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Align saliency map media instantiation over tasks (#1447)
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@negvet
negvet authored Dec 16, 2022
1 parent 18fe77e commit 8cb95ab
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Showing 6 changed files with 30 additions and 25 deletions.
23 changes: 12 additions & 11 deletions external/deep-object-reid/torchreid_tasks/openvino_task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,28 +199,29 @@ def infer(self, dataset: DatasetEntity,
if saliency_map.ndim == 2:
# Single saliency map per image, support e.g. EigenCAM use case
actmap = get_actmap(saliency_map, (dataset_item.width, dataset_item.height))
saliency_media = ResultMediaEntity(name="Saliency Map", type="saliency_map",
saliency_media = ResultMediaEntity(name="Saliency Map",
type="saliency_map",
annotation_scene=dataset_item.annotation_scene,
numpy=actmap, roi=dataset_item.roi,
label=predicted_scene.annotations[0].get_labels()[0].label)
numpy=actmap,
roi=dataset_item.roi)
dataset_item.append_metadata_item(saliency_media, model=self.model)
elif saliency_map.ndim == 3:
# Multiple saliency maps per image (class-wise saliency map), support e.g. Recipro-CAM use case
predicted_class_set = set()
for label in predicted_scene.annotations[0].get_labels():
predicted_class_set.add(label.name)
predicted_labels = set()
for scored_label in predicted_scene.annotations[0].get_labels():
predicted_labels.add(scored_label.label)

for class_id, class_wise_saliency_map in enumerate(saliency_map):
class_name_str = self.task_environment.get_labels()[class_id].name
if class_name_str in predicted_class_set:
label = self.task_environment.get_labels()[class_id]
if label in predicted_labels:
# TODO (negvet): Support more advanced use case,
# when all/configurable set of saliency maps is returned
actmap = get_actmap(class_wise_saliency_map, (dataset_item.width, dataset_item.height))
label = predicted_scene.annotations[0].get_labels()[0].label
saliency_media = ResultMediaEntity(name=class_name_str,
saliency_media = ResultMediaEntity(name=label.name,
type="saliency_map",
annotation_scene=dataset_item.annotation_scene,
numpy=actmap, roi=dataset_item.roi,
numpy=actmap,
roi=dataset_item.roi,
label=label)
dataset_item.append_metadata_item(saliency_media, model=self.model)
else:
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16 changes: 10 additions & 6 deletions external/mmdetection/detection_tasks/apis/detection/openvino_task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -417,28 +417,32 @@ def infer(
dataset_item.append_metadata_item(saliency_media, model=self.model)
elif saliency_map.ndim == 3:
# Multiple saliency maps per image (class-wise saliency map)
predicted_class_set = set()
predicted_labels = set()
for bbox in predicted_scene.annotations:
predicted_class_set.add(bbox.get_labels()[0].name)
scored_label = bbox.get_labels()[0]
predicted_labels.add(scored_label.label)

labels = self.task_environment.get_labels()
num_saliency_maps = saliency_map.shape[0]
if num_saliency_maps == len(labels) + 1:
# Include the background as the last category
labels.append(LabelEntity('background', Domain.DETECTION))

for class_id, class_wise_saliency_map in enumerate(saliency_map):
class_name_str = labels[class_id].name
if class_name_str in predicted_class_set:
label = labels[class_id]
if label in predicted_labels:
# TODO (negvet): Support more advanced use case,
# when all/configurable set of saliency maps is returned
actmap = get_actmap(
class_wise_saliency_map, (dataset_item.width, dataset_item.height)
)
saliency_media = ResultMediaEntity(
name=class_name_str,
name=label.name,
type="saliency_map",
annotation_scene=dataset_item.annotation_scene,
numpy=actmap, roi=dataset_item.roi
numpy=actmap,
roi=dataset_item.roi,
label=label
)
dataset_item.append_metadata_item(saliency_media, model=self.model)
else:
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2 changes: 1 addition & 1 deletion external/mmsegmentation/segmentation_tasks/apis/segmentation/inference_task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,7 +229,7 @@ def _add_predictions_to_dataset(self, prediction_results, dataset, dump_soft_pre
current_label_soft_prediction = soft_prediction[:, :, label_index]

class_act_map = get_activation_map(current_label_soft_prediction)
result_media = ResultMediaEntity(name='Soft Prediction',
result_media = ResultMediaEntity(name=label.name,
type='soft_prediction',
label=label,
annotation_scene=dataset_item.annotation_scene,
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2 changes: 1 addition & 1 deletion external/mmsegmentation/segmentation_tasks/apis/segmentation/openvino_task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -185,7 +185,7 @@ def infer(self,
continue
current_label_soft_prediction = soft_prediction[:, :, label_index]
class_act_map = get_activation_map(current_label_soft_prediction)
result_media = ResultMediaEntity(name='Soft Prediction',
result_media = ResultMediaEntity(name=label.name,
type='soft_prediction',
label=label,
annotation_scene=dataset_item.annotation_scene,
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7 changes: 3 additions & 4 deletions external/model-preparation-algorithm/mpa_tasks/apis/classification/task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -289,7 +289,6 @@ def _add_predictions_to_dataset(self, prediction_results, dataset, update_progre
annotation_scene=dataset_item.annotation_scene,
numpy=saliency_map,
roi=dataset_item.roi,
label=item_labels[0].label,
)
dataset_item.append_metadata_item(saliency_map_media, model=self._task_environment.model)
elif saliency_map.ndim == 3:
Expand All @@ -298,14 +297,14 @@ def _add_predictions_to_dataset(self, prediction_results, dataset, update_progre
class_wise_saliency_map = get_actmap(
class_wise_saliency_map, (dataset_item.width, dataset_item.height)
)
class_name_str = self._labels[class_id].name
label = self._labels[class_id]
saliency_map_media = ResultMediaEntity(
name=class_name_str,
name=label.name,
type="saliency_map",
annotation_scene=dataset_item.annotation_scene,
numpy=class_wise_saliency_map,
roi=dataset_item.roi,
label=item_labels[0].label,
label=label,
)
dataset_item.append_metadata_item(saliency_map_media, model=self._task_environment.model)
else:
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5 changes: 3 additions & 2 deletions external/model-preparation-algorithm/mpa_tasks/apis/detection/task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -327,13 +327,14 @@ def _add_predictions_to_dataset(self, prediction_results, dataset, confidence_th
labels.append(LabelEntity("background", Domain.DETECTION))
for class_id, class_wise_saliency_map in enumerate(saliency_map):
actmap = get_actmap(class_wise_saliency_map, (dataset_item.width, dataset_item.height))
class_name_str = labels[class_id].name
label = labels[class_id]
saliency_media = ResultMediaEntity(
name=class_name_str,
name=label.name,
type="saliency_map",
annotation_scene=dataset_item.annotation_scene,
numpy=actmap,
roi=dataset_item.roi,
label=label,
)
dataset_item.append_metadata_item(saliency_media, model=self._task_environment.model)
else:
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