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new 5292b423ec [SYSTEMDS-3913] Adapt visual representations to image
modality
5292b423ec is described below
commit 5292b423ecf1eda53d2966b948d0211f45286fca
Author: Christina Dionysio <[email protected]>
AuthorDate: Mon Dec 8 13:28:36 2025 +0100
[SYSTEMDS-3913] Adapt visual representations to image modality
This patch fixes some errors in visual representations in order to make
them work for video and images.
---
src/main/python/systemds/scuro/dataloader/image_loader.py | 2 +-
src/main/python/systemds/scuro/dataloader/json_loader.py | 2 +-
src/main/python/systemds/scuro/drsearch/hyperparameter_tuner.py | 4 ++--
src/main/python/systemds/scuro/representations/color_histogram.py | 8 +++++---
src/main/python/systemds/scuro/representations/fusion.py | 2 +-
src/main/python/systemds/scuro/representations/resnet.py | 2 +-
src/main/python/systemds/scuro/representations/vgg.py | 2 +-
src/main/python/systemds/scuro/utils/torch_dataset.py | 1 -
8 files changed, 12 insertions(+), 11 deletions(-)
diff --git a/src/main/python/systemds/scuro/dataloader/image_loader.py
b/src/main/python/systemds/scuro/dataloader/image_loader.py
index 0667e703b1..21ad27bf04 100644
--- a/src/main/python/systemds/scuro/dataloader/image_loader.py
+++ b/src/main/python/systemds/scuro/dataloader/image_loader.py
@@ -54,7 +54,7 @@ class ImageLoader(BaseLoader):
else:
height, width, channels = image.shape
- image = image.astype(np.float32) / 255.0
+ image = image.astype(np.uint8, copy=False)
self.metadata[file] = self.modality_type.create_metadata(
width, height, channels
diff --git a/src/main/python/systemds/scuro/dataloader/json_loader.py
b/src/main/python/systemds/scuro/dataloader/json_loader.py
index ed15448597..53e98e7e19 100644
--- a/src/main/python/systemds/scuro/dataloader/json_loader.py
+++ b/src/main/python/systemds/scuro/dataloader/json_loader.py
@@ -55,6 +55,6 @@ class JSONLoader(BaseLoader):
except:
text = json_file[self.field]
- text = " ".join(text)
+ text = " ".join(text) if isinstance(text, list) else text
self.data.append(text)
self.metadata[idx] =
self.modality_type.create_metadata(len(text), text)
diff --git a/src/main/python/systemds/scuro/drsearch/hyperparameter_tuner.py
b/src/main/python/systemds/scuro/drsearch/hyperparameter_tuner.py
index a90485dfa2..2a5f826283 100644
--- a/src/main/python/systemds/scuro/drsearch/hyperparameter_tuner.py
+++ b/src/main/python/systemds/scuro/drsearch/hyperparameter_tuner.py
@@ -177,11 +177,11 @@ class HyperparameterTuner:
if self.maximize_metric:
best_params, best_score = max(
- all_results, key=lambda x: x[1].scores[self.scoring_metric]
+ all_results, key=lambda x:
x[1].average_scores[self.scoring_metric]
)
else:
best_params, best_score = min(
- all_results, key=lambda x: x[1].scores[self.scoring_metric]
+ all_results, key=lambda x:
x[1].average_scores[self.scoring_metric]
)
tuning_time = time.time() - start_time
diff --git a/src/main/python/systemds/scuro/representations/color_histogram.py
b/src/main/python/systemds/scuro/representations/color_histogram.py
index 6412b1979d..2d780939e1 100644
--- a/src/main/python/systemds/scuro/representations/color_histogram.py
+++ b/src/main/python/systemds/scuro/representations/color_histogram.py
@@ -22,17 +22,19 @@
import numpy as np
import cv2
+from systemds.scuro.drsearch.operator_registry import register_representation
from systemds.scuro.modality.type import ModalityType
from systemds.scuro.representations.unimodal import UnimodalRepresentation
from systemds.scuro.modality.transformed import TransformedModality
+@register_representation(ModalityType.IMAGE)
class ColorHistogram(UnimodalRepresentation):
def __init__(
self,
color_space="RGB",
- bins=32,
- normalize=True,
+ bins=64,
+ normalize=False,
aggregation="mean",
output_file=None,
):
@@ -48,7 +50,7 @@ class ColorHistogram(UnimodalRepresentation):
def _get_parameters(self):
return {
"color_space": ["RGB", "HSV", "GRAY"],
- "bins": [8, 16, 32, 64, 128, 256, (8, 8, 8), (16, 16, 16)],
+ "bins": [8, 16, 32, 64, 128, 256],
"normalize": [True, False],
"aggregation": ["mean", "max", "concat"],
}
diff --git a/src/main/python/systemds/scuro/representations/fusion.py
b/src/main/python/systemds/scuro/representations/fusion.py
index d491dcad6b..693689bf92 100644
--- a/src/main/python/systemds/scuro/representations/fusion.py
+++ b/src/main/python/systemds/scuro/representations/fusion.py
@@ -86,7 +86,7 @@ class Fusion(Representation):
(len(modalities[0].data), transformed_train.shape[1])
)
transformed_data[task.train_indices] = transformed_train
- transformed_data[task.val_indices] = transformed_val
+ transformed_data[task.test_indices] = transformed_other
return transformed_data
diff --git a/src/main/python/systemds/scuro/representations/resnet.py
b/src/main/python/systemds/scuro/representations/resnet.py
index f544e6a46f..55e7d36948 100644
--- a/src/main/python/systemds/scuro/representations/resnet.py
+++ b/src/main/python/systemds/scuro/representations/resnet.py
@@ -114,7 +114,7 @@ class ResNet(UnimodalRepresentation):
return parameters
def transform(self, modality):
- self.data_type = numpy_dtype_to_torch_dtype(modality.data_type)
+ self.data_type = torch.float32
if next(self.model.parameters()).dtype != self.data_type:
self.model = self.model.to(self.data_type)
diff --git a/src/main/python/systemds/scuro/representations/vgg.py
b/src/main/python/systemds/scuro/representations/vgg.py
index 4d0212883c..4f4324a372 100644
--- a/src/main/python/systemds/scuro/representations/vgg.py
+++ b/src/main/python/systemds/scuro/representations/vgg.py
@@ -65,7 +65,7 @@ class VGG19(UnimodalRepresentation):
return parameters
def transform(self, modality):
- self.data_type = numpy_dtype_to_torch_dtype(modality.data_type)
+ self.data_type = torch.float32
if next(self.model.parameters()).dtype != self.data_type:
self.model = self.model.to(self.data_type)
diff --git a/src/main/python/systemds/scuro/utils/torch_dataset.py
b/src/main/python/systemds/scuro/utils/torch_dataset.py
index 19875f8802..9c462e3675 100644
--- a/src/main/python/systemds/scuro/utils/torch_dataset.py
+++ b/src/main/python/systemds/scuro/utils/torch_dataset.py
@@ -62,7 +62,6 @@ class CustomDataset(torch.utils.data.Dataset):
if isinstance(data, np.ndarray) and data.ndim == 3:
# image
- data = torch.tensor(data).permute(2, 0, 1)
output = self.tf(data).to(self.device)
else:
for i, d in enumerate(data):
