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Unverified Commit 97760186 authored by Jonathan Huang's avatar Jonathan Huang Committed by GitHub
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Merge pull request #4460 from pkulzc/master 

Release evaluation code for OI Challenge 2018 and minor fixes.
parents ed901b73 a703fc0c
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Showing with 3299 additions and 202 deletions
research/object_detection/builders/dataset_builder.py
View file @ 97760186
......@@ -56,15 +56,26 @@ def _get_padding_shapes(dataset, max_num_boxes=None, num_classes=None,
else:
height, width = spatial_image_shape # pylint: disable=unpacking-non-sequence
num_additional_channels = 0
if fields.InputDataFields.image_additional_channels in dataset.output_shapes:
num_additional_channels = dataset.output_shapes[
fields.InputDataFields.image_additional_channels].dims[2].value
padding_shapes = {
fields.InputDataFields.image: [height, width, 3],
# Additional channels are merged before batching.
fields.InputDataFields.image: [
height, width, 3 + num_additional_channels
],
fields.InputDataFields.image_additional_channels: [
height, width, num_additional_channels
],
fields.InputDataFields.source_id: [],
fields.InputDataFields.filename: [],
fields.InputDataFields.key: [],
fields.InputDataFields.groundtruth_difficult: [max_num_boxes],
fields.InputDataFields.groundtruth_boxes: [max_num_boxes, 4],
fields.InputDataFields.groundtruth_instance_masks: [max_num_boxes, height,
width],
fields.InputDataFields.groundtruth_instance_masks: [
max_num_boxes, height, width
],
fields.InputDataFields.groundtruth_is_crowd: [max_num_boxes],
fields.InputDataFields.groundtruth_group_of: [max_num_boxes],
fields.InputDataFields.groundtruth_area: [max_num_boxes],
......@@ -74,7 +85,8 @@ def _get_padding_shapes(dataset, max_num_boxes=None, num_classes=None,
fields.InputDataFields.groundtruth_label_scores: [max_num_boxes],
fields.InputDataFields.true_image_shape: [3],
fields.InputDataFields.multiclass_scores: [
max_num_boxes, num_classes + 1 if num_classes is not None else None],
max_num_boxes, num_classes + 1 if num_classes is not None else None
],
}
# Determine whether groundtruth_classes are integers or one-hot encodings, and
# apply batching appropriately.
......@@ -90,7 +102,9 @@ def _get_padding_shapes(dataset, max_num_boxes=None, num_classes=None,
'rank 2 tensor (one-hot encodings)')
if fields.InputDataFields.original_image in dataset.output_shapes:
padding_shapes[fields.InputDataFields.original_image] = [None, None, 3]
padding_shapes[fields.InputDataFields.original_image] = [
None, None, 3 + num_additional_channels
]
if fields.InputDataFields.groundtruth_keypoints in dataset.output_shapes:
tensor_shape = dataset.output_shapes[fields.InputDataFields.
groundtruth_keypoints]
......@@ -108,9 +122,13 @@ def _get_padding_shapes(dataset, max_num_boxes=None, num_classes=None,
for tensor_key, _ in dataset.output_shapes.items()}
def build(input_reader_config, transform_input_data_fn=None,
batch_size=None, max_num_boxes=None, num_classes=None,
spatial_image_shape=None):
def build(input_reader_config,
transform_input_data_fn=None,
batch_size=None,
max_num_boxes=None,
num_classes=None,
spatial_image_shape=None,
num_additional_channels=0):
"""Builds a tf.data.Dataset.
Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all
......@@ -128,6 +146,7 @@ def build(input_reader_config, transform_input_data_fn=None,
spatial_image_shape: A list of two integers of the form [height, width]
containing expected spatial shape of the image after applying
transform_input_data_fn. If None, will use dynamic shapes.
num_additional_channels: Number of additional channels to use in the input.
Returns:
A tf.data.Dataset based on the input_reader_config.
......@@ -152,7 +171,9 @@ def build(input_reader_config, transform_input_data_fn=None,
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=input_reader_config.load_instance_masks,
instance_mask_type=input_reader_config.mask_type,
label_map_proto_file=label_map_proto_file)
label_map_proto_file=label_map_proto_file,
use_display_name=input_reader_config.use_display_name,
num_additional_channels=num_additional_channels)
def process_fn(value):
processed = decoder.decode(value)
......
research/object_detection/builders/dataset_builder_test.py
View file @ 97760186
......@@ -30,49 +30,50 @@ from object_detection.utils import dataset_util
class DatasetBuilderTest(tf.test.TestCase):
def create_tf_record(self):
def create_tf_record(self, has_additional_channels=False):
path = os.path.join(self.get_temp_dir(), 'tfrecord')
writer = tf.python_io.TFRecordWriter(path)
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
additional_channels_tensor = np.random.randint(
255, size=(4, 5, 1)).astype(np.uint8)
flat_mask = (4 * 5) * [1.0]
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
encoded_additional_channels_jpeg = tf.image.encode_jpeg(
tf.constant(additional_channels_tensor)).eval()
features = {
'image/encoded':
feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format':
feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(
value=['jpeg'.encode('utf-8')])),
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])
),
'image/height':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=[4])),
'image/width':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
}
if has_additional_channels:
features['image/additional_channels/encoded'] = feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(
value=[encoded_additional_channels_jpeg] * 2))
example = example_pb2.Example(
features=feature_pb2.Features(feature=features))
writer.write(example.SerializeToString())
writer.close()
......@@ -218,6 +219,31 @@ class DatasetBuilderTest(tf.test.TestCase):
[2, 2, 4, 5],
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
def test_build_tf_record_input_reader_with_additional_channels(self):
tf_record_path = self.create_tf_record(has_additional_channels=True)
input_reader_text_proto = """
shuffle: false
num_readers: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_util.make_initializable_iterator(
dataset_builder.build(
input_reader_proto, batch_size=2,
num_additional_channels=2)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertEquals((2, 4, 5, 5),
output_dict[fields.InputDataFields.image].shape)
def test_raises_error_with_no_input_paths(self):
input_reader_text_proto = """
shuffle: false
......
research/object_detection/builders/image_resizer_builder.py
View file @ 97760186
......@@ -79,12 +79,17 @@ def build(image_resizer_config):
keep_aspect_ratio_config.max_dimension):
raise ValueError('min_dimension > max_dimension')
method = _tf_resize_method(keep_aspect_ratio_config.resize_method)
per_channel_pad_value = (0, 0, 0)
if keep_aspect_ratio_config.per_channel_pad_value:
per_channel_pad_value = tuple(keep_aspect_ratio_config.
per_channel_pad_value)
image_resizer_fn = functools.partial(
preprocessor.resize_to_range,
min_dimension=keep_aspect_ratio_config.min_dimension,
max_dimension=keep_aspect_ratio_config.max_dimension,
method=method,
pad_to_max_dimension=keep_aspect_ratio_config.pad_to_max_dimension)
pad_to_max_dimension=keep_aspect_ratio_config.pad_to_max_dimension,
per_channel_pad_value=per_channel_pad_value)
if not keep_aspect_ratio_config.convert_to_grayscale:
return image_resizer_fn
elif image_resizer_oneof == 'fixed_shape_resizer':
......
research/object_detection/builders/image_resizer_builder_test.py
View file @ 97760186
......@@ -52,6 +52,9 @@ class ImageResizerBuilderTest(tf.test.TestCase):
min_dimension: 10
max_dimension: 20
pad_to_max_dimension: true
per_channel_pad_value: 3
per_channel_pad_value: 4
per_channel_pad_value: 5
}
"""
input_shape = (50, 25, 3)
......
research/object_detection/core/box_list_ops.py
View file @ 97760186
......@@ -778,7 +778,7 @@ def to_absolute_coordinates(boxlist,
height,
width,
check_range=True,
maximum_normalized_coordinate=1.01,
maximum_normalized_coordinate=1.1,
scope=None):
"""Converts normalized box coordinates to absolute pixel coordinates.
......@@ -792,7 +792,7 @@ def to_absolute_coordinates(boxlist,
width: Maximum value for width of absolute box coordinates.
check_range: If True, checks if the coordinates are normalized or not.
maximum_normalized_coordinate: Maximum coordinate value to be considered
as normalized, default to 1.01.
as normalized, default to 1.1.
scope: name scope.
Returns:
......
research/object_detection/core/box_list_ops_test.py
View file @ 97760186
......@@ -931,6 +931,21 @@ class CoordinatesConversionTest(tf.test.TestCase):
out = sess.run(boxlist.get())
self.assertAllClose(out, coordinates)
def test_to_absolute_coordinates_maximum_coordinate_check(self):
coordinates = tf.constant([[0, 0, 1.2, 1.2],
[0.25, 0.25, 0.75, 0.75]], tf.float32)
img = tf.ones((128, 100, 100, 3))
boxlist = box_list.BoxList(coordinates)
absolute_boxlist = box_list_ops.to_absolute_coordinates(
boxlist,
tf.shape(img)[1],
tf.shape(img)[2],
maximum_normalized_coordinate=1.1)
with self.test_session() as sess:
with self.assertRaisesOpError('assertion failed'):
sess.run(absolute_boxlist.get())
class BoxRefinementTest(tf.test.TestCase):
......
research/object_detection/core/box_predictor.py
View file @ 97760186
......@@ -79,10 +79,12 @@ class BoxPredictor(object):
Returns:
A dictionary containing at least the following tensors.
box_encodings: A list of float tensors of shape
[batch_size, num_anchors_i, q, code_size] representing the location of
the objects, where q is 1 or the number of classes. Each entry in the
list corresponds to a feature map in the input `image_features` list.
box_encodings: A list of float tensors. Each entry in the list
corresponds to a feature map in the input `image_features` list. All
tensors in the list have one of the two following shapes:
a. [batch_size, num_anchors_i, q, code_size] representing the location
of the objects, where q is 1 or the number of classes.
b. [batch_size, num_anchors_i, code_size].
class_predictions_with_background: A list of float tensors of shape
[batch_size, num_anchors_i, num_classes + 1] representing the class
predictions for the proposals. Each entry in the list corresponds to a
......@@ -120,10 +122,12 @@ class BoxPredictor(object):
Returns:
A dictionary containing at least the following tensors.
box_encodings: A list of float tensors of shape
[batch_size, num_anchors_i, q, code_size] representing the location of
the objects, where q is 1 or the number of classes. Each entry in the
list corresponds to a feature map in the input `image_features` list.
box_encodings: A list of float tensors. Each entry in the list
corresponds to a feature map in the input `image_features` list. All
tensors in the list have one of the two following shapes:
a. [batch_size, num_anchors_i, q, code_size] representing the location
of the objects, where q is 1 or the number of classes.
b. [batch_size, num_anchors_i, code_size].
class_predictions_with_background: A list of float tensors of shape
[batch_size, num_anchors_i, num_classes + 1] representing the class
predictions for the proposals. Each entry in the list corresponds to a
......@@ -765,6 +769,13 @@ class ConvolutionalBoxPredictor(BoxPredictor):
}
# TODO(rathodv): Replace with slim.arg_scope_func_key once its available
# externally.
def _arg_scope_func_key(op):
"""Returns a key that can be used to index arg_scope dictionary."""
return getattr(op, '_key_op', str(op))
# TODO(rathodv): Merge the implementation with ConvolutionalBoxPredictor above
# since they are very similar.
class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
......@@ -773,8 +784,12 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
Defines the box predictor as defined in
https://arxiv.org/abs/1708.02002. This class differs from
ConvolutionalBoxPredictor in that it shares weights and biases while
predicting from different feature maps. Separate multi-layer towers are
constructed for the box encoding and class predictors respectively.
predicting from different feature maps. However, batch_norm parameters are not
shared because the statistics of the activations vary among the different
feature maps.
Also note that separate multi-layer towers are constructed for the box
encoding and class predictors respectively.
