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remove deprecated DAG v1 #4313

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16a7f01
remove deprecated DAG v1
pgladkows Jun 22, 2026
72d1f37
remove Resnet model from SimpleMediaPipe
pgladkows Jun 25, 2026
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287 changes: 0 additions & 287 deletions tests/functional/constants/pipelines.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,16 +26,11 @@
from ovms.constants.model_dataset import RandomDataset
from ovms.constants.models import ModelInfo
from ovms.constants.models import (
AgeGender,
ArgMax,
CrnnTf,
Dummy,
DummyAdd2Inputs,
DummyIncrement,
DummyIncrementDecrement,
EastFp32,
Emotion,
FaceDetectionRetail,
GoogleNetV2Fp32,
Increment4d,
Resnet,
Expand All @@ -51,13 +46,9 @@
CustomNodeAddSub,
CustomNodeChooseMaximum,
CustomNodeDemultiply,
CustomNodeDemultiplyGather,
CustomNodeDifferentOperations,
CustomNodeDynamicDemultiplex,
CustomNodeEastOcr,
CustomNodeElastic1T,
CustomNodeFaces,
CustomNodeImageTransformation,
CustomNodeVehicles,
)
from tests.functional.object_model.mediapipe_calculators import (
Expand Down Expand Up @@ -700,100 +691,6 @@ def _create_nodes(self, models=None):
return nodes


class SameModelsPipeline(Pipeline):

def __init__(self, **kwargs):
super().__init__("multiple_versions_of_the_same_model_pipeline", **kwargs)
self._initialize()

def _create_nodes(self, models=None):
node1 = Node("node_1", DummyIncrement())
node2 = Node("node_2", DummyIncrement())

request = Node("request", node_type=NodeType.Input)
output = Node("output", node_type=NodeType.Output)

NodesConnection.connect(node1, 0, request, 0)
NodesConnection.connect(node2, 0, node1, 0)
NodesConnection.connect(output, 0, node2, 0)

nodes = [request, node1, node2, output]
return nodes


class NodeReferringMultipleOutputsFromPreviousNodePipeline(Pipeline):

def __init__(self, **kwargs):
super().__init__("pipeline_multiple_inputs_from_the_same_model", **kwargs)
self._initialize()

def _create_nodes(self, models=None):
node1 = Node("node_1", DummyIncrementDecrement())
node2 = Node("node_2", DummyAdd2Inputs())

request = Node("request", node_type=NodeType.Input)
output = Node("output", node_type=NodeType.Output)

NodesConnection.connect(node1, 0, request, 0)
NodesConnection.connect(node1, 1, request, 1)
NodesConnection.connect(node2, 0, node1, 0)
NodesConnection.connect(node2, 1, node1, 1)
NodesConnection.connect(output, 0, node2, 0)

nodes = [request, node1, node2, output]
return nodes

def get_expected_output(self, input_data, client_type: str = None):
models = self.get_regular_models()
input_model = models[0]
output_model = models[1]
input_model_output = input_model.get_expected_output(input_data)
node2 = {
output_model.input_names[0]: input_model_output[input_model.output_names[0]],
output_model.input_names[1]: input_model_output[input_model.output_names[1]],
}
output_model_output = output_model.get_expected_output(node2)
pipeline_output = self.map_model_output_to_pipeline_output(output_model_output)
return pipeline_output


class EastAndOcrPipeline(Pipeline):

def __init__(self, **kwargs):
super().__init__("east_and_ocr_pipeline", **kwargs)
self._initialize()
self.set_expected_output_shape()

def set_expected_output_shape(self):
self.outputs["texts"]["shape"].insert(0, 0)

def _create_nodes(self, models=None):
east_node = Node("east_node", EastFp32(), output_names=["scores", "geometry"])
extract_node = Node("extract_node", CustomNodeEastOcr(), NodeType.Custom, demultiply_count=0)
crnn_model = CrnnTf()
crnn_model.inputs["input"]["layout"] = "NHWC:NCHW"
crnn_node = Node("crnn_node", crnn_model)

