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Unverified Commit 3f78f4cf authored by derekjchow's avatar derekjchow Committed by GitHub
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Merge pull request #3494 from pkulzc/master 

Update object detection with internal changes and remove unused BUILD files.
parents 73748d01 0319908c
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Showing with 378 additions and 842 deletions
research/object_detection/meta_architectures/ssd_meta_arch_test.py
View file @ 3f78f4cf
......@@ -61,12 +61,12 @@ class MockAnchorGenerator2x2(anchor_generator.AnchorGenerator):
return [1]
def _generate(self, feature_map_shape_list, im_height, im_width):
return box_list.BoxList(
return [box_list.BoxList(
tf.constant([[0, 0, .5, .5],
[0, .5, .5, 1],
[.5, 0, 1, .5],
[1., 1., 1.5, 1.5] # Anchor that is outside clip_window.
], tf.float32))
], tf.float32))]
def num_anchors(self):
return 4
......@@ -74,7 +74,8 @@ class MockAnchorGenerator2x2(anchor_generator.AnchorGenerator):
class SsdMetaArchTest(test_case.TestCase):
def _create_model(self, apply_hard_mining=True):
def _create_model(self, apply_hard_mining=True,
normalize_loc_loss_by_codesize=False):
is_training = False
num_classes = 1
mock_anchor_generator = MockAnchorGenerator2x2()
......@@ -98,6 +99,7 @@ class SsdMetaArchTest(test_case.TestCase):
max_total_size=5)
classification_loss_weight = 1.0
localization_loss_weight = 1.0
negative_class_weight = 1.0
normalize_loss_by_num_matches = False
hard_example_miner = None
......@@ -111,10 +113,11 @@ class SsdMetaArchTest(test_case.TestCase):
model = ssd_meta_arch.SSDMetaArch(
is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
fake_feature_extractor, mock_matcher, region_similarity_calculator,
encode_background_as_zeros, image_resizer_fn, non_max_suppression_fn,
tf.identity, classification_loss, localization_loss,
classification_loss_weight, localization_loss_weight,
normalize_loss_by_num_matches, hard_example_miner, add_summaries=False)
encode_background_as_zeros, negative_class_weight, image_resizer_fn,
non_max_suppression_fn, tf.identity, classification_loss,
localization_loss, classification_loss_weight, localization_loss_weight,
normalize_loss_by_num_matches, hard_example_miner, add_summaries=False,
normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize)
return model, num_classes, mock_anchor_generator.num_anchors(), code_size
def test_preprocess_preserves_shapes_with_dynamic_input_image(self):
......@@ -287,6 +290,37 @@ class SsdMetaArchTest(test_case.TestCase):
self.assertAllClose(localization_loss, expected_localization_loss)
self.assertAllClose(classification_loss, expected_classification_loss)
def test_loss_results_are_correct_with_normalize_by_codesize_true(self):
with tf.Graph().as_default():
_, _, _, _ = 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,
normalize_loc_loss_by_codesize=True)
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'],)
batch_size = 2
preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
groundtruth_boxes1 = np.array([[0, 0, 1, 1]], dtype=np.float32)
groundtruth_boxes2 = np.array([[0, 0, 1, 1]], dtype=np.float32)
groundtruth_classes1 = np.array([[1]], dtype=np.float32)
groundtruth_classes2 = np.array([[1]], dtype=np.float32)
expected_localization_loss = 0.5 / 4
localization_loss = self.execute(graph_fn, [preprocessed_input,
groundtruth_boxes1,
groundtruth_boxes2,
groundtruth_classes1,
groundtruth_classes2])
self.assertAllClose(localization_loss, expected_localization_loss)
def test_loss_results_are_correct_with_hard_example_mining(self):
with tf.Graph().as_default():
......