"""
def __init__(self,
......@@ -833,14 +848,15 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
Returns:
box_encodings: A list of float tensors of shape
[batch_size, num_anchors_i, q, code_size] representing the location of
the objects, where q is 1 or the number of classes. Each entry in the
list corresponds to a feature map in the input `image_features` list.
[batch_size, num_anchors_i, code_size] representing the location of
the objects. Each entry in the list corresponds to a feature map in the
input `image_features` list.
class_predictions_with_background: A list of float tensors of shape
[batch_size, num_anchors_i, num_classes + 1] representing the class
predictions for the proposals. Each entry in the list corresponds to a
feature map in the input `image_features` list.
Raises:
ValueError: If the image feature maps do not have the same number of
channels or if the num predictions per locations is differs between the
......@@ -858,15 +874,18 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
'channels, found: {}'.format(feature_channels))
box_encodings_list = []
class_predictions_list = []
for (image_feature, num_predictions_per_location) in zip(
image_features, num_predictions_per_location_list):
for feature_index, (image_feature,
num_predictions_per_location) in enumerate(
zip(image_features,
num_predictions_per_location_list)):
# Add a slot for the background class.
with tf.variable_scope('WeightSharedConvolutionalBoxPredictor',
reuse=tf.AUTO_REUSE):
num_class_slots = self.num_classes + 1
box_encodings_net = image_feature
class_predictions_net = image_feature
with slim.arg_scope(self._conv_hyperparams_fn()):
with slim.arg_scope(self._conv_hyperparams_fn()) as sc:
apply_batch_norm = _arg_scope_func_key(slim.batch_norm) in sc
for i in range(self._num_layers_before_predictor):
box_encodings_net = slim.conv2d(
box_encodings_net,
......@@ -874,14 +893,22 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
[self._kernel_size, self._kernel_size],
stride=1,
padding='SAME',
scope='BoxEncodingPredictionTower/conv2d_{}'.format(i))
activation_fn=None,
normalizer_fn=(tf.identity if apply_batch_norm else None),
scope='BoxPredictionTower/conv2d_{}'.format(i))
if apply_batch_norm:
box_encodings_net = slim.batch_norm(
box_encodings_net,
scope='BoxPredictionTower/conv2d_{}/BatchNorm/feature_{}'.
format(i, feature_index))
box_encodings_net = tf.nn.relu6(box_encodings_net)
box_encodings = slim.conv2d(
box_encodings_net,
num_predictions_per_location * self._box_code_size,
[self._kernel_size, self._kernel_size],
activation_fn=None, stride=1, padding='SAME',
normalizer_fn=None,
scope='BoxEncodingPredictor')
scope='BoxPredictor')
for i in range(self._num_layers_before_predictor):
class_predictions_net = slim.conv2d(
......@@ -890,7 +917,15 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
[self._kernel_size, self._kernel_size],
stride=1,
padding='SAME',
activation_fn=None,
normalizer_fn=(tf.identity if apply_batch_norm else None),
scope='ClassPredictionTower/conv2d_{}'.format(i))
if apply_batch_norm:
class_predictions_net = slim.batch_norm(
class_predictions_net,
scope='ClassPredictionTower/conv2d_{}/BatchNorm/feature_{}'
.format(i, feature_index))
class_predictions_net = tf.nn.relu6(class_predictions_net)
if self._use_dropout:
class_predictions_net = slim.dropout(
class_predictions_net, keep_prob=self._dropout_keep_prob)
......@@ -912,7 +947,7 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
combined_feature_map_shape[1] *
combined_feature_map_shape[2] *
num_predictions_per_location,
1, self._box_code_size]))
self._box_code_size]))
box_encodings_list.append(box_encodings)
class_predictions_with_background = tf.reshape(
class_predictions_with_background,
......
research/object_detection/core/box_predictor_test.py
View file @ 97760186
......@@ -442,6 +442,24 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.build(conv_hyperparams, is_training=True)
def _build_conv_arg_scope_no_batch_norm(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
activation: RELU_6
regularizer {
l2_regularizer {
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
"""
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.build(conv_hyperparams, is_training=True)
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
......@@ -463,7 +481,7 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(
graph_fn, [image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(box_encodings.shape, [4, 320, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
def test_bias_predictions_to_background_with_sigmoid_score_conversion(self):
......@@ -512,7 +530,7 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(box_encodings.shape, [4, 320, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 320, num_classes_without_background+1])
......@@ -543,11 +561,12 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
image_features2 = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features1, image_features2])
self.assertAllEqual(box_encodings.shape, [4, 640, 1, 4])
self.assertAllEqual(box_encodings.shape, [4, 640, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 640, num_classes_without_background+1])
def test_predictions_from_multiple_feature_maps_share_weights(self):
def test_predictions_from_multiple_feature_maps_share_weights_not_batchnorm(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = box_predictor.WeightSharedConvolutionalBoxPredictor(
......@@ -574,26 +593,95 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Box prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_0/weights'),
'BoxPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_0/BatchNorm/beta'),
'BoxPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_1/weights'),
'BoxPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_1/BatchNorm/beta'),
'BoxPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/beta'),
'ClassPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/beta'),
'ClassPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/biases')])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_no_batchnorm_params_when_batchnorm_is_not_configured(self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_conv_arg_scope_no_batch_norm(),
depth=32,
num_layers_before_predictor=2,
box_code_size=4)
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Box prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictor/weights'),
'BoxPredictionTower/conv2d_1/biases'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictor/biases'),
'BoxPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/biases'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
......@@ -628,7 +716,7 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
......
research/object_detection/core/preprocessor.py
View file @ 97760186
......@@ -2128,7 +2128,8 @@ def resize_to_range(image,
max_dimension=None,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False,
pad_to_max_dimension=False):
pad_to_max_dimension=False,
per_channel_pad_value=(0, 0, 0)):
"""Resizes an image so its dimensions are within the provided value.
The output size can be described by two cases:
......@@ -2153,6 +2154,8 @@ def resize_to_range(image,
so the resulting image is of the spatial size
[max_dimension, max_dimension]. If masks are included they are padded
similarly.
per_channel_pad_value: A tuple of per-channel scalar value to use for
padding. By default pads zeros.
Returns:
Note that the position of the resized_image_shape changes based on whether
......@@ -2181,8 +2184,20 @@ def resize_to_range(image,
image, new_size[:-1], method=method, align_corners=align_corners)
if pad_to_max_dimension:
new_image = tf.image.pad_to_bounding_box(
new_image, 0, 0, max_dimension, max_dimension)
channels = tf.unstack(new_image, axis=2)
if len(channels) != len(per_channel_pad_value):
raise ValueError('Number of channels must be equal to the length of '
'per-channel pad value.')
new_image = tf.stack(
[
tf.pad(
channels[i], [[0, max_dimension - new_size[0]],
[0, max_dimension - new_size[1]]],
constant_values=per_channel_pad_value[i])
for i in range(len(channels))
],
axis=2)
new_image.set_shape([max_dimension, max_dimension, 3])
result = [new_image]
if masks is not None:
......
research/object_detection/core/preprocessor_test.py
View file @ 97760186
......@@ -2316,6 +2316,46 @@ class PreprocessorTest(tf.test.TestCase):
np.random.randn(*in_shape)})
self.assertAllEqual(out_image_shape, expected_shape)
def testResizeToRangeWithPadToMaxDimensionReturnsCorrectShapes(self):
in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
min_dim = 50
max_dim = 100
expected_shape_list = [[100, 100, 3], [100, 100, 3], [100, 100, 3]]
for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
out_image, _ = preprocessor.resize_to_range(
in_image,
min_dimension=min_dim,
max_dimension=max_dim,
pad_to_max_dimension=True)
self.assertAllEqual(out_image.shape.as_list(), expected_shape)
out_image_shape = tf.shape(out_image)
with self.test_session() as sess:
out_image_shape = sess.run(
out_image_shape, feed_dict={in_image: np.random.randn(*in_shape)})
self.assertAllEqual(out_image_shape, expected_shape)
def testResizeToRangeWithPadToMaxDimensionReturnsCorrectTensor(self):
in_image_np = np.array([[[0, 1, 2]]], np.float32)
ex_image_np = np.array(
[[[0, 1, 2], [123.68, 116.779, 103.939]],
[[123.68, 116.779, 103.939], [123.68, 116.779, 103.939]]], np.float32)
min_dim = 1
max_dim = 2
in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
out_image, _ = preprocessor.resize_to_range(
in_image,
min_dimension=min_dim,
max_dimension=max_dim,
pad_to_max_dimension=True,
per_channel_pad_value=(123.68, 116.779, 103.939))
with self.test_session() as sess:
out_image_np = sess.run(out_image, feed_dict={in_image: in_image_np})
self.assertAllClose(ex_image_np, out_image_np)
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
"""Tests image resizing, checking output sizes."""
in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
......
research/object_detection/core/standard_fields.py
View file @ 97760186
......@@ -34,6 +34,7 @@ class InputDataFields(object):
Attributes:
image: image.
image_additional_channels: additional channels.
original_image: image in the original input size.
key: unique key corresponding to image.
source_id: source of the original image.
......@@ -66,6 +67,7 @@ class InputDataFields(object):
multiclass_scores: the label score per class for each box.
"""
image = 'image'
image_additional_channels = 'image_additional_channels'
original_image = 'original_image'
key = 'key'
source_id = 'source_id'
......@@ -161,6 +163,8 @@ class TfExampleFields(object):
height: height of image in pixels, e.g. 462
width: width of image in pixels, e.g. 581
source_id: original source of the image
image_class_text: image-level label in text format
image_class_label: image-level label in numerical format
object_class_text: labels in text format, e.g. ["person", "cat"]
object_class_label: labels in numbers, e.g. [16, 8]
object_bbox_xmin: xmin coordinates of groundtruth box, e.g. 10, 30
......@@ -195,6 +199,8 @@ class TfExampleFields(object):
height = 'image/height'
width = 'image/width'
source_id = 'image/source_id'
image_class_text = 'image/class/text'
image_class_label = 'image/class/label'
object_class_text = 'image/object/class/text'
object_class_label = 'image/object/class/label'
object_bbox_ymin = 'image/object/bbox/ymin'
......
