padding:
padding = 'VALID'
intermediate_layer = ops.fixed_padding(
intermediate_layer, conv_kernel_size)
if use_depthwise: if use_depthwise:
feature_map = slim.separable_conv2d( feature_map = slim.separable_conv2d(
intermediate_layer, intermediate_layer,
None, [conv_kernel_size, conv_kernel_size], None, [conv_kernel_size, conv_kernel_size],
depth_multiplier=1, depth_multiplier=1,
padding='SAME', padding=padding,
stride=stride, stride=stride,
scope=layer_name + '_depthwise') scope=layer_name + '_depthwise')
feature_map = slim.conv2d( feature_map = slim.conv2d(
...@@ -161,10 +170,56 @@ def multi_resolution_feature_maps(feature_map_layout, depth_multiplier, ...@@ -161,10 +170,56 @@ def multi_resolution_feature_maps(feature_map_layout, depth_multiplier,
feature_map = slim.conv2d( feature_map = slim.conv2d(
intermediate_layer, intermediate_layer,
depth_fn(layer_depth), [conv_kernel_size, conv_kernel_size], depth_fn(layer_depth), [conv_kernel_size, conv_kernel_size],
padding='SAME', padding=padding,
stride=stride, stride=stride,
scope=layer_name) scope=layer_name)
feature_map_keys.append(layer_name) feature_map_keys.append(layer_name)
feature_maps.append(feature_map) feature_maps.append(feature_map)
return collections.OrderedDict( return collections.OrderedDict(
[(x, y) for (x, y) in zip(feature_map_keys, feature_maps)]) [(x, y) for (x, y) in zip(feature_map_keys, feature_maps)])
def fpn_top_down_feature_maps(image_features, depth, scope=None):
"""Generates `top-down` feature maps for Feature Pyramid Networks.
See https://arxiv.org/abs/1612.03144 for details.
Args:
image_features: list of image feature tensors. Spatial resolutions of
succesive tensors must reduce exactly by a factor of 2.
depth: depth of output feature maps.
scope: A scope name to wrap this op under.
Returns:
feature_maps: an OrderedDict mapping keys (feature map names) to
tensors where each tensor has shape [batch, height_i, width_i, depth_i].
"""
with tf.variable_scope(
scope, 'top_down', image_features):
num_levels = len(image_features)
output_feature_maps_list = []
output_feature_map_keys = []
with slim.arg_scope(
[slim.conv2d],
activation_fn=None, normalizer_fn=None, padding='SAME', stride=1):
top_down = slim.conv2d(
image_features[-1],
depth, [1, 1], scope='projection_%d' % num_levels)
output_feature_maps_list.append(top_down)
output_feature_map_keys.append(
'top_down_feature_map_%d' % (num_levels - 1))
for level in reversed(range(num_levels - 1)):
top_down = ops.nearest_neighbor_upsampling(top_down, 2)
residual = slim.conv2d(
image_features[level], depth, [1, 1],
scope='projection_%d' % (level + 1))
top_down = 0.5 * top_down + 0.5 * residual
output_feature_maps_list.append(slim.conv2d(
top_down,
depth, [3, 3],
activation_fn=None,
scope='smoothing_%d' % (level + 1)))
output_feature_map_keys.append('top_down_feature_map_%d' % level)
return collections.OrderedDict(
reversed(zip(output_feature_map_keys, output_feature_maps_list)))
