test_misc.py
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"""
Copyright 2017-2018 Fizyr (https://fizyr.com)
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.
"""
from tensorflow import keras
import keras_retinanet.backend
import keras_retinanet.layers
import numpy as np
class TestAnchors(object):
def test_simple(self):
# create simple Anchors layer
anchors_layer = keras_retinanet.layers.Anchors(
size=32,
stride=8,
ratios=np.array([1], keras.backend.floatx()),
scales=np.array([1], keras.backend.floatx()),
)
# create fake features input (only shape is used anyway)
features = np.zeros((1, 2, 2, 1024), dtype=keras.backend.floatx())
features = keras.backend.variable(features)
# call the Anchors layer
anchors = anchors_layer.call(features)
anchors = keras.backend.eval(anchors)
# expected anchor values
expected = np.array([[
[-12, -12, 20, 20],
[-4 , -12, 28, 20],
[-12, -4 , 20, 28],
[-4 , -4 , 28, 28],
]], dtype=keras.backend.floatx())
# test anchor values
np.testing.assert_array_equal(anchors, expected)
# mark test to fail
def test_mini_batch(self):
# create simple Anchors layer
anchors_layer = keras_retinanet.layers.Anchors(
size=32,
stride=8,
ratios=np.array([1], dtype=keras.backend.floatx()),
scales=np.array([1], dtype=keras.backend.floatx()),
)
# create fake features input with batch_size=2
features = np.zeros((2, 2, 2, 1024), dtype=keras.backend.floatx())
features = keras.backend.variable(features)
# call the Anchors layer
anchors = anchors_layer.call(features)
anchors = keras.backend.eval(anchors)
# expected anchor values
expected = np.array([[
[-12, -12, 20, 20],
[-4 , -12, 28, 20],
[-12, -4 , 20, 28],
[-4 , -4 , 28, 28],
]], dtype=keras.backend.floatx())
expected = np.tile(expected, (2, 1, 1))
# test anchor values
np.testing.assert_array_equal(anchors, expected)
class TestUpsampleLike(object):
def test_simple(self):
# create simple UpsampleLike layer
upsample_like_layer = keras_retinanet.layers.UpsampleLike()
# create input source
source = np.zeros((1, 2, 2, 1), dtype=keras.backend.floatx())
source = keras.backend.variable(source)
target = np.zeros((1, 5, 5, 1), dtype=keras.backend.floatx())
expected = target
target = keras.backend.variable(target)
# compute output
actual = upsample_like_layer.call([source, target])
actual = keras.backend.eval(actual)
np.testing.assert_array_equal(actual, expected)
def test_mini_batch(self):
# create simple UpsampleLike layer
upsample_like_layer = keras_retinanet.layers.UpsampleLike()
# create input source
source = np.zeros((2, 2, 2, 1), dtype=keras.backend.floatx())
source = keras.backend.variable(source)
target = np.zeros((2, 5, 5, 1), dtype=keras.backend.floatx())
expected = target
target = keras.backend.variable(target)
# compute output
actual = upsample_like_layer.call([source, target])
actual = keras.backend.eval(actual)
np.testing.assert_array_equal(actual, expected)
class TestRegressBoxes(object):
def test_simple(self):
mean = [0, 0, 0, 0]
std = [0.2, 0.2, 0.2, 0.2]
# create simple RegressBoxes layer
regress_boxes_layer = keras_retinanet.layers.RegressBoxes(mean=mean, std=std)
# create input
anchors = np.array([[
[0 , 0 , 10 , 10 ],
[50, 50, 100, 100],
[20, 20, 40 , 40 ],
]], dtype=keras.backend.floatx())
anchors = keras.backend.variable(anchors)
regression = np.array([[
[0 , 0 , 0 , 0 ],
[0.1, 0.1, 0 , 0 ],
[0 , 0 , 0.1, 0.1],
]], dtype=keras.backend.floatx())
regression = keras.backend.variable(regression)
# compute output
actual = regress_boxes_layer.call([anchors, regression])
actual = keras.backend.eval(actual)
# compute expected output
expected = np.array([[
[0 , 0 , 10 , 10 ],
[51, 51, 100 , 100 ],
[20, 20, 40.4, 40.4],
]], dtype=keras.backend.floatx())
np.testing.assert_array_almost_equal(actual, expected, decimal=2)
# mark test to fail
def test_mini_batch(self):
mean = [0, 0, 0, 0]
std = [0.2, 0.2, 0.2, 0.2]
# create simple RegressBoxes layer
regress_boxes_layer = keras_retinanet.layers.RegressBoxes(mean=mean, std=std)
# create input
anchors = np.array([
[
[0 , 0 , 10 , 10 ], # 1
[50, 50, 100, 100], # 2
[20, 20, 40 , 40 ], # 3
],
[
[20, 20, 40 , 40 ], # 3
[0 , 0 , 10 , 10 ], # 1
[50, 50, 100, 100], # 2
],
], dtype=keras.backend.floatx())
anchors = keras.backend.variable(anchors)
regression = np.array([
[
[0 , 0 , 0 , 0 ], # 1
[0.1, 0.1, 0 , 0 ], # 2
[0 , 0 , 0.1, 0.1], # 3
],
[
[0 , 0 , 0.1, 0.1], # 3
[0 , 0 , 0 , 0 ], # 1
[0.1, 0.1, 0 , 0 ], # 2
],
], dtype=keras.backend.floatx())
regression = keras.backend.variable(regression)
# compute output
actual = regress_boxes_layer.call([anchors, regression])
actual = keras.backend.eval(actual)
# compute expected output
expected = np.array([
[
[0 , 0 , 10 , 10 ], # 1
[51, 51, 100 , 100 ], # 2
[20, 20, 40.4, 40.4], # 3
],
[
[20, 20, 40.4, 40.4], # 3
[0 , 0 , 10 , 10 ], # 1
[51, 51, 100 , 100 ], # 2
],
], dtype=keras.backend.floatx())
np.testing.assert_array_almost_equal(actual, expected, decimal=2)