research/object_detection/metrics/BUILD deleted 100644 → 0
View file @ 73748d01
# Tensorflow Object Detection API: main runnables.
package(
default_visibility = ["//visibility:public"],
)
licenses(["notice"])
# 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(
name = "offline_eval_map_corloc",
srcs = [
"offline_eval_map_corloc.py",
],
deps = [
":tf_example_parser",
"//tensorflow/models/research/object_detection:evaluator",
"//tensorflow/models/research/object_detection/builders:input_reader_builder",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:label_map_util",
],
)
py_test(
name = "offline_eval_map_corloc_test",
srcs = [
"offline_eval_map_corloc_test.py",
],
deps = [
":offline_eval_map_corloc",
"//tensorflow",
],
)
py_library(
name = "tf_example_parser",
srcs = ["tf_example_parser.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/core:data_parser",
"//tensorflow/models/research/object_detection/core:standard_fields",
],
)
py_test(
name = "tf_example_parser_test",
srcs = ["tf_example_parser_test.py"],
deps = [
":tf_example_parser",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:standard_fields",
],
)
research/object_detection/metrics/coco_evaluation.py
View file @ 3f78f4cf
......@@ -74,23 +74,30 @@ class CocoDetectionEvaluator(object_detection_evaluation.DetectionEvaluator):
[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_is_crowd (optional): integer numpy array of
shape [num_boxes] containing iscrowd flag for groundtruth 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
groundtruth_is_crowd = groundtruth_dict.get(
standard_fields.InputDataFields.groundtruth_is_crowd)
# Drop groundtruth_is_crowd if empty tensor.
if groundtruth_is_crowd is not None and not groundtruth_is_crowd.shape[0]:
groundtruth_is_crowd = None
self._groundtruth_list.extend(
coco_tools.
ExportSingleImageGroundtruthToCoco(
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_boxes=groundtruth_dict[
standard_fields.InputDataFields.groundtruth_boxes],
groundtruth_classes=groundtruth_dict[
standard_fields.InputDataFields.groundtruth_classes],
groundtruth_is_crowd=groundtruth_is_crowd))
self._annotation_id += groundtruth_dict[standard_fields.InputDataFields.
groundtruth_boxes].shape[0]
self._image_ids[image_id] = False
......
research/object_detection/metrics/coco_evaluation_test.py
View file @ 3f78f4cf
......@@ -86,6 +86,78 @@ class CocoDetectionEvaluationTest(tf.test.TestCase):
metrics = coco_evaluator.evaluate()
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
def testGetOneMAPWithMatchingGroundtruthAndDetectionsSkipCrowd(self):
"""Tests computing mAP with is_crowd GT boxes skipped."""
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.], [99., 99., 200., 200.]]),
standard_fields.InputDataFields.groundtruth_classes:
np.array([1, 2]),
standard_fields.InputDataFields.groundtruth_is_crowd:
np.array([0, 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()
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
def testGetOneMAPWithMatchingGroundtruthAndDetectionsEmptyCrowd(self):
"""Tests computing mAP with empty is_crowd array passed in."""
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]),
standard_fields.InputDataFields.groundtruth_is_crowd:
np.array([])
})
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()
self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
def testRejectionOnDuplicateGroundtruth(self):
"""Tests that groundtruth cannot be added more than once for an image."""
categories = [{'id': 1, 'name': 'cat'},
......
research/object_detection/metrics/coco_tools.py
View file @ 3f78f4cf
......@@ -17,7 +17,7 @@
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
TODO(jonathanhuang): wrap as a slim metric in metrics.py
Usage example: given a set of images with ids in the list image_ids
......@@ -327,7 +327,8 @@ def ExportSingleImageGroundtruthToCoco(image_id,
category_id_set,
groundtruth_boxes,
groundtruth_classes,
groundtruth_masks=None):
groundtruth_masks=None,
groundtruth_is_crowd=None):
"""Export groundtruth of a single image to COCO format.
This function converts groundtruth detection annotations represented as numpy
......@@ -338,8 +339,7 @@ def ExportSingleImageGroundtruthToCoco(image_id,
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.
groundtruth bounding box.
