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An API for streamlining unsupervised ML ops such as visualizations, clustering, CNN insights, etc.

Project description

opticaltoolkit_logo

A collection of deep learning -- computer vision utility functions

Installation

pip install optical_toolkit

Visualize

  • Visualize a dataset in a grid
from sklearn.datasets import load_digits
from optical_toolkit.visualize import plot_images

X, y = load_digits()

plot_images(X, targets=y)

dataset

  • Summarize a dataset by classes
from sklearn.datasets import load_digits
from optical_toolkit.visualize import plot_images

X, y = load_digits()

summarize_images(X, targets=y, num_images_per_class=10, num_classes=10)

dataset

  • Visualize the 2d and 3d embeddings of images
from sklearn.datasets import load_digits
from optical_toolkit.visualize.embeddings import get_embeddings

X, y = load_digits()

2d_embeddings, fig_2d = get_embeddings(X, y, dims=2, embedding_type="tsne", return_plot=True)
3d_embeddings, fig_3d = get_embeddings(X, y, dims=3, embedding_type="tsne", return_plot=True)

embedding2d embedding3d

embedding2d_comp embedding3d_comp

Insight

  • Visualize the filters of a (trained) CNN model
from optical_toolkit.cnn_filters import display_filters, display_model_filters

model_name = "xception"

layer_names = [
    "block2_sepconv1",
    "block5_sepconv1",
    "block9_sepconv1",
    "block14_sepconv1",
]

for layer_name in layer_names:
    display_filters(
    model=model_name,
    layer_name=layer_name,
)

filters filters filters filters

display_model_filters(model=model_name)

model_filters

  • Visualize the filters of your custom CNN with custom objects
import keras

model_name = "examples/custom_models/svdnet.keras"
dir_name = "examples/insights"

@keras.saving.register_keras_serializable()
class ResidualConvBlock(keras.layers.Layer):
    ...

display_model_filters(
    model_name,
    custom_layer_prefix="residual",
)

model_filters

Analyze

  • A high level function for image dataset analysis
from sklearn.datasets import load_digits

from optical_toolkit.analyze.analyze import analyze_image_dataset

digits = load_digits()
X = digits.images
y = digits.target

analyze_image_dataset(X, y, output_path="examples/analyze/analysis.pdf")

View full analysis (PDF)

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