request = Node("request", node_type=NodeType.Input)
output = Node(
"output",
node_type=NodeType.Output,
input_names=["text_images", "text_coordinates", "confidence_levels", "texts"],
)
NodesConnection.connect(east_node, 0, request, 0)
NodesConnection.connect(extract_node, 0, request, 0)
NodesConnection.connect(extract_node, 1, east_node, 0)
NodesConnection.connect(extract_node, 2, east_node, 1)
NodesConnection.connect(crnn_node, 0, extract_node, 0)
NodesConnection.connect(output, 0, extract_node, 0)
NodesConnection.connect(output, 1, extract_node, 1)
NodesConnection.connect(output, 2, extract_node, 2)
NodesConnection.connect(output, 3, crnn_node, 0)

nodes = [request, east_node, extract_node, crnn_node, output]
return nodes


class DemultiplyPipeline(Pipeline):

def __init__(self, demultiply_value, **kwargs):
Expand Down Expand Up @@ -944,59 +841,6 @@ def _create_nodes(self, models=None):
return nodes


class CyclicGraphPipeline(Pipeline):

def __init__(self, **kwargs):
super().__init__("cyclic_graph_pipeline", **kwargs)
self._initialize()

def _create_nodes(self, models=None):
model_two_inputs_two_outputs = DummyIncrementDecrement()
model_two_inputs_one_output = DummyAdd2Inputs()

node1 = Node("node_1", model_two_inputs_one_output)
node2 = Node("node_2", model_two_inputs_two_outputs)

request = Node("request", node_type=NodeType.Input, output_names=self.input_names)
output = Node("output", node_type=NodeType.Output, input_names=self.output_names)

NodesConnection.connect(node1, 0, request, 0)
NodesConnection.connect(node1, 1, node2, 1)
NodesConnection.connect(output, 0, node2, 0)

nodes = [request, node1, node2, output]
return nodes


class AgeGenderAndEmotionPipeline(Pipeline):

def __init__(self, **kwargs):
super().__init__("combined-recognition", **kwargs)
self._initialize()

def _create_nodes(self, models=None):
model_gender = AgeGender()
model_gender.set_input_shape_for_ovms([1, 3, 64, 64])

model_emotion = Emotion()

age_gender_node = Node("age_gender", model_gender, output_names=["age", "gender"])
emotion_node = Node("emotion_node", model_emotion, output_names=["emotion"])

request = Node("request", node_type=NodeType.Input, output_names=["image"])
output = Node("output", node_type=NodeType.Output, input_names=["age", "gender", "emotion"])

NodesConnection.connect(age_gender_node, 0, request, 0)
NodesConnection.connect(emotion_node, 0, request, 0)

NodesConnection.connect(output, 0, age_gender_node, 0)
NodesConnection.connect(output, 1, age_gender_node, 1)
NodesConnection.connect(output, 2, emotion_node, 0)

nodes = [request, age_gender_node, emotion_node, output]
return nodes


class VehiclesAnalysisPipeline(Pipeline):

def __init__(self, **kwargs):
Expand Down Expand Up @@ -1043,76 +887,6 @@ def _create_nodes(self, models=None):
return nodes


class FacesAnalysisPipeline(Pipeline):

def __init__(self, **kwargs):
super().__init__("find_face_images", **kwargs)
self._initialize()

def _create_nodes(self, models=None):
model_face = FaceDetectionRetail()
model_face.input_shapes = [1, 3, 400, 600]
model_face.set_input_shape_for_ovms([1, 3, 400, 600])

model_gender = AgeGender()
model_gender.input_shapes = [1, 3, 64, 64]
model_gender.set_input_shape_for_ovms([1, 3, 64, 64])

model_emotion = Emotion()
model_emotion.input_shapes = [1, 3, 64, 64]
model_emotion.set_input_shape_for_ovms([1, 3, 64, 64])

faces_custom_node = CustomNodeFaces()

model_gender.inputs["data"]["shape"] = deepcopy(model_emotion.inputs["data"]["shape"])
model_face.inputs["data"]["shape"] = [1, 3, 400, 600]

face_detection_node = Node("face_detection_node", model_face, output_names=["detection_out"])
extract_node = Node(
"extract_node",
faces_custom_node,
NodeType.Custom,
demultiply_count=0,
output_names=["face_images", "face_coordinates", "confidence_levels"],
)
age_gender_recognition_node = Node("age_gender_recognition_node", model_gender, output_names=["age", "gender"])
emotion_recognition_node = Node("emotion_recognition_node", model_emotion, output_names=["emotion"])

request = Node("request", node_type=NodeType.Input, output_names=["image"])
output = Node(
"output",
node_type=NodeType.Output,
input_names=["face_images", "face_coordinates", "confidence_levels", "ages", "genders", "emotions"],
)