Args:
image_id: a unique image identifier either of type integer or string.
......@@ -352,6 +352,8 @@ def ExportSingleImageGroundtruthToCoco(image_id,
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.
groundtruth_is_crowd: optional numpy array (int) with shape [num_gt_boxes]
indicating whether groundtruth boxes are crowd.
Returns:
a list of groundtruth annotations for a single image in the COCO format.
......@@ -379,17 +381,27 @@ def ExportSingleImageGroundtruthToCoco(image_id,
'Classes shape: %d. Boxes shape: %d. Image ID: %s' % (
groundtruth_classes.shape[0],
groundtruth_boxes.shape[0], image_id))
has_is_crowd = groundtruth_is_crowd is not None
if has_is_crowd and len(groundtruth_is_crowd.shape) != 1:
raise ValueError('groundtruth_is_crowd is expected to be of rank 1.')
groundtruth_list = []
for i in range(num_boxes):
if groundtruth_classes[i] in category_id_set:
iscrowd = groundtruth_is_crowd[i] if has_is_crowd else 0
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
'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':
iscrowd
}
if groundtruth_masks is not None:
export_dict['segmentation'] = _RleCompress(groundtruth_masks[i])
......@@ -416,7 +428,7 @@ def ExportGroundtruthToCOCO(image_ids,
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.
TODO(jonathanhuang): pass in "iscrowd" array for evaluating on COCO dataset.
Args:
image_ids: a list of unique image identifier either of type integer or
......
research/object_detection/metrics/coco_tools_test.py
View file @ 3f78f4cf
......@@ -248,7 +248,11 @@ class CocoToolsTest(tf.test.TestCase):
[0, 0, .5, .5],
[.5, .5, .5, .5]], dtype=np.float32)
classes = np.array([1, 2, 3], dtype=np.int32)
is_crowd = np.array([0, 1, 0], dtype=np.int32)
next_annotation_id = 1
expected_counts = ['04', '31', '4']
# Tests exporting without passing in is_crowd (for backward compatibility).
coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
image_id='first_image',
category_id_set=set([1, 2, 3]),
......@@ -256,7 +260,6 @@ class CocoToolsTest(tf.test.TestCase):
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])
......@@ -267,6 +270,26 @@ class CocoToolsTest(tf.test.TestCase):
self.assertEqual(annotation['category_id'], classes[i])
self.assertEqual(annotation['id'], i + next_annotation_id)
# Tests exporting with is_crowd.
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,
groundtruth_is_crowd=is_crowd)
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['iscrowd'], is_crowd[i])
self.assertEqual(annotation['id'], i + next_annotation_id)
if __name__ == '__main__':
tf.test.main()
research/object_detection/model.py
View file @ 3f78f4cf
......@@ -32,6 +32,7 @@ import tensorflow as tf
from google.protobuf import text_format
from tensorflow.contrib.learn.python.learn import learn_runner
from tensorflow.contrib.tpu.python.tpu import tpu_optimizer
from tensorflow.python.lib.io import file_io
from object_detection import eval_util
from object_detection import inputs
from object_detection import model_hparams
......@@ -54,6 +55,20 @@ tf.flags.DEFINE_integer('num_eval_steps', 10000, 'Number of train steps.')
FLAGS = tf.flags.FLAGS
# A map of names to methods that help build the model.
MODEL_BUILD_UTIL_MAP = {
'get_configs_from_pipeline_file':
config_util.get_configs_from_pipeline_file,
'create_pipeline_proto_from_configs':
config_util.create_pipeline_proto_from_configs,
'merge_external_params_with_configs':
config_util.merge_external_params_with_configs,
'create_train_input_fn': inputs.create_train_input_fn,
'create_eval_input_fn': inputs.create_eval_input_fn,
'create_predict_input_fn': inputs.create_predict_input_fn,
}
def _get_groundtruth_data(detection_model, class_agnostic):
"""Extracts groundtruth data from detection_model.
