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Is it possible to apply GradCam to a TF Lite model

I have been studying about GradCam and I noticed most cases are used on a Keras/Tensorflow model. However I have a tensorflow lite model that has been compiled to .tflite format. I am not sure if it's even possible to access my CNN layers after it's been compiled, given that I tried using keras library to load the model and it only accepts specific file types, not exactly .tflite since it threw errors:
from tensorflow.keras.models import load_model
model = load_model("/content/drive/My Drive/tensorflow_lite_model.tflite")
It gives the error:
OSError: SavedModel file does not exist
What I was trying to do was to print the .tflite models using model.summary as a way to confirm If I could perform any operation with the model layers. If that is so, then I don't think it's possible to use Grad-Cam with a tensorflow lite model.
Therefore, I would like to know If that is true, or did I just try to validate it, the wrong way?

TFLite model file is a different serialization format with the TensorFlow model formats, keras and saved model.
Since you already have a TFLite model, you need to use the TensorFlow Lite Interpreter API, instead of using the TensorFlow API.
interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()
Please refer to this link for the details.
The TF GradCam model can be converted into the TFLite model. It is technically possible to convert any TF models to the corresponding TFLite model. If you have any issues with the conversion, please file bug at the tensorflow github.

How to convert a .h5 model to a .tflite and also get image labels?

I am pretty new to Deep Learning, I have a custom dataset which is quite large, how do I convert the .h5 model to a .tflite model and also how do I generate all the labels without doing it manually?

From Tensorflow documentation
Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory
tflite_model = converter.convert()

Saving model in pytorch and keras

I have trained model with keras and saved in with the help of pytorch. Will it cause any problems in the future. As far as I know the only difference between them is Keras saves its model's weights as doubles while PyTorch saves its weights as floats.

You can convert your model to double by doing
model.double()
Note that after this, you will need your input to be DoubleTensor.

Convert data-00000-of-00001 file to Tensorflow Lite

Is there any way to convert data-00000-of-00001 to Tensorflow Lite model?
The file structure is like this
|-semantic_model.data-00000-of-00001
|-semantic_model.index
|-semantic_model.meta

Using TensorFlow Version: 1.15
The following 2 steps will convert it to a .tflite model.
1. Generate a TensorFlow Model for Inference (a frozen graph .pb file) using the answer posted here
What you currently have is model checkpoint (a TensorFlow 1 model saved in 3 files: .data..., .meta and .index. This model can be further trained if needed). You need to convert this to a frozen graph (a TensorFlow 1 model saved in a single .pb file. This model cannot be trained further and is optimized for inference/prediction).
2. Generate a TensorFlow lite model ( .tflite file)
A. Initialize the TFLiteConverter: The .from_frozen_graph API can be defined this way and the attributes which can be added are here. To find the names of these arrays, visualize the .pb file in Netron
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
graph_def_file='....path/to/frozen_graph.pb',
input_arrays=...,
output_arrays=....,
input_shapes={'...' : [_, _,....]}
)
B. Optional: Perform the simplest optimization known as post-training dynamic range quantization. You can refer to the same document for other types of optimizations/quantization methods.
converter.optimizations = [tf.lite.Optimize.DEFAULT]
C. Convert it to a .tflite file and save it
tflite_model = converter.convert()
tflite_model_size = open('model.tflite', 'wb').write(tflite_model)
print('TFLite Model is %d bytes' % tflite_model_size)

Load keras model with tflearn

I've trained models in Keras and I need to load this models in code made in Tflearn.
I can save the Keras trained models with the line:
model.save('my_model.hdf5') #keras
It saves the architecture and weights of the trained model.
or the line:
model.save_weights('my_model.hdf5') #keras
It saves only the weights of the trained model.
The purpose is to load the model into tflearn using for example this line:
model.load ('example.tflearn') #tflearn
This line just load the weights, requires that the architecture be passed before.
I tried loading the files generated by .save and .save_weights from Keras with .load of tflearn but it did not work.