research/object_detection/data/oid_object_detection_challenge_500_label_map.pbtxt 0 → 100644
View file @ 97760186
item {
name: "/m/061hd_"
id: 1
display_name: "Infant bed"
}
item {
name: "/m/06m11"
id: 2
display_name: "Rose"
}
item {
name: "/m/03120"
id: 3
display_name: "Flag"
}
item {
name: "/m/01kb5b"
id: 4
display_name: "Flashlight"
}
item {
name: "/m/0120dh"
id: 5
display_name: "Sea turtle"
}
item {
name: "/m/0dv5r"
id: 6
display_name: "Camera"
}
item {
name: "/m/0jbk"
id: 7
display_name: "Animal"
}
item {
name: "/m/0174n1"
id: 8
display_name: "Glove"
}
item {
name: "/m/09f_2"
id: 9
display_name: "Crocodile"
}
item {
name: "/m/01xq0k1"
id: 10
display_name: "Cattle"
}
item {
name: "/m/03jm5"
id: 11
display_name: "House"
}
item {
name: "/m/02g30s"
id: 12
display_name: "Guacamole"
}
item {
name: "/m/05z6w"
id: 13
display_name: "Penguin"
}
item {
name: "/m/01jfm_"
id: 14
display_name: "Vehicle registration plate"
}
item {
name: "/m/076lb9"
id: 15
display_name: "Training bench"
}
item {
name: "/m/0gj37"
id: 16
display_name: "Ladybug"
}
item {
name: "/m/0k0pj"
id: 17
display_name: "Human nose"
}
item {
name: "/m/0kpqd"
id: 18
display_name: "Watermelon"
}
item {
name: "/m/0l14j_"
id: 19
display_name: "Flute"
}
item {
name: "/m/0cyf8"
id: 20
display_name: "Butterfly"
}
item {
name: "/m/0174k2"
id: 21
display_name: "Washing machine"
}
item {
name: "/m/0dq75"
id: 22
display_name: "Raccoon"
}
item {
name: "/m/076bq"
id: 23
display_name: "Segway"
}
item {
name: "/m/07crc"
id: 24
display_name: "Taco"
}
item {
name: "/m/0d8zb"
id: 25
display_name: "Jellyfish"
}
item {
name: "/m/0fszt"
id: 26
display_name: "Cake"
}
item {
name: "/m/0k1tl"
id: 27
display_name: "Pen"
}
item {
name: "/m/020kz"
id: 28
display_name: "Cannon"
}
item {
name: "/m/09728"
id: 29
display_name: "Bread"
}
item {
name: "/m/07j7r"
id: 30
display_name: "Tree"
}
item {
name: "/m/0fbdv"
id: 31
display_name: "Shellfish"
}
item {
name: "/m/03ssj5"
id: 32
display_name: "Bed"
}
item {
name: "/m/03qrc"
id: 33
display_name: "Hamster"
}
item {
name: "/m/02dl1y"
id: 34
display_name: "Hat"
}
item {
name: "/m/01k6s3"
id: 35
display_name: "Toaster"
}
item {
name: "/m/02jfl0"
id: 36
display_name: "Sombrero"
}
item {
name: "/m/01krhy"
id: 37
display_name: "Tiara"
}
item {
name: "/m/04kkgm"
id: 38
display_name: "Bowl"
}
item {
name: "/m/0ft9s"
id: 39
display_name: "Dragonfly"
}
item {
name: "/m/0d_2m"
id: 40
display_name: "Moths and butterflies"
}
item {
name: "/m/0czz2"
id: 41
display_name: "Antelope"
}
item {
name: "/m/0f4s2w"
id: 42
display_name: "Vegetable"
}
item {
name: "/m/07dd4"
id: 43
display_name: "Torch"
}
item {
name: "/m/0cgh4"
id: 44
display_name: "Building"
}
item {
name: "/m/03bbps"
id: 45
display_name: "Power plugs and sockets"
}
item {
name: "/m/02pjr4"
id: 46
display_name: "Blender"
}
item {
name: "/m/04p0qw"
id: 47
display_name: "Billiard table"
}
item {
name: "/m/02pdsw"
id: 48
display_name: "Cutting board"
}
item {
name: "/m/01yx86"
id: 49
display_name: "Bronze sculpture"
}
item {
name: "/m/09dzg"
id: 50
display_name: "Turtle"
}
item {
name: "/m/0hkxq"
id: 51
display_name: "Broccoli"
}
item {
name: "/m/07dm6"
id: 52
display_name: "Tiger"
}
item {
name: "/m/054_l"
id: 53
display_name: "Mirror"
}
item {
name: "/m/01dws"
id: 54
display_name: "Bear"
}
item {
name: "/m/027pcv"
id: 55
display_name: "Zucchini"
}
item {
name: "/m/01d40f"
id: 56
display_name: "Dress"
}
item {
name: "/m/02rgn06"
id: 57
display_name: "Volleyball"
}
item {
name: "/m/0342h"
id: 58
display_name: "Guitar"
}
item {
name: "/m/06bt6"
id: 59
display_name: "Reptile"
}
item {
name: "/m/0323sq"
id: 60
display_name: "Golf cart"
}
item {
name: "/m/02zvsm"
id: 61
display_name: "Tart"
}
item {
name: "/m/02fq_6"
id: 62
display_name: "Fedora"
}
item {
name: "/m/01lrl"
id: 63
display_name: "Carnivore"
}
item {
name: "/m/0k4j"
id: 64
display_name: "Car"
}
item {
name: "/m/04h7h"
id: 65
display_name: "Lighthouse"
}
item {
name: "/m/07xyvk"
id: 66
display_name: "Coffeemaker"
}
item {
name: "/m/03y6mg"
id: 67
display_name: "Food processor"
}
item {
name: "/m/07r04"
id: 68
display_name: "Truck"
}
item {
name: "/m/03__z0"
id: 69
display_name: "Bookcase"
}
item {
name: "/m/019w40"
id: 70
display_name: "Surfboard"
}
item {
name: "/m/09j5n"
id: 71
display_name: "Footwear"
}
item {
name: "/m/0cvnqh"
id: 72
display_name: "Bench"
}
item {
name: "/m/01llwg"
id: 73
display_name: "Necklace"
}
item {
name: "/m/0c9ph5"
id: 74
display_name: "Flower"
}
item {
name: "/m/015x5n"
id: 75
display_name: "Radish"
}
item {
name: "/m/0gd2v"
id: 76
display_name: "Marine mammal"
}
item {
name: "/m/04v6l4"
id: 77
display_name: "Frying pan"
}
item {
name: "/m/02jz0l"
id: 78
display_name: "Tap"
}
item {
name: "/m/0dj6p"
id: 79
display_name: "Peach"
}
item {
name: "/m/04ctx"
id: 80
display_name: "Knife"
}
item {
name: "/m/080hkjn"
id: 81
display_name: "Handbag"
}
item {
name: "/m/01c648"
id: 82
display_name: "Laptop"
}
item {
name: "/m/01j61q"
id: 83
display_name: "Tent"
}
item {
name: "/m/012n7d"
id: 84
display_name: "Ambulance"
}
item {
name: "/m/025nd"
id: 85
display_name: "Christmas tree"
}
item {
name: "/m/09csl"
id: 86
display_name: "Eagle"
}
item {
name: "/m/01lcw4"
id: 87
display_name: "Limousine"
}
item {
name: "/m/0h8n5zk"
id: 88
display_name: "Kitchen & dining room table"
}
item {
name: "/m/0633h"
id: 89
display_name: "Polar bear"
}
item {
name: "/m/01fdzj"
id: 90
display_name: "Tower"
}
item {
name: "/m/01226z"
id: 91
display_name: "Football"
}
item {
name: "/m/0mw_6"
id: 92
display_name: "Willow"
}
item {
name: "/m/04hgtk"
id: 93
display_name: "Human head"
}
item {
name: "/m/02pv19"
id: 94
display_name: "Stop sign"
}
item {
name: "/m/09qck"
id: 95
display_name: "Banana"
}
item {
name: "/m/063rgb"
id: 96
display_name: "Mixer"
}
item {
name: "/m/0lt4_"
id: 97
display_name: "Binoculars"
}
item {
name: "/m/0270h"
id: 98
display_name: "Dessert"
}
item {
name: "/m/01h3n"
id: 99
display_name: "Bee"
}
item {
name: "/m/01mzpv"
id: 100
display_name: "Chair"
}
item {
name: "/m/04169hn"
id: 101
display_name: "Wood-burning stove"
}
item {
name: "/m/0fm3zh"
id: 102
display_name: "Flowerpot"
}
item {
name: "/m/0d20w4"
id: 103
display_name: "Beaker"
}
item {
name: "/m/0_cp5"
id: 104
display_name: "Oyster"
}
item {
name: "/m/01dy8n"
id: 105
display_name: "Woodpecker"
}
item {
name: "/m/03m5k"
id: 106
display_name: "Harp"
}
item {
name: "/m/03dnzn"
id: 107
display_name: "Bathtub"
}
item {
name: "/m/0h8mzrc"
id: 108
display_name: "Wall clock"
}
item {
name: "/m/0h8mhzd"
id: 109
display_name: "Sports uniform"
}
item {
name: "/m/03d443"
id: 110
display_name: "Rhinoceros"
}
item {
name: "/m/01gllr"
id: 111
display_name: "Beehive"
}
item {
name: "/m/0642b4"
id: 112
display_name: "Cupboard"
}
item {
name: "/m/09b5t"
id: 113
display_name: "Chicken"
}
item {
name: "/m/04yx4"
id: 114
display_name: "Man"
}
item {
name: "/m/01f8m5"
id: 115
display_name: "Blue jay"
}
item {
name: "/m/015x4r"
id: 116
display_name: "Cucumber"
}
item {
name: "/m/01j51"
id: 117
display_name: "Balloon"
}
item {
name: "/m/02zt3"
id: 118
display_name: "Kite"
}
item {
name: "/m/03tw93"
id: 119
display_name: "Fireplace"
}
item {
name: "/m/01jfsr"
id: 120
display_name: "Lantern"
}
item {
name: "/m/04ylt"
id: 121
display_name: "Missile"
}
item {
name: "/m/0bt_c3"
id: 122
display_name: "Book"
}
item {
name: "/m/0cmx8"
id: 123
display_name: "Spoon"
}
item {
name: "/m/0hqkz"
id: 124
display_name: "Grapefruit"
}
item {
name: "/m/071qp"
id: 125
display_name: "Squirrel"
}
item {
name: "/m/0cyhj_"
id: 126
display_name: "Orange"
}
item {
name: "/m/01xygc"
id: 127
display_name: "Coat"
}
item {
name: "/m/0420v5"
id: 128
display_name: "Punching bag"
}
item {
name: "/m/0898b"
id: 129
display_name: "Zebra"
}
item {
name: "/m/01knjb"
id: 130
display_name: "Billboard"
}
item {
name: "/m/0199g"
id: 131
display_name: "Bicycle"
}
item {
name: "/m/03c7gz"
id: 132
display_name: "Door handle"
}
item {
name: "/m/02x984l"
id: 133
display_name: "Mechanical fan"
}
item {
name: "/m/04zwwv"
id: 134
display_name: "Ring binder"
}
item {
name: "/m/04bcr3"
id: 135
display_name: "Table"
}
item {
name: "/m/0gv1x"
id: 136
display_name: "Parrot"
}
item {
name: "/m/01nq26"
id: 137
display_name: "Sock"
}
item {
name: "/m/02s195"
id: 138
display_name: "Vase"
}
item {
name: "/m/083kb"
id: 139
display_name: "Weapon"
}
item {
name: "/m/06nrc"
id: 140
display_name: "Shotgun"
}
item {
name: "/m/0jyfg"
id: 141
display_name: "Glasses"
}
item {
name: "/m/0nybt"
id: 142
display_name: "Seahorse"
}
item {
name: "/m/0176mf"
id: 143
display_name: "Belt"
}
item {
name: "/m/01rzcn"
id: 144
display_name: "Watercraft"
}
item {
name: "/m/0d4v4"
id: 145
display_name: "Window"
}
item {
name: "/m/03bk1"
id: 146
display_name: "Giraffe"
}
item {
name: "/m/096mb"
id: 147
display_name: "Lion"
}
item {
name: "/m/0h9mv"
id: 148
display_name: "Tire"
}
item {
name: "/m/07yv9"
id: 149
display_name: "Vehicle"
}
item {
name: "/m/0ph39"
id: 150
display_name: "Canoe"
}
item {
name: "/m/01rkbr"
id: 151
display_name: "Tie"
}
item {
name: "/m/0gjbg72"
id: 152
display_name: "Shelf"
}
item {
name: "/m/06z37_"
id: 153
display_name: "Picture frame"
}
item {
name: "/m/01m4t"
id: 154
display_name: "Printer"
}
item {
name: "/m/035r7c"
id: 155
display_name: "Human leg"
}
item {
name: "/m/019jd"
id: 156
display_name: "Boat"
}
item {
name: "/m/02tsc9"
id: 157
display_name: "Slow cooker"
}
item {
name: "/m/015wgc"
id: 158
display_name: "Croissant"
}
item {
name: "/m/0c06p"
id: 159
display_name: "Candle"
}
item {
name: "/m/01dwwc"
id: 160
display_name: "Pancake"
}
item {
name: "/m/034c16"
id: 161
display_name: "Pillow"
