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Commit 855d29db authored by Sara Beery's avatar Sara Beery Committed by TF Object Detection Team
Browse files

Converting context r-cnn dataset tools to TF2 

PiperOrigin-RevId: 323805090
parent a565d720
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Showing with 120 additions and 191 deletions
research/object_detection/dataset_tools/context_rcnn/add_context_to_examples.py
View file @ 855d29db
...@@ -53,7 +53,7 @@ import os ...@@ -53,7 +53,7 @@ import os
import numpy as np import numpy as np
import PIL.Image import PIL.Image
import six import six
import tensorflow.compat.v1 as tf import tensorflow as tf
try: try:
import apache_beam as beam # pylint:disable=g-import-not-at-top import apache_beam as beam # pylint:disable=g-import-not-at-top
......
research/object_detection/dataset_tools/context_rcnn/add_context_to_examples_tf1_test.py → research/object_detection/dataset_tools/context_rcnn/add_context_to_examples_tf2_test.py
View file @ 855d29db
...@@ -25,11 +25,12 @@ import unittest ...@@ -25,11 +25,12 @@ import unittest
import numpy as np import numpy as np
import six import six
import tensorflow.compat.v1 as tf import tensorflow as tf
from object_detection.dataset_tools.context_rcnn import add_context_to_examples
from object_detection.utils import tf_version from object_detection.utils import tf_version
if tf_version.is_tf2():
from object_detection.dataset_tools.context_rcnn import add_context_to_examples # pylint:disable=g-import-not-at-top
try: try:
import apache_beam as beam # pylint:disable=g-import-not-at-top import apache_beam as beam # pylint:disable=g-import-not-at-top
...@@ -42,7 +43,7 @@ def InMemoryTFRecord(entries): ...@@ -42,7 +43,7 @@ def InMemoryTFRecord(entries):
temp = tempfile.NamedTemporaryFile(delete=False) temp = tempfile.NamedTemporaryFile(delete=False)
filename = temp.name filename = temp.name
try: try:
with tf.python_io.TFRecordWriter(filename) as writer: with tf.io.TFRecordWriter(filename) as writer:
for value in entries: for value in entries:
writer.write(value) writer.write(value)
yield filename yield filename
...@@ -70,13 +71,12 @@ def FloatListFeature(value): ...@@ -70,13 +71,12 @@ def FloatListFeature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value)) return tf.train.Feature(float_list=tf.train.FloatList(value=value))
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.') @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class GenerateContextDataTest(tf.test.TestCase): class GenerateContextDataTest(tf.test.TestCase):
def _create_first_tf_example(self): def _create_first_tf_example(self):
with self.test_session(): encoded_image = tf.io.encode_jpeg(
encoded_image = tf.image.encode_jpeg( tf.constant(np.ones((4, 4, 3)).astype(np.uint8))).numpy()
tf.constant(np.ones((4, 4, 3)).astype(np.uint8))).eval()
example = tf.train.Example(features=tf.train.Features(feature={ example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': BytesFeature(encoded_image), 'image/encoded': BytesFeature(encoded_image),
...@@ -105,9 +105,8 @@ class GenerateContextDataTest(tf.test.TestCase): ...@@ -105,9 +105,8 @@ class GenerateContextDataTest(tf.test.TestCase):
return example.SerializeToString() return example.SerializeToString()
def _create_second_tf_example(self): def _create_second_tf_example(self):
with self.test_session(): encoded_image = tf.io.encode_jpeg(
encoded_image = tf.image.encode_jpeg( tf.constant(np.ones((4, 4, 3)).astype(np.uint8))).numpy()
tf.constant(np.ones((4, 4, 3)).astype(np.uint8))).eval()
example = tf.train.Example(features=tf.train.Features(feature={ example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': BytesFeature(encoded_image), 'image/encoded': BytesFeature(encoded_image),
...@@ -353,7 +352,8 @@ class GenerateContextDataTest(tf.test.TestCase): ...@@ -353,7 +352,8 @@ class GenerateContextDataTest(tf.test.TestCase):
p.run() p.run()
filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????') filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????')