research/object_detection/models/feature_map_generators_test.py
View file @ 7a9934df
...@@ -40,7 +40,7 @@ EMBEDDED_SSD_MOBILENET_V1_LAYOUT = { ...@@ -40,7 +40,7 @@ EMBEDDED_SSD_MOBILENET_V1_LAYOUT = {
} }
# TODO(rathodv): add tests with different anchor strides. # TODO: add tests with different anchor strides.
class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase): class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
def test_get_expected_feature_map_shapes_with_inception_v2(self): def test_get_expected_feature_map_shapes_with_inception_v2(self):
...@@ -134,6 +134,34 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase): ...@@ -134,6 +134,34 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)
class FPNFeatureMapGeneratorTest(tf.test.TestCase):
def test_get_expected_feature_map_shapes(self):
image_features = [
tf.random_uniform([4, 8, 8, 256], dtype=tf.float32),
tf.random_uniform([4, 4, 4, 256], dtype=tf.float32),
tf.random_uniform([4, 2, 2, 256], dtype=tf.float32),
tf.random_uniform([4, 1, 1, 256], dtype=tf.float32),
]
feature_maps = feature_map_generators.fpn_top_down_feature_maps(
image_features=image_features, depth=128)
expected_feature_map_shapes = {
'top_down_feature_map_0': (4, 8, 8, 128),
'top_down_feature_map_1': (4, 4, 4, 128),
'top_down_feature_map_2': (4, 2, 2, 128),
'top_down_feature_map_3': (4, 1, 1, 128)
}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = {key: value.shape
for key, value in out_feature_maps.items()}
self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)
class GetDepthFunctionTest(tf.test.TestCase): class GetDepthFunctionTest(tf.test.TestCase):
def test_return_min_depth_when_multiplier_is_small(self): def test_return_min_depth_when_multiplier_is_small(self):
......
research/object_detection/models/ssd_feature_extractor_test.py
View file @ 7a9934df
...@@ -17,33 +17,14 @@ ...@@ -17,33 +17,14 @@
from abc import abstractmethod from abc import abstractmethod
import itertools
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
from object_detection.utils import test_case
class SsdFeatureExtractorTestBase(object):
def _validate_features_shape(self, class SsdFeatureExtractorTestBase(test_case.TestCase):
feature_extractor,
preprocessed_inputs,
expected_feature_map_shapes):
"""Checks the extracted features are of correct shape.
Args:
feature_extractor: The feature extractor to test.
preprocessed_inputs: A [batch, height, width, 3] tensor to extract
features with.
expected_feature_map_shapes: The expected shape of the extracted features.
"""
feature_maps = feature_extractor.extract_features(preprocessed_inputs)
feature_map_shapes = [tf.shape(feature_map) for feature_map in feature_maps]
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
feature_map_shapes_out = sess.run(feature_map_shapes)
for shape_out, exp_shape_out in zip(
feature_map_shapes_out, expected_feature_map_shapes):
self.assertAllEqual(shape_out, exp_shape_out)
@abstractmethod @abstractmethod
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple): def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
...@@ -59,14 +40,39 @@ class SsdFeatureExtractorTestBase(object): ...@@ -59,14 +40,39 @@ class SsdFeatureExtractorTestBase(object):
pass pass
def check_extract_features_returns_correct_shape( def check_extract_features_returns_correct_shape(
self, image_height, image_width, depth_multiplier, pad_to_multiple, self, batch_size, image_height, image_width, depth_multiplier,
expected_feature_map_shapes_out): pad_to_multiple, expected_feature_map_shapes):
feature_extractor = self._create_feature_extractor(depth_multiplier, def graph_fn(image_tensor):
pad_to_multiple) feature_extractor = self._create_feature_extractor(depth_multiplier,
preprocessed_inputs = tf.random_uniform( pad_to_multiple)
[4, image_height, image_width, 3], dtype=tf.float32) feature_maps = feature_extractor.extract_features(image_tensor)
self._validate_features_shape( return feature_maps
feature_extractor, preprocessed_inputs, expected_feature_map_shapes_out)
image_tensor = np.random.rand(batch_size, image_height, image_width,
3).astype(np.float32)
feature_maps = self.execute(graph_fn, [image_tensor])
for feature_map, expected_shape in itertools.izip(
feature_maps, expected_feature_map_shapes):
self.assertAllEqual(feature_map.shape, expected_shape)
def check_extract_features_returns_correct_shapes_with_dynamic_inputs(
self, batch_size, image_height, image_width, depth_multiplier,
pad_to_multiple, expected_feature_map_shapes):
def graph_fn(image_height, image_width):
feature_extractor = self._create_feature_extractor(depth_multiplier,
pad_to_multiple)
image_tensor = tf.random_uniform([batch_size, image_height, image_width,
3], dtype=tf.float32)
feature_maps = feature_extractor.extract_features(image_tensor)
return feature_maps
feature_maps = self.execute_cpu(graph_fn, [
np.array(image_height, dtype=np.int32),
np.array(image_width, dtype=np.int32)
])
for feature_map, expected_shape in itertools.izip(
feature_maps, expected_feature_map_shapes):
self.assertAllEqual(feature_map.shape, expected_shape)
def check_extract_features_raises_error_with_invalid_image_size( def check_extract_features_raises_error_with_invalid_image_size(
self, image_height, image_width, depth_multiplier, pad_to_multiple): self, image_height, image_width, depth_multiplier, pad_to_multiple):
......