}
item {
name: "/m/0242l"
id: 162
display_name: "Coin"
}
item {
name: "/m/02lbcq"
id: 163
display_name: "Stretcher"
}
item {
name: "/m/03nfch"
id: 164
display_name: "Sandal"
}
item {
name: "/m/03bt1vf"
id: 165
display_name: "Woman"
}
item {
name: "/m/01lynh"
id: 166
display_name: "Stairs"
}
item {
name: "/m/03q5t"
id: 167
display_name: "Harpsichord"
}
item {
name: "/m/0fqt361"
id: 168
display_name: "Stool"
}
item {
name: "/m/01bjv"
id: 169
display_name: "Bus"
}
item {
name: "/m/01s55n"
id: 170
display_name: "Suitcase"
}
item {
name: "/m/0283dt1"
id: 171
display_name: "Human mouth"
}
item {
name: "/m/01z1kdw"
id: 172
display_name: "Juice"
}
item {
name: "/m/016m2d"
id: 173
display_name: "Skull"
}
item {
name: "/m/02dgv"
id: 174
display_name: "Door"
}
item {
name: "/m/07y_7"
id: 175
display_name: "Violin"
}
item {
name: "/m/01_5g"
id: 176
display_name: "Chopsticks"
}
item {
name: "/m/06_72j"
id: 177
display_name: "Digital clock"
}
item {
name: "/m/0ftb8"
id: 178
display_name: "Sunflower"
}
item {
name: "/m/0c29q"
id: 179
display_name: "Leopard"
}
item {
name: "/m/0jg57"
id: 180
display_name: "Bell pepper"
}
item {
name: "/m/02l8p9"
id: 181
display_name: "Harbor seal"
}
item {
name: "/m/078jl"
id: 182
display_name: "Snake"
}
item {
name: "/m/0llzx"
id: 183
display_name: "Sewing machine"
}
item {
name: "/m/0dbvp"
id: 184
display_name: "Goose"
}
item {
name: "/m/09ct_"
id: 185
display_name: "Helicopter"
}
item {
name: "/m/0dkzw"
id: 186
display_name: "Seat belt"
}
item {
name: "/m/02p5f1q"
id: 187
display_name: "Coffee cup"
}
item {
name: "/m/0fx9l"
id: 188
display_name: "Microwave oven"
}
item {
name: "/m/01b9xk"
id: 189
display_name: "Hot dog"
}
item {
name: "/m/0b3fp9"
id: 190
display_name: "Countertop"
}
item {
name: "/m/0h8n27j"
id: 191
display_name: "Serving tray"
}
item {
name: "/m/0h8n6f9"
id: 192
display_name: "Dog bed"
}
item {
name: "/m/01599"
id: 193
display_name: "Beer"
}
item {
name: "/m/017ftj"
id: 194
display_name: "Sunglasses"
}
item {
name: "/m/044r5d"
id: 195
display_name: "Golf ball"
}
item {
name: "/m/01dwsz"
id: 196
display_name: "Waffle"
}
item {
name: "/m/0cdl1"
id: 197
display_name: "Palm tree"
}
item {
name: "/m/07gql"
id: 198
display_name: "Trumpet"
}
item {
name: "/m/0hdln"
id: 199
display_name: "Ruler"
}
item {
name: "/m/0zvk5"
id: 200
display_name: "Helmet"
}
item {
name: "/m/012w5l"
id: 201
display_name: "Ladder"
}
item {
name: "/m/021sj1"
id: 202
display_name: "Office building"
}
item {
name: "/m/0bh9flk"
id: 203
display_name: "Tablet computer"
}
item {
name: "/m/09gtd"
id: 204
display_name: "Toilet paper"
}
item {
name: "/m/0jwn_"
id: 205
display_name: "Pomegranate"
}
item {
name: "/m/02wv6h6"
id: 206
display_name: "Skirt"
}
item {
name: "/m/02wv84t"
id: 207
display_name: "Gas stove"
}
item {
name: "/m/021mn"
id: 208
display_name: "Cookie"
}
item {
name: "/m/018p4k"
id: 209
display_name: "Cart"
}
item {
name: "/m/06j2d"
id: 210
display_name: "Raven"
}
item {
name: "/m/033cnk"
id: 211
display_name: "Egg"
}
item {
name: "/m/01j3zr"
id: 212
display_name: "Burrito"
}
item {
name: "/m/03fwl"
id: 213
display_name: "Goat"
}
item {
name: "/m/058qzx"
id: 214
display_name: "Kitchen knife"
}
item {
name: "/m/06_fw"
id: 215
display_name: "Skateboard"
}
item {
name: "/m/02x8cch"
id: 216
display_name: "Salt and pepper shakers"
}
item {
name: "/m/04g2r"
id: 217
display_name: "Lynx"
}
item {
name: "/m/01b638"
id: 218
display_name: "Boot"
}
item {
name: "/m/099ssp"
id: 219
display_name: "Platter"
}
item {
name: "/m/071p9"
id: 220
display_name: "Ski"
}
item {
name: "/m/01gkx_"
id: 221
display_name: "Swimwear"
}
item {
name: "/m/0b_rs"
id: 222
display_name: "Swimming pool"
}
item {
name: "/m/03v5tg"
id: 223
display_name: "Drinking straw"
}
item {
name: "/m/01j5ks"
id: 224
display_name: "Wrench"
}
item {
name: "/m/026t6"
id: 225
display_name: "Drum"
}
item {
name: "/m/0_k2"
id: 226
display_name: "Ant"
}
item {
name: "/m/039xj_"
id: 227
display_name: "Human ear"
}
item {
name: "/m/01b7fy"
id: 228
display_name: "Headphones"
}
item {
name: "/m/0220r2"
id: 229
display_name: "Fountain"
}
item {
name: "/m/015p6"
id: 230
display_name: "Bird"
}
item {
name: "/m/0fly7"
id: 231
display_name: "Jeans"
}
item {
name: "/m/07c52"
id: 232
display_name: "Television"
}
item {
name: "/m/0n28_"
id: 233
display_name: "Crab"
}
item {
name: "/m/0hg7b"
id: 234
display_name: "Microphone"
}
item {
name: "/m/019dx1"
id: 235
display_name: "Home appliance"
}
item {
name: "/m/04vv5k"
id: 236
display_name: "Snowplow"
}
item {
name: "/m/020jm"
id: 237
display_name: "Beetle"
}
item {
name: "/m/047v4b"
id: 238
display_name: "Artichoke"
}
item {
name: "/m/01xs3r"
id: 239
display_name: "Jet ski"
}
item {
name: "/m/03kt2w"
id: 240
display_name: "Stationary bicycle"
}
item {
name: "/m/03q69"
id: 241
display_name: "Human hair"
}
item {
name: "/m/01dxs"
id: 242
display_name: "Brown bear"
}
item {
name: "/m/01h8tj"
id: 243
display_name: "Starfish"
}
item {
name: "/m/0dt3t"
id: 244
display_name: "Fork"
}
item {
name: "/m/0cjq5"
id: 245
display_name: "Lobster"
}
item {
name: "/m/0h8lkj8"
id: 246
display_name: "Corded phone"
}
item {
name: "/m/0271t"
id: 247
display_name: "Drink"
}
item {
name: "/m/03q5c7"
id: 248
display_name: "Saucer"
}
item {
name: "/m/0fj52s"
id: 249
display_name: "Carrot"
}
item {
name: "/m/03vt0"
id: 250
display_name: "Insect"
}
item {
name: "/m/01x3z"
id: 251
display_name: "Clock"
}
item {
name: "/m/0d5gx"
id: 252
display_name: "Castle"
}
item {
name: "/m/0h8my_4"
id: 253
display_name: "Tennis racket"
}
item {
name: "/m/03ldnb"
id: 254
display_name: "Ceiling fan"
}
item {
name: "/m/0cjs7"
id: 255
display_name: "Asparagus"
}
item {
name: "/m/0449p"
id: 256
display_name: "Jaguar"
}
item {
name: "/m/04szw"
id: 257
display_name: "Musical instrument"
}
item {
name: "/m/07jdr"
id: 258
display_name: "Train"
}
item {
name: "/m/01yrx"
id: 259
display_name: "Cat"
}
item {
name: "/m/06c54"
id: 260
display_name: "Rifle"
}
item {
name: "/m/04h8sr"
id: 261
display_name: "Dumbbell"
}
item {
name: "/m/050k8"
id: 262
display_name: "Mobile phone"
}
item {
name: "/m/0pg52"
id: 263
display_name: "Taxi"
}
item {
name: "/m/02f9f_"
id: 264
display_name: "Shower"
}
item {
name: "/m/054fyh"
id: 265
display_name: "Pitcher"
}
item {
name: "/m/09k_b"
id: 266
display_name: "Lemon"
}
item {
name: "/m/03xxp"
id: 267
display_name: "Invertebrate"
}
item {
name: "/m/0jly1"
id: 268
display_name: "Turkey"
}
item {
name: "/m/06k2mb"
id: 269
display_name: "High heels"
}
item {
name: "/m/04yqq2"
id: 270
display_name: "Bust"
}
item {
name: "/m/0bwd_0j"
id: 271
display_name: "Elephant"
}
item {
name: "/m/02h19r"
id: 272
display_name: "Scarf"
}
item {
name: "/m/02zn6n"
id: 273
display_name: "Barrel"
}
item {
name: "/m/07c6l"
id: 274
display_name: "Trombone"
}
item {
name: "/m/05zsy"
id: 275
display_name: "Pumpkin"
}
item {
name: "/m/025dyy"
id: 276
display_name: "Box"
}
item {
name: "/m/07j87"
id: 277
display_name: "Tomato"
}
item {
name: "/m/09ld4"
id: 278
display_name: "Frog"
}
item {
name: "/m/01vbnl"
id: 279
display_name: "Bidet"
}
item {
name: "/m/0dzct"
id: 280
display_name: "Human face"
}
item {
name: "/m/03fp41"
id: 281
display_name: "Houseplant"
}
item {
name: "/m/0h2r6"
id: 282
display_name: "Van"
}
item {
name: "/m/0by6g"
id: 283
display_name: "Shark"
}
item {
name: "/m/0cxn2"
id: 284
display_name: "Ice cream"
}
item {
name: "/m/04tn4x"
id: 285
display_name: "Swim cap"
}
item {
name: "/m/0f6wt"
id: 286
display_name: "Falcon"
}
item {
name: "/m/05n4y"
id: 287
display_name: "Ostrich"
}
item {
name: "/m/0gxl3"
id: 288
display_name: "Handgun"
}
item {
name: "/m/02d9qx"
id: 289
display_name: "Whiteboard"
}
item {
name: "/m/04m9y"
id: 290
display_name: "Lizard"
}
item {
name: "/m/05z55"
id: 291
display_name: "Pasta"
}
item {
name: "/m/01x3jk"
id: 292
display_name: "Snowmobile"
}
item {
name: "/m/0h8l4fh"
id: 293
display_name: "Light bulb"
}
item {
name: "/m/031b6r"
id: 294
display_name: "Window blind"
}
item {
name: "/m/01tcjp"
id: 295
display_name: "Muffin"
}
item {
name: "/m/01f91_"
id: 296
display_name: "Pretzel"
}
item {
name: "/m/02522"
id: 297
display_name: "Computer monitor"
}
item {
name: "/m/0319l"
id: 298
display_name: "Horn"
}
item {
name: "/m/0c_jw"
id: 299
display_name: "Furniture"
}
item {
name: "/m/0l515"
id: 300
display_name: "Sandwich"
}
item {
name: "/m/0306r"
id: 301
display_name: "Fox"
}
item {
name: "/m/0crjs"
id: 302
display_name: "Convenience store"
}
item {
name: "/m/0ch_cf"
id: 303
display_name: "Fish"
}
item {
name: "/m/02xwb"
id: 304
display_name: "Fruit"
}
item {
name: "/m/01r546"
id: 305
display_name: "Earrings"
}
item {
name: "/m/03rszm"
id: 306
display_name: "Curtain"
}
item {
name: "/m/0388q"
id: 307
display_name: "Grape"
}
item {
name: "/m/03m3pdh"
id: 308
display_name: "Sofa bed"
}
item {
name: "/m/03k3r"
id: 309
display_name: "Horse"
}
item {
name: "/m/0hf58v5"
id: 310
display_name: "Luggage and bags"
}
item {
name: "/m/01y9k5"
id: 311
display_name: "Desk"
}
item {
name: "/m/05441v"
id: 312
display_name: "Crutch"
}
item {
name: "/m/03p3bw"
id: 313
display_name: "Bicycle helmet"
}
item {
name: "/m/0175cv"
id: 314
display_name: "Tick"
}
item {
name: "/m/0cmf2"
id: 315
display_name: "Airplane"
}
item {
name: "/m/0ccs93"
id: 316
display_name: "Canary"
}
item {
name: "/m/02d1br"
id: 317
display_name: "Spatula"
}
item {
name: "/m/0gjkl"
id: 318
display_name: "Watch"
}
item {
name: "/m/0jqgx"
id: 319
display_name: "Lily"
}
item {
name: "/m/0h99cwc"
id: 320
display_name: "Kitchen appliance"
}
item {
name: "/m/047j0r"
id: 321
display_name: "Filing cabinet"
}
item {
name: "/m/0k5j"