......@@ -319,9 +334,11 @@ def create_model_fn(detection_model_fn, configs, hparams, use_tpu=False):
else:
category_index = label_map_util.create_category_index_from_labelmap(
eval_input_config.label_map_path)
detection_and_groundtruth = vis_utils.draw_side_by_side_evaluation_image(
eval_dict, category_index, max_boxes_to_draw=20, min_score_thresh=0.2)
if not use_tpu:
detection_and_groundtruth = (
vis_utils.draw_side_by_side_evaluation_image(
eval_dict, category_index, max_boxes_to_draw=20,
min_score_thresh=0.2))
tf.summary.image('Detections_Left_Groundtruth_Right',
detection_and_groundtruth)
......@@ -411,8 +428,18 @@ def populate_experiment(run_config,
An `Experiment` that defines all aspects of training, evaluation, and
export.
"""
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
get_configs_from_pipeline_file = MODEL_BUILD_UTIL_MAP[
'get_configs_from_pipeline_file']
create_pipeline_proto_from_configs = MODEL_BUILD_UTIL_MAP[
'create_pipeline_proto_from_configs']
merge_external_params_with_configs = MODEL_BUILD_UTIL_MAP[
'merge_external_params_with_configs']
create_train_input_fn = MODEL_BUILD_UTIL_MAP['create_train_input_fn']
create_eval_input_fn = MODEL_BUILD_UTIL_MAP['create_eval_input_fn']
create_predict_input_fn = MODEL_BUILD_UTIL_MAP['create_predict_input_fn']
configs = get_configs_from_pipeline_file(pipeline_config_path)
configs = merge_external_params_with_configs(
configs,
hparams,
train_steps=train_steps,
......@@ -424,28 +451,28 @@ def populate_experiment(run_config,
eval_config = configs['eval_config']
eval_input_config = configs['eval_input_config']
if train_steps is None:
train_steps = train_config.num_steps if train_config.num_steps else None
if train_steps is None and train_config.num_steps:
train_steps = train_config.num_steps
if eval_steps is None:
eval_steps = eval_config.num_examples if eval_config.num_examples else None
if eval_steps is None and eval_config.num_examples:
eval_steps = eval_config.num_examples
detection_model_fn = functools.partial(
model_builder.build, model_config=model_config)
# Create the input functions for TRAIN/EVAL.
train_input_fn = inputs.create_train_input_fn(
train_input_fn = create_train_input_fn(
train_config=train_config,
train_input_config=train_input_config,
model_config=model_config)
eval_input_fn = inputs.create_eval_input_fn(
eval_input_fn = create_eval_input_fn(
eval_config=eval_config,
eval_input_config=eval_input_config,
model_config=model_config)
export_strategies = [
tf.contrib.learn.utils.saved_model_export_utils.make_export_strategy(
serving_input_fn=inputs.create_predict_input_fn(
serving_input_fn=create_predict_input_fn(
model_config=model_config))
]
......@@ -455,8 +482,10 @@ def populate_experiment(run_config,
if run_config.is_chief:
# Store the final pipeline config for traceability.
pipeline_config_final = config_util.create_pipeline_proto_from_configs(
pipeline_config_final = create_pipeline_proto_from_configs(
configs)
if not file_io.file_exists(estimator.model_dir):
file_io.recursive_create_dir(estimator.model_dir)
pipeline_config_final_path = os.path.join(estimator.model_dir,
'pipeline.config')
config_text = text_format.MessageToString(pipeline_config_final)
......
research/object_detection/model_tpu.py
View file @ 3f78f4cf
......@@ -77,6 +77,10 @@ tf.flags.DEFINE_integer('min_eval_interval_secs', 180,
tf.flags.DEFINE_integer(
'eval_timeout_secs', None,
'Maximum seconds between checkpoints before evaluation terminates.')
tf.flags.DEFINE_string('hparams_overrides', None, 'Comma-separated list of '
'hyperparameters to override defaults.')
tf.flags.DEFINE_boolean('eval_training_data', False,
'If training data should be evaluated for this job.')