actual_output = [] actual_output = []
record_iterator = tf.python_io.tf_record_iterator(path=filenames[0]) record_iterator = tf.data.TFRecordDataset(
tf.convert_to_tensor(filenames)).as_numpy_iterator()
for record in record_iterator: for record in record_iterator:
actual_output.append(record) actual_output.append(record)
self.assertEqual(len(actual_output), 2) self.assertEqual(len(actual_output), 2)
...@@ -383,8 +383,8 @@ class GenerateContextDataTest(tf.test.TestCase): ...@@ -383,8 +383,8 @@ class GenerateContextDataTest(tf.test.TestCase):
p.run() p.run()
filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????') filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????')
actual_output = [] actual_output = []
record_iterator = tf.python_io.tf_record_iterator( record_iterator = tf.data.TFRecordDataset(
path=filenames[0]) tf.convert_to_tensor(filenames)).as_numpy_iterator()
for record in record_iterator: for record in record_iterator:
actual_output.append(record) actual_output.append(record)
self.assertEqual(len(actual_output), 1) self.assertEqual(len(actual_output), 1)
......
research/object_detection/dataset_tools/context_rcnn/create_cococameratraps_tfexample_main.py
View file @ 855d29db
...@@ -37,11 +37,10 @@ import argparse ...@@ -37,11 +37,10 @@ import argparse
import hashlib import hashlib
import io import io
import json import json
import logging
import os import os
import numpy as np import numpy as np
import PIL.Image import PIL.Image
import tensorflow.compat.v1 as tf import tensorflow as tf
from object_detection.utils import dataset_util from object_detection.utils import dataset_util
try: try:
...@@ -110,16 +109,9 @@ class ParseImage(beam.DoFn): ...@@ -110,16 +109,9 @@ class ParseImage(beam.DoFn):
encoded_jpg = fid.read() encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg) encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io) image = PIL.Image.open(encoded_jpg_io)
# Ensure the image can be read by tf image = tf.io.decode_jpeg(encoded_jpg, channels=3)
with tf.Graph().as_default(): except Exception: # pylint: disable=broad-except
image = tf.image.decode_jpeg(encoded_jpg, channels=3)
init_op = tf.initialize_all_tables()
with tf.Session() as sess:
sess.run(init_op)
sess.run(image)
except Exception as e: # pylint: disable=broad-except
# The image file is missing or corrupt # The image file is missing or corrupt
tf.logging.error(str(e))
return [] return []
key = hashlib.sha256(encoded_jpg).hexdigest() key = hashlib.sha256(encoded_jpg).hexdigest()
...@@ -257,8 +249,6 @@ def create_pipeline(pipeline, ...@@ -257,8 +249,6 @@ def create_pipeline(pipeline,
keep_bboxes: Whether to keep any bounding boxes that exist in the json file keep_bboxes: Whether to keep any bounding boxes that exist in the json file
""" """
logging.info('Reading data from COCO-CameraTraps Dataset.')
data = load_json_data(input_annotations_file) data = load_json_data(input_annotations_file)
num_shards = int(np.ceil(float(len(data['images']))/num_images_per_shard)) num_shards = int(np.ceil(float(len(data['images']))/num_images_per_shard))
......
research/object_detection/dataset_tools/context_rcnn/create_cococameratraps_tfexample_tf1_test.py → research/object_detection/dataset_tools/context_rcnn/create_cococameratraps_tfexample_tf2_test.py
View file @ 855d29db
...@@ -25,17 +25,19 @@ import unittest ...@@ -25,17 +25,19 @@ import unittest
import numpy as np import numpy as np
from PIL import Image from PIL import Image
import tensorflow.compat.v1 as tf import tensorflow as tf
from object_detection.dataset_tools.context_rcnn import create_cococameratraps_tfexample_main
from object_detection.utils import tf_version from object_detection.utils import tf_version
if tf_version.is_tf2():
from object_detection.dataset_tools.context_rcnn import create_cococameratraps_tfexample_main # pylint:disable=g-import-not-at-top
try: try:
import apache_beam as beam # pylint:disable=g-import-not-at-top import apache_beam as beam # pylint:disable=g-import-not-at-top
except ModuleNotFoundError: except ModuleNotFoundError:
pass pass
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.') @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class CreateCOCOCameraTrapsTfexampleTest(tf.test.TestCase): class CreateCOCOCameraTrapsTfexampleTest(tf.test.TestCase):
IMAGE_HEIGHT = 360 IMAGE_HEIGHT = 360
...@@ -175,7 +177,8 @@ class CreateCOCOCameraTrapsTfexampleTest(tf.test.TestCase): ...@@ -175,7 +177,8 @@ class CreateCOCOCameraTrapsTfexampleTest(tf.test.TestCase):
p.run() p.run()
filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????') filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????')