id: 322
display_name: "Aircraft"
}
item {
name: "/m/0h8n6ft"
id: 323
display_name: "Cake stand"
}
item {
name: "/m/0gm28"
id: 324
display_name: "Candy"
}
item {
name: "/m/0130jx"
id: 325
display_name: "Sink"
}
item {
name: "/m/04rmv"
id: 326
display_name: "Mouse"
}
item {
name: "/m/081qc"
id: 327
display_name: "Wine"
}
item {
name: "/m/0qmmr"
id: 328
display_name: "Wheelchair"
}
item {
name: "/m/03fj2"
id: 329
display_name: "Goldfish"
}
item {
name: "/m/040b_t"
id: 330
display_name: "Refrigerator"
}
item {
name: "/m/02y6n"
id: 331
display_name: "French fries"
}
item {
name: "/m/0fqfqc"
id: 332
display_name: "Drawer"
}
item {
name: "/m/030610"
id: 333
display_name: "Treadmill"
}
item {
name: "/m/07kng9"
id: 334
display_name: "Picnic basket"
}
item {
name: "/m/029b3"
id: 335
display_name: "Dice"
}
item {
name: "/m/0fbw6"
id: 336
display_name: "Cabbage"
}
item {
name: "/m/07qxg_"
id: 337
display_name: "Football helmet"
}
item {
name: "/m/068zj"
id: 338
display_name: "Pig"
}
item {
name: "/m/01g317"
id: 339
display_name: "Person"
}
item {
name: "/m/01bfm9"
id: 340
display_name: "Shorts"
}
item {
name: "/m/02068x"
id: 341
display_name: "Gondola"
}
item {
name: "/m/0fz0h"
id: 342
display_name: "Honeycomb"
}
item {
name: "/m/0jy4k"
id: 343
display_name: "Doughnut"
}
item {
name: "/m/05kyg_"
id: 344
display_name: "Chest of drawers"
}
item {
name: "/m/01prls"
id: 345
display_name: "Land vehicle"
}
item {
name: "/m/01h44"
id: 346
display_name: "Bat"
}
item {
name: "/m/08pbxl"
id: 347
display_name: "Monkey"
}
item {
name: "/m/02gzp"
id: 348
display_name: "Dagger"
}
item {
name: "/m/04brg2"
id: 349
display_name: "Tableware"
}
item {
name: "/m/031n1"
id: 350
display_name: "Human foot"
}
item {
name: "/m/02jvh9"
id: 351
display_name: "Mug"
}
item {
name: "/m/046dlr"
id: 352
display_name: "Alarm clock"
}
item {
name: "/m/0h8ntjv"
id: 353
display_name: "Pressure cooker"
}
item {
name: "/m/0k65p"
id: 354
display_name: "Human hand"
}
item {
name: "/m/011k07"
id: 355
display_name: "Tortoise"
}
item {
name: "/m/03grzl"
id: 356
display_name: "Baseball glove"
}
item {
name: "/m/06y5r"
id: 357
display_name: "Sword"
}
item {
name: "/m/061_f"
id: 358
display_name: "Pear"
}
item {
name: "/m/01cmb2"
id: 359
display_name: "Miniskirt"
}
item {
name: "/m/01mqdt"
id: 360
display_name: "Traffic sign"
}
item {
name: "/m/05r655"
id: 361
display_name: "Girl"
}
item {
name: "/m/02p3w7d"
id: 362
display_name: "Roller skates"
}
item {
name: "/m/029tx"
id: 363
display_name: "Dinosaur"
}
item {
name: "/m/04m6gz"
id: 364
display_name: "Porch"
}
item {
name: "/m/015h_t"
id: 365
display_name: "Human beard"
}
item {
name: "/m/06pcq"
id: 366
display_name: "Submarine sandwich"
}
item {
name: "/m/01bms0"
id: 367
display_name: "Screwdriver"
}
item {
name: "/m/07fbm7"
id: 368
display_name: "Strawberry"
}
item {
name: "/m/09tvcd"
id: 369
display_name: "Wine glass"
}
item {
name: "/m/06nwz"
id: 370
display_name: "Seafood"
}
item {
name: "/m/0dv9c"
id: 371
display_name: "Racket"
}
item {
name: "/m/083wq"
id: 372
display_name: "Wheel"
}
item {
name: "/m/0gd36"
id: 373
display_name: "Sea lion"
}
item {
name: "/m/0138tl"
id: 374
display_name: "Toy"
}
item {
name: "/m/07clx"
id: 375
display_name: "Tea"
}
item {
name: "/m/05ctyq"
id: 376
display_name: "Tennis ball"
}
item {
name: "/m/0bjyj5"
id: 377
display_name: "Waste container"
}
item {
name: "/m/0dbzx"
id: 378
display_name: "Mule"
}
item {
name: "/m/02ctlc"
id: 379
display_name: "Cricket ball"
}
item {
name: "/m/0fp6w"
id: 380
display_name: "Pineapple"
}
item {
name: "/m/0djtd"
id: 381
display_name: "Coconut"
}
item {
name: "/m/0167gd"
id: 382
display_name: "Doll"
}
item {
name: "/m/078n6m"
id: 383
display_name: "Coffee table"
}
item {
name: "/m/0152hh"
id: 384
display_name: "Snowman"
}
item {
name: "/m/04gth"
id: 385
display_name: "Lavender"
}
item {
name: "/m/0ll1f78"
id: 386
display_name: "Shrimp"
}
item {
name: "/m/0cffdh"
id: 387
display_name: "Maple"
}
item {
name: "/m/025rp__"
id: 388
display_name: "Cowboy hat"
}
item {
name: "/m/02_n6y"
id: 389
display_name: "Goggles"
}
item {
name: "/m/0wdt60w"
id: 390
display_name: "Rugby ball"
}
item {
name: "/m/0cydv"
id: 391
display_name: "Caterpillar"
}
item {
name: "/m/01n5jq"
id: 392
display_name: "Poster"
}
item {
name: "/m/09rvcxw"
id: 393
display_name: "Rocket"
}
item {
name: "/m/013y1f"
id: 394
display_name: "Organ"
}
item {
name: "/m/06ncr"
id: 395
display_name: "Saxophone"
}
item {
name: "/m/015qff"
id: 396
display_name: "Traffic light"
}
item {
name: "/m/024g6"
id: 397
display_name: "Cocktail"
}
item {
name: "/m/05gqfk"
id: 398
display_name: "Plastic bag"
}
item {
name: "/m/0dv77"
id: 399
display_name: "Squash"
}
item {
name: "/m/052sf"
id: 400
display_name: "Mushroom"
}
item {
name: "/m/0cdn1"
id: 401
display_name: "Hamburger"
}
item {
name: "/m/03jbxj"
id: 402
display_name: "Light switch"
}
item {
name: "/m/0cyfs"
id: 403
display_name: "Parachute"
}
item {
name: "/m/0kmg4"
id: 404
display_name: "Teddy bear"
}
item {
name: "/m/02cvgx"
id: 405
display_name: "Winter melon"
}
item {
name: "/m/09kx5"
id: 406
display_name: "Deer"
}
item {
name: "/m/057cc"
id: 407
display_name: "Musical keyboard"
}
item {
name: "/m/02pkr5"
id: 408
display_name: "Plumbing fixture"
}
item {
name: "/m/057p5t"
id: 409
display_name: "Scoreboard"
}
item {
name: "/m/03g8mr"
id: 410
display_name: "Baseball bat"
}
item {
name: "/m/0frqm"
id: 411
display_name: "Envelope"
}
item {
name: "/m/03m3vtv"
id: 412
display_name: "Adhesive tape"
}
item {
name: "/m/0584n8"
id: 413
display_name: "Briefcase"
}
item {
name: "/m/014y4n"
id: 414
display_name: "Paddle"
}
item {
name: "/m/01g3x7"
id: 415
display_name: "Bow and arrow"
}
item {
name: "/m/07cx4"
id: 416
display_name: "Telephone"
}
item {
name: "/m/07bgp"
id: 417
display_name: "Sheep"
}
item {
name: "/m/032b3c"
id: 418
display_name: "Jacket"
}
item {
name: "/m/01bl7v"
id: 419
display_name: "Boy"
}
item {
name: "/m/0663v"
id: 420
display_name: "Pizza"
}
item {
name: "/m/0cn6p"
id: 421
display_name: "Otter"
}
item {
name: "/m/02rdsp"
id: 422
display_name: "Office supplies"
}
item {
name: "/m/02crq1"
id: 423
display_name: "Couch"
}
item {
name: "/m/01xqw"
id: 424
display_name: "Cello"
}
item {
name: "/m/0cnyhnx"
id: 425
display_name: "Bull"
}
item {
name: "/m/01x_v"
id: 426
display_name: "Camel"
}
item {
name: "/m/018xm"
id: 427
display_name: "Ball"
}
item {
name: "/m/09ddx"
id: 428
display_name: "Duck"
}
item {
name: "/m/084zz"
id: 429
display_name: "Whale"
}
item {
name: "/m/01n4qj"
id: 430
display_name: "Shirt"
}
item {
name: "/m/07cmd"
id: 431
display_name: "Tank"
}
item {
name: "/m/04_sv"
id: 432
display_name: "Motorcycle"
}
item {
name: "/m/0mkg"
id: 433
display_name: "Accordion"
}
item {
name: "/m/09d5_"
id: 434
display_name: "Owl"
}
item {
name: "/m/0c568"
id: 435
display_name: "Porcupine"
}
item {
name: "/m/02wbtzl"
id: 436
display_name: "Sun hat"
}
item {
name: "/m/05bm6"
id: 437
display_name: "Nail"
}
item {
name: "/m/01lsmm"
id: 438
display_name: "Scissors"
}
item {
name: "/m/0dftk"
id: 439
display_name: "Swan"
}
item {
name: "/m/0dtln"
id: 440
display_name: "Lamp"
}
item {
name: "/m/0nl46"
id: 441
display_name: "Crown"
}
item {
name: "/m/05r5c"
id: 442
display_name: "Piano"
}
item {
name: "/m/06msq"
id: 443
display_name: "Sculpture"
}
item {
name: "/m/0cd4d"
id: 444
display_name: "Cheetah"
}
item {
name: "/m/05kms"
id: 445
display_name: "Oboe"
}
item {
name: "/m/02jnhm"
id: 446
display_name: "Tin can"
}
item {
name: "/m/0fldg"
id: 447
display_name: "Mango"
}
item {
name: "/m/073bxn"
id: 448
display_name: "Tripod"
}
item {
name: "/m/029bxz"
id: 449
display_name: "Oven"
}
item {
name: "/m/020lf"
id: 450
display_name: "Computer mouse"
}
item {
name: "/m/01btn"
id: 451
display_name: "Barge"
}
item {
name: "/m/02vqfm"
id: 452
display_name: "Coffee"
}
item {
name: "/m/06__v"
id: 453
display_name: "Snowboard"
}
item {
name: "/m/043nyj"
id: 454
display_name: "Common fig"
}
item {
name: "/m/0grw1"
id: 455
display_name: "Salad"
}
item {
name: "/m/03hl4l9"
id: 456
display_name: "Marine invertebrates"
}
item {
name: "/m/0hnnb"
id: 457
display_name: "Umbrella"
}
item {
name: "/m/04c0y"
id: 458
display_name: "Kangaroo"
}
item {
name: "/m/0dzf4"
id: 459
display_name: "Human arm"
}
item {
name: "/m/07v9_z"
id: 460
display_name: "Measuring cup"
}
item {
name: "/m/0f9_l"
id: 461
display_name: "Snail"
}
item {
name: "/m/0703r8"
id: 462
display_name: "Loveseat"
}
item {
name: "/m/01xyhv"
id: 463
display_name: "Suit"
}
item {
name: "/m/01fh4r"
id: 464
display_name: "Teapot"
}
item {
name: "/m/04dr76w"
id: 465
display_name: "Bottle"
}
item {
name: "/m/0pcr"
id: 466
display_name: "Alpaca"
}
item {
name: "/m/03s_tn"
id: 467
display_name: "Kettle"
}
item {
name: "/m/07mhn"
id: 468
display_name: "Trousers"
}
item {
name: "/m/01hrv5"
id: 469
display_name: "Popcorn"
}
item {
name: "/m/019h78"
id: 470
display_name: "Centipede"
}
item {
name: "/m/09kmb"
id: 471
display_name: "Spider"
}
item {
name: "/m/0h23m"
id: 472
display_name: "Sparrow"
}
item {
name: "/m/050gv4"
id: 473
display_name: "Plate"
}
item {
name: "/m/01fb_0"
id: 474
display_name: "Bagel"
}
item {
name: "/m/02w3_ws"
id: 475
display_name: "Personal care"
}
item {
name: "/m/014j1m"
id: 476
display_name: "Apple"
}
item {
name: "/m/01gmv2"
id: 477
display_name: "Brassiere"
}
item {
name: "/m/04y4h8h"
id: 478
display_name: "Bathroom cabinet"
}
item {
name: "/m/026qbn5"
id: 479
display_name: "Studio couch"
}
item {
name: "/m/01m2v"
id: 480
display_name: "Computer keyboard"
}
item {
name: "/m/05_5p_0"
id: 481
display_name: "Table tennis racket"
}
item {
