chitra

chitra (चित्र) is a Deep Learning library for Model Building, Explainable AI, Data Visualization, API Building & Deployment. Load Image from Internet url, filepath or numpy array and plot Bounding Boxes on the images easily. Model Training and Explainable AI. Easily create UI for Machine Learning models or Rest API backend that can be deployed for serving ML Models in Production.

📌 Highlights:

  • [New] Framework Agnostic Model Serving & Interactive UI prototype app ✨🌟
  • [New] Data Visualization, Bounding Box Visualization 🐶🎨
  • Model interpretation using GradCAM/GradCAM++ with no extra code 🔥
  • Faster data loading without any boilerplate 🤺
  • Progressive resizing of images 🎨
  • Rapid experiments with different models using chitra.trainer module 🚀

🚘 Implementation Roadmap

  • One click deployment to serverless platform.
  • Auto Dockerization of Models.

If you have more use case please raise an issue/PR with the feature you want.
If you want to contribute, feel free to raise a PR. It doesn't need to be perfect.
We will help you get there.

📀 Installation

Downloads
Downloads
GitHub License

Using pip (recommended)

pip install -U chitra

From source

pip install git+https://github.com/aniketmaurya/[email protected]

Or,

git clone https://github.com/aniketmaurya/chitra.git
cd chitra
pip install .

🧑‍💻 Usage

Loading data for image classification

Chitra dataloader and datagenerator modules for loading data. dataloader is a minimal dataloader that
returns tf.data.Dataset object. datagenerator provides flexibility to users on how they want to load and manipulate
the data.

import numpy as np
import chitra
from chitra.dataloader import Clf, show_batch
import matplotlib.pyplot as plt


clf_dl = Clf()
data = clf_dl.from_folder(cat_dog_path, target_shape=(224, 224))
clf_dl.show_batch(8, figsize=(8, 8))

Show Batch

Image datagenerator

Dataset class provides the flexibility to load image dataset by updating components of the class.

Components of Dataset class are:

  • image file generator
  • resizer
  • label generator
  • image loader

These components can be updated with custom function by the user according to their dataset structure. For example the
Tiny Imagenet dataset is organized as-

train_folder/
.....folder1/
    .....file.txt
    .....folder2/
           .....image1.jpg
           .....image2.jpg
                     .
                     .
                     .
           ......imageN.jpg

The inbuilt file generator search for images on the folder1, now we can just update the image file generator and
rest of the functionality will remain same.

Dataset also support progressive resizing of images.

Updating component

from chitra.datagenerator import Dataset

ds = Dataset(data_path)
# it will load the folders and NOT images
ds.filenames[:3]
Output
No item present in the image size list

['/Users/aniket/Pictures/data/tiny-imagenet-200/train/n02795169/n02795169_boxes.txt',
 '/Users/aniket/Pictures/data/tiny-imagenet-200/train/n02795169/images',
 '/Users/aniket/Pictures/data/tiny-imagenet-200/train/n02769748/images']
def load_files(path):
    return glob(f'{path}/*/images/*')


def get_label(path):
    return path.split('/')[-3]


ds.update_component('get_filenames', load_files)
ds.filenames[:3]
Output
get_filenames updated with <function load_files at 0x7fad6916d0e0>
No item present in the image size list

['/Users/aniket/Pictures/data/tiny-imagenet-200/train/n02795169/images/n02795169_369.JPEG',
 '/Users/aniket/Pictures/data/tiny-imagenet-200/train/n02795169/images/n02795169_386.JPEG',
 '/Users/aniket/Pictures/data/tiny-imagenet-200/train/n02795169/images/n02795169_105.JPEG']

Progressive resizing

It is the technique to sequentially resize all the images while training the CNNs on smaller to bigger image sizes. Progressive Resizing is described briefly in his terrific fastai course, “Practical Deep Learning for Coders”. A great way to use this technique is to train a model with smaller image size say 64x64, then use the weights of this model to train another model on images of size 128x128 and so on. Each larger-scale model incorporates the previous smaller-scale model layers and weights in its architecture.
~KDnuggets

image_sz_list = [(28, 28), (32, 32), (64, 64)]

ds = Dataset(data_path, image_size=image_sz_list)
ds.update_component('get_filenames', load_files)
ds.update_component('get_label', get_label)

# first call to generator
for img, label in ds.generator():
    print('first call to generator:', img.shape)
    break

# seconds call to generator
for img, label in ds.generator():
    print('seconds call to generator:', img.shape)
    break

# third call to generator
for img, label in ds.generator():
    print('third call to generator:', img.shape)
    break
Output
get_filenames updated with <function load_files at 0x7fad6916d0e0>
get_label updated with <function get_label at 0x7fad6916d8c0>

first call to generator: (28, 28, 3)
seconds call to generator: (32, 32, 3)
third call to generator: (64, 64, 3)

tf.data support

Creating a tf.data dataloader was never as easy as this one liner. It converts the Python generator
into tf.data.Dataset for a faster data loading, prefetching, caching and everything provided by tf.data.

