Rahul0985
commented
4 years ago hi @jiafatom hi @jiafatom , i tried your solution of getting the tensorflow frozen graph and then using tf2onnx to convert it. I am getting following error: .......................................................................................................................................... 2020-03-30 00:41:37.689103: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found 2020-03-30 00:41:37.701017: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. WARNING:tensorflow:From C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\tf2onnx\verbose_logging.py:72: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_ve rbosity instead.
2020-03-30 00:41:41,006 - WARNING - From C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\tf2onnx\verbose_logging.py:72: The name tf.logging.set_verbosity is deprecated. Please use tf.compat. v1.logging.set_verbosity instead.
2020-03-30 00:41:41.009392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-03-30 00:41:41.756002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1050 major: 6 minor: 1 memoryClockRate(GHz): 1.493
pciBusID: 0000:02:00.0
2020-03-30 00:41:41.767085: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found
2020-03-30 00:41:41.773644: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cublas64_100.dll'; dlerror: cublas64_100.dll not found
2020-03-30 00:41:41.782547: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cufft64_100.dll'; dlerror: cufft64_100.dll not found
2020-03-30 00:41:41.801789: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'curand64_100.dll'; dlerror: curand64_100.dll not found
2020-03-30 00:41:41.811840: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusolver64_100.dll'; dlerror: cusolver64_100.dll not found
2020-03-30 00:41:41.830418: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusparse64_100.dll'; dlerror: cusparse64_100.dll not found
2020-03-30 00:41:41.841328: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudnn64_7.dll'; dlerror: cudnn64_7.dll not found
2020-03-30 00:41:41.855089: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you woul
d like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2020-03-30 00:41:41.890716: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-03-30 00:41:41.909786: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-30 00:41:41.919578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]
2020-03-30 00:41:42,547 - WARNING - inputs [input_1:0] is not in frozen graph, delete them
2020-03-30 00:41:42.886264: I tensorflow/tools/graph_transforms/transform_graph.cc:317] Applying fold_batch_norms
2020-03-30 00:41:42.906852: I tensorflow/tools/graph_transforms/transform_graph.cc:317] Applying fold_old_batch_norms
2020-03-30 00:41:43.283588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-30 00:41:43.292791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]
2020-03-30 00:41:43,296 - INFO - Using tensorflow=1.15.0, onnx=1.6.0, tf2onnx=1.5.5/3c8f90
2020-03-30 00:41:43,296 - INFO - Using opset <onnx, 8>
2020-03-30 00:41:43,881 - ERROR - Tensorflow op [bidirectional_1/ReverseV2: ReverseV2] is not supported
2020-03-30 00:41:43,893 - ERROR - Tensorflow op [bidirectional_1/ReverseV2_1: ReverseV2] is not supported
2020-03-30 00:41:44,016 - ERROR - Tensorflow op [crf_1/ReverseV2: ReverseV2] is not supported
2020-03-30 00:41:44,067 - ERROR - Tensorflow op [crf_1/ReverseV2_1: ReverseV2] is not supported
2020-03-30 00:41:44,067 - ERROR - Failed to convert node crf_1/one_hot
OP=OneHot
Name=crf_1/one_hot
Inputs:
crf_1/Squeeze:0=Squeeze, [-1, 100], 6
crf_1/one_hot/depth:0=Const, [], 6
crf_1/one_hot/on_value:0=Const, [], 1
crf_1/one_hot/off_value:0=Const, [], 1
Outpus:
crf_1/one_hot:0=[-1, 100, 100], 1
Traceback (most recent call last):
File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\tf2onnx\tfonnx.py", line 356, in tensorflow_onnx_mapping
func(g, node, kwargs)
File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\tf2onnx\onnx_opset\tensor.py", line 1053, in version_1
raise ValueError("onehot op: only rank1 is supported")
ValueError: onehot op: only rank1 is supported
2020-03-30 00:41:44,070 - ERROR - Unsupported ops: Counter({'ReverseV2': 4})
Traceback (most recent call last):
File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\tf2onnx\convert.py", line 161, in
import os import keras; from keras import backend as K import tensorflow.compat.v1 as tf from keras.models import Model from keras.layers import LSTM,Input, Embedding, Dense, TimeDistributed, Dropout, Bidirectional, Lambda
from keras_contrib.layers.crf import CRF
from tensorflow.python.tools import freeze_graph from keras_contrib.layers.crf import CRF, crf_loss, crf_viterbi_accuracy
K.set_learning_phase(0);
print(keras.name) def create_model(): maxlen = 100; n_tags = 100 n_words = 100 input = Input(shape=(maxlen,), name="input") model = Embedding(input_dim=n_words + 1, output_dim=250, input_length=maxlen)(input) model = Bidirectional(LSTM(units=150, return_sequences=True, recurrent_dropout=0.1))(model) # variational biLSTM model = TimeDistributed(Dense(150, activation="relu"))(model) # a dense layer as suggested by neuralNer crf = CRF(n_tags) # CRF layer out = crf(model) # output model = Model(input, out) model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
model.summary()
return modelmodel = create_model()
save_dir = "C:\Users\rahul\PycharmProjects\Review Conflation\model"
tf.saved_model.simple_save(K.get_session(), save_dir, inputs={"input": model.inputs[0]}, outputs={"output": model.outputs[0]} )
freeze_graph.freeze_graph(None, None, None, None, model.outputs[0].op.name, None, None, os.path.join(save_dir, "frozen_model.pb"), False, "", input_saved_model_dir=save_dir
................................................................................................................................................................................................... Then i use python -m tf2onnx.convert --input frozen_model.pb --output model.onnx --outputs crf_1/cond/Merge:0 --inputs input_1:0 to convert into onnx model. packages Version: tf(1.15) , keras-contrib(2.0.8) , onnx(1.6.0) , tf2onnx(1.5.5) and keras(2.3.1)
Can you please look into it ?
wenbingl
Hi team, i was trying to convert (BILSTM + CRF) network from keras to onnnx . i am getting following error with keras2onnx (1.6.5) and keras (2.3.1) and tensorflow(2.0) ......................................................................................... tf executing eager_mode: True The tf.op node crf_1/cond of type StatelessIf cannot be converted There is an error(<class 'AssertionError'>) happened during optimizing on the converted model!
Traceback (most recent call last): File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\keras2onnx\topology.py", line 315, in convert_topology target_opset=container.target_opset) File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\onnxconverter_common\optimizer.py", line 1614, in optimize_onnx_graph initializers) File "C:\Users\rahul\Anaconda3\envs\Review Conflation\lib\site-packages\onnxconverter_common\optimizer.py", line 303, in build_fromonnx assert var == '' or var_ in inputs AssertionError .................................................................................................................... Code: import keras2onnx import onnx from keras.models import Model, Input from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional, Lambda from keras_contrib.layers.crf import CRF onnxfilepath= "E:/xyz/xyz.onnx" maxlen = 100; n_tags = 100 n_words=100 input = Input(shape=(maxlen,),name="input_1") model = Embedding(input_dim=n_words + 1, output_dim=250, input_length=maxlen)(input) model = Bidirectional(LSTM(units=150, return_sequences=True, recurrent_dropout=0.1))(model) # variational biLSTM model = TimeDistributed(Dense(150, activation="relu"))(model) # a dense layer as suggested by neuralNer crf = CRF(n_tags) # CRF layer out = crf(model) # output model = Model(input, out) model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy]) model.summary() onnx_model = keras2onnx.convert_keras(model,target_opset=9); onnx.save_model(onnx_model,onnxfilepath)