FLAGS = tf.flags.FLAGS
......@@ -122,7 +126,10 @@ def create_estimator(run_config,
Returns:
Estimator: A estimator object used for training and evaluation
train_input_fn: Input function for the training loop
eval_input_fn: Input function for the evaluation run
eval_validation_input_fn: Input function to run for evaluation on
validation data.
eval_training_input_fn: Input function to run for evaluation on
training data.
train_steps: Number of training steps either from arg `train_steps` or
`TrainConfig` proto
eval_steps: Number of evaluation steps either from arg `eval_steps` or
......@@ -141,15 +148,17 @@ def create_estimator(run_config,
train_input_config = configs['train_input_config']
eval_config = configs['eval_config']
eval_input_config = configs['eval_input_config']
if FLAGS.eval_training_data:
eval_input_config = configs['train_input_config']
if params is None:
params = {}
if train_steps is None:
train_steps = train_config.num_steps if train_config.num_steps else None
if train_steps is None and train_config.num_steps:
train_steps = train_config.num_steps
if eval_steps is None:
eval_steps = eval_config.num_examples if eval_config.num_examples else None
if eval_steps is None and eval_config.num_examples:
eval_steps = eval_config.num_examples
detection_model_fn = functools.partial(
model_builder.build, model_config=model_config)
......@@ -159,10 +168,14 @@ def create_estimator(run_config,
train_config=train_config,
train_input_config=train_input_config,
model_config=model_config)
eval_input_fn = inputs.create_eval_input_fn(
eval_validation_input_fn = inputs.create_eval_input_fn(
eval_config=eval_config,
eval_input_config=eval_input_config,
model_config=model_config)
eval_training_input_fn = inputs.create_eval_input_fn(
eval_config=eval_config,
eval_input_config=train_input_config,
model_config=model_config)
estimator = tpu_estimator.TPUEstimator(
model_fn=model_fn_creator(detection_model_fn, configs, hparams,
......@@ -173,7 +186,8 @@ def create_estimator(run_config,
use_tpu=use_tpu,
config=run_config,
params=params)
return estimator, train_input_fn, eval_input_fn, train_steps, eval_steps
return (estimator, train_input_fn, eval_validation_input_fn,
eval_training_input_fn, train_steps, eval_steps)
def main(unused_argv):
......@@ -204,24 +218,27 @@ def main(unused_argv):
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_shards))
params = {}
estimator, train_input_fn, eval_input_fn, train_steps, eval_steps = (
create_estimator(
config,
model_hparams.create_hparams(),
FLAGS.pipeline_config_path,
train_steps=FLAGS.num_train_steps,
eval_steps=FLAGS.num_eval_steps,
train_batch_size=FLAGS.train_batch_size,
use_tpu=FLAGS.use_tpu,
num_shards=FLAGS.num_shards,
params=params))
(estimator, train_input_fn, eval_validation_input_fn, eval_training_input_fn,
train_steps, eval_steps) = (
create_estimator(
config,
model_hparams.create_hparams(
hparams_overrides=FLAGS.hparams_overrides),
FLAGS.pipeline_config_path,
train_steps=FLAGS.num_train_steps,
eval_steps=FLAGS.num_eval_steps,
train_batch_size=FLAGS.train_batch_size,
use_tpu=FLAGS.use_tpu,
num_shards=FLAGS.num_shards,
params=params))
if FLAGS.mode in ['train', 'train_and_eval']:
estimator.train(input_fn=train_input_fn, max_steps=train_steps)
if FLAGS.mode == 'train_and_eval':
# Eval one time.
eval_results = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
eval_results = estimator.evaluate(
input_fn=eval_validation_input_fn, steps=eval_steps)
tf.logging.info('Eval results: %s' % eval_results)
# Continuously evaluating.
......@@ -239,11 +256,18 @@ def main(unused_argv):
timeout_fn=terminate_eval):
tf.logging.info('Starting to evaluate.')
