I get this error when trying to convert .h5 model to .tflite
OSError: SavedModel file does not exist at: /home/xyz/Desktop/facial-expression-recognition-using-cnn-master/model/{saved_model.pbtxt|saved_model.pb}
The model is saved as .h5 but it does not save a .pb or .pbtext file. Can I have a solution to convert the .h5 file to .tflite please?
Thank you
Convert keras h5 model to tflite
tflite_convert \
--keras_model_file=/tmp/keras_model.h5
--output_file=/tmp/keras_model.tflite
Related
I'm exporting model to .h5 format using PyTorch like this.
torch.save(model.state_dict(), 'model.h5')
But when I load this model.h5 in TensorFlow, I got this error.
File "h5py/h5f.pyx", line 106, in h5py.h5f.open
OSError: Unable to open file (file signature not found)
I try both ways of saving models.
# 1.
torch.save(model.state_dict(), 'model.h5')
# 2.
torch.save(model, 'model.h5')
In both ways, I'm getting the same error.
You cant do that!
according to the official TF documentation, TensorFlow uses the HDF5 file format. PyTorch doesnt. Pytorch uses Python’s pickle to store the weights.
Just passing a random file extension doesn't magically convert it to that file format! you have to explicitly save your weights as hdf5.
I have used Yolov5 to train the model in google colab and how can I export the .tflite file of the trained model so that it can be used for mobile applications. Thanks for help me
Assuming that you have the latest TensorFlow version 2.7.0 use the general method for that purpose that it is shown here:
import tensorflow as tf
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
Get back with your results and tag me if you have problems
You can look in this github(https://github.com/hunglc007/tensorflow-yolov4-tflite), i think you find something useful
After using YOLOv5 to train model weights as .pt file,
how can I convert the weights file (model.pt) to hdf5 file (model.h5)?
Running python train.py --batch 16 --epochs 3 --data mydata.yaml --weights yolov5s.pt, the result is given by best.pt file at subfolder of YOLOv5, how can I convert it to h5 file?
Do this installation and get started here
This could be a lengthy procedure ... as you are aware that .pt only contains weights and not model architecture hence your model class should also be present in your conversion code
Edit: New links are added
How do I convert a .h5 weight file to .pb file ? I trained keras pre-trained model and saved the file as something.h5 . How do i convert it to .pb file for using it for tensorflow serving ?
P.S: Don't degrade the question, I couldn't find any solution online. Do mention the reason why you degraded the question. Otherwise, help with solving the question.
import tensorflow as tf
model = tf.keras.models.load_model(keras_model_path)
tf.saved_model.save(model, saved_model_path)
Bingo.
I need to train from scratch a CNN over a COCO dataset with a specific configuration: https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/embedded_ssd_mobilenet_v1_coco.config
Thus, I installed TF Object Detection API and I downloaded the COCO dataset. However the dataset is in .h5 extension.
Is it possible to run the training with this kind of file or do I need to convert it in images in someway? If that is possible, what would the command be?
PS: I was not able to find a pre-trained model with that config, this is why I need to train a cnn from scratch.
My suggestion would be to convert the .hdf5 file to a .tfrecord file, you can find examples of how to do this here.