actual_output = [] actual_output = []
record_iterator = tf.python_io.tf_record_iterator(path=filenames[0]) record_iterator = tf.data.TFRecordDataset(
tf.convert_to_tensor(filenames)).as_numpy_iterator()
for record in record_iterator: for record in record_iterator:
actual_output.append(record) actual_output.append(record)
self.assertEqual(len(actual_output), num_frames) self.assertEqual(len(actual_output), num_frames)
...@@ -198,7 +201,8 @@ class CreateCOCOCameraTrapsTfexampleTest(tf.test.TestCase): ...@@ -198,7 +201,8 @@ class CreateCOCOCameraTrapsTfexampleTest(tf.test.TestCase):
p.run() p.run()
filenames = tf.io.gfile.glob(output_tfrecord+'-?????-of-?????') filenames = tf.io.gfile.glob(output_tfrecord+'-?????-of-?????')
actual_output = [] actual_output = []
record_iterator = tf.python_io.tf_record_iterator(path=filenames[0]) record_iterator = tf.data.TFRecordDataset(
tf.convert_to_tensor(filenames)).as_numpy_iterator()
for record in record_iterator: for record in record_iterator:
actual_output.append(record) actual_output.append(record)
self.assertEqual(len(actual_output), num_frames) self.assertEqual(len(actual_output), num_frames)
......
research/object_detection/dataset_tools/context_rcnn/generate_detection_data.py
View file @ 855d29db
...@@ -48,7 +48,8 @@ from __future__ import print_function ...@@ -48,7 +48,8 @@ from __future__ import print_function
import argparse import argparse
import os import os
import threading import threading
import tensorflow.compat.v1 as tf import tensorflow as tf
try: try:
import apache_beam as beam # pylint:disable=g-import-not-at-top import apache_beam as beam # pylint:disable=g-import-not-at-top
except ModuleNotFoundError: except ModuleNotFoundError:
...@@ -85,22 +86,7 @@ class GenerateDetectionDataFn(beam.DoFn): ...@@ -85,22 +86,7 @@ class GenerateDetectionDataFn(beam.DoFn):
# one instance across all threads in the worker. This is possible since # one instance across all threads in the worker. This is possible since
# tf.Session.run() is thread safe. # tf.Session.run() is thread safe.
with self.session_lock: with self.session_lock:
if self._session is None: self._detect_fn = tf.saved_model.load(self._model_dir)
graph = tf.Graph()
self._session = tf.Session(graph=graph)
with graph.as_default():
meta_graph = tf.saved_model.loader.load(
self._session, [tf.saved_model.tag_constants.SERVING],
self._model_dir)
signature = meta_graph.signature_def['serving_default']
input_tensor_name = signature.inputs['inputs'].name
self._input = graph.get_tensor_by_name(input_tensor_name)
self._boxes_node = graph.get_tensor_by_name(
signature.outputs['detection_boxes'].name)
self._scores_node = graph.get_tensor_by_name(
signature.outputs['detection_scores'].name)
self._num_detections_node = graph.get_tensor_by_name(
signature.outputs['num_detections'].name)
def process(self, tfrecord_entry): def process(self, tfrecord_entry):
return self._run_inference_and_generate_detections(tfrecord_entry) return self._run_inference_and_generate_detections(tfrecord_entry)
...@@ -112,9 +98,11 @@ class GenerateDetectionDataFn(beam.DoFn): ...@@ -112,9 +98,11 @@ class GenerateDetectionDataFn(beam.DoFn):
# There are already ground truth boxes for this image, just keep them. # There are already ground truth boxes for this image, just keep them.
return [input_example] return [input_example]
detection_boxes, detection_scores, num_detections = self._session.run( detections = self._detect_fn.signatures['serving_default'](
[self._boxes_node, self._scores_node, self._num_detections_node], (tf.expand_dims(tf.convert_to_tensor(tfrecord_entry), 0)))
feed_dict={self._input: [tfrecord_entry]}) detection_boxes = detections['detection_boxes']
num_detections = detections['num_detections']
detection_scores = detections['detection_scores']
example = tf.train.Example() example = tf.train.Example()
......
