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[release/0.4.0][Bug-fix] Convert NaN to num to make visible in geti UI #1413

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merged 3 commits into from
Dec 5, 2022
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[release/0.4.0][Bug-fix] Convert NaN to num to make visible in geti UI #1413

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cd175cc
Change submodule to develop (#1409)
supersoob Dec 5, 2022
41cb1f7
Send full dataset to POT optimization function (#1379)
AlexanderDokuchaev Dec 5, 2022
dc56b29
nan learning curve
Dec 5, 2022
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6 changes: 6 additions & 0 deletions external/anomaly/tasks/openvino.py
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Expand Up @@ -271,6 +271,12 @@ def optimize(
if optimization_type is not OptimizationType.POT:
raise ValueError("POT is the only supported optimization type for OpenVINO models")

# Training subset does not contain example of anomalous images.
# Anomalous examples from all dataset used to get statistics for quantization.
dataset = DatasetEntity(
items=[item for item in dataset if item.get_shapes_labels()[0].is_anomalous], purpose=dataset.purpose
)

logger.info("Starting POT optimization.")
data_loader = OTEOpenVINOAnomalyDataloader(config=self.config, dataset=dataset, inferencer=self.inferencer)

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2 changes: 1 addition & 1 deletion external/anomaly/tools/sample.py
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Expand Up @@ -264,7 +264,7 @@ def optimize(self) -> None:

self.openvino_task.optimize(
optimization_type=OptimizationType.POT,
dataset=self.dataset.get_subset(Subset.TRAINING),
dataset=self.dataset,
output_model=optimized_model,
optimization_parameters=OptimizationParameters(),
)
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2 changes: 2 additions & 0 deletions external/deep-object-reid/torchreid_tasks/openvino_task.py
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Expand Up @@ -40,6 +40,7 @@
from ote_sdk.entities.tensor import TensorEntity
from ote_sdk.entities.resultset import ResultSetEntity
from ote_sdk.entities.result_media import ResultMediaEntity
from ote_sdk.entities.subset import Subset
from ote_sdk.entities.task_environment import TaskEnvironment
from ote_sdk.serialization.label_mapper import LabelSchemaMapper, label_schema_to_bytes
from ote_sdk.usecases.exportable_code.inference import BaseInferencer
Expand Down Expand Up @@ -277,6 +278,7 @@ def optimize(self,
if optimization_type is not OptimizationType.POT:
raise ValueError("POT is the only supported optimization type for OpenVino models")

dataset = dataset.get_subset(Subset.TRAINING)
data_loader = OTEOpenVinoDataLoader(dataset, self.inferencer)

with tempfile.TemporaryDirectory() as tempdir:
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2 changes: 2 additions & 0 deletions external/mmdetection/detection_tasks/apis/detection/openvino_task.py
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Expand Up @@ -49,6 +49,7 @@
from ote_sdk.entities.optimization_parameters import OptimizationParameters
from ote_sdk.entities.result_media import ResultMediaEntity
from ote_sdk.entities.resultset import ResultSetEntity
from ote_sdk.entities.subset import Subset
from ote_sdk.entities.task_environment import TaskEnvironment
from ote_sdk.entities.tensor import TensorEntity
from ote_sdk.entities.label import Domain, LabelEntity
Expand Down Expand Up @@ -589,6 +590,7 @@ def optimize(
"POT is the only supported optimization type for OpenVino models"
)

dataset = dataset.get_subset(Subset.TRAINING)
data_loader = OTEOpenVinoDataLoader(dataset, self.inferencer)

with tempfile.TemporaryDirectory() as tempdir:
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2 changes: 2 additions & 0 deletions external/mmsegmentation/segmentation_tasks/apis/segmentation/openvino_task.py
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Expand Up @@ -39,6 +39,7 @@
from ote_sdk.entities.tensor import TensorEntity
from ote_sdk.entities.resultset import ResultSetEntity
from ote_sdk.entities.result_media import ResultMediaEntity
from ote_sdk.entities.subset import Subset
from ote_sdk.entities.task_environment import TaskEnvironment
from ote_sdk.usecases.evaluation.metrics_helper import MetricsHelper
from ote_sdk.usecases.exportable_code.inference import BaseInferencer
Expand Down Expand Up @@ -252,6 +253,7 @@ def optimize(self,
if optimization_type is not OptimizationType.POT:
raise ValueError("POT is the only supported optimization type for OpenVino models")

