def test_raises_error_with_no_input_paths(self):
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
"""
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
with self.assertRaises(ValueError):
input_reader_builder.build(input_reader_proto)
def test_raises_error_with_no_input_paths(self):
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
"""
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
with self.assertRaises(ValueError):
input_reader_builder.build(input_reader_proto)
def test_build_tf_record_input_reader_and_load_instance_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
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 = input_reader_builder.build(input_reader_proto)
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertEquals((4, 5, 3),
output_dict[fields.InputDataFields.image].shape)
self.assertEquals(
[2], output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 4),
output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0])
self.assertAllEqual((1, 4, 5), output_dict[
fields.InputDataFields.groundtruth_instance_masks].shape)
def test_build_tf_record_input_reader_and_load_instance_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
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 = input_reader_builder.build(input_reader_proto)
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertEquals(
(4, 5, 3), output_dict[fields.InputDataFields.image].shape)
self.assertEquals(
[2], output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0])
self.assertAllEqual(
(1, 4, 5),
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
def test_build_tf_record_input_reader(self):
tf_record_path = self.create_tf_record()
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 = input_reader_builder.build(input_reader_proto)
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.assertTrue(fields.InputDataFields.groundtruth_instance_masks
not in output_dict)
self.assertEquals((4, 5, 3),
output_dict[fields.InputDataFields.image].shape)
self.assertEquals(
[2], output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 4),
output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0])
def test_build_tf_record_input_reader(self):
tf_record_path = self.create_tf_record()
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 = input_reader_builder.build(input_reader_proto)
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(
(4, 5, 3), output_dict[fields.InputDataFields.image].shape)
self.assertEquals(
[2], output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0])
def test_build_tf_record_input_reader_sequence_example(self):
tf_record_path = self.create_tf_record_sequence_example()
input_reader_text_proto = """
shuffle: false
num_readers: 1
input_type: TF_SEQUENCE_EXAMPLE
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
input_reader_proto.label_map_path = _get_labelmap_path()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = input_reader_builder.build(input_reader_proto)
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
expected_groundtruth_classes = [[-1, -1], [1, -1], [1, 2], [-1, -1]]
expected_groundtruth_boxes = [[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 1.0, 1.0],
[0.1, 0.1, 0.2, 0.2]],
[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]]
expected_num_groundtruth_boxes = [0, 1, 2, 0]
self.assertNotIn(fields.InputDataFields.groundtruth_instance_masks,
output_dict)
# sequence example images are encoded
self.assertEqual((4, ),
output_dict[fields.InputDataFields.image].shape)
self.assertAllEqual(
expected_groundtruth_classes,
output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEqual(
(4, 2, 4),
output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllClose(
expected_groundtruth_boxes,
output_dict[fields.InputDataFields.groundtruth_boxes])
self.assertAllClose(
expected_num_groundtruth_boxes,
output_dict[fields.InputDataFields.num_groundtruth_boxes])
# tensor_dict[fields.InputDataFields.image] = float_images
# input_queue = batcher.BatchQueue(
# tensor_dict,
# batch_size=30,
# batch_queue_capacity=100,
# num_batch_queue_threads=4,
# prefetch_queue_capacity=100)
### it seems that the code must have a feed_dict, so I have to run the queue.pop(tensor_dict) to get the data then feed the feed_dict.
# Commonly, if we have a queue, can we direct use the tensor, and run the final output tensor?
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = input_reader_builder.build(input_reader_proto)
print 'type tensor_dict', type(tensor_dict)
#print 'tensor_dict', tensor_dict
# sv.start_queue_runners(sess)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
outfileset = {}
