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# Copyright 2018 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 object_detection.export_tflite_ssd_graph."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import six
import tensorflow as tf
from tensorflow.core.framework import types_pb2
from object_detection import export_tflite_ssd_graph_lib
from object_detection import exporter
from object_detection.builders import graph_rewriter_builder
from object_detection.builders import model_builder
from object_detection.core import model
from object_detection.protos import graph_rewriter_pb2
from object_detection.protos import pipeline_pb2
from object_detection.protos import post_processing_pb2
if six.PY2:
import mock # pylint: disable=g-import-not-at-top
else:
from unittest import mock # pylint: disable=g-import-not-at-top
class FakeModel(model.DetectionModel):
def __init__(self, add_detection_masks=False):
self._add_detection_masks = add_detection_masks
def preprocess(self, inputs):
pass
def predict(self, preprocessed_inputs, true_image_shapes):
features = tf.contrib.slim.conv2d(preprocessed_inputs, 3, 1)
with tf.control_dependencies([features]):
prediction_tensors = {
'box_encodings':
tf.constant([[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]],
tf.float32),
'class_predictions_with_background':
tf.constant([[[0.7, 0.6], [0.9, 0.0]]], tf.float32),
}
with tf.control_dependencies(
[tf.convert_to_tensor(features.get_shape().as_list()[1:3])]):
prediction_tensors['anchors'] = tf.constant(
[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 1.0]], tf.float32)
return prediction_tensors
def postprocess(self, prediction_tensors, true_image_shapes):
pass
def restore_map(self, checkpoint_path, from_detection_checkpoint):
pass
def loss(self, prediction_dict, true_image_shapes):
pass
def regularization_losses(self):
pass
def updates(self):
pass
class ExportTfliteGraphTest(tf.test.TestCase):
def _save_checkpoint_from_mock_model(self,
checkpoint_path,
use_moving_averages,
quantize=False,
num_channels=3):
g = tf.Graph()
with g.as_default():
mock_model = FakeModel()
inputs = tf.placeholder(tf.float32, shape=[1, 10, 10, num_channels])
mock_model.predict(inputs, true_image_shapes=None)
if use_moving_averages:
tf.train.ExponentialMovingAverage(0.0).apply()
tf.train.get_or_create_global_step()
if quantize:
graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
graph_rewriter_config.quantization.delay = 500000
graph_rewriter_fn = graph_rewriter_builder.build(
graph_rewriter_config, is_training=False)
graph_rewriter_fn()
saver = tf.train.Saver()
init = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init)
saver.save(sess, checkpoint_path)
def _assert_quant_vars_exists(self, tflite_graph_file):
with tf.gfile.Open(tflite_graph_file) as f:
graph_string = f.read()
print(graph_string)
self.assertTrue('quant' in graph_string)
def _import_graph_and_run_inference(self, tflite_graph_file, num_channels=3):
"""Imports a tflite graph, runs single inference and returns outputs."""
graph = tf.Graph()
with graph.as_default():
graph_def = tf.GraphDef()
with tf.gfile.Open(tflite_graph_file) as f:
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
input_tensor = graph.get_tensor_by_name('normalized_input_image_tensor:0')
box_encodings = graph.get_tensor_by_name('raw_outputs/box_encodings:0')
class_predictions = graph.get_tensor_by_name(
'raw_outputs/class_predictions:0')
with self.test_session(graph) as sess:
[box_encodings_np, class_predictions_np] = sess.run(
[box_encodings, class_predictions],
feed_dict={input_tensor: np.random.rand(1, 10, 10, num_channels)})
return box_encodings_np, class_predictions_np
def _export_graph(self, pipeline_config, num_channels=3):
"""Exports a tflite graph."""
output_dir = self.get_temp_dir()
trained_checkpoint_prefix = os.path.join(output_dir, 'model.ckpt')
tflite_graph_file = os.path.join(output_dir, 'tflite_graph.pb')
quantize = pipeline_config.HasField('graph_rewriter')
self._save_checkpoint_from_mock_model(
trained_checkpoint_prefix,
use_moving_averages=pipeline_config.eval_config.use_moving_averages,
quantize=quantize,
num_channels=num_channels)
with mock.patch.object(
model_builder, 'build', autospec=True) as mock_builder:
mock_builder.return_value = FakeModel()
with tf.Graph().as_default():
export_tflite_ssd_graph_lib.export_tflite_graph(
pipeline_config=pipeline_config,
trained_checkpoint_prefix=trained_checkpoint_prefix,
output_dir=output_dir,
add_postprocessing_op=False,
max_detections=10,
max_classes_per_detection=1)
return tflite_graph_file
def _export_graph_with_postprocessing_op(self,
pipeline_config,
num_channels=3):
"""Exports a tflite graph with custom postprocessing op."""
