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Merged
MechaCritter merged 13 commits into from
Jun 10, 2026
Merged

Bugfix/fix ruff errors #12

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b0b542b
Fix ruff I001: sort imports in _base_classes.py
MechaCritter Jun 10, 2026
6154081
Fix ruff F401: declare OxfordFlowerDataset re-export via __all__
MechaCritter Jun 10, 2026
df4b123
Fix ruff B027: remove empty __init__ from FeatureExtractorBase
MechaCritter Jun 10, 2026
b51f347
Fix ruff B028: add stacklevel=2 to warnings.warn calls
MechaCritter Jun 10, 2026
321d3e8
Fix ruff B904: chain exceptions with 'raise ... from e' in except cla…
MechaCritter Jun 10, 2026
24e4051
Fix ruff B008: replace function-call defaults with None and lazy init
MechaCritter Jun 10, 2026
24f33a9
Fix ruff B905: pass strict=True to zip() calls
MechaCritter Jun 10, 2026
c2e9a6e
Fix ruff F403/F405: replace star imports in example notebooks
MechaCritter Jun 10, 2026
aa77bc6
added ruff-check in pre commit
MechaCritter Jun 10, 2026
707622a
Potential fix for pull request finding
MechaCritter Jun 10, 2026
9722282
Potential fix for pull request finding
MechaCritter Jun 10, 2026
24aa859
Potential fix for pull request finding
MechaCritter Jun 10, 2026
08b2652
ran ruff formatter again
MechaCritter Jun 10, 2026
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2 changes: 2 additions & 0 deletions .pre-commit-config.yaml
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Expand Up @@ -19,6 +19,8 @@ repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.15.2
hooks:
- id: ruff-check
args: [ --fix ]
- id: ruff-format