research/object_detection/dataset_tools/context_rcnn/generate_detection_data_tf1_test.py → research/object_detection/dataset_tools/context_rcnn/generate_detection_data_tf2_test.py
View file @ 855d29db
...@@ -24,15 +24,17 @@ import tempfile ...@@ -24,15 +24,17 @@ import tempfile
import unittest import unittest
import numpy as np import numpy as np
import six import six
import tensorflow.compat.v1 as tf import tensorflow as tf
from object_detection import exporter from object_detection import exporter_lib_v2
from object_detection.builders import model_builder from object_detection.builders import model_builder
from object_detection.core import model from object_detection.core import model
from object_detection.dataset_tools.context_rcnn import generate_detection_data
from object_detection.protos import pipeline_pb2 from object_detection.protos import pipeline_pb2
from object_detection.utils import tf_version from object_detection.utils import tf_version
if tf_version.is_tf2():
from object_detection.dataset_tools.context_rcnn import generate_detection_data # pylint:disable=g-import-not-at-top
if six.PY2: if six.PY2:
import mock # pylint: disable=g-import-not-at-top import mock # pylint: disable=g-import-not-at-top
else: else:
...@@ -45,17 +47,23 @@ except ModuleNotFoundError: ...@@ -45,17 +47,23 @@ except ModuleNotFoundError:
class FakeModel(model.DetectionModel): class FakeModel(model.DetectionModel):
"""A Fake Detection model with expected output nodes from post-processing."""
def __init__(self, conv_weight_scalar=1.0):
super(FakeModel, self).__init__(num_classes=5)
self._conv = tf.keras.layers.Conv2D(
filters=1, kernel_size=1, strides=(1, 1), padding='valid',
kernel_initializer=tf.keras.initializers.Constant(
value=conv_weight_scalar))
def preprocess(self, inputs): def preprocess(self, inputs):
true_image_shapes = [] # Doesn't matter for the fake model. true_image_shapes = [] # Doesn't matter for the fake model.
return tf.identity(inputs), true_image_shapes return tf.identity(inputs), true_image_shapes
def predict(self, preprocessed_inputs, true_image_shapes): def predict(self, preprocessed_inputs, true_image_shapes):
return {'image': tf.layers.conv2d(preprocessed_inputs, 3, 1)} return {'image': self._conv(preprocessed_inputs)}
def postprocess(self, prediction_dict, true_image_shapes): def postprocess(self, prediction_dict, true_image_shapes):
with tf.control_dependencies(prediction_dict.values()): with tf.control_dependencies(list(prediction_dict.values())):
postprocessed_tensors = { postprocessed_tensors = {
'detection_boxes': tf.constant([[[0.0, 0.1, 0.5, 0.6], 'detection_boxes': tf.constant([[[0.0, 0.1, 0.5, 0.6],
[0.5, 0.5, 0.8, 0.8]]], tf.float32), [0.5, 0.5, 0.8, 0.8]]], tf.float32),
...@@ -89,7 +97,7 @@ def InMemoryTFRecord(entries): ...@@ -89,7 +97,7 @@ def InMemoryTFRecord(entries):
temp = tempfile.NamedTemporaryFile(delete=False) temp = tempfile.NamedTemporaryFile(delete=False)
filename = temp.name filename = temp.name
try: try:
with tf.python_io.TFRecordWriter(filename) as writer: with tf.io.TFRecordWriter(filename) as writer:
for value in entries: for value in entries:
writer.write(value) writer.write(value)
yield filename yield filename
...@@ -97,7 +105,7 @@ def InMemoryTFRecord(entries): ...@@ -97,7 +105,7 @@ def InMemoryTFRecord(entries):
os.unlink(filename) os.unlink(filename)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.') @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class GenerateDetectionDataTest(tf.test.TestCase): class GenerateDetectionDataTest(tf.test.TestCase):
def _save_checkpoint_from_mock_model(self, checkpoint_path): def _save_checkpoint_from_mock_model(self, checkpoint_path):
...@@ -106,64 +114,39 @@ class GenerateDetectionDataTest(tf.test.TestCase): ...@@ -106,64 +114,39 @@ class GenerateDetectionDataTest(tf.test.TestCase):
Args: Args:
checkpoint_path: Path to save checkpoint from Fake model. checkpoint_path: Path to save checkpoint from Fake model.