if FLAGS.eval_training_data:
name = 'training_data'
input_fn = eval_training_input_fn
else:
name = 'validation_data'
input_fn = eval_validation_input_fn
try:
eval_results = estimator.evaluate(
input_fn=eval_input_fn,
input_fn=input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
checkpoint_path=ckpt,
name=name)
tf.logging.info('Eval results: %s' % eval_results)
# Terminate eval job when final checkpoint is reached
......
research/object_detection/models/BUILD deleted 100644 → 0
View file @ 73748d01
# Tensorflow Object Detection API: Models.
package(
default_visibility = ["//visibility:public"],
)
licenses(["notice"])
# Apache 2.0
py_library(
name = "feature_map_generators",
srcs = [
"feature_map_generators.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/utils:ops",
],
)
py_test(
name = "feature_map_generators_test",
srcs = [
"feature_map_generators_test.py",
],
deps = [
":feature_map_generators",
"//tensorflow",
],
)
py_library(
name = "ssd_feature_extractor_test",
srcs = [
"ssd_feature_extractor_test.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/utils:test_case",
],
)
py_library(
name = "ssd_inception_v2_feature_extractor",
srcs = [
"ssd_inception_v2_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:inception_v2",
],
)
py_library(
name = "ssd_inception_v3_feature_extractor",
srcs = [
"ssd_inception_v3_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:inception_v3",
],
)
py_library(
name = "ssd_mobilenet_v1_feature_extractor",
srcs = ["ssd_mobilenet_v1_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:mobilenet_v1",
],
)
py_library(
name = "embedded_ssd_mobilenet_v1_feature_extractor",
srcs = ["embedded_ssd_mobilenet_v1_feature_extractor.py"],
deps = [
":feature_map_generators",
":ssd_mobilenet_v1_feature_extractor",
"//tensorflow",
"//tensorflow/models/research/object_detection/utils:ops",
"//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",
timeout = "long",
srcs = ["ssd_resnet_v1_fpn_feature_extractor_test.py"],
deps = [
":ssd_resnet_v1_fpn_feature_extractor",
":ssd_resnet_v1_fpn_feature_extractor_testbase",
"//tensorflow",
],
)
py_test(
name = "ssd_inception_v2_feature_extractor_test",
srcs = [
"ssd_inception_v2_feature_extractor_test.py",
],
deps = [
":ssd_feature_extractor_test",
":ssd_inception_v2_feature_extractor",
"//tensorflow",
],
)
py_test(
name = "ssd_inception_v3_feature_extractor_test",
srcs = [
"ssd_inception_v3_feature_extractor_test.py",
],
deps = [
":ssd_feature_extractor_test",
":ssd_inception_v3_feature_extractor",
"//tensorflow",
],
)
py_test(
name = "ssd_mobilenet_v1_feature_extractor_test",
srcs = ["ssd_mobilenet_v1_feature_extractor_test.py"],
deps = [
":ssd_feature_extractor_test",
":ssd_mobilenet_v1_feature_extractor",
"//tensorflow",
],
)
py_test(
name = "embedded_ssd_mobilenet_v1_feature_extractor_test",
srcs = ["embedded_ssd_mobilenet_v1_feature_extractor_test.py"],
deps = [
":embedded_ssd_mobilenet_v1_feature_extractor",
":ssd_feature_extractor_test",
"//tensorflow",
],
)
py_test(
name = "faster_rcnn_nas_feature_extractor_test",
srcs = [
"faster_rcnn_nas_feature_extractor_test.py",
],
deps = [
":faster_rcnn_nas_feature_extractor",
"//tensorflow",
],
)
py_library(
name = "faster_rcnn_nas_feature_extractor",
srcs = [
"faster_rcnn_nas_feature_extractor.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//third_party/tensorflow_models/slim:nasnet",
],
)
py_library(
name = "faster_rcnn_inception_resnet_v2_feature_extractor",
srcs = [
