CodeFactor found an issue: Duplicate Code

It's currently on:
chitra\dataloader.py:25-37
chitra\dataloader.py:69-81
Commit de8c0af

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Integrate KivyMD

Describe the solution you'd like

https://analyticsindiamag.com/deploying-machine-learning-models-in-android-apps-using-python/

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A module to benchmark Models based resources and speed 🚄 🍀 .

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Describe alternatives you've considered

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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.

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

Bug description

---------------------------------------------------------------------------
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: []

Expected result

Actual result

Steps to reproduce

Context

Your Environment

• Version used:
• Operating System and version:
• Link to your fork:

Is your feature request related to a problem? Please describe.

Right now there is no way to resize image along with the bounding boxes.

Describe the solution you'd like

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)

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A clear and concise description of any alternative solutions or features you've considered.

Additional context

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Is your feature request related to a problem? Please describe.

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

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Additional context

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Bug description

converter module is not being used and is buggy.

Expected result

Actual result

Steps to reproduce

Context

Your Environment

• Version used:
• Operating System and version:
• Link to your fork:

Is your feature request related to a problem? Please describe.

There should be a way to plot confusion matrix easily

Describe the solution you'd like

from chitra.plot import confusion_matrix
confusion_matrix(y_truth, y_pred)

Describe alternatives you've considered

A clear and concise description of any alternative solutions or features you've considered.

Additional context

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Is your feature request related to a problem? Please describe.

from chitra.image import Chitra

# image is downloaded from internet
image = Chitra(IMAGE_URL)

# same image is downloaded again!
image = Chitra(IMAGE_URL)

Describe the solution you'd like

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)

Describe alternatives you've considered

A clear and concise description of any alternative solutions or features you've considered.

Additional context

Add any other context or screenshots about the feature request here.

The bot created this issue to inform you that pyup.io has been set up on this repo.
Once you have closed it, the bot will open pull requests for updates as soon as they are available.

Is your feature request related to a problem? Please describe.

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Describe the solution you'd like

A clear and concise description of what you want to happen.

Describe alternatives you've considered

A clear and concise description of any alternative solutions or features you've considered.

Additional context

Add any other context or screenshots about the feature request here.

Reformat installation step for from source.

Expected result

Should appear all together rather disconnected grey boxes.

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.

Is your feature request related to a problem?

Create app UI with Gradio or Streamlit for model predictions.

Describe the solution you'd like

Right now we have create_api function, there should be a create_app function that can create Gradio or Streamlit app automatically.

Describe alternatives you've considered

A clear and concise description of any alternative solutions or features you've considered.

Additional context

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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?

Is your feature request related to a problem? Please describe.

Automatically create documentation from docstrings

Here are some changes that I am proposing to the README to make it look better and according to the convention.

• Change the banner image to 1280x640 px image.
• Make the headings and subheadings better.
• Add links to create an issue and contributing guide where they are mentioned in the README.
• Change "How to use section" to "Usage" as it is the convention.

You can also use the banner image as the repo preview image.

I will take this issue and make a PR.

Is your feature request related to a problem? Please describe.

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

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Additional context

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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!

Problem

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.

Solution and Alternatives

Making the generator yields inexhaustible data and simultaneously adding training steps in model training.

Is your feature request related to a problem? Please describe.

Create a label manager for multilabel classification tasks.

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A clear and concise description of what you want to happen.

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Additional context

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``

Adding issue and feature templates are good practice for making things easier for the contributors and making sure that everything is managed properly.

Is your feature request related to a problem? Please describe.

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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Is your feature request related to a problem? Please describe.

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

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Additional context

Add any other context or screenshots about the feature request here.

Is your feature request related to a problem? Please describe.

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

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Additional context

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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

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

https://news.ycombinator.com/show:

https://www.reddit.com/

https://www.producthunt.com/

https://dev.to

https://twitter.com/home

Test trainer.compile method

Is your feature request related to a problem? Please describe.

☁️ Integrate AWS Chalice for deploying models directly to AWS Lambda.

Describe the solution you'd like

🚀 One Click Deployment!

Additional context

How to Build a Serverless Application using AWS Chalice by Siben Nayak

Bug description

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

Expected result

Actual result

Steps to reproduce

Context

Your Environment

• Version used:
• Operating System and version:
• Link to your fork:

ubuntu 20.04
anconda env
install local packages
$ pip install .
Installing collected packages: chitra
Attempting uninstall: chitra
Found existing installation: chitra 0.2.0a1
Uninstalling chitra-0.2.0a1:
Successfully uninstalled chitra-0.2.0a1
Successfully installed chitra-0.2.0a1

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

from chitra.dataloader import Clf, show_batch
ImportError: cannot import name 'show_batch' from 'chitra.dataloader'

Is your feature request related to a problem? Please describe.

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

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Describe alternatives you've considered

A clear and concise description of any alternative solutions or features you've considered.

Additional context

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Bug description

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.

Expected result

Calling Chitra(url1) and Chitra(url2) should return correct image.