image_sz_list = [(28, 28), (32, 32), (64, 64)]

ds = Dataset(data_path, image_size=image_sz_list)
ds.update_component('get_filenames', load_files)
ds.update_component('get_label', get_label)

dl = ds.get_tf_dataset()

for e in dl.take(1):
    print(e[0].shape)

for e in dl.take(1):
    print(e[0].shape)

for e in dl.take(1):
    print(e[0].shape)
Output
get_filenames updated with <function load_files at 0x7fad6916d0e0>
get_label updated with <detn get_label at 0x7fad6916d8c0>
(28, 28, 3)
(32, 32, 3)
(64, 64, 3)

Trainer

The Trainer class inherits from tf.keras.Model, it contains everything that is required for training. It exposes
trainer.cyclic_fit method which trains the model using Cyclic Learning rate discovered
by Leslie Smith.

from chitra.trainer import Trainer, create_cnn
from chitra.datagenerator import Dataset


ds = Dataset(cat_dog_path, image_size=(224, 224))
model = create_cnn('mobilenetv2', num_classes=2, name='Cat_Dog_Model')
trainer = Trainer(ds, model)
# trainer.summary()
trainer.compile2(batch_size=8,
    optimizer=tf.keras.optimizers.SGD(1e-3, momentum=0.9, nesterov=True),
    lr_range=(1e-6, 1e-3),
    loss='binary_crossentropy',
    metrics=['binary_accuracy'])

trainer.cyclic_fit(epochs=5,
    batch_size=8,
    lr_range=(0.00001, 0.0001),
)
Training Loop... cyclic learning rate already set!
Epoch 1/5
1/1 [==============================] - 0s 14ms/step - loss: 6.4702 - binary_accuracy: 0.2500
Epoch 2/5
Returning the last set size which is: (224, 224)
1/1 [==============================] - 0s 965us/step - loss: 5.9033 - binary_accuracy: 0.5000
Epoch 3/5
Returning the last set size which is: (224, 224)
1/1 [==============================] - 0s 977us/step - loss: 5.9233 - binary_accuracy: 0.5000
Epoch 4/5
Returning the last set size which is: (224, 224)
1/1 [==============================] - 0s 979us/step - loss: 2.1408 - binary_accuracy: 0.7500
Epoch 5/5
Returning the last set size which is: (224, 224)
1/1 [==============================] - 0s 982us/step - loss: 1.9062 - binary_accuracy: 0.8750

<tensorflow.python.keras.callbacks.History at 0x7f8b1c3f2410>

✨ Model Interpretability

It is important to understand what is going inside the model. Techniques like GradCam and Saliency Maps can visualize
what the Network is learning. trainer module has InterpretModel class which creates GradCam and GradCam++
visualization with almost no additional code.

from chitra.trainer import InterpretModel

trainer = Trainer(ds, create_cnn('mobilenetv2', num_classes=1000, keras_applications=False))
model_interpret = InterpretModel(True, trainer)

image = ds[1][0].numpy().astype('uint8')
image = Image.fromarray(image)
model_interpret(image)
print(IMAGENET_LABELS[285])
Returning the last set size which is: (224, 224)
index: 282
Egyptian Mau

png

🎨 Data Visualization

Image annotation

Bounding Box creation is based on top of imgaug library.

from chitra.image import Chitra


bbox = [70, 25, 190, 210]
label = 'Dog'

image = Chitra(image_path, bboxes=bbox, labels=label)
plt.imshow(image.draw_boxes())

png

See Play with Images for detailed
example!

🚀 Model Serving (Framework Agnostic)

Chitra can Create Rest API or Interactive UI app for Any Learning Model -
ML, DL, Image Classification, NLP, Tensorflow, PyTorch or SKLearn.
It provides chitra.serve.GradioApp for building Interactive UI app for ML/DL models
and chitra.serve.API for building Rest API endpoint.

from chitra.serve import create_api
from chitra.trainer import create_cnn

model = create_cnn('mobilenetv2', num_classes=2)
create_api(model, run=True, api_type='image-classification')
API Docs Preview

Preview Model Server

See Example Section for detailed
explanation!

🛠 Utility

Limit GPU memory or enable dynamic GPU memory growth for Tensorflow.

from chitra.utility import tf_limit_gpu, tf_gpu_dynamic_mem_growth

# limit the amount of GPU required for your training
tf_limit_gpu(gpu_id=0, memory_limit=1024 * 2)
No GPU:0 found in your system!
tf_gpu_dynamic_mem_growth()
No GPU found on the machine!

GitHub

https://github.com/aniketmaurya/chitra