"faster_rcnn_inception_resnet_v2_feature_extractor.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//third_party/tensorflow_models/slim:inception_resnet_v2",
],
)
py_test(
name = "faster_rcnn_inception_resnet_v2_feature_extractor_test",
srcs = [
"faster_rcnn_inception_resnet_v2_feature_extractor_test.py",
],
deps = [
":faster_rcnn_inception_resnet_v2_feature_extractor",
"//tensorflow",
],
)
py_library(
name = "faster_rcnn_inception_v2_feature_extractor",
srcs = [
"faster_rcnn_inception_v2_feature_extractor.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//third_party/tensorflow_models/slim:inception_v2",
],
)
py_test(
name = "faster_rcnn_inception_v2_feature_extractor_test",
srcs = [
"faster_rcnn_inception_v2_feature_extractor_test.py",
],
deps = [
":faster_rcnn_inception_v2_feature_extractor",
"//tensorflow",
],
)
py_library(
name = "faster_rcnn_resnet_v1_feature_extractor",
srcs = [
"faster_rcnn_resnet_v1_feature_extractor.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//third_party/tensorflow_models/slim:resnet_utils",
"//third_party/tensorflow_models/slim:resnet_v1",
],
)
py_test(
name = "faster_rcnn_resnet_v1_feature_extractor_test",
srcs = [
"faster_rcnn_resnet_v1_feature_extractor_test.py",
],
deps = [
":faster_rcnn_resnet_v1_feature_extractor",
"//tensorflow",
],
)
research/object_detection/models/faster_rcnn_nas_feature_extractor.py
View file @ 3f78f4cf
......@@ -108,7 +108,7 @@ def _build_nasnet_base(hidden_previous,
return net
# TODO: Only fixed_shape_resizer is currently supported for NASNet
# TODO(shlens): Only fixed_shape_resizer is currently supported for NASNet
# featurization. The reason for this is that nasnet.py only supports
# inputs with fully known shapes. We need to update nasnet.py to handle
# shapes not known at compile time.
......@@ -182,10 +182,14 @@ class FasterRCNNNASFeatureExtractor(
with slim.arg_scope(nasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._train_batch_norm)):
_, end_points = nasnet.build_nasnet_large(
preprocessed_inputs, num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_11')
with arg_scope([slim.conv2d,
slim.batch_norm,
slim.separable_conv2d],
reuse=self._reuse_weights):
_, end_points = nasnet.build_nasnet_large(
preprocessed_inputs, num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_11')
# Note that both 'Cell_10' and 'Cell_11' have equal depth = 2016.
rpn_feature_map = tf.concat([end_points['Cell_10'],
......
research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py
View file @ 3f78f4cf
......@@ -111,7 +111,7 @@ class FasterRCNNResnetV1FeatureExtractor(
with tf.control_dependencies([shape_assert]):
# Disables batchnorm for fine-tuning with smaller batch sizes.
# TODO: Figure out if it is needed when image
# TODO(chensun): Figure out if it is needed when image
# batch size is bigger.
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
......
research/object_detection/models/feature_map_generators_test.py
View file @ 3f78f4cf
......@@ -40,7 +40,7 @@ EMBEDDED_SSD_MOBILENET_V1_LAYOUT = {
}
# TODO: add tests with different anchor strides.
# TODO(rathodv): add tests with different anchor strides.
class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
def test_get_expected_feature_map_shapes_with_inception_v2(self):
......
research/object_detection/models/ssd_feature_extractor_test.py
View file @ 3f78f4cf
......@@ -27,13 +27,17 @@ from object_detection.utils import test_case
class SsdFeatureExtractorTestBase(test_case.TestCase):
@abstractmethod
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
use_explicit_padding=False):
"""Constructs a new feature extractor.
