# chitra [](https://www.codefactor.io/repository/github/gradsflow/chitra) [](https://sonarcloud.io/dashboard?id=aniketmaurya_chitra)  [](https://chitra.readthedocs.io/en/latest/?badge=latest) ## What is chitra? **chitra** (**चित्र**) is a multi-functional library for full-stack Deep Learning. It simplifies Model Building, API development, and Model Deployment. ### Components 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] [Auto Dockerization of Models](https://chitra.readthedocs.io/en/latest/source/cli/builder/builder-create/) 🐳 - [New] [Framework Agnostic Model Serving & Interactive UI prototype app](https://chitra.readthedocs.io/en/latest/source/api/serve/model_server/) ✨🌟 - [New] [Data Visualization, Bounding Box Visualization](https://chitra.readthedocs.io/en/latest/source/api/image/chitra-class/) 🐶🎨 - 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. > If you have more use case please [**raise an issue/PR**](https://github.com/aniketmaurya/chitra/issues/new/choose) 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 [](https://pepy.tech/project/chitra) [](https://pepy.tech/project/chitra)  ### Using pip (recommended) 1. Minimum installation `pip install -U chitra` 1. Full Installation `pip install -U 'chitra[all]'` 1. Install for Training `pip install -U 'chitra[nn]'` 1. Install for Serving `pip install -U 'chitra[serve]'` ### From source ``` pip install git+https://github.com/aniketmaurya/chitra@master ``` 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. ```python import numpy as np import chitra from chitra.dataloader import Clf 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)) ```  ## 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 ```python 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']

```python 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 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](https://www.kdnuggets.com/2019/05/boost-your-image-classification-model.html) ```python 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 get_label updated with 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. ```python 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 get_label updated with (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](https://arxiv.org/abs/1506.01186). ```python 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() ``` ```python 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

## ✨ 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. ```python 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  ## 🎨 Data Visualization ### Image annotation Bounding Box creation is based on top of `imgaug` library. ```python from chitra.image import Chitra import matplotlib.pyplot as plt bbox = [70, 25, 190, 210] label = 'Dog' image = Chitra(image_path, bboxes=bbox, labels=label) plt.imshow(image.draw_boxes()) ```  See [Play with Images](https://chitra.readthedocs.io/en/latest/examples/chitra-class/chitra-class.html) 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. ```python 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


See [Example Section](https://chitra.readthedocs.io/en/latest/source/api/serve/model_server/) for detailed explanation! ## 🛠 Utility Limit GPU memory or enable dynamic GPU memory growth for Tensorflow. ```python from chitra.utility.tf_utils import limit_gpu, gpu_dynamic_mem_growth # limit the amount of GPU required for your training limit_gpu(gpu_id=0, memory_limit=1024 * 2) ``` No GPU:0 found in your system! ```python gpu_dynamic_mem_growth() ``` No GPU found on the machine! ## 🤗 Contribute Contributions of any kind are welcome. Please check the [**Contributing Guidelines**](https://github.com/aniketmaurya/chitra/blob/master/CONTRIBUTING.md) before contributing. ## Code Of Conduct We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. Read full [**Contributor Covenant Code of Conduct**](https://github.com/aniketmaurya/chitra/blob/master/CODE_OF_CONDUCT.md) ## Acknowledgement *chitra* is built with help of awesome libraries like [Tensorflow 2.x](https://github.com/tensorflow/tensorflow), [imgaug](https://github.com/aleju/imgaug), [FastAPI](https://github.com/tiangolo/fastapi) and [Gradio](https://gradio.app).