research/object_detection/models/ssd_inception_v2_feature_extractor.py
View file @ 7a9934df
...@@ -19,6 +19,7 @@ import tensorflow as tf ...@@ -19,6 +19,7 @@ import tensorflow as tf
from object_detection.meta_architectures import ssd_meta_arch from object_detection.meta_architectures import ssd_meta_arch
from object_detection.models import feature_map_generators from object_detection.models import feature_map_generators
from object_detection.utils import ops from object_detection.utils import ops
from object_detection.utils import shape_utils
from nets import inception_v2 from nets import inception_v2
slim = tf.contrib.slim slim = tf.contrib.slim
...@@ -34,7 +35,8 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor): ...@@ -34,7 +35,8 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
pad_to_multiple, pad_to_multiple,
conv_hyperparams, conv_hyperparams,
batch_norm_trainable=True, batch_norm_trainable=True,
reuse_weights=None): reuse_weights=None,
use_explicit_padding=False):
"""InceptionV2 Feature Extractor for SSD Models. """InceptionV2 Feature Extractor for SSD Models.
Args: Args:
...@@ -49,10 +51,13 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor): ...@@ -49,10 +51,13 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
(e.g. 1), it is desirable to disable batch norm update and use (e.g. 1), it is desirable to disable batch norm update and use
pretrained batch norm params. pretrained batch norm params.
reuse_weights: Whether to reuse variables. Default is None. reuse_weights: Whether to reuse variables. Default is None.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
""" """
super(SSDInceptionV2FeatureExtractor, self).__init__( super(SSDInceptionV2FeatureExtractor, self).__init__(
is_training, depth_multiplier, min_depth, pad_to_multiple, is_training, depth_multiplier, min_depth, pad_to_multiple,
conv_hyperparams, batch_norm_trainable, reuse_weights) conv_hyperparams, batch_norm_trainable, reuse_weights,
use_explicit_padding)
def preprocess(self, resized_inputs): def preprocess(self, resized_inputs):
"""SSD preprocessing. """SSD preprocessing.
...@@ -80,32 +85,29 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor): ...@@ -80,32 +85,29 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
feature_maps: a list of tensors where the ith tensor has shape feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i] [batch, height_i, width_i, depth_i]
""" """
preprocessed_inputs.get_shape().assert_has_rank(4) preprocessed_inputs = shape_utils.check_min_image_dim(
shape_assert = tf.Assert( 33, preprocessed_inputs)
tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
['image size must at least be 33 in both height and width.'])