output_dir = self.get_temp_dir()
trained_checkpoint_prefix = os.path.join(output_dir, 'model.ckpt')
tflite_graph_file = os.path.join(output_dir, 'tflite_graph.pb')
quantize = pipeline_config.HasField('graph_rewriter')
self._save_checkpoint_from_mock_model(
trained_checkpoint_prefix,
use_moving_averages=pipeline_config.eval_config.use_moving_averages,
quantize=quantize,
num_channels=num_channels)
with mock.patch.object(
model_builder, 'build', autospec=True) as mock_builder:
mock_builder.return_value = FakeModel()
with tf.Graph().as_default():
export_tflite_ssd_graph_lib.export_tflite_graph(
pipeline_config=pipeline_config,
trained_checkpoint_prefix=trained_checkpoint_prefix,
output_dir=output_dir,
add_postprocessing_op=True,
max_detections=10,
max_classes_per_detection=1)
return tflite_graph_file
def test_export_tflite_graph_with_moving_averages(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = True
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
(box_encodings_np, class_predictions_np
) = self._import_graph_and_run_inference(tflite_graph_file)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
def test_export_tflite_graph_without_moving_averages(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
(box_encodings_np, class_predictions_np
) = self._import_graph_and_run_inference(tflite_graph_file)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
def test_export_tflite_graph_grayscale(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
(pipeline_config.model.ssd.image_resizer.fixed_shape_resizer
).convert_to_grayscale = True
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config, num_channels=1)
self.assertTrue(os.path.exists(tflite_graph_file))
(box_encodings_np,
class_predictions_np) = self._import_graph_and_run_inference(
tflite_graph_file, num_channels=1)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
def test_export_tflite_graph_with_quantization(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.graph_rewriter.quantization.delay = 500000
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
self._assert_quant_vars_exists(tflite_graph_file)
(box_encodings_np, class_predictions_np
) = self._import_graph_and_run_inference(tflite_graph_file)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
def test_export_tflite_graph_with_softmax_score_conversion(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.post_processing.score_converter = (
post_processing_pb2.PostProcessing.SOFTMAX)
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
(box_encodings_np, class_predictions_np
) = self._import_graph_and_run_inference(tflite_graph_file)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np,
[[[0.524979, 0.475021], [0.710949, 0.28905]]])
def test_export_tflite_graph_with_sigmoid_score_conversion(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.post_processing.score_converter = (
post_processing_pb2.PostProcessing.SIGMOID)
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
(box_encodings_np, class_predictions_np
) = self._import_graph_and_run_inference(tflite_graph_file)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np,
[[[0.668188, 0.645656], [0.710949, 0.5]]])
def test_export_tflite_graph_with_postprocessing_op(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.post_processing.score_converter = (
post_processing_pb2.PostProcessing.SIGMOID)
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph_with_postprocessing_op(
pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
graph = tf.Graph()
with graph.as_default():
graph_def = tf.GraphDef()
with tf.gfile.Open(tflite_graph_file) as f:
graph_def.ParseFromString(f.read())
all_op_names = [node.name for node in graph_def.node]
self.assertTrue('TFLite_Detection_PostProcess' in all_op_names)
for node in graph_def.node:
if node.name == 'TFLite_Detection_PostProcess':
self.assertTrue(node.attr['_output_quantized'].b is True)
self.assertTrue(
node.attr['_support_output_type_float_in_quantized_op'].b is True)
self.assertTrue(node.attr['y_scale'].f == 10.0)
self.assertTrue(node.attr['x_scale'].f == 10.0)
self.assertTrue(node.attr['h_scale'].f == 5.0)
self.assertTrue(node.attr['w_scale'].f == 5.0)
self.assertTrue(node.attr['num_classes'].i == 2)
self.assertTrue(
all([
t == types_pb2.DT_FLOAT
for t in node.attr['_output_types'].list.type
]))
@mock.patch.object(exporter, 'rewrite_nn_resize_op')
def test_export_with_nn_resize_op_not_called_without_fpn(self, mock_get):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
tflite_graph_file = self._export_graph_with_postprocessing_op(
pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
mock_get.assert_not_called()
@mock.patch.object(exporter, 'rewrite_nn_resize_op')
def test_export_with_nn_resize_op_called_with_fpn(self, mock_get):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.feature_extractor.fpn.min_level = 3
pipeline_config.model.ssd.feature_extractor.fpn.max_level = 7
tflite_graph_file = self._export_graph_with_postprocessing_op(
pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
mock_get.assert_called_once()
if __name__ == '__main__':
tf.test.main()
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