Args:
depth_multiplier: float depth multiplier for feature extractor
pad_to_multiple: the nearest multiple to zero pad the input height and
width dimensions to.
use_explicit_padding: use 'VALID' padding for convolutions, but prepad
inputs so that the output dimensions are the same as if 'SAME' padding
were used.
Returns:
an ssd_meta_arch.SSDFeatureExtractor object.
"""
......@@ -41,10 +45,11 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
def check_extract_features_returns_correct_shape(
self, batch_size, image_height, image_width, depth_multiplier,
pad_to_multiple, expected_feature_map_shapes):
pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False):
def graph_fn(image_tensor):
feature_extractor = self._create_feature_extractor(depth_multiplier,
pad_to_multiple)
pad_to_multiple,
use_explicit_padding)
feature_maps = feature_extractor.extract_features(image_tensor)
return feature_maps
......@@ -57,10 +62,11 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
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):
pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False):
def graph_fn(image_height, image_width):
feature_extractor = self._create_feature_extractor(depth_multiplier,
pad_to_multiple)
pad_to_multiple,
use_explicit_padding)
image_tensor = tf.random_uniform([batch_size, image_height, image_width,
3], dtype=tf.float32)
feature_maps = feature_extractor.extract_features(image_tensor)
......
research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py
View file @ 3f78f4cf
......@@ -53,8 +53,9 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
(e.g. 1), it is desirable to disable batch norm update and use
pretrained batch norm params.
reuse_weights: Whether to reuse variables. Default is None.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
inputs so that the output dimensions are the same as if 'SAME' padding
were used.
use_depthwise: Whether to use depthwise convolutions. Default is False.
"""
super(SSDMobileNetV1FeatureExtractor, self).__init__(
......@@ -100,7 +101,7 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
}
with slim.arg_scope(self._conv_hyperparams):
# TODO: Enable fused batch norm once quantization supports it.
# TODO(skligys): Enable fused batch norm once quantization supports it.
with slim.arg_scope([slim.batch_norm], fused=False):
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
......@@ -109,6 +110,7 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
......
research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py
View file @ 3f78f4cf
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""SSD Feature Pyramid Network (FPN) feature extractors based on Resnet v1.
See https://arxiv.org/abs/1708.02002 for details.
......@@ -87,7 +101,7 @@ class _SSDResnetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
return resized_inputs - [[channel_means]]
def _filter_features(self, image_features):
# TODO: Change resnet endpoint to strip scope prefixes instead
# TODO(rathodv): Change resnet endpoint to strip scope prefixes instead
# of munging the scope here.
filtered_image_features = dict({})
for key, feature in image_features.items():
......
research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py
View file @ 3f78f4cf
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for ssd resnet v1 FPN feature extractors."""
import abc
import numpy as np
......
research/object_detection/protos/BUILD deleted 100644 → 0
View file @ 73748d01
This diff is collapsed.
research/object_detection/protos/hyperparams.proto
View file @ 3f78f4cf
......@@ -65,6 +65,7 @@ message Initializer {
oneof initializer_oneof {
TruncatedNormalInitializer truncated_normal_initializer = 1;
VarianceScalingInitializer variance_scaling_initializer = 2;
RandomNormalInitializer random_normal_initializer = 3;
}
}
......@@ -89,6 +90,13 @@ message VarianceScalingInitializer {
optional Mode mode = 3 [default = FAN_IN];
}
// Configuration proto for random normal initializer. See
// https://www.tensorflow.org/api_docs/python/tf/random_normal_initializer
message RandomNormalInitializer {
optional float mean = 1 [default = 0.0];
optional float stddev = 2 [default = 1.0];
}
// Configuration proto for batch norm to apply after convolution op. See
// https://www.tensorflow.org/api_docs/python/tf/contrib/layers/batch_norm
message BatchNorm {
......
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