feature_map_layout = { feature_map_layout = {
'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''], 'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128], 'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
} }
with tf.control_dependencies([shape_assert]): with slim.arg_scope(self._conv_hyperparams):
with slim.arg_scope(self._conv_hyperparams): with tf.variable_scope('InceptionV2',
with tf.variable_scope('InceptionV2', reuse=self._reuse_weights) as scope:
reuse=self._reuse_weights) as scope: _, image_features = inception_v2.inception_v2_base(
_, image_features = inception_v2.inception_v2_base( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), final_endpoint='Mixed_5c',
final_endpoint='Mixed_5c', min_depth=self._min_depth,
min_depth=self._min_depth, depth_multiplier=self._depth_multiplier,
depth_multiplier=self._depth_multiplier, scope=scope)
scope=scope) feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_maps = feature_map_generators.multi_resolution_feature_maps( feature_map_layout=feature_map_layout,
feature_map_layout=feature_map_layout, depth_multiplier=self._depth_multiplier,
depth_multiplier=self._depth_multiplier, min_depth=self._min_depth,
min_depth=self._min_depth, insert_1x1_conv=True,
insert_1x1_conv=True, image_features=image_features)
image_features=image_features)
return feature_maps.values() return feature_maps.values()
research/object_detection/models/ssd_inception_v2_feature_extractor_test.py
View file @ 7a9934df
...@@ -22,7 +22,7 @@ from object_detection.models import ssd_inception_v2_feature_extractor ...@@ -22,7 +22,7 @@ from object_detection.models import ssd_inception_v2_feature_extractor
class SsdInceptionV2FeatureExtractorTest( class SsdInceptionV2FeatureExtractorTest(
ssd_feature_extractor_test.SsdFeatureExtractorTestBase, tf.test.TestCase): ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
is_training=True, batch_norm_trainable=True): is_training=True, batch_norm_trainable=True):
...@@ -49,11 +49,23 @@ class SsdInceptionV2FeatureExtractorTest( ...@@ -49,11 +49,23 @@ class SsdInceptionV2FeatureExtractorTest(
image_width = 128 image_width = 128
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 8, 8, 576), (4, 4, 4, 1024), expected_feature_map_shape = [(2, 8, 8, 576), (2, 4, 4, 1024),
(4, 2, 2, 512), (4, 1, 1, 256), (2, 2, 2, 512), (2, 1, 1, 256),
(4, 1, 1, 256), (4, 1, 1, 128)] (2, 1, 1, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
image_height = 128
image_width = 128
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 8, 8, 576), (2, 4, 4, 1024),
(2, 2, 2, 512), (2, 1, 1, 256),
(2, 1, 1, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_299(self): def test_extract_features_returns_correct_shapes_299(self):
...@@ -61,11 +73,11 @@ class SsdInceptionV2FeatureExtractorTest( ...@@ -61,11 +73,11 @@ class SsdInceptionV2FeatureExtractorTest(
image_width = 299 image_width = 299
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 19, 19, 576), (4, 10, 10, 1024), expected_feature_map_shape = [(2, 19, 19, 576), (2, 10, 10, 1024),
(4, 5, 5, 512), (4, 3, 3, 256), (2, 5, 5, 512), (2, 3, 3, 256),
(4, 2, 2, 256), (4, 1, 1, 128)] (2, 2, 2, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_enforcing_min_depth(self): def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
...@@ -73,11 +85,11 @@ class SsdInceptionV2FeatureExtractorTest( ...@@ -73,11 +85,11 @@ class SsdInceptionV2FeatureExtractorTest(
image_width = 299 image_width = 299
depth_multiplier = 0.5**12 depth_multiplier = 0.5**12
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 19, 19, 128), (4, 10, 10, 128), expected_feature_map_shape = [(2, 19, 19, 128), (2, 10, 10, 128),
(4, 5, 5, 32), (4, 3, 3, 32), (2, 5, 5, 32), (2, 3, 3, 32),
(4, 2, 2, 32), (4, 1, 1, 32)] (2, 2, 2, 32), (2, 1, 1, 32)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self): def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
...@@ -85,11 +97,11 @@ class SsdInceptionV2FeatureExtractorTest( ...@@ -85,11 +97,11 @@ class SsdInceptionV2FeatureExtractorTest(
image_width = 299 image_width = 299
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 32 pad_to_multiple = 32
expected_feature_map_shape = [(4, 20, 20, 576), (4, 10, 10, 1024), expected_feature_map_shape = [(2, 20, 20, 576), (2, 10, 10, 1024),
(4, 5, 5, 512), (4, 3, 3, 256), (2, 5, 5, 512), (2, 3, 3, 256),
(4, 2, 2, 256), (4, 1, 1, 128)] (2, 2, 2, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_raises_error_with_invalid_image_size(self): def test_extract_features_raises_error_with_invalid_image_size(self):
......
