def test_03(self):
with self.assertRaises(AssertionError):
RetinanetToPmml(self.model,
input_shape=(224, 224, 3),
backbone_name='resnet50',
pmml_file_name="retinanet_with_coco_2.pmml")
def test_03(self):
model = load_model('resnet50_coco_best_v2.1.0.h5',
backbone_name='resnet50')
with self.assertRaises(AssertionError):
RetinanetToPmml(model,
input_shape=(224, 224, 3),
backbone_name='resnet50',
pmml_file_name="retinanet_with_coco_2.pmml")
def test_05(self):
backbone = 'resnet'
script_content = open("nyoka/tests/preprocess.py", 'r').read()
RetinanetToPmml(self.model,
input_shape=(224, 224, 3),
backbone_name=backbone,
pmml_file_name="retinanet_with_coco_2.pmml",
script_args={
"content": script_content,
"def_name": "getBase64EncodedString",
"return_type": "string",
"encode": True
})
recon_pmml_obj = pml.parse("retinanet_with_coco_2.pmml", True)
content = recon_pmml_obj.TransformationDictionary.DefineFunction[
0].Apply.Extension[0].anytypeobjs_[0]
content = base64.b64decode(content).decode()
self.assertEqual(script_content, content)
def test_02(self):
backbone = 'mobilenet'
RetinanetToPmml(self.model,
input_shape=(224, 224, 3),
backbone_name=backbone,
pmml_file_name="retinanet_with_coco_2.pmml")
recon_pmml_obj = pml.parse("retinanet_with_coco_2.pmml", True)
binary_buffered = recon_pmml_obj.DataDictionary.DataField[0].Extension[
0].value
self.assertEqual(binary_buffered, 'true')
function = recon_pmml_obj.DeepNetwork[
0].LocalTransformations.DerivedField[0].Apply.function
self.assertEqual(function,
'KerasRetinaNet:getBase64StringFromBufferedInput')
scaling = recon_pmml_obj.DeepNetwork[
0].LocalTransformations.DerivedField[0].Apply.Constant[0].valueOf_
self.assertEqual(scaling, 'tf')
def test_01(self):
RetinanetToPmml(model=self.model,
input_shape=(224, 224, 3),
input_format='image',
backbone_name='resnet',
trained_classes=classes,
pmml_file_name="RetinaNet.pmml")
model_name = self.adapa_utility.upload_to_zserver('RetinaNet.pmml')
z_boxes, z_scores, z_labels = self.adapa_utility.score_in_zserver(
model_name, 'nyoka/tests/test_image_retinanet.png', 'RN')
img = load_img('nyoka/tests/test_image_retinanet.png')
img = img_to_array(img)
img = preprocess_input(img)
test = np.expand_dims(img, axis=0)
boxes, scores, labels = self.model.predict(test)
scores = scores.flatten()
boxes = boxes.reshape(-1, 4)
labels = labels.flatten()
scores_cnt = 0
for a, b in zip(scores, z_scores):
a = "{:.4f}".format(a)
b = "{:.4f}".format(b)
if a != b:
scores_cnt += 1
labels_cnt = 0
for a, b in zip(labels, z_labels):
b = self.classes.index(b)
if a != b:
labels_cnt += 1
boxes_cnt = 0
for a, b in zip(boxes, z_boxes):
for a_, b_ in zip(a, b):
a_ = "{:.2f}".format(a_)
b_ = "{:.2f}".format(b_)
if a_ != b_:
boxes_cnt += 1
self.assertEqual(scores_cnt, 0)
self.assertEqual(labels_cnt, 0)
self.assertEqual(boxes_cnt, 0)