A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
Home Page: https://chitra.readthedocs.io/en/latest/
License: Apache License 2.0
Add a new Python module for loading Pascal VOC data format to tf.data
The function should receive an annotation and image folder path and will return the tf.Dataset object.
CodeFactor found an issue: Duplicate Code
It's currently on:
chitra\dataloader.py:25-37
chitra\dataloader.py:69-81
Commit de8c0af
Function for reading TFrecord dataset
Inputs should be dataset path and feature dictionary.
def read_tfrecord(dataset_path, feature_dictionary):
# parse the data using feature dictionary
return data
from chitra.image import Chitra
# image is downloaded from internet
image = Chitra(IMAGE_URL)
# same image is downloaded again!
image = Chitra(IMAGE_URL)Cache the image in local dir
What I want is -
from chitra.image import Chitra
# image is downloaded from internet
image = Chitra(IMAGE_URL, cache=True)
# image is loaded from cached dir
image = Chitra(IMAGE_URL, cache=True)A clear and concise description of any alternative solutions or features you've considered.
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There is a limited data yield in the generator which stops data iteration (hence no data will now yield by generator) in 1 complete cycle. Limiting developer to reassess the same data twice in a loop.
Making the generator yields inexhaustible data and simultaneously adding training steps in model training.
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CodeFactor found an issue: Duplication of key "repos" in mapping
It's currently on:
.pre-commit-config.yaml:34
Commit f26a6b8
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---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-91-b64613cd9976> in <module>
----> 1 interpret(img.image, auto_resize=False)
~/miniconda3/envs/aniket/lib/python3.7/site-packages/chitra/trainer.py in __call__(self, image, auto_resize, image_size)
352 X,
353 penultimate_layer=-1, # model.layers number
--> 354 seek_penultimate_conv_layer=True,
355 )
356 cam = normalize(cam)
~/miniconda3/envs/aniket/lib/python3.7/site-packages/tf_keras_vis/gradcam.py in __call__(self, loss, seed_input, penultimate_layer, seek_penultimate_conv_layer, activation_modifier, expand_cam, training)
148 # Processing gradcam
149 model = tf.keras.Model(inputs=self.model.inputs,
--> 150 outputs=self.model.outputs + [penultimate_output_tensor])
151
152 with tf.GradientTape(watch_accessed_variables=False) as tape:
~/miniconda3/envs/aniket/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
515 self._self_setattr_tracking = False # pylint: disable=protected-access
516 try:
--> 517 result = method(self, *args, **kwargs)
518 finally:
519 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/miniconda3/envs/aniket/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py in __init__(self, inputs, outputs, name, trainable, **kwargs)
118 generic_utils.validate_kwargs(kwargs, {})
119 super(Functional, self).__init__(name=name, trainable=trainable)
--> 120 self._init_graph_network(inputs, outputs)
121
122 @trackable.no_automatic_dependency_tracking
~/miniconda3/envs/aniket/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
515 self._self_setattr_tracking = False # pylint: disable=protected-access
516 try:
--> 517 result = method(self, *args, **kwargs)
518 finally:
519 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/miniconda3/envs/aniket/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py in _init_graph_network(self, inputs, outputs)
202 # Keep track of the network's nodes and layers.
203 nodes, nodes_by_depth, layers, _ = _map_graph_network(
--> 204 self.inputs, self.outputs)
205 self._network_nodes = nodes
206 self._nodes_by_depth = nodes_by_depth
~/miniconda3/envs/aniket/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py in _map_graph_network(inputs, outputs)
988 'The following previous layers '
989 'were accessed without issue: ' +
--> 990 str(layers_with_complete_input))
991 for x in nest.flatten(node.outputs):
992 computable_tensors.add(id(x))
ValueError: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 512, 512, 3), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'") at layer "rescaling". The following previous layers were accessed without issue: []
InvalidArgumentError while training an image classification model
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Unknown image file format. One of JPEG, PNG, GIF, BMP required.
[[{{node decode_image/DecodeImage}}]]
[[StatefulPartitionedCall]]
[[IteratorGetNext]]
(1) Cancelled: Function was cancelled before it was started
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_1024160]
Function call stack:
train_function -> train_function -> train_function -> train_function
Here are some changes that I am proposing to the README to make it look better and according to the convention.
You can also use the banner image as the repo preview image.
I will take this issue and make a PR.
converter module is not being used and is buggy.
Create app UI with Gradio or Streamlit for model predictions.
Right now we have create_api function, there should be a create_app function that can create Gradio or Streamlit app automatically.
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There should be a way to plot confusion matrix easily
from chitra.plot import confusion_matrix
confusion_matrix(y_truth, y_pred)A clear and concise description of any alternative solutions or features you've considered.
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Reformat installation step for from source.
Should appear all together rather disconnected grey boxes.
A module to benchmark Models based resources and speed 🚄 🍀 .
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NotImplementedError: TEXT-GENERATION is not implemented! Available types are - ['IMAGE-CLASSIFICATION', 'OBJECT-DETECTION', 'TEXT-CLASSIFICATION', 'QUESTION-ANS']
why there is no support for text generation? would you consider adding it?
Adding issue and feature templates are good practice for making things easier for the contributors and making sure that everything is managed properly.
from chitra.dataloader import Clf, show_batch
ImportError: cannot import name 'show_batch' from 'chitra.dataloader'
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These are some ideas I have to promote this project.
We can share ideas here and grow the community around this project.
Sharing in the following platforms
☁️ Integrate AWS Chalice for deploying models directly to AWS Lambda.
🚀 One Click Deployment!
How to Build a Serverless Application using AWS Chalice by Siben Nayak
Right now DL libraries are tightly coupled and impact module import time. Need to refactor imports and make some libraries optional.
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First off, Apologies for opening an issue for this. I'm currently not active on Social Media but wanted a way to share this.
Second, Thanks so much for this amazing open source library. This is amazing work! Kudos to @aniketmaurya and the contributors.
Here's my small video contribution to spread the word - https://youtu.be/0RJfKJEXUDg
Once again, I'm sorry for raising an issue for this. please close this issue after you read the message.
Thanks!
Test trainer.compile method
Chitra(..., cache=True) caching is not working correctly.
URL_1 = 'https://one.com/image.jpg'
URL_2 = 'https://two.com/image.jpg'
In this case image is cached with the first URL only and subsequence call will return older image from cache.
Calling Chitra(url1) and Chitra(url2) should return correct image.
Right now there is no way to resize image along with the bounding boxes.
from chitra.image import Chitra
label = 'Dog'
bbox = [ 70, 25, 190, 210]
image = Chitra(image_path, bboxes=bbox, labels=label)
# the resize function should resize image along with bounding box
image.resize((224, 224)A clear and concise description of any alternative solutions or features you've considered.
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Automatically create documentation from docstrings
Sending images to TF serving with REST API is slower than gRPC.
Create a gRPC client class that will take server and port and will create a connection. The client object can be reused for sending data multiple times.
Integrate KivyMD
https://analyticsindiamag.com/deploying-machine-learning-models-in-android-apps-using-python/
Create a label manager for multilabel classification tasks.
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``
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