""" """
g = tf.Graph() mock_model = FakeModel()
with g.as_default(): fake_image = tf.zeros(shape=[1, 10, 10, 3], dtype=tf.float32)
mock_model = FakeModel(num_classes=5) preprocessed_inputs, true_image_shapes = mock_model.preprocess(fake_image)
preprocessed_inputs, true_image_shapes = mock_model.preprocess(
tf.placeholder(tf.float32, shape=[None, None, None, 3]))
predictions = mock_model.predict(preprocessed_inputs, true_image_shapes) predictions = mock_model.predict(preprocessed_inputs, true_image_shapes)
mock_model.postprocess(predictions, true_image_shapes) mock_model.postprocess(predictions, true_image_shapes)
tf.train.get_or_create_global_step() ckpt = tf.train.Checkpoint(model=mock_model)
saver = tf.train.Saver() exported_checkpoint_manager = tf.train.CheckpointManager(
init = tf.global_variables_initializer() ckpt, checkpoint_path, max_to_keep=1)
with self.test_session(graph=g) as sess: exported_checkpoint_manager.save(checkpoint_number=0)
sess.run(init)
saver.save(sess, checkpoint_path)
def _export_saved_model(self): def _export_saved_model(self):
tmp_dir = self.get_temp_dir() tmp_dir = self.get_temp_dir()
checkpoint_path = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(tmp_dir)
self._save_checkpoint_from_mock_model(checkpoint_path)
output_directory = os.path.join(tmp_dir, 'output') output_directory = os.path.join(tmp_dir, 'output')
saved_model_path = os.path.join(output_directory, 'saved_model') saved_model_path = os.path.join(output_directory, 'saved_model')
tf.io.gfile.makedirs(output_directory) tf.io.gfile.makedirs(output_directory)
with mock.patch.object( with mock.patch.object(
model_builder, 'build', autospec=True) as mock_builder: model_builder, 'build', autospec=True) as mock_builder:
mock_builder.return_value = FakeModel(num_classes=5) mock_builder.return_value = FakeModel()
output_directory = os.path.join(tmp_dir, 'output')
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False exporter_lib_v2.export_inference_graph(
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
outputs, placeholder_tensor = exporter.build_detection_graph(
input_type='tf_example', input_type='tf_example',
detection_model=detection_model, pipeline_config=pipeline_config,
input_shape=None, trained_checkpoint_dir=tmp_dir,
output_collection_name='inference_op', output_directory=output_directory)
graph_hook_fn=None) saved_model_path = os.path.join(output_directory, 'saved_model')
output_node_names = ','.join(outputs.keys())
saver = tf.train.Saver()
input_saver_def = saver.as_saver_def()
frozen_graph_def = exporter.freeze_graph_with_def_protos(
input_graph_def=tf.get_default_graph().as_graph_def(),
input_saver_def=input_saver_def,
input_checkpoint=checkpoint_path,
output_node_names=output_node_names,
restore_op_name='save/restore_all',
filename_tensor_name='save/Const:0',
output_graph='',
clear_devices=True,
initializer_nodes='')
exporter.write_saved_model(
saved_model_path=saved_model_path,
frozen_graph_def=frozen_graph_def,
inputs=placeholder_tensor,
outputs=outputs)
return saved_model_path return saved_model_path
def _create_tf_example(self): def _create_tf_example(self):
with self.test_session(): with self.test_session():
encoded_image = tf.image.encode_jpeg( encoded_image = tf.io.encode_jpeg(
tf.constant(np.ones((4, 6, 3)).astype(np.uint8))).eval() tf.constant(np.ones((4, 6, 3)).astype(np.uint8))).numpy()
def BytesFeature(value): def BytesFeature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
...@@ -264,7 +247,8 @@ class GenerateDetectionDataTest(tf.test.TestCase): ...@@ -264,7 +247,8 @@ class GenerateDetectionDataTest(tf.test.TestCase):
p.run() p.run()
filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????') filenames = tf.io.gfile.glob(output_tfrecord + '-?????-of-?????')
actual_output = [] actual_output = []
record_iterator = tf.python_io.tf_record_iterator(path=filenames[0]) record_iterator = tf.data.TFRecordDataset(
tf.convert_to_tensor(filenames)).as_numpy_iterator()
for record in record_iterator: for record in record_iterator:
actual_output.append(record) actual_output.append(record)
self.assertEqual(len(actual_output), 1) self.assertEqual(len(actual_output), 1)
......