NodesConnection.connect(face_detection_node, 0, request, 0)

NodesConnection.connect(extract_node, 0, request, 0)
NodesConnection.connect(extract_node, 1, face_detection_node, 0)

NodesConnection.connect(age_gender_recognition_node, 0, extract_node, 0)

NodesConnection.connect(emotion_recognition_node, 0, extract_node, 0)

NodesConnection.connect(output, 0, extract_node, 0)
NodesConnection.connect(output, 1, extract_node, 1)
NodesConnection.connect(output, 2, extract_node, 2)

NodesConnection.connect(output, 3, age_gender_recognition_node, 0)
NodesConnection.connect(output, 4, age_gender_recognition_node, 1)
NodesConnection.connect(output, 5, emotion_recognition_node, 0)

nodes = [
request,
face_detection_node,
extract_node,
age_gender_recognition_node,
emotion_recognition_node,
output,
]
return nodes


class TenDummySerialPipeline(Pipeline):

def __init__(self, **kwargs):
Expand Down Expand Up @@ -1227,65 +1001,6 @@ def _create_nodes(self, models=None):
return nodes


class DemultiplexerAndGatherPipeline(Pipeline):

def __init__(self, demultiply_count, **kwargs):
super().__init__("pipeline_with_demultiplexer_and_gather", **kwargs)
self._demultiply_count = demultiply_count
self._initialize()

def _create_nodes(self, models=None):
diff_node = Node(
"diff_node", CustomNodeDifferentOperations(), NodeType.Custom, demultiply_count=self._demultiply_count
)
dummy_node = Node("dummy_node", Dummy())
demultiply_gather_node = Node(
"demultiply_gather_node",
CustomNodeDemultiplyGather(),
NodeType.Custom,
demultiply_count=self._demultiply_count,
gather_from_node="diff_node",
)

request = Node("request", node_type=NodeType.Input, output_names=self.input_names)
output = Node("output", node_type=NodeType.Output, input_names=self.output_names)

NodesConnection.connect(diff_node, 0, request, 0)
NodesConnection.connect(diff_node, 1, request, 1)
NodesConnection.connect(dummy_node, 0, diff_node, 0)
NodesConnection.connect(demultiply_gather_node, 0, dummy_node, 0)
NodesConnection.connect(output, 0, demultiply_gather_node, 0)

nodes = [request, dummy_node, demultiply_gather_node, diff_node, output]
return nodes


class ImageTransformationPipeline(Pipeline):

def __init__(self, **kwargs):
super().__init__("image_transformation_test", **kwargs)
self._initialize()

def _create_nodes(self, models=None):
self.demultiply_count = 0
image_transformation_node = Node(
"image_transformation_node", CustomNodeImageTransformation(), NodeType.Custom, output_names=["image"]
)
resnet_node = Node("resnet_node", Resnet())

request = Node("request", node_type=NodeType.Input, output_names=["image"])
output = Node("output", node_type=NodeType.Output, input_names=["image_0", "image_1"])

NodesConnection.connect(image_transformation_node, 0, request, 0)
NodesConnection.connect(resnet_node, 0, image_transformation_node, 0)
NodesConnection.connect(output, 0, resnet_node, 0)

NodesConnection.connect(output, 1, image_transformation_node, 0)

nodes = [request, image_transformation_node, resnet_node, output]
return nodes


class SingleLevelPipeline(Pipeline):
def __init__(self, list_of_models, predict_shape, **kwargs):
super().__init__("single_level_pipeline", **kwargs)
Expand Down Expand Up @@ -1512,8 +1227,6 @@ def graph_refresh(self):
class SimpleMediaPipe(MediaPipe):
def __init__(self, model=None, demultiply_count=None, **kwargs):
pipeline = SimplePipeline
if model is None:
model = Resnet()
super().__init__(model, pipeline, demultiply_count, **kwargs)
self.calculators = [OpenVINOModelServerSessionCalculator(model=self), OpenVINOInferenceCalculator(model=self)]
self._initialize([model])
Expand Down