research/object_detection/models/ssd_inception_v3_feature_extractor.py
View file @ 7a9934df
...@@ -19,6 +19,7 @@ import tensorflow as tf ...@@ -19,6 +19,7 @@ import tensorflow as tf
from object_detection.meta_architectures import ssd_meta_arch from object_detection.meta_architectures import ssd_meta_arch
from object_detection.models import feature_map_generators from object_detection.models import feature_map_generators
from object_detection.utils import ops from object_detection.utils import ops
from object_detection.utils import shape_utils
from nets import inception_v3 from nets import inception_v3
slim = tf.contrib.slim slim = tf.contrib.slim
...@@ -34,7 +35,8 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor): ...@@ -34,7 +35,8 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
pad_to_multiple, pad_to_multiple,
conv_hyperparams, conv_hyperparams,
batch_norm_trainable=True, batch_norm_trainable=True,
reuse_weights=None): reuse_weights=None,
use_explicit_padding=False):
"""InceptionV3 Feature Extractor for SSD Models. """InceptionV3 Feature Extractor for SSD Models.
Args: Args:
...@@ -49,10 +51,13 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor): ...@@ -49,10 +51,13 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
(e.g. 1), it is desirable to disable batch norm update and use (e.g. 1), it is desirable to disable batch norm update and use
pretrained batch norm params. pretrained batch norm params.
reuse_weights: Whether to reuse variables. Default is None. reuse_weights: Whether to reuse variables. Default is None.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
""" """
super(SSDInceptionV3FeatureExtractor, self).__init__( super(SSDInceptionV3FeatureExtractor, self).__init__(
is_training, depth_multiplier, min_depth, pad_to_multiple, is_training, depth_multiplier, min_depth, pad_to_multiple,
conv_hyperparams, batch_norm_trainable, reuse_weights) conv_hyperparams, batch_norm_trainable, reuse_weights,
use_explicit_padding)
def preprocess(self, resized_inputs): def preprocess(self, resized_inputs):
"""SSD preprocessing. """SSD preprocessing.
...@@ -80,32 +85,28 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor): ...@@ -80,32 +85,28 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
feature_maps: a list of tensors where the ith tensor has shape feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i] [batch, height_i, width_i, depth_i]
""" """
preprocessed_inputs.get_shape().assert_has_rank(4) preprocessed_inputs = shape_utils.check_min_image_dim(
shape_assert = tf.Assert( 33, preprocessed_inputs)
tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
['image size must at least be 33 in both height and width.'])
feature_map_layout = { feature_map_layout = {
'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''], 'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
'layer_depth': [-1, -1, -1, 512, 256, 128], 'layer_depth': [-1, -1, -1, 512, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
} }
with tf.control_dependencies([shape_assert]): with slim.arg_scope(self._conv_hyperparams):
with slim.arg_scope(self._conv_hyperparams): with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope:
with tf.variable_scope('InceptionV3', _, image_features = inception_v3.inception_v3_base(
reuse=self._reuse_weights) as scope: ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
_, image_features = inception_v3.inception_v3_base( final_endpoint='Mixed_7c',
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), min_depth=self._min_depth,
final_endpoint='Mixed_7c', depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth, scope=scope)
depth_multiplier=self._depth_multiplier, feature_maps = feature_map_generators.multi_resolution_feature_maps(
scope=scope) feature_map_layout=feature_map_layout,
