Package: chitra
chitraWhat is chitra?chitra(चित्र) is a multi-functional library for full-stack Deep Learning. It simplifies Model Building, API development, and Model Deployment.ComponentsLoad Image from Internet url, filepath ornumpyarray 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]Auto Dockerization of Models🐳[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 usingchitra.trainermodule 🚀🚘 Implementation RoadmapOne click deployment toserverlessplatform.If you have more use case pleaseraise an issue/PRwith 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.📀 InstallationUsing pip (recommended)Minimum installationpip install -U chitraFull Installationpip install -U 'chitra[all]'Install for Trainingpip install -U 'chitra[nn]'Install for Servingpip install -U 'chitra[serve]'From sourcepip install git+https://github.com/aniketmaurya/chitra@masterOr,git clone https://github.com/aniketmaurya/chitra.git
cd chitra
pip install .🧑‍💻 UsageLoading data for image classificationChitradataloaderanddatageneratormodules for loading data.dataloaderis a minimal dataloader that
returnstf.data.Datasetobject.datageneratorprovides flexibility to users on how they want to load and manipulate
the data.importnumpyasnpimportchitrafromchitra.dataloaderimportClfimportmatplotlib.pyplotaspltclf_dl=Clf()data=clf_dl.from_folder(cat_dog_path,target_shape=(224,224))clf_dl.show_batch(8,figsize=(8,8))Image datageneratorDataset class provides the flexibility to load image dataset by updating components of the class.Components of Dataset class are:image file generatorresizerlabel generatorimage loaderThese 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.jpgThe inbuilt file generator search for images on thefolder1, now we can just update theimage file generatorand
rest of the functionality will remain same.Dataset also support progressive resizing of images.Updating componentfromchitra.datageneratorimportDatasetds=Dataset(data_path)# it will load the folders and NOT imagesds.filenames[:3]OutputNo 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']defload_files(path):returnglob(f'{path}/*/images/*')defget_label(path):returnpath.split('/')[-3]ds.update_component('get_filenames',load_files)ds.filenames[:3]Outputget_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 resizingIt 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.
~KDnuggetsimage_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 generatorforimg,labelinds.generator():print('first call to generator:',img.shape)break# seconds call to generatorforimg,labelinds.generator():print('seconds call to generator:',img.shape)break# third call to generatorforimg,labelinds.generator():print('third call to generator:',img.shape)breakOutputget_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 supportCreating atf.datadataloader was never as easy as this one liner. It converts the Python generator
intotf.data.Datasetfor 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()foreindl.take(1):print(e[0].shape)foreindl.take(1):print(e[0].shape)foreindl.take(1):print(e[0].shape)Outputget_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)TrainerThe Trainer class inherits fromtf.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
byLeslie Smith.fromchitra.trainerimportTrainer,create_cnnfromchitra.datageneratorimportDatasetds=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 InterpretabilityIt is important to understand what is going inside the model. Techniques like GradCam and Saliency Maps can visualize
what the Network is learning.trainermodule has InterpretModel class which creates GradCam and GradCam++
visualization with almost no additional code.fromchitra.trainerimportInterpretModeltrainer=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🎨 Data VisualizationImage annotationBounding Box creation is based on top ofimgauglibrary.fromchitra.imageimportChitraimportmatplotlib.pyplotaspltbbox=[70,25,190,210]label='Dog'image=Chitra(image_path,bboxes=bbox,labels=label)plt.imshow(image.draw_boxes())SeePlay with Imagesfor 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 provideschitra.serve.GradioAppfor building Interactive UI app for ML/DL models
andchitra.serve.APIfor building Rest API endpoint.fromchitra.serveimportcreate_apifromchitra.trainerimportcreate_cnnmodel=create_cnn('mobilenetv2',num_classes=2)create_api(model,run=True,api_type='image-classification')API Docs PreviewSeeExample Sectionfor detailed
explanation!🛠 UtilityLimit GPU memory or enable dynamic GPU memory growth for Tensorflow.fromchitra.utility.tf_utilsimportlimit_gpu,gpu_dynamic_mem_growth# limit the amount of GPU required for your traininglimit_gpu(gpu_id=0,memory_limit=1024*2)No GPU:0 found in your system!gpu_dynamic_mem_growth()No GPU found on the machine!🤗 ContributeContributions of any kind are welcome. Please check theContributing
Guidelinesbefore contributing.Code Of ConductWe pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.Read fullContributor Covenant Code of ConductAcknowledgementchitrais built with help of awesome libraries likeTensorflow 2.x,imgaug,FastAPIandGradio.