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MechaCritterclaudeCopilot
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Bugfix/fix ruff errors (#12)
* Fix ruff I001: sort imports in _base_classes.py Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix ruff F401: declare OxfordFlowerDataset re-export via __all__ Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix ruff B027: remove empty __init__ from FeatureExtractorBase Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix ruff B028: add stacklevel=2 to warnings.warn calls Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix ruff B904: chain exceptions with 'raise ... from e' in except clauses Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix ruff B008: replace function-call defaults with None and lazy init RootSIFT() and vgg16(weights=...) were evaluated once at import time; vgg16 in particular triggered a model download on import. They are now instantiated per-call inside __init__ when the argument is omitted. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix ruff B905: pass strict=True to zip() calls All zipped sequences are required to have equal lengths, so strict=True turns silent truncation on a length mismatch into an immediate ValueError. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix ruff F403/F405: replace star imports in example notebooks Replaces 'from pyvisim._utils import *' with explicit imports in the five example notebooks. This required restoring plot_image, cluster_and_return_labels, cluster_images_and_generate_statistics, plot_and_save_heatmap and plot_and_save_barplot to pyvisim/_utils.py: they were deleted as dead code in b2832a7, but the notebooks still call them (the dead-code scan did not cover notebooks). Restored versions are modernized to pass the current lint config, and the bogus check_is_image decorator was not restored. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * added ruff-check in pre commit * Potential fix for pull request finding fixed indentation in pre commit config file Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * Potential fix for pull request finding in plot_image, detach if it's a torch tensor Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * Potential fix for pull request finding in plot_and_save_heatmap, turn everything to numpy before plotting Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * ran ruff formatter again --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
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‎.pre-commit-config.yaml‎

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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.15.2
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hooks:
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- id: ruff-format
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- id: ruff-check
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args: [--fix]
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- id: ruff-format
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- repo: https://github.com/codespell-project/codespell
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rev: v2.4.1
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hooks:

‎examples/clustering_images_using_fv.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "9d64d8aebd89bdcd",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-01-18T23:05:14.526690Z",
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"start_time": "2025-01-18T23:05:00.750503Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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",
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"2025-01-19 00:05:08,128 - numexpr.utils - INFO - NumExpr defaulting to 8 threads.\n"
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]
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}
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],
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"source": [
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"from torchvision.models import VGG16_Weights, vgg16\n",
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"\n",
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"from pyvisim._utils import *\n",
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"from pyvisim.datasets import OxfordFlowerDataset\n",
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"from pyvisim.encoders import FisherVectorEncoder, GMMWeights\n",
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"from pyvisim.features import DeepConvFeature"
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]
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"outputs": [],
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"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"
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},
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{
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"cell_type": "markdown",
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

‎examples/clustering_images_using_pipeline.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"id": "9d64d8aebd89bdcd",
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"metadata": {
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"ExecuteTime": {
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}
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},
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"outputs": [],
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"source": [
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"from torchvision.models import VGG16_Weights, vgg16\n",
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"\n",
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"from pyvisim._utils import *\n",
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"from pyvisim.datasets import OxfordFlowerDataset\n",
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"from pyvisim.encoders import (\n",
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" FisherVectorEncoder,\n",
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" GMMWeights,\n",
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" KMeansWeights,\n",
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" Pipeline,\n",
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" VLADEncoder,\n",
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")\n",
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"from pyvisim.features import DeepConvFeature"
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]
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"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"
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},
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{
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"cell_type": "markdown",
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

‎examples/clustering_images_using_vlad.ipynb‎

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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"id": "9d64d8aebd89bdcd",
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"metadata": {
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"ExecuteTime": {
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}
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},
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"outputs": [],
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"source": [
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"from torchvision.models import VGG16_Weights, vgg16\n",
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"\n",
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"from pyvisim._utils import *\n",
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"from pyvisim.datasets import OxfordFlowerDataset\n",
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"from pyvisim.encoders import KMeansWeights, VLADEncoder\n",
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"from pyvisim.features import DeepConvFeature"
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]
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"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"
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},
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{
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"cell_type": "markdown",
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

‎examples/m_ap_and_top_k_accuracy.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"id": "45cf34f1532e800e",
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"metadata": {
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"ExecuteTime": {
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},
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"outputs": [],
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"source": [
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"train_imgs, train_labels = zip(*[(img, label) for img, label, _ in train_dataset])\n",
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"val_imgs, val_labels = zip(*[(img, label) for img, label, _ in val_dataset])"
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"train_imgs, train_labels = zip(\n",
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" *[(img, label) for img, label, _ in train_dataset], strict=True\n",
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")\n",
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"val_imgs, val_labels = zip(\n",
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" *[(img, label) for img, label, _ in val_dataset], strict=True\n",
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")"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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"id": "b41f8223d09379ac",
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"metadata": {
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"ExecuteTime": {
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},
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"outputs": [],
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"source": [
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"train_paths, train_labels = zip(*[(path, label) for _, label, path in train_dataset])\n",
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"train_paths, train_labels = zip(\n",
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" *[(path, label) for _, label, path in train_dataset], strict=True\n",
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")\n",
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"encodings_vlad = vlad_encoder.generate_encoding_map(train_paths)\n",
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"encodings_fisher = fisher_vector_encoder.generate_encoding_map(train_paths)\n",
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"encodings_pipeline = pipeline_with_pca.generate_encoding_map(train_paths)\n",
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"dataset_labels_dict = dict(zip(train_paths, train_labels))"
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"dataset_labels_dict = dict(zip(train_paths, train_labels, strict=True))"
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]
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},
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{

‎examples/pipeline.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "3cfeb663c836d2a9",
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}
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},
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"outputs": [],
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"source": [
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"from torchvision.models import VGG16_Weights, vgg16\n",
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"\n",
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"from pyvisim._utils import *\n",
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"from pyvisim.datasets import OxfordFlowerDataset # or any dataset you have\n",
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"from pyvisim.encoders import (\n",
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" FisherVectorEncoder,\n",
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" GMMWeights,\n",
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" KMeansWeights,\n",
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" Pipeline,\n",
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" VLADEncoder,\n",
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")\n",
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"from pyvisim.features import DeepConvFeature"
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]
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"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"
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},
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{
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"cell_type": "markdown",
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

‎examples/vlad_and_fisher_with_vgg16_deep_features.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "169b9b052dc0e9b4",
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"metadata": {
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}
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},
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"outputs": [],
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"source": [
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"from torchvision.models import VGG16_Weights, vgg16\n",
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"\n",
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"from pyvisim._utils import *\n",
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"from pyvisim.datasets import OxfordFlowerDataset # We'll create this in a moment\n",
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"from pyvisim.encoders._base_encoder import GMMWeights, KMeansWeights\n",
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"from pyvisim.encoders.fisher_vector import FisherVectorEncoder\n",
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"from pyvisim.encoders.vlad import VLADEncoder\n",
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"from pyvisim.eval import retrieve_top_k_similar\n",
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"\n",
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"# Our library imports\n",
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"from pyvisim.features import DeepConvFeature"
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]
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"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"
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"cell_type": "markdown",
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},
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"nbformat": 4,
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}
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}

‎pyvisim/_base_classes.py‎

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import numpy as np
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class SimilarityMetric(abc.ABC):
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"""
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Abstract base for all similarity encoders.
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def __init__(self):
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pass
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@abc.abstractmethod
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def __call__(self, image: np.ndarray):
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"""

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