Hello everyone,
I have a question regarding converting tensorflow v2 saved_model to an IR model.
Can you please provide some information on this?
After training the following sample code using keras.LSTM in tensorflow v2, I tried to save the trained model in saved_model format and convert it using mo, but I encountered the following error.
Is it true that openVINO 2022.3 does not support keras.LSTM ?
If there is a workaround, please let me know.
Thank you in advance.
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OS Ubuntu20.04
Python 3.9.2
tensorflow 2.9.3
cuda 11.6
cpu intel i9
GPU nvidia RTX2080
openVINO 2022.3
----------------------------------------------
//
////////////////////////////////////////////////
// sample code : lstm_code.py
//
import numpy as np
from pathlib import Path
import shutil
import tensorflow as tf
#
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, LSTM, Input
#
cwdPath = Path().cwd()
savedModelPath = cwdPath / "saved_model"
if savedModelPath.exists():
shutil.rmtree(savedModelPath)
savedModelPath.mkdir()
#
# time series data
nSteps = 100
num_samples = 1000
#
wt = np.linspace(0, 20*np.pi, num_samples)
w = np.exp(-wt/wt[-1])
signal = w * np.sin(wt)
print(f'*signal.shape = {signal.shape}')
X = []
y = []
batchSiz = len(signal) - nSteps - 1
print(f'*Batch L = {batchSiz}')
for i in range(batchSiz):
X.append(signal[i:i+nSteps])
y.append(signal[i+nSteps]) # L-1 = i + nSteps
X = np.array(X)
y = np.array(y)
X = X.reshape((X.shape[0], X.shape[1], 1))
y = y.reshape((y.shape[0], 1))
#
# keras model
hdim = 50
miniBatchSiz= 32
nOutputs = 1
nEpochs = 5
#
# keras model
inputs = Input(shape=(nSteps, 1), name="inp")
x = LSTM(hdim, input_shape=inputs.shape)(inputs)
outputs= Dense(1, activation="linear", name="outp")(x)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
#
model.fit(X, y, epochs=nEpochs, batch_size=miniBatchSiz)
#
tf.saved_model.save(model, str(savedModelPath))
#
print(f"-> prediction ... ", end="")
future_signal = signal[-nSteps:]
for i in range(nSteps):
X_pred = np.reshape(future_signal[-nSteps:], (1, nSteps, 1))
y_pred = model.predict(X_pred, verbose=None)[0][0]
future_signal = np.append(future_signal, y_pred)
print(f"done.")
#
import matplotlib.pyplot as plt
plt.plot(signal, label='original signal')
plt.plot(range(len(signal)-nSteps, len(signal)), future_signal[nSteps:], label='predicted signal')
plt.legend()
plt.savefig('result.png')
# node name and shape
saved_model = tf.saved_model.load(str(str(savedModelPath)))
print(f"\n======================================")
print(f" node information")
# Print the input and output node names for a signature
for signature_name in saved_model.signatures:
print(f" signature name : {signature_name}")
print(f" in : {saved_model.signatures[signature_name].inputs[0]}" )
print(f" out: {saved_model.signatures[signature_name].outputs[0]}")
print(f"======================================\n")
//
////////////////////////////////////////////////
// tf node information on screen
//
======================================
node information
signature name : serving_default
in : Tensor("inp:0", shape=(None, 100, 1), dtype=float32)
out: Tensor("Identity:0", shape=(None, 1), dtype=float32)
======================================
//
////////////////////////////////////////////////
// mo command
//
mo --use_new_frontend \
--log_level DEBUG \
--saved_model_dir ~/Projects/ovConvert/saved_model \
--input inp \
--input_shape "[?,100,1]" \
--output Identity \
--output_dir ~/Projects/ovConvert/ir_model
//
////////////////////////////////////////////////
// mo output message
//
[ ERROR ] -------------------------------------------------
[ ERROR ] ----------------- INTERNAL ERROR ----------------
[ ERROR ] Unexpected exception happened.
[ ERROR ] Please contact Model Optimizer developers and forward the following information:
[ ERROR ] Check 'translate_map.count(operation_decoder->get_op_type())' failed at src/frontends/tensorflow/src/frontend.cpp:177:
FrontEnd API failed with OpConversionFailure: :
No translator found for TensorListFromTensor node.
[ ERROR ] Traceback (most recent call last):
File "/home/tats/Projects/ovConvert/venv/lib/python3.9/site-packages/openvino/tools/mo/main.py", line 50, in main
ngraph_function = convert_model(**argv)
File "/home/tats/Projects/ovConvert/venv/lib/python3.9/site-packages/openvino/tools/mo/convert.py", line 47, in convert_model
return _convert(**args)
File "/home/tats/Projects/ovConvert/venv/lib/python3.9/site-packages/openvino/tools/mo/convert_impl.py", line 937, in _convert
raise e.with_traceback(None)
openvino._pyopenvino.OpConversionFailure: Check 'translate_map.count(operation_decoder->get_op_type())' failed at src/frontends/tensorflow/src/frontend.cpp:177:
FrontEnd API failed with OpConversionFailure: :
No translator found for TensorListFromTensor node.
[ ERROR ] ---------------- END OF BUG REPORT --------------
[ ERROR ] -------------------------------------------------