research/object_detection/dataset_tools/context_rcnn/generate_embedding_data.py
View file @ 855d29db
...@@ -55,7 +55,7 @@ import threading ...@@ -55,7 +55,7 @@ import threading
import numpy as np import numpy as np
import six import six
import tensorflow.compat.v1 as tf import tensorflow as tf
try: try:
import apache_beam as beam # pylint:disable=g-import-not-at-top import apache_beam as beam # pylint:disable=g-import-not-at-top
...@@ -95,27 +95,7 @@ class GenerateEmbeddingDataFn(beam.DoFn): ...@@ -95,27 +95,7 @@ class GenerateEmbeddingDataFn(beam.DoFn):
# one instance across all threads in the worker. This is possible since # one instance across all threads in the worker. This is possible since
# tf.Session.run() is thread safe. # tf.Session.run() is thread safe.
with self.session_lock: with self.session_lock:
if self._session is None: self._detect_fn = tf.saved_model.load(self._model_dir)
graph = tf.Graph()
self._session = tf.Session(graph=graph)
with graph.as_default():
meta_graph = tf.saved_model.loader.load(
self._session, [tf.saved_model.tag_constants.SERVING],
self._model_dir)
signature = meta_graph.signature_def['serving_default']
input_tensor_name = signature.inputs['inputs'].name
detection_features_name = signature.outputs['detection_features'].name
detection_boxes_name = signature.outputs['detection_boxes'].name
num_detections_name = signature.outputs['num_detections'].name
self._input = graph.get_tensor_by_name(input_tensor_name)
self._embedding_node = graph.get_tensor_by_name(detection_features_name)
self._box_node = graph.get_tensor_by_name(detection_boxes_name)
self._scores_node = graph.get_tensor_by_name(
signature.outputs['detection_scores'].name)
self._num_detections = graph.get_tensor_by_name(num_detections_name)
tf.logging.info(signature.outputs['detection_features'].name)
tf.logging.info(signature.outputs['detection_boxes'].name)
tf.logging.info(signature.outputs['num_detections'].name)
def process(self, tfrecord_entry): def process(self, tfrecord_entry):
return self._run_inference_and_generate_embedding(tfrecord_entry) return self._run_inference_and_generate_embedding(tfrecord_entry)
...@@ -184,13 +164,12 @@ class GenerateEmbeddingDataFn(beam.DoFn): ...@@ -184,13 +164,12 @@ class GenerateEmbeddingDataFn(beam.DoFn):
example.features.feature['image/unix_time'].float_list.value.extend( example.features.feature['image/unix_time'].float_list.value.extend(
[unix_time]) [unix_time])
(detection_features, detection_boxes, num_detections, detections = self._detect_fn.signatures['serving_default'](
detection_scores) = self._session.run( (tf.expand_dims(tf.convert_to_tensor(tfrecord_entry), 0)))
[ detection_features = detections['detection_features']
self._embedding_node, self._box_node, self._num_detections[0], detection_boxes = detections['detection_boxes']
self._scores_node num_detections = detections['num_detections']
], detection_scores = detections['detection_scores']
feed_dict={self._input: [tfrecord_entry]})
num_detections = int(num_detections) num_detections = int(num_detections)
embed_all = [] embed_all = []
......
research/object_detection/dataset_tools/context_rcnn/generate_embedding_data_tf1_test.py → research/object_detection/dataset_tools/context_rcnn/generate_embedding_data_tf2_test.py
View file @ 855d29db
...@@ -23,14 +23,15 @@ import tempfile ...@@ -23,14 +23,15 @@ import tempfile
import unittest import unittest
import numpy as np import numpy as np
import six import six
import tensorflow.compat.v1 as tf import tensorflow as tf
from object_detection import exporter from object_detection import exporter_lib_v2
from object_detection.builders import model_builder from object_detection.builders import model_builder
from object_detection.core import model from object_detection.core import model
from object_detection.dataset_tools.context_rcnn import generate_embedding_data
from object_detection.protos import pipeline_pb2 from object_detection.protos import pipeline_pb2
from object_detection.utils import tf_version from object_detection.utils import tf_version
if tf_version.is_tf2():
from object_detection.dataset_tools.context_rcnn import generate_embedding_data # pylint:disable=g-import-not-at-top
if six.PY2: if six.PY2:
import mock # pylint: disable=g-import-not-at-top import mock # pylint: disable=g-import-not-at-top
...@@ -44,14 +45,20 @@ except ModuleNotFoundError: ...@@ -44,14 +45,20 @@ except ModuleNotFoundError:
class FakeModel(model.DetectionModel): class FakeModel(model.DetectionModel):
"""A Fake Detection model with expected output nodes from post-processing."""