name: "/m/07030"
id: 482
display_name: "Sushi"
}
item {
name: "/m/01s105"
id: 483
display_name: "Cabinetry"
}
item {
name: "/m/033rq4"
id: 484
display_name: "Street light"
}
item {
name: "/m/0162_1"
id: 485
display_name: "Towel"
}
item {
name: "/m/02z51p"
id: 486
display_name: "Nightstand"
}
item {
name: "/m/06mf6"
id: 487
display_name: "Rabbit"
}
item {
name: "/m/02hj4"
id: 488
display_name: "Dolphin"
}
item {
name: "/m/0bt9lr"
id: 489
display_name: "Dog"
}
item {
name: "/m/08hvt4"
id: 490
display_name: "Jug"
}
item {
name: "/m/084rd"
id: 491
display_name: "Wok"
}
item {
name: "/m/01pns0"
id: 492
display_name: "Fire hydrant"
}
item {
name: "/m/014sv8"
id: 493
display_name: "Human eye"
}
item {
name: "/m/079cl"
id: 494
display_name: "Skyscraper"
}
item {
name: "/m/01940j"
id: 495
display_name: "Backpack"
}
item {
name: "/m/05vtc"
id: 496
display_name: "Potato"
}
item {
name: "/m/02w3r3"
id: 497
display_name: "Paper towel"
}
item {
name: "/m/054xkw"
id: 498
display_name: "Lifejacket"
}
item {
name: "/m/01bqk0"
id: 499
display_name: "Bicycle wheel"
}
item {
name: "/m/09g1w"
id: 500
display_name: "Toilet"
}
research/object_detection/data_decoders/tf_example_decoder.py
View file @ 97760186
......@@ -112,7 +112,8 @@ class TfExampleDecoder(data_decoder.DataDecoder):
label_map_proto_file=None,
use_display_name=False,
dct_method='',
num_keypoints=0):
num_keypoints=0,
num_additional_channels=0):
"""Constructor sets keys_to_features and items_to_handlers.
Args:
......@@ -133,6 +134,7 @@ class TfExampleDecoder(data_decoder.DataDecoder):
are ['INTEGER_FAST', 'INTEGER_ACCURATE']. The hint may be ignored, for
example, the jpeg library does not have that specific option.
num_keypoints: the number of keypoints per object.
num_additional_channels: how many additional channels to use.
Raises:
ValueError: If `instance_mask_type` option is not one of
......@@ -178,15 +180,28 @@ class TfExampleDecoder(data_decoder.DataDecoder):
'image/object/weight':
tf.VarLenFeature(tf.float32),
}
# We are checking `dct_method` instead of passing it directly in order to
# ensure TF version 1.6 compatibility.
if dct_method:
image = slim_example_decoder.Image(
image_key='image/encoded',
format_key='image/format',
channels=3,
dct_method=dct_method)
additional_channel_image = slim_example_decoder.Image(
image_key='image/additional_channels/encoded',
format_key='image/format',
channels=1,
repeated=True,
dct_method=dct_method)
else:
image = slim_example_decoder.Image(
image_key='image/encoded', format_key='image/format', channels=3)
additional_channel_image = slim_example_decoder.Image(
image_key='image/additional_channels/encoded',
format_key='image/format',
channels=1,
repeated=True)
self.items_to_handlers = {
fields.InputDataFields.image:
image,
......@@ -211,6 +226,13 @@ class TfExampleDecoder(data_decoder.DataDecoder):
fields.InputDataFields.groundtruth_weights: (
slim_example_decoder.Tensor('image/object/weight')),
}
if num_additional_channels > 0:
self.keys_to_features[
'image/additional_channels/encoded'] = tf.FixedLenFeature(
(num_additional_channels,), tf.string)
self.items_to_handlers[
fields.InputDataFields.
image_additional_channels] = additional_channel_image
self._num_keypoints = num_keypoints
if num_keypoints > 0:
self.keys_to_features['image/object/keypoint/x'] = (
......@@ -294,6 +316,9 @@ class TfExampleDecoder(data_decoder.DataDecoder):
[None] indicating if the boxes enclose a crowd.
Optional:
fields.InputDataFields.image_additional_channels - 3D uint8 tensor of
shape [None, None, num_additional_channels]. 1st dim is height; 2nd dim
is width; 3rd dim is the number of additional channels.
fields.InputDataFields.groundtruth_difficult - 1D bool tensor of shape
[None] indicating if the boxes represent `difficult` instances.
fields.InputDataFields.groundtruth_group_of - 1D bool tensor of shape
......@@ -316,6 +341,12 @@ class TfExampleDecoder(data_decoder.DataDecoder):
tensor_dict[fields.InputDataFields.num_groundtruth_boxes] = tf.shape(
tensor_dict[fields.InputDataFields.groundtruth_boxes])[0]
if fields.InputDataFields.image_additional_channels in tensor_dict:
channels = tensor_dict[fields.InputDataFields.image_additional_channels]
channels = tf.squeeze(channels, axis=3)
channels = tf.transpose(channels, perm=[1, 2, 0])
tensor_dict[fields.InputDataFields.image_additional_channels] = channels
def default_groundtruth_weights():
return tf.ones(
[tf.shape(tensor_dict[fields.InputDataFields.groundtruth_boxes])[0]],
......
research/object_detection/data_decoders/tf_example_decoder_test.py
View file @ 97760186
......@@ -23,6 +23,7 @@ from tensorflow.core.example import example_pb2
from tensorflow.core.example import feature_pb2
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import lookup_ops
from tensorflow.python.ops import parsing_ops
......@@ -72,10 +73,41 @@ class TfExampleDecoderTest(tf.test.TestCase):
def _BytesFeatureFromList(self, ndarray):
values = ndarray.flatten().tolist()
for i in range(len(values)):
values[i] = values[i].encode('utf-8')
return feature_pb2.Feature(bytes_list=feature_pb2.BytesList(value=values))
def testDecodeAdditionalChannels(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
additional_channel_tensor = np.random.randint(
256, size=(4, 5, 1)).astype(np.uint8)
encoded_additional_channel = self._EncodeImage(additional_channel_tensor)
decoded_additional_channel = self._DecodeImage(encoded_additional_channel)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
self._BytesFeature(encoded_jpeg),
'image/additional_channels/encoded':
self._BytesFeatureFromList(
np.array([encoded_additional_channel] * 2)),
'image/format':
self._BytesFeature('jpeg'),
'image/source_id':
self._BytesFeature('image_id'),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
num_additional_channels=2)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
np.concatenate([decoded_additional_channel] * 2, axis=2),
tensor_dict[fields.InputDataFields.image_additional_channels])
def testDecodeExampleWithBranchedBackupHandler(self):
example1 = example_pb2.Example(
features=feature_pb2.Features(
......@@ -304,6 +336,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual(
2, tensor_dict[fields.InputDataFields.num_groundtruth_boxes])
@test_util.enable_c_shapes
def testDecodeKeypoint(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
......@@ -331,7 +364,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
get_shape().as_list()), [None, 4])
self.assertAllEqual((tensor_dict[fields.InputDataFields.
groundtruth_keypoints].
get_shape().as_list()), [None, 3, 2])
get_shape().as_list()), [2, 3, 2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
......@@ -376,6 +409,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllClose(tensor_dict[fields.InputDataFields.groundtruth_weights],
np.ones(2, dtype=np.float32))
@test_util.enable_c_shapes
def testDecodeObjectLabel(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
......@@ -391,7 +425,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
[None])
[2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
......@@ -522,6 +556,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual([3, 1],
tensor_dict[fields.InputDataFields.groundtruth_classes])
@test_util.enable_c_shapes
def testDecodeObjectArea(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
......@@ -536,13 +571,14 @@ class TfExampleDecoderTest(tf.test.TestCase):
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_area].
get_shape().as_list()), [None])
get_shape().as_list()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(object_area,
tensor_dict[fields.InputDataFields.groundtruth_area])
@test_util.enable_c_shapes
def testDecodeObjectIsCrowd(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
......@@ -558,7 +594,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()),
[None])
[2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
......@@ -566,6 +602,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
tensor_dict[
fields.InputDataFields.groundtruth_is_crowd])
@test_util.enable_c_shapes
def testDecodeObjectDifficult(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
......@@ -581,7 +618,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_difficult].get_shape().as_list()),
[None])
[2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
......@@ -589,6 +626,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
tensor_dict[
fields.InputDataFields.groundtruth_difficult])
@test_util.enable_c_shapes
def testDecodeObjectGroupOf(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
......@@ -605,7 +643,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_group_of].get_shape().as_list()),
[None])
[2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
......@@ -637,6 +675,7 @@ class TfExampleDecoderTest(tf.test.TestCase):
object_weights,
tensor_dict[fields.InputDataFields.groundtruth_weights])
@test_util.enable_c_shapes
def testDecodeInstanceSegmentation(self):
num_instances = 4
image_height = 5
......@@ -673,11 +712,11 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual((
tensor_dict[fields.InputDataFields.groundtruth_instance_masks].
get_shape().as_list()), [None, None, None])
get_shape().as_list()), [4, 5, 3])
self.assertAllEqual((
tensor_dict[fields.InputDataFields.groundtruth_classes].
get_shape().as_list()), [None])
get_shape().as_list()), [4])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
......
research/object_detection/dataset_tools/create_oid_tf_record.py
View file @ 97760186
......@@ -16,7 +16,8 @@ r"""Creates TFRecords of Open Images dataset for object detection.