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- repo: https://github.com/codespell-project/codespell
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24 changes: 4 additions & 20 deletions examples/clustering_images_using_fv.ipynb
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Expand Up @@ -20,32 +20,16 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "9d64d8aebd89bdcd",
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-18T23:05:14.526690Z",
"start_time": "2025-01-18T23:05:00.750503Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2025-01-19 00:05:08,127 - numexpr.utils - INFO - Note: NumExpr detected 64 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
"2025-01-19 00:05:08,128 - numexpr.utils - INFO - NumExpr defaulting to 8 threads.\n"
]
}
],
"source": [
"from torchvision.models import VGG16_Weights, vgg16\n",
"\n",
"from pyvisim._utils import *\n",
"from pyvisim.datasets import OxfordFlowerDataset\n",
"from pyvisim.encoders import FisherVectorEncoder, GMMWeights\n",
"from pyvisim.features import DeepConvFeature"
]
"outputs": [],
"source": "import numpy as np\nfrom torchvision.models import VGG16_Weights, vgg16\n\nfrom pyvisim._utils import (\n cluster_images_and_generate_statistics,\n cosine_similarity,\n plot_and_save_heatmap,\n)\nfrom pyvisim.datasets import OxfordFlowerDataset\nfrom pyvisim.encoders import FisherVectorEncoder, GMMWeights\nfrom pyvisim.features import DeepConvFeature"
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -316,4 +300,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
19 changes: 3 additions & 16 deletions examples/clustering_images_using_pipeline.ipynb
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Expand Up @@ -22,7 +22,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "9d64d8aebd89bdcd",
"metadata": {
"ExecuteTime": {
Expand All @@ -31,20 +31,7 @@
}
},
"outputs": [],
"source": [
"from torchvision.models import VGG16_Weights, vgg16\n",
"\n",
"from pyvisim._utils import *\n",
"from pyvisim.datasets import OxfordFlowerDataset\n",
"from pyvisim.encoders import (\n",
" FisherVectorEncoder,\n",
" GMMWeights,\n",
" KMeansWeights,\n",
" Pipeline,\n",
" VLADEncoder,\n",
")\n",
"from pyvisim.features import DeepConvFeature"
]
"source": "import numpy as np\nfrom torchvision.models import VGG16_Weights, vgg16\n\nfrom pyvisim._utils import (\n cluster_images_and_generate_statistics,\n cosine_similarity,\n plot_and_save_heatmap,\n)\nfrom pyvisim.datasets import OxfordFlowerDataset\nfrom pyvisim.encoders import (\n FisherVectorEncoder,\n GMMWeights,\n KMeansWeights,\n Pipeline,\n VLADEncoder,\n)\nfrom pyvisim.features import DeepConvFeature"
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -320,4 +307,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
13 changes: 3 additions & 10 deletions examples/clustering_images_using_vlad.ipynb
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Expand Up @@ -20,7 +20,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "9d64d8aebd89bdcd",
"metadata": {
"ExecuteTime": {
Expand All @@ -29,14 +29,7 @@
}
},
"outputs": [],
"source": [
"from torchvision.models import VGG16_Weights, vgg16\n",
"\n",
"from pyvisim._utils import *\n",
"from pyvisim.datasets import OxfordFlowerDataset\n",
"from pyvisim.encoders import KMeansWeights, VLADEncoder\n",
"from pyvisim.features import DeepConvFeature"
]
"source": "import numpy as np\nfrom torchvision.models import VGG16_Weights, vgg16\n\nfrom pyvisim._utils import (\n cluster_images_and_generate_statistics,\n cosine_similarity,\n plot_and_save_heatmap,\n)\nfrom pyvisim.datasets import OxfordFlowerDataset\nfrom pyvisim.encoders import KMeansWeights, VLADEncoder\nfrom pyvisim.features import DeepConvFeature"
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -305,4 +298,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
19 changes: 5 additions & 14 deletions examples/m_ap_and_top_k_accuracy.ipynb
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Expand Up @@ -71,7 +71,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "45cf34f1532e800e",
"metadata": {
"ExecuteTime": {
Expand All @@ -80,10 +80,7 @@
}
},
"outputs": [],
"source": [
"train_imgs, train_labels = zip(*[(img, label) for img, label, _ in train_dataset])\n",
"val_imgs, val_labels = zip(*[(img, label) for img, label, _ in val_dataset])"
]
"source": "train_imgs, train_labels = zip(\n *[(img, label) for img, label, _ in train_dataset], strict=True\n)\nval_imgs, val_labels = zip(*[(img, label) for img, label, _ in val_dataset], strict=True)"
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -170,7 +167,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"id": "b41f8223d09379ac",
"metadata": {
"ExecuteTime": {
Expand All @@ -179,13 +176,7 @@
}
},
"outputs": [],
"source": [
"train_paths, train_labels = zip(*[(path, label) for _, label, path in train_dataset])\n",
"encodings_vlad = vlad_encoder.generate_encoding_map(train_paths)\n",
"encodings_fisher = fisher_vector_encoder.generate_encoding_map(train_paths)\n",
"encodings_pipeline = pipeline_with_pca.generate_encoding_map(train_paths)\n",
"dataset_labels_dict = dict(zip(train_paths, train_labels))"
]
"source": "train_paths, train_labels = zip(\n *[(path, label) for _, label, path in train_dataset], strict=True\n)\nencodings_vlad = vlad_encoder.generate_encoding_map(train_paths)\nencodings_fisher = fisher_vector_encoder.generate_encoding_map(train_paths)\nencodings_pipeline = pipeline_with_pca.generate_encoding_map(train_paths)\ndataset_labels_dict = dict(zip(train_paths, train_labels, strict=True))"
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -579,4 +570,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
19 changes: 3 additions & 16 deletions examples/pipeline.ipynb
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Expand Up @@ -23,7 +23,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "3cfeb663c836d2a9",
"metadata": {
"ExecuteTime": {
Expand All @@ -32,20 +32,7 @@
}
},
"outputs": [],
"source": [
"from torchvision.models import VGG16_Weights, vgg16\n",
"\n",
"from pyvisim._utils import *\n",
"from pyvisim.datasets import OxfordFlowerDataset # or any dataset you have\n",
"from pyvisim.encoders import (\n",
" FisherVectorEncoder,\n",
" GMMWeights,\n",
" KMeansWeights,\n",
" Pipeline,\n",
" VLADEncoder,\n",
")\n",
"from pyvisim.features import DeepConvFeature"
]
"source": "import os\n\nimport numpy as np\nimport torch\nfrom torchvision.models import VGG16_Weights, vgg16\n\nfrom pyvisim._utils import cosine_similarity, plot_and_save_heatmap, plot_image\nfrom pyvisim.datasets import OxfordFlowerDataset # or any dataset you have\nfrom pyvisim.encoders import (\n FisherVectorEncoder,\n GMMWeights,\n KMeansWeights,\n Pipeline,\n VLADEncoder,\n)\nfrom pyvisim.features import DeepConvFeature"
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -553,4 +540,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
18 changes: 3 additions & 15 deletions examples/vlad_and_fisher_with_vgg16_deep_features.ipynb
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Expand Up @@ -34,7 +34,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "169b9b052dc0e9b4",
"metadata": {
"ExecuteTime": {
Expand All @@ -43,19 +43,7 @@
}
},
"outputs": [],
"source": [
"from torchvision.models import VGG16_Weights, vgg16\n",
"\n",
"from pyvisim._utils import *\n",
"from pyvisim.datasets import OxfordFlowerDataset # We'll create this in a moment\n",
"from pyvisim.encoders._base_encoder import GMMWeights, KMeansWeights\n",
"from pyvisim.encoders.fisher_vector import FisherVectorEncoder\n",
"from pyvisim.encoders.vlad import VLADEncoder\n",
"from pyvisim.eval import retrieve_top_k_similar\n",
"\n",
"# Our library imports\n",
"from pyvisim.features import DeepConvFeature"
]
"source": "import os\n\nimport cv2\nimport matplotlib.pyplot as plt\nfrom torchvision.models import VGG16_Weights, vgg16\n\nfrom pyvisim._utils import plot_image\nfrom pyvisim.datasets import OxfordFlowerDataset # We'll create this in a moment\nfrom pyvisim.encoders._base_encoder import GMMWeights, KMeansWeights\nfrom pyvisim.encoders.fisher_vector import FisherVectorEncoder\nfrom pyvisim.encoders.vlad import VLADEncoder\nfrom pyvisim.eval import retrieve_top_k_similar\n\n# Our library imports\nfrom pyvisim.features import DeepConvFeature"
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -1093,4 +1081,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
4 changes: 0 additions & 4 deletions pyvisim/_base_classes.py
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Expand Up @@ -5,7 +5,6 @@
import numpy as np



class SimilarityMetric(abc.ABC):
"""
Abstract base for all similarity encoders.
Expand Down Expand Up @@ -39,9 +38,6 @@ class FeatureExtractorBase(abc.ABC):

_logger = logging.getLogger("Feature_Extractor")

def __init__(self):
pass

@abc.abstractmethod
def __call__(self, image: np.ndarray):
"""
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