def __init__(self, conv_weight_scalar=1.0):
super(FakeModel, self).__init__(num_classes=5)
self._conv = tf.keras.layers.Conv2D(
filters=1, kernel_size=1, strides=(1, 1), padding='valid',
kernel_initializer=tf.keras.initializers.Constant(
value=conv_weight_scalar))
def preprocess(self, inputs): def preprocess(self, inputs):
true_image_shapes = [] # Doesn't matter for the fake model. true_image_shapes = [] # Doesn't matter for the fake model.
return tf.identity(inputs), true_image_shapes return tf.identity(inputs), true_image_shapes
def predict(self, preprocessed_inputs, true_image_shapes): def predict(self, preprocessed_inputs, true_image_shapes):
return {'image': tf.layers.conv2d(preprocessed_inputs, 3, 1)} return {'image': self._conv(preprocessed_inputs)}
def postprocess(self, prediction_dict, true_image_shapes): def postprocess(self, prediction_dict, true_image_shapes):
with tf.control_dependencies(prediction_dict.values()): with tf.control_dependencies(prediction_dict.values()):
...@@ -96,7 +103,7 @@ def InMemoryTFRecord(entries): ...@@ -96,7 +103,7 @@ def InMemoryTFRecord(entries):
temp = tempfile.NamedTemporaryFile(delete=False) temp = tempfile.NamedTemporaryFile(delete=False)
filename = temp.name filename = temp.name
try: try:
with tf.python_io.TFRecordWriter(filename) as writer: with tf.io.TFRecordWriter(filename) as writer:
for value in entries: for value in entries:
writer.write(value) writer.write(value)
yield filename yield filename
...@@ -104,7 +111,7 @@ def InMemoryTFRecord(entries): ...@@ -104,7 +111,7 @@ def InMemoryTFRecord(entries):
os.unlink(temp.name) os.unlink(temp.name)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.') @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class GenerateEmbeddingData(tf.test.TestCase): class GenerateEmbeddingData(tf.test.TestCase):
def _save_checkpoint_from_mock_model(self, checkpoint_path): def _save_checkpoint_from_mock_model(self, checkpoint_path):
...@@ -113,64 +120,38 @@ class GenerateEmbeddingData(tf.test.TestCase): ...@@ -113,64 +120,38 @@ class GenerateEmbeddingData(tf.test.TestCase):
Args: Args:
checkpoint_path: Path to save checkpoint from Fake model. checkpoint_path: Path to save checkpoint from Fake model.
""" """
g = tf.Graph() mock_model = FakeModel()
with g.as_default(): fake_image = tf.zeros(shape=[1, 10, 10, 3], dtype=tf.float32)
mock_model = FakeModel(num_classes=5) preprocessed_inputs, true_image_shapes = mock_model.preprocess(fake_image)
preprocessed_inputs, true_image_shapes = mock_model.preprocess(
tf.placeholder(tf.float32, shape=[None, None, None, 3]))
predictions = mock_model.predict(preprocessed_inputs, true_image_shapes) predictions = mock_model.predict(preprocessed_inputs, true_image_shapes)
mock_model.postprocess(predictions, true_image_shapes) mock_model.postprocess(predictions, true_image_shapes)
tf.train.get_or_create_global_step() ckpt = tf.train.Checkpoint(model=mock_model)
saver = tf.train.Saver() exported_checkpoint_manager = tf.train.CheckpointManager(
init = tf.global_variables_initializer() ckpt, checkpoint_path, max_to_keep=1)
with self.test_session(graph=g) as sess: exported_checkpoint_manager.save(checkpoint_number=0)
sess.run(init)
saver.save(sess, checkpoint_path)
def _export_saved_model(self): def _export_saved_model(self):
tmp_dir = self.get_temp_dir() tmp_dir = self.get_temp_dir()
checkpoint_path = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(tmp_dir)
self._save_checkpoint_from_mock_model(checkpoint_path)
output_directory = os.path.join(tmp_dir, 'output') output_directory = os.path.join(tmp_dir, 'output')
saved_model_path = os.path.join(output_directory, 'saved_model') saved_model_path = os.path.join(output_directory, 'saved_model')
tf.io.gfile.makedirs(output_directory) tf.io.gfile.makedirs(output_directory)
with mock.patch.object( with mock.patch.object(