Example usage:
python object_detection/dataset_tools/create_oid_tf_record.py \
--input_annotations_csv=/path/to/input/annotations-human-bbox.csv \
--input_box_annotations_csv=/path/to/input/annotations-human-bbox.csv \
--input_image_label_annotations_csv=/path/to/input/annotations-label.csv \
--input_images_directory=/path/to/input/image_pixels_directory \
--input_label_map=/path/to/input/labels_bbox_545.labelmap \
--output_tf_record_path_prefix=/path/to/output/prefix.tfrecord
......@@ -27,7 +28,9 @@ https://github.com/openimages/dataset
This script will include every image found in the input_images_directory in the
output TFRecord, even if the image has no corresponding bounding box annotations
in the input_annotations_csv.
in the input_annotations_csv. If input_image_label_annotations_csv is specified,
it will add image-level labels as well. Note that the information of whether a
label is positivelly or negativelly verified is NOT added to tfrecord.
"""
from __future__ import absolute_import
from __future__ import division
......@@ -40,13 +43,16 @@ import pandas as pd
import tensorflow as tf
from object_detection.dataset_tools import oid_tfrecord_creation
from object_detection.dataset_tools import tf_record_creation_util
from object_detection.utils import label_map_util
tf.flags.DEFINE_string('input_annotations_csv', None,
tf.flags.DEFINE_string('input_box_annotations_csv', None,
'Path to CSV containing image bounding box annotations')
tf.flags.DEFINE_string('input_images_directory', None,
'Directory containing the image pixels '
'downloaded from the OpenImages GitHub repository.')
tf.flags.DEFINE_string('input_image_label_annotations_csv', None,
'Path to CSV containing image-level labels annotations')
tf.flags.DEFINE_string('input_label_map', None, 'Path to the label map proto')
tf.flags.DEFINE_string(
'output_tf_record_path_prefix', None,
......@@ -61,7 +67,7 @@ def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
required_flags = [
'input_annotations_csv', 'input_images_directory', 'input_label_map',
'input_box_annotations_csv', 'input_images_directory', 'input_label_map',
'output_tf_record_path_prefix'
]
for flag_name in required_flags:
......@@ -69,17 +75,24 @@ def main(_):
raise ValueError('Flag --{} is required'.format(flag_name))
label_map = label_map_util.get_label_map_dict(FLAGS.input_label_map)
all_annotations = pd.read_csv(FLAGS.input_annotations_csv)
all_box_annotations = pd.read_csv(FLAGS.input_box_annotations_csv)
if FLAGS.input_image_label_annotations_csv:
all_label_annotations = pd.read_csv(FLAGS.input_image_label_annotations_csv)
all_label_annotations.rename(
columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
else:
all_label_annotations = None
all_images = tf.gfile.Glob(
os.path.join(FLAGS.input_images_directory, '*.jpg'))
all_image_ids = [os.path.splitext(os.path.basename(v))[0] for v in all_images]
all_image_ids = pd.DataFrame({'ImageID': all_image_ids})
all_annotations = pd.concat([all_annotations, all_image_ids])
all_annotations = pd.concat(
[all_box_annotations, all_image_ids, all_label_annotations])
tf.logging.log(tf.logging.INFO, 'Found %d images...', len(all_image_ids))
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = oid_tfrecord_creation.open_sharded_output_tfrecords(
output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
tf_record_close_stack, FLAGS.output_tf_record_path_prefix,
FLAGS.num_shards)
......
research/object_detection/dataset_tools/create_pet_tf_record.py
View file @ 97760186
......@@ -33,11 +33,13 @@ import os
import random
import re
import contextlib2
from lxml import etree
import numpy as np
import PIL.Image
import tensorflow as tf
from object_detection.dataset_tools import tf_record_creation_util
from object_detection.utils import dataset_util
from object_detection.utils import label_map_util
......@@ -52,6 +54,8 @@ flags.DEFINE_boolean('faces_only', True, 'If True, generates bounding boxes '
'in the latter case, the resulting files are much larger.')
flags.DEFINE_string('mask_type', 'png', 'How to represent instance '
'segmentation masks. Options are "png" or "numerical".')
flags.DEFINE_integer('num_shards', 10, 'Number of TFRecord shards')
FLAGS = flags.FLAGS
......@@ -208,6 +212,7 @@ def dict_to_tf_example(data,
def create_tf_record(output_filename,
num_shards,
label_map_dict,
annotations_dir,
image_dir,
......@@ -218,6 +223,7 @@ def create_tf_record(output_filename,
Args:
output_filename: Path to where output file is saved.
num_shards: Number of shards for output file.
label_map_dict: The label map dictionary.
annotations_dir: Directory where annotation files are stored.
image_dir: Directory where image files are stored.
......@@ -227,7 +233,9 @@ def create_tf_record(output_filename,
mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
smaller file sizes.
"""
writer = tf.python_io.TFRecordWriter(output_filename)
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
tf_record_close_stack, output_filename, num_shards)
for idx, example in enumerate(examples):
if idx % 100 == 0:
logging.info('On image %d of %d', idx, len(examples))
......@@ -250,12 +258,12 @@ def create_tf_record(output_filename,
image_dir,
faces_only=faces_only,
mask_type=mask_type)
writer.write(tf_example.SerializeToString())
if tf_example:
shard_idx = idx % num_shards
output_tfrecords[shard_idx].write(tf_example.SerializeToString())
except ValueError:
logging.warning('Invalid example: %s, ignoring.', xml_path)
writer.close()
# TODO(derekjchow): Add test for pet/PASCAL main files.
def main(_):
......@@ -279,15 +287,16 @@ def main(_):
logging.info('%d training and %d validation examples.',
len(train_examples), len(val_examples))
train_output_path = os.path.join(FLAGS.output_dir, 'pet_train.record')
val_output_path = os.path.join(FLAGS.output_dir, 'pet_val.record')
if FLAGS.faces_only:
train_output_path = os.path.join(FLAGS.output_dir, 'pet_faces_train.record')
val_output_path = os.path.join(FLAGS.output_dir, 'pet_faces_val.record')
if not FLAGS.faces_only:
train_output_path = os.path.join(FLAGS.output_dir,
'pet_train_with_masks.record')
'pets_fullbody_with_masks_train.record')
val_output_path = os.path.join(FLAGS.output_dir,
'pet_val_with_masks.record')
'pets_fullbody_with_masks_val.record')
create_tf_record(
train_output_path,
FLAGS.num_shards,
label_map_dict,
annotations_dir,
image_dir,
......@@ -296,6 +305,7 @@ def main(_):
mask_type=FLAGS.mask_type)
create_tf_record(
val_output_path,
FLAGS.num_shards,
label_map_dict,
annotations_dir,
image_dir,
......
research/object_detection/dataset_tools/oid_hierarchical_labels_expansion.py 0 → 100644
View file @ 97760186
# 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.
# ==============================================================================
"""A class and executable to expand hierarchically image-level labels and boxes.
Example usage:
./hierarchical_labels_expansion <path to JSON hierarchy> <input csv file>
<output csv file> [optional]labels_file
"""
import json
import sys
def _update_dict(initial_dict, update):
"""Updates dictionary with update content.
Args:
initial_dict: initial dictionary.
update: updated dictionary.
"""
for key, value_list in update.iteritems():
if key in initial_dict:
initial_dict[key].extend(value_list)
else:
initial_dict[key] = value_list
def _build_plain_hierarchy(hierarchy, skip_root=False):
"""Expands tree hierarchy representation to parent-child dictionary.
Args:
hierarchy: labels hierarchy as JSON file.
skip_root: if true skips root from the processing (done for the case when all
classes under hierarchy are collected under virtual node).
Returns:
keyed_parent - dictionary of parent - all its children nodes.
keyed_child - dictionary of children - all its parent nodes
children - all children of the current node.
"""
all_children = []
all_keyed_parent = {}
all_keyed_child = {}
if 'Subcategory' in hierarchy:
for node in hierarchy['Subcategory']:
keyed_parent, keyed_child, children = _build_plain_hierarchy(node)
# Update is not done through dict.update() since some children have multi-
# ple parents in the hiearchy.
_update_dict(all_keyed_parent, keyed_parent)
_update_dict(all_keyed_child, keyed_child)
all_children.extend(children)
if not skip_root:
all_keyed_parent[hierarchy['LabelName']] = all_children
all_children = [hierarchy['LabelName']] + all_children
for child, _ in all_keyed_child.iteritems():
all_keyed_child[child].append(hierarchy['LabelName'])
all_keyed_child[hierarchy['LabelName']] = []
return all_keyed_parent, all_keyed_child, all_children
class OIDHierarchicalLabelsExpansion(object):
""" Main class to perform labels hierachical expansion."""
def __init__(self, hierarchy):
"""Constructor.
Args:
hierarchy: labels hierarchy as JSON file.
"""
self._hierarchy_keyed_parent, self._hierarchy_keyed_child, _ = (
_build_plain_hierarchy(hierarchy, skip_root=True))
def expand_boxes_from_csv(self, csv_row):
"""Expands a row containing bounding boxes from CSV file.
Args:
csv_row: a single row of Open Images released groundtruth file.
Returns:
a list of strings (including the initial row) corresponding to the ground
truth expanded to multiple annotation for evaluation with Open Images
Challenge 2018 metric.
"""
# Row header is expected to be exactly:
# ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,
# IsTruncated,IsGroupOf,IsDepiction,IsInside
cvs_row_splited = csv_row.split(',')
assert len(cvs_row_splited) == 13
result = [csv_row]
assert cvs_row_splited[2] in self._hierarchy_keyed_child
parent_nodes = self._hierarchy_keyed_child[cvs_row_splited[2]]
for parent_node in parent_nodes:
cvs_row_splited[2] = parent_node
result.append(','.join(cvs_row_splited))
return result
def expand_labels_from_csv(self, csv_row):
"""Expands a row containing bounding boxes from CSV file.
Args:
csv_row: a single row of Open Images released groundtruth file.
Returns:
a list of strings (including the initial row) corresponding to the ground
truth expanded to multiple annotation for evaluation with Open Images
Challenge 2018 metric.
