Hi there 
,
I’m developing a React Native mobile app which should detect and recognize (using a TensorCamera) text from live stream.
I’m totally new and, reading documentation, my code would do something like that:
I found in the hub this models:
They are all .tflite!!!
How can I use in my code ?
Thank you 
PS:
I’ve tried to convert using:
tensorflowjs_converter --input_format=tf_hub 'https://tfhub.dev/tulasiram58827/lite-model/keras-ocr/float16/2' path/my/folder
but it raises a lot of errors:
If you want to generate a TensorFlow.js model, you can use the following procedure to inverse-quantify tflite to generate onnx, then generate TensorFlow saved_model again, and convert from saved_model to TFJS.
pip install -U tf2onnx
python -m tf2onnx.convert \
--opset 11 \
--tflite lite-model_craft-text-detector_dr_1.tflite \
--dequantize \
--output lite-model_craft-text-detector_dr_1.onnx
pip install onnx2tf
onnx2tf \
-i lite-model_craft-text-detector_dr_1.onnx \
-ois input:1,3,800,600 -osd
pip install protobuf==3.20
tensorflowjs_converter \
--input_format tf_saved_model \
--output_format tfjs_graph_model \
saved_model \
tfjs_model_float32
tensorflowjs_converter \
--input_format tf_saved_model \
--output_format tfjs_graph_model \
--quantize_float16 "*" \
saved_model \
tfjs_model_float16
Thank you so much for your kind and very detailed response!
I’ve tried it but, unfortunately, on running this command:
onnx2tf \
-i lite-model_craft-text-detector_dr_1.onnx \
-ois input:1,3,800,600 -osd
I get these errors:
TypeError: argument of type 'NoneType' is not iterable
ERROR: input_onnx_file_path: /Users/someuser/Downloads/myModel/lite-model_craft-text-detector_dr_1.onnx
ERROR: onnx_op_name: onnx_tf_prefix_MatMul_84;StatefulPartitionedCall/onnx_tf_prefix_MatMul_845
ERROR: Read this and deal with it. https://github.com/PINTO0309/onnx2tf#parameter-replacement
ERROR: Alternatively, if the input OP has a dynamic dimension, use the -b or -ois option to rewrite it to a static shape and try again.
ERROR: If the input OP of ONNX before conversion is NHWC or an irregular channel arrangement other than NCHW, use the -kt or -kat option.
ERROR: Also, for models that include NonMaxSuppression in the post-processing, try the -onwdt option.
Any tips?
I performed the same procedure again, but unfortunately no such error occurs in my environment.
tf.nn.convolution_25 (TFOpLambda) (1, 400, 300, 2) 0 ['tf.split_26[0][0]']
tf.math.add_46 (TFOpLambda) (1, 400, 300, 2) 0 ['tf.nn.convolution_25[0][0]']
Identity (TFOpLambda) (1, 400, 300, 2) 0 ['tf.math.add_46[0][0]']
============================================================================================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
____________________________________________________________________________________________________________________________________________
saved_model output started ==========================================================
saved_model output complete!
Float32 tflite output complete!
Float16 tflite output complete!
I expect that this is probably due to an outdated version of some package used in the back end, etc. Therefore, I recommend trying the following. If you are a Windows user, running the commands inside WSL2 Ubuntu should go very smoothly.
docker login ghcr.io
Username (xxxx): {Enter}
Password: {Personal Access Token}
Login Succeeded
docker run --rm -it \
-v `pwd`:/workdir \
-w /workdir \
ghcr.io/pinto0309/onnx2tf:1.13.3
wget -O lite-model_craft-text-detector_dr_1.tflite https://tfhub.dev/tulasiram58827/lite-model/craft-text-detector/dr/1?lite-format=tflite
pip install -U tf2onnx
python -m tf2onnx.convert \
--opset 11 \
--tflite lite-model_craft-text-detector_dr_1.tflite \
--dequantize \
--output lite-model_craft-text-detector_dr_1.onnx
onnx2tf \
-i lite-model_craft-text-detector_dr_1.onnx \
-ois input:1,3,800,600 -osd
pip install tensorflowjs
tensorflowjs_converter \
--input_format tf_saved_model \
--output_format tfjs_graph_model \
saved_model \
tfjs_model_float32
tensorflowjs_converter \
--input_format tf_saved_model \
--output_format tfjs_graph_model \
--quantize_float16 "*" \
saved_model \
tfjs_model_float16
Now it kills himself 
...
Model optimizing complete!
Automatic generation of each OP name started ========================================
Automatic generation of each OP name complete!
Model loaded ========================================================================
Model convertion started ============================================================
INFO: input_op_name: input shape: [1, 3, 800, 600] dtype: float32
Killed
I used the docker approach you suggest above.
My computer is a Macbook (13.3.1 (a) )
I am not going to advise you on issues specific to your environment. Killed is probably due to lack of RAM or something. TensorFlow unnecessarily consumes a lot of RAM, not an onnx2tf problem.
Thank you again, you are very kind and patient with me. I’m learning a lot with your help! 
With Colab it works and I was able to convert lite-model_craft-text-detector_dr_1.onnx easy.
I was trying with another model from hub, rosetta, but in the command:
!onnx2tf -i lite-model_rosetta_dr_1.onnx -ois input:1,3,800,600 -osd
I get following errors:
ERROR: input_onnx_file_path: lite-model_rosetta_dr_1.onnx
ERROR: onnx_op_name: transpose_1;StatefulPartitionedCall/transpose_11
ERROR: Read this and deal with it. https://github.com/PINTO0309/onnx2tf#parameter-replacement
ERROR: Alternatively, if the input OP has a dynamic dimension, use the -b or -ois option to rewrite it to a static shape and try again.
ERROR: If the input OP of ONNX before conversion is NHWC or an irregular channel arrangement other than NCHW, use the -kt or -kat option.
ERROR: Also, for models that include NonMaxSuppression in the post-processing, try the -onwdt option.
Could you please suggest me a way to fix it?
Thank you in advance
PS: I’ve tried to use in my code the model exported as json+bin but it gave me following error:
Error: layer: Improper config format:
{
... <- all json content here
}
'className' and 'config' must set.
There was an omission in the conversion process of Slice in onnx2tf, which will be fixed and released later.
https://github.com/PINTO0309/onnx2tf/pull/377
I fixed it. From today rosetta can also be converted. Note that I do not know how to implement React. You’ll have to rely on other engineers.
Your work is amazing!
Thank you so much. I’m going to try it soon
I will definitely ask in another thread about other application’s errors.
Hello ,
I was trying to convert keras-ocr tflite using previous Colab but I crash with this error:
ERROR - rewriter <function rewrite_cond at 0x7f207905cdc0>: exception make_sure failure: Cannot find node with output 'std.constant107' in graph 'main'
Traceback (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
Any suggestions?
