def test_buildtfgraphforname(self):
""""
Run the graph produced by _buildtfgraphforname using tensorflow and compare to keras result.
"""
imageArray = self.imageArray
kerasPredict = self.kerasPredict
modelGraphInfo = _buildTFGraphForName(self.name, False)
graph = modelGraphInfo["graph"]
sess = tf.Session(graph=graph)
with sess.as_default():
inputTensor = graph.get_tensor_by_name(modelGraphInfo["inputTensorName"])
outputTensor = graph.get_tensor_by_name(modelGraphInfo["outputTensorName"])
tfPredict = sess.run(outputTensor, {inputTensor: imageArray})
self.assertEqual(kerasPredict.shape, tfPredict.shape)
np.testing.assert_array_almost_equal(kerasPredict, tfPredict)
def test_buildtfgraphforname(self):
""""
Run the graph produced by _buildtfgraphforname using tensorflow and compare to keras result.
"""
imageArray = self.imageArray
kerasPredict = self.kerasPredict
modelGraphInfo = _buildTFGraphForName(self.name, False)
graph = modelGraphInfo["graph"]
sess = tf.Session(graph=graph)
with sess.as_default():
inputTensor = graph.get_tensor_by_name(
modelGraphInfo["inputTensorName"])
outputTensor = graph.get_tensor_by_name(
modelGraphInfo["outputTensorName"])
tfPredict = sess.run(outputTensor, {inputTensor: imageArray})
self.assertEqual(kerasPredict.shape, tfPredict.shape)
np.testing.assert_array_almost_equal(kerasPredict, tfPredict)
def test_inceptionV3_prediction(self):
"""
Test inceptionV3 using keras, tensorflow and sparkDL
We run the sparkDL test with and without resizing beforehand
"""
imgFiles, images = getSampleImageList()
imageArray = np.empty((len(images), 299, 299, 3), 'uint8')
for i, img in enumerate(images):
assert img is not None and img.mode == "RGB"
imageArray[i] = np.array(img.resize((299, 299)))
# Basic keras flow
# We predict the class probabilities for the images in our test library using keras API.
prepedImaged = inception_v3.preprocess_input(
imageArray.astype('float32'))
model = inception_v3.InceptionV3()
kerasPredict = model.predict(prepedImaged)
# test: _buildTfGraphForName
# Run the graph produced by _buildTfGraphForName and compare the result to above keras
# result.
modelGraphInfo = _buildTFGraphForName("InceptionV3", False)
graph = modelGraphInfo["graph"]
sess = tf.Session(graph=graph)
with sess.as_default():
inputTensor = graph.get_tensor_by_name(
modelGraphInfo["inputTensorName"])
outputTensor = graph.get_tensor_by_name(
modelGraphInfo["outputTensorName"])
tfPredict = sess.run(outputTensor, {inputTensor: imageArray})
self.assertEqual(kerasPredict.shape, tfPredict.shape)
np.testing.assert_array_almost_equal(kerasPredict, tfPredict)
imageType = imageIO.pilModeLookup["RGB"]
def rowWithImage(img):
# return [imageIO.imageArrayToStruct(img.astype('uint8'), imageType.sparkMode)]
row = imageIO.imageArrayToStruct(img.astype('uint8'),
imageType.sparkMode)
# re-order row to avoid pyspark bug
return [[
getattr(row, field.name) for field in imageIO.imageSchema
]]
# test: predictor vs keras on resized images
# Run sparkDL inceptionV3 transformer on resized images and compare result to above keras
# result.
rdd = self.sc.parallelize([rowWithImage(img) for img in imageArray])
dfType = StructType([StructField("image", imageIO.imageSchema)])
imageDf = rdd.toDF(dfType)
transformer = DeepImagePredictor(
inputCol='image',
modelName="InceptionV3",
outputCol="prediction",
)
dfPredict = transformer.transform(imageDf).collect()
dfPredict = np.array([i.prediction for i in dfPredict])
self.assertEqual(kerasPredict.shape, dfPredict.shape)
np.testing.assert_array_almost_equal(kerasPredict, dfPredict)
# test: predictor vs keras on raw images
# Run sparkDL inceptionV3 transformer on raw (original size) images and compare result to
# above keras (using keras resizing) result.
origImgDf = getSampleImageDF()
fullPredict = transformer.transform(origImgDf).collect()
fullPredict = np.array([i.prediction for i in fullPredict])
self.assertEqual(kerasPredict.shape, fullPredict.shape)
# We use a large tolerance below because of differences in the resize step
# TODO: match keras resize step to get closer prediction
np.testing.assert_array_almost_equal(kerasPredict,
fullPredict,
decimal=6)