dataset = dataset.get_subset(Subset.TRAINING)
data_loader = OTEOpenVinoDataLoader(dataset, self.inferencer)

with tempfile.TemporaryDirectory() as tempdir:
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6 changes: 5 additions & 1 deletion external/model-preparation-algorithm/mpa_tasks/apis/classification/task.py
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Expand Up @@ -548,7 +548,11 @@ def _generate_training_metrics_group(self, learning_curves) -> Optional[List[Met
return output

for key, curve in learning_curves.items():
metric_curve = CurveMetric(xs=curve.x, ys=curve.y, name=key)
metric_curve = CurveMetric(
xs=np.nan_to_num(curve.x).tolist(),
ys=np.nan_to_num(curve.y).tolist(),
name=key
)
if key == metric_key:
best_acc = max(curve.y)
visualization_info = LineChartInfo(name=key, x_axis_label="Timestamp", y_axis_label=key)
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4 changes: 1 addition & 3 deletions external/model-preparation-algorithm/mpa_tasks/samples/cls_cls_il_sample.py
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Expand Up @@ -308,9 +308,7 @@ def main():
dataset,
environment.get_model_configuration(),
)
openvino_task.optimize(
OptimizationType.POT, dataset.get_subset(Subset.TRAINING), optimized_model, OptimizationParameters()
)
openvino_task.optimize(OptimizationType.POT, dataset, optimized_model, OptimizationParameters())

logger.info("Get predictions on the validation set")
predicted_validation_dataset = openvino_task.infer(
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4 changes: 1 addition & 3 deletions external/model-preparation-algorithm/mpa_tasks/samples/det_cls_il_sample.py
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Expand Up @@ -252,9 +252,7 @@ def main(args):
dataset,
environment.get_model_configuration(),
)
openvino_task.optimize(
OptimizationType.POT, dataset.get_subset(Subset.TRAINING), optimized_model, OptimizationParameters()
)
openvino_task.optimize(OptimizationType.POT, dataset, optimized_model, OptimizationParameters())

logger.info("Get predictions on the validation set")
predicted_validation_dataset = openvino_task.infer(
Expand Down
4 changes: 1 addition & 3 deletions external/model-preparation-algorithm/mpa_tasks/samples/inst_seg_cls_il_sample.py
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Expand Up @@ -300,9 +300,7 @@ def main(args):
dataset,
environment.get_model_configuration(),
)
openvino_task.optimize(
OptimizationType.POT, dataset.get_subset(Subset.TRAINING), optimized_model, OptimizationParameters()
)
openvino_task.optimize(OptimizationType.POT, dataset, optimized_model, OptimizationParameters())

logger.info("Get predictions on the validation set")
predicted_validation_dataset = openvino_task.infer(
Expand Down
4 changes: 1 addition & 3 deletions external/model-preparation-algorithm/mpa_tasks/samples/seg_cls_il_sample.py
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Expand Up @@ -271,9 +271,7 @@ def main(args):
dataset,
environment.get_model_configuration(),
)
openvino_task.optimize(
OptimizationType.POT, dataset.get_subset(Subset.TRAINING), optimized_model, OptimizationParameters()
)
openvino_task.optimize(OptimizationType.POT, dataset, optimized_model, OptimizationParameters())

logger.info("Get predictions on the validation set")
predicted_validation_dataset = openvino_task.infer(
Expand Down
2 changes: 1 addition & 1 deletion external/model-preparation-algorithm/submodule
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Submodule submodule updated 0 files
5 changes: 1 addition & 4 deletions ote_sdk/ote_sdk/test_suite/training_tests_actions.py
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Expand Up @@ -410,9 +410,6 @@ class OTETestPotAction(BaseOTETestAction):
_name = "pot"
_depends_stages_names = ["export"]

def __init__(self, pot_subset=Subset.TRAINING):
self.pot_subset = pot_subset

def _run_ote_pot(
self, data_collector, model_template, dataset, environment_for_export
):
Expand All @@ -431,7 +428,7 @@ def _run_ote_pot(
try:
self.openvino_task_pot.optimize(
OptimizationType.POT,
dataset.get_subset(self.pot_subset),
dataset,
self.optimized_model_pot,
OptimizationParameters(),
)
Expand Down