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# 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 object_detection.predictors.rfcn_box_predictor."""
import numpy as np
import tensorflow as tf
from google.protobuf import text_format
from object_detection.builders import hyperparams_builder
from object_detection.predictors import rfcn_box_predictor as box_predictor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
class RfcnBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_conv_hyperparams(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
"""
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.build(conv_hyperparams, is_training=True)
def test_get_correct_box_encoding_and_class_prediction_shapes(self):
def graph_fn(image_features, proposal_boxes):
rfcn_box_predictor = box_predictor.RfcnBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4
)
box_predictions = rfcn_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor',
proposal_boxes=proposal_boxes)
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)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
proposal_boxes = np.random.rand(4, 2, 4).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features, proposal_boxes])
self.assertAllEqual(box_encodings.shape, [8, 1, 2, 4])
self.assertAllEqual(class_predictions_with_background.shape, [8, 1, 3])
if __name__ == '__main__':
tf.test.main()
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