model_builder, 'build', autospec=True) as mock_builder: model_builder, 'build', autospec=True) as mock_builder:
mock_builder.return_value = FakeModel(num_classes=5) mock_builder.return_value = FakeModel()
output_directory = os.path.join(tmp_dir, 'output')
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False exporter_lib_v2.export_inference_graph(
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
outputs, placeholder_tensor = exporter.build_detection_graph(
input_type='tf_example', input_type='tf_example',
detection_model=detection_model, pipeline_config=pipeline_config,
input_shape=None, trained_checkpoint_dir=tmp_dir,
output_collection_name='inference_op', output_directory=output_directory)
graph_hook_fn=None) saved_model_path = os.path.join(output_directory, 'saved_model')
output_node_names = ','.join(outputs.keys())
saver = tf.train.Saver()
input_saver_def = saver.as_saver_def()
frozen_graph_def = exporter.freeze_graph_with_def_protos(
input_graph_def=tf.get_default_graph().as_graph_def(),
input_saver_def=input_saver_def,
input_checkpoint=checkpoint_path,
output_node_names=output_node_names,
restore_op_name='save/restore_all',
filename_tensor_name='save/Const:0',
output_graph='',
clear_devices=True,
initializer_nodes='')
exporter.write_saved_model(
saved_model_path=saved_model_path,
frozen_graph_def=frozen_graph_def,
inputs=placeholder_tensor,
outputs=outputs)
return saved_model_path return saved_model_path
def _create_tf_example(self): def _create_tf_example(self):
with self.test_session(): encoded_image = tf.io.encode_jpeg(
encoded_image = tf.image.encode_jpeg( tf.constant(np.ones((4, 4, 3)).astype(np.uint8))).numpy()
tf.constant(np.ones((4, 4, 3)).astype(np.uint8))).eval()
def BytesFeature(value): def BytesFeature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
...@@ -335,7 +316,8 @@ class GenerateEmbeddingData(tf.test.TestCase): ...@@ -335,7 +316,8 @@ class GenerateEmbeddingData(tf.test.TestCase):
filenames = tf.io.gfile.glob( filenames = tf.io.gfile.glob(
output_tfrecord + '-?????-of-?????') output_tfrecord + '-?????-of-?????')
actual_output = [] actual_output = []
record_iterator = tf.python_io.tf_record_iterator(path=filenames[0]) record_iterator = tf.data.TFRecordDataset(
tf.convert_to_tensor(filenames)).as_numpy_iterator()
for record in record_iterator: for record in record_iterator:
actual_output.append(record) actual_output.append(record)
self.assertEqual(len(actual_output), 1) self.assertEqual(len(actual_output), 1)
......
research/object_detection/packages/tf1/setup.py
View file @ 855d29db
...@@ -3,7 +3,7 @@ import os ...@@ -3,7 +3,7 @@ import os
from setuptools import find_packages from setuptools import find_packages
from setuptools import setup from setuptools import setup
REQUIRED_PACKAGES = ['apache-beam', 'pillow', 'lxml', 'matplotlib', 'Cython', REQUIRED_PACKAGES = ['pillow', 'lxml', 'matplotlib', 'Cython',
'contextlib2', 'tf-slim', 'six', 'pycocotools', 'scipy', 'contextlib2', 'tf-slim', 'six', 'pycocotools', 'scipy',
'pandas'] 'pandas']
......
research/object_detection/packages/tf2/setup.py
View file @ 855d29db
...@@ -6,7 +6,9 @@ from setuptools import setup ...@@ -6,7 +6,9 @@ from setuptools import setup
# Note: adding apache-beam to required packages causes conflict with # Note: adding apache-beam to required packages causes conflict with
# tf-models-offical requirements. These packages request for incompatible # tf-models-offical requirements. These packages request for incompatible
# oauth2client package. # oauth2client package.
REQUIRED_PACKAGES = ['pillow', 'lxml', 'matplotlib', 'Cython', 'contextlib2', REQUIRED_PACKAGES = ['avro-python3==1.8.1', 'apache-beam',
'pillow', 'lxml',
'matplotlib', 'Cython', 'contextlib2',
'tf-slim', 'six', 'pycocotools', 'scipy', 'pandas', 'tf-slim', 'six', 'pycocotools', 'scipy', 'pandas',
'tf-models-official'] 'tf-models-official']
......
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