"""
# Row header is expected to be exactly:
# ImageID,Source,LabelName,Confidence
cvs_row_splited = csv_row.split(',')
assert len(cvs_row_splited) == 4
result = [csv_row]
if int(cvs_row_splited[3]) == 1:
assert cvs_row_splited[2] in self._hierarchy_keyed_child
parent_nodes = self._hierarchy_keyed_child[cvs_row_splited[2]]
for parent_node in parent_nodes:
cvs_row_splited[2] = parent_node
result.append(','.join(cvs_row_splited))
else:
assert cvs_row_splited[2] in self._hierarchy_keyed_parent
child_nodes = self._hierarchy_keyed_parent[cvs_row_splited[2]]
for child_node in child_nodes:
cvs_row_splited[2] = child_node
result.append(','.join(cvs_row_splited))
return result
def main(argv):
if len(argv) < 4:
print """Missing arguments. \n
Usage: ./hierarchical_labels_expansion <path to JSON hierarchy>
<input csv file> <output csv file> [optional]labels_file"""
return
with open(argv[1]) as f:
hierarchy = json.load(f)
expansion_generator = OIDHierarchicalLabelsExpansion(hierarchy)
labels_file = False
if len(argv) > 4 and argv[4] == 'labels_file':
labels_file = True
with open(argv[2], 'r') as source:
with open(argv[3], 'w') as target:
header_skipped = False
for line in source:
if not header_skipped:
header_skipped = True
continue
if labels_file:
expanded_lines = expansion_generator.expand_labels_from_csv(line)
else:
expanded_lines = expansion_generator.expand_boxes_from_csv(line)
target.writelines(expanded_lines)
if __name__ == '__main__':
main(sys.argv)
research/object_detection/dataset_tools/oid_hierarchical_labels_expansion_test.py 0 → 100644
View file @ 97760186
# 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 the OpenImages label expansion (OIDHierarchicalLabelsExpansion)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from object_detection.dataset_tools import oid_hierarchical_labels_expansion
def create_test_data():
hierarchy = {
'LabelName':
'a',
'Subcategory': [{
'LabelName': 'b'
}, {
'LabelName': 'c',
'Subcategory': [{
'LabelName': 'd'
}, {
'LabelName': 'e'
}]
}, {
'LabelName': 'f',
'Subcategory': [{
'LabelName': 'd'
},]
}]
}
bbox_rows = [
'123,xclick,b,1,0.1,0.2,0.1,0.2,1,1,0,0,0',
'123,xclick,d,1,0.2,0.3,0.1,0.2,1,1,0,0,0'
]
label_rows = [
'123,verification,b,0', '123,verification,c,0', '124,verification,d,1'
]
return hierarchy, bbox_rows, label_rows
class HierarchicalLabelsExpansionTest(tf.test.TestCase):
def test_bbox_expansion(self):
hierarchy, bbox_rows, _ = create_test_data()
expansion_generator = (
oid_hierarchical_labels_expansion.OIDHierarchicalLabelsExpansion(
hierarchy))
all_result_rows = []
for row in bbox_rows:
all_result_rows.extend(expansion_generator.expand_boxes_from_csv(row))
self.assertItemsEqual([
'123,xclick,b,1,0.1,0.2,0.1,0.2,1,1,0,0,0',
'123,xclick,d,1,0.2,0.3,0.1,0.2,1,1,0,0,0',
'123,xclick,f,1,0.2,0.3,0.1,0.2,1,1,0,0,0',
'123,xclick,c,1,0.2,0.3,0.1,0.2,1,1,0,0,0'
], all_result_rows)
def test_labels_expansion(self):
hierarchy, _, label_rows = create_test_data()
expansion_generator = (
oid_hierarchical_labels_expansion.OIDHierarchicalLabelsExpansion(
hierarchy))
all_result_rows = []
for row in label_rows:
all_result_rows.extend(expansion_generator.expand_labels_from_csv(row))
self.assertItemsEqual([
'123,verification,b,0', '123,verification,c,0', '123,verification,d,0',
'123,verification,e,0', '124,verification,d,1', '124,verification,f,1',
'124,verification,c,1'
], all_result_rows)
if __name__ == '__main__':
tf.test.main()
research/object_detection/dataset_tools/oid_tfrecord_creation.py
View file @ 97760186
......@@ -41,24 +41,31 @@ def tf_example_from_annotations_data_frame(annotations_data_frame, label_map,
filtered_data_frame = annotations_data_frame[
annotations_data_frame.LabelName.isin(label_map)]
filtered_data_frame_boxes = filtered_data_frame[
~filtered_data_frame.YMin.isnull()]
filtered_data_frame_labels = filtered_data_frame[
filtered_data_frame.YMin.isnull()]
image_id = annotations_data_frame.ImageID.iloc[0]
feature_map = {
standard_fields.TfExampleFields.object_bbox_ymin:
dataset_util.float_list_feature(filtered_data_frame.YMin.as_matrix()),
dataset_util.float_list_feature(
filtered_data_frame_boxes.YMin.as_matrix()),
standard_fields.TfExampleFields.object_bbox_xmin:
dataset_util.float_list_feature(filtered_data_frame.XMin.as_matrix()),
dataset_util.float_list_feature(
filtered_data_frame_boxes.XMin.as_matrix()),
standard_fields.TfExampleFields.object_bbox_ymax:
dataset_util.float_list_feature(filtered_data_frame.YMax.as_matrix()),
dataset_util.float_list_feature(
filtered_data_frame_boxes.YMax.as_matrix()),
standard_fields.TfExampleFields.object_bbox_xmax:
dataset_util.float_list_feature(filtered_data_frame.XMax.as_matrix()),
dataset_util.float_list_feature(
filtered_data_frame_boxes.XMax.as_matrix()),
standard_fields.TfExampleFields.object_class_text:
dataset_util.bytes_list_feature(
filtered_data_frame.LabelName.as_matrix()),
filtered_data_frame_boxes.LabelName.as_matrix()),
standard_fields.TfExampleFields.object_class_label:
dataset_util.int64_list_feature(
filtered_data_frame.LabelName.map(lambda x: label_map[x])
filtered_data_frame_boxes.LabelName.map(lambda x: label_map[x])
.as_matrix()),
standard_fields.TfExampleFields.filename:
dataset_util.bytes_feature('{}.jpg'.format(image_id)),
......@@ -71,43 +78,29 @@ def tf_example_from_annotations_data_frame(annotations_data_frame, label_map,
if 'IsGroupOf' in filtered_data_frame.columns:
feature_map[standard_fields.TfExampleFields.
object_group_of] = dataset_util.int64_list_feature(
filtered_data_frame.IsGroupOf.as_matrix().astype(int))
filtered_data_frame_boxes.IsGroupOf.as_matrix().astype(int))
if 'IsOccluded' in filtered_data_frame.columns:
feature_map[standard_fields.TfExampleFields.
object_occluded] = dataset_util.int64_list_feature(
filtered_data_frame.IsOccluded.as_matrix().astype(int))
filtered_data_frame_boxes.IsOccluded.as_matrix().astype(
int))
if 'IsTruncated' in filtered_data_frame.columns:
feature_map[standard_fields.TfExampleFields.
object_truncated] = dataset_util.int64_list_feature(
filtered_data_frame.IsTruncated.as_matrix().astype(int))
filtered_data_frame_boxes.IsTruncated.as_matrix().astype(
int))
if 'IsDepiction' in filtered_data_frame.columns:
feature_map[standard_fields.TfExampleFields.
object_depiction] = dataset_util.int64_list_feature(
filtered_data_frame.IsDepiction.as_matrix().astype(int))
filtered_data_frame_boxes.IsDepiction.as_matrix().astype(
int))
if 'ConfidenceImageLabel' in filtered_data_frame_labels.columns:
feature_map[standard_fields.TfExampleFields.
image_class_label] = dataset_util.int64_list_feature(
filtered_data_frame_labels.LabelName.map(
lambda x: label_map[x]).as_matrix())
feature_map[standard_fields.TfExampleFields.
image_class_text] = dataset_util.bytes_list_feature(
filtered_data_frame_labels.LabelName.as_matrix()),
return tf.train.Example(features=tf.train.Features(feature=feature_map))
def open_sharded_output_tfrecords(exit_stack, base_path, num_shards):
"""Opens all TFRecord shards for writing and adds them to an exit stack.
Args:
exit_stack: A context2.ExitStack used to automatically closed the TFRecords
opened in this function.
base_path: The base path for all shards
num_shards: The number of shards
Returns:
The list of opened TFRecords. Position k in the list corresponds to shard k.
"""
tf_record_output_filenames = [
'{}-{:05d}-of-{:05d}'.format(base_path, idx, num_shards)
for idx in range(num_shards)
]
tfrecords = [
exit_stack.enter_context(tf.python_io.TFRecordWriter(file_name))
for file_name in tf_record_output_filenames
]
return tfrecords
research/object_detection/dataset_tools/oid_tfrecord_creation_test.py
View file @ 97760186
......@@ -14,8 +14,6 @@
# ==============================================================================
"""Tests for oid_tfrecord_creation.py."""
import os
import contextlib2
import pandas as pd
import tensorflow as tf
......@@ -24,16 +22,17 @@ from object_detection.dataset_tools import oid_tfrecord_creation
def create_test_data():
data = {
'ImageID': ['i1', 'i1', 'i1', 'i1', 'i2', 'i2'],
'LabelName': ['a', 'a', 'b', 'b', 'b', 'c'],
'YMin': [0.3, 0.6, 0.8, 0.1, 0.0, 0.0],
'XMin': [0.1, 0.3, 0.7, 0.0, 0.1, 0.1],
'XMax': [0.2, 0.3, 0.8, 0.5, 0.9, 0.9],
'YMax': [0.3, 0.6, 1, 0.8, 0.8, 0.8],
'IsOccluded': [0, 1, 1, 0, 0, 0],
'IsTruncated': [0, 0, 0, 1, 0, 0],
'IsGroupOf': [0, 0, 0, 0, 0, 1],
'IsDepiction': [1, 0, 0, 0, 0, 0],
'ImageID': ['i1', 'i1', 'i1', 'i1', 'i1', 'i2', 'i2'],
'LabelName': ['a', 'a', 'b', 'b', 'c', 'b', 'c'],
'YMin': [0.3, 0.6, 0.8, 0.1, None, 0.0, 0.0],
'XMin': [0.1, 0.3, 0.7, 0.0, None, 0.1, 0.1],
'XMax': [0.2, 0.3, 0.8, 0.5, None, 0.9, 0.9],
'YMax': [0.3, 0.6, 1, 0.8, None, 0.8, 0.8],
'IsOccluded': [0, 1, 1, 0, None, 0, 0],
'IsTruncated': [0, 0, 0, 1, None, 0, 0],
'IsGroupOf': [0, 0, 0, 0, None, 0, 1],
'IsDepiction': [1, 0, 0, 0, None, 0, 0],
'ConfidenceImageLabel': [None, None, None, None, 0, None, None],
}
df = pd.DataFrame(data=data)
label_map = {'a': 0, 'b': 1, 'c': 2}
......@@ -47,7 +46,8 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
self.assertProtoEquals("""
self.assertProtoEquals(
"""
features {
feature {
key: "image/encoded"
......@@ -87,7 +87,13 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
value { int64_list { value: [0, 1, 1, 0] } } }
feature {
key: "image/object/truncated"
value { int64_list { value: [0, 0, 0, 1] } } } }
value { int64_list { value: [0, 0, 0, 1] } } }
feature {
key: "image/class/label"
value { int64_list { value: [2] } } }
feature {
key: "image/class/text"
value { bytes_list { value: ["c"] } } } }
""", tf_example)
def test_no_attributes(self):
......@@ -97,6 +103,7 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
del df['IsGroupOf']
del df['IsOccluded']
del df['IsTruncated']
del df['ConfidenceImageLabel']
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i2'], label_map, 'encoded_image_test')
......@@ -138,7 +145,8 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
self.assertProtoEquals("""
self.assertProtoEquals(
"""
features {
feature {
key: "image/encoded"
......@@ -178,26 +186,15 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
value { int64_list { value: [0, 1] } } }
feature {
key: "image/object/truncated"
value { int64_list { value: [0, 0] } } } }
value { int64_list { value: [0, 0] } } }
feature {
key: "image/class/label"
value { int64_list { } } }
feature {
key: "image/class/text"
value { bytes_list { } } } }
""", tf_example)
class OpenOutputTfrecordsTests(tf.test.TestCase):
def test_sharded_tfrecord_writes(self):
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = oid_tfrecord_creation.open_sharded_output_tfrecords(
tf_record_close_stack,
os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), 10)
for idx in range(10):
output_tfrecords[idx].write('test_{}'.format(idx))
for idx in range(10):
tf_record_path = '{}-{:05d}-of-00010'.format(
os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), idx)
records = list(tf.python_io.tf_record_iterator(tf_record_path))
self.assertAllEqual(records, ['test_{}'.format(idx)])
if __name__ == '__main__':
tf.test.main()
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