feature_maps = feature_map_generators.multi_resolution_feature_maps( depth_multiplier=self._depth_multiplier,
feature_map_layout=feature_map_layout, min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier, insert_1x1_conv=True,
min_depth=self._min_depth, image_features=image_features)
insert_1x1_conv=True,
image_features=image_features)
return feature_maps.values() return feature_maps.values()
research/object_detection/models/ssd_inception_v3_feature_extractor_test.py
View file @ 7a9934df
...@@ -22,7 +22,7 @@ from object_detection.models import ssd_inception_v3_feature_extractor ...@@ -22,7 +22,7 @@ from object_detection.models import ssd_inception_v3_feature_extractor
class SsdInceptionV3FeatureExtractorTest( class SsdInceptionV3FeatureExtractorTest(
ssd_feature_extractor_test.SsdFeatureExtractorTestBase, tf.test.TestCase): ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
is_training=True, batch_norm_trainable=True): is_training=True, batch_norm_trainable=True):
...@@ -49,11 +49,23 @@ class SsdInceptionV3FeatureExtractorTest( ...@@ -49,11 +49,23 @@ class SsdInceptionV3FeatureExtractorTest(
image_width = 128 image_width = 128
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 13, 13, 288), (4, 6, 6, 768), expected_feature_map_shape = [(2, 13, 13, 288), (2, 6, 6, 768),
(4, 2, 2, 2048), (4, 1, 1, 512), (2, 2, 2, 2048), (2, 1, 1, 512),
(4, 1, 1, 256), (4, 1, 1, 128)] (2, 1, 1, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
image_height = 128
image_width = 128
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 13, 13, 288), (2, 6, 6, 768),
(2, 2, 2, 2048), (2, 1, 1, 512),
(2, 1, 1, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_299(self): def test_extract_features_returns_correct_shapes_299(self):
...@@ -61,11 +73,11 @@ class SsdInceptionV3FeatureExtractorTest( ...@@ -61,11 +73,11 @@ class SsdInceptionV3FeatureExtractorTest(
image_width = 299 image_width = 299
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 35, 35, 288), (4, 17, 17, 768), expected_feature_map_shape = [(2, 35, 35, 288), (2, 17, 17, 768),
(4, 8, 8, 2048), (4, 4, 4, 512), (2, 8, 8, 2048), (2, 4, 4, 512),
(4, 2, 2, 256), (4, 1, 1, 128)] (2, 2, 2, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_enforcing_min_depth(self): def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
...@@ -73,11 +85,11 @@ class SsdInceptionV3FeatureExtractorTest( ...@@ -73,11 +85,11 @@ class SsdInceptionV3FeatureExtractorTest(
image_width = 299 image_width = 299
depth_multiplier = 0.5**12 depth_multiplier = 0.5**12
pad_to_multiple = 1 pad_to_multiple = 1
expected_feature_map_shape = [(4, 35, 35, 128), (4, 17, 17, 128), expected_feature_map_shape = [(2, 35, 35, 128), (2, 17, 17, 128),
(4, 8, 8, 192), (4, 4, 4, 32), (2, 8, 8, 192), (2, 4, 4, 32),
(4, 2, 2, 32), (4, 1, 1, 32)] (2, 2, 2, 32), (2, 1, 1, 32)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self): def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
...@@ -85,11 +97,11 @@ class SsdInceptionV3FeatureExtractorTest( ...@@ -85,11 +97,11 @@ class SsdInceptionV3FeatureExtractorTest(
image_width = 299 image_width = 299
depth_multiplier = 1.0 depth_multiplier = 1.0
pad_to_multiple = 32 pad_to_multiple = 32
expected_feature_map_shape = [(4, 37, 37, 288), (4, 18, 18, 768), expected_feature_map_shape = [(2, 37, 37, 288), (2, 18, 18, 768),
(4, 8, 8, 2048), (4, 4, 4, 512), (2, 8, 8, 2048), (2, 4, 4, 512),
(4, 2, 2, 256), (4, 1, 1, 128)] (2, 2, 2, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape( self.check_extract_features_returns_correct_shape(
image_height, image_width, depth_multiplier, pad_to_multiple, 2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape) expected_feature_map_shape)
def test_extract_features_raises_error_with_invalid_image_size(self): def test_extract_features_raises_error_with_invalid_image_size(self):
......
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