i have already trained a model by ssd inception v2 model and use "export_inference_graph.py" to generated the frozen_inference_graph.pb
i tried to use below command to generated the model then try to use mvNCCompiler to convert it to movidius graph
python3 /home/chanchihang/Special_Topic/models/research/object_detection/export_inference_graph.py
--input_type image_tensor
--pipeline_config_path /home/chanchihang/Special_Topic/ssd_inception_v2_coco.config
--trained_checkpoint_prefix /home/chanchihang/Special_Topic/models/research/inceptionV2_Training/model.ckpt-20000
--output_directory /home/chanchihang/Special_Topic/models/research/inceptionV2_Training/
then i tried to convert model.ckpt.meta by mvNCCompiler to movidius graph and get error below
mvNCCompile model.ckpt.meta -in=image_tensor -on=detection_scores -is 224 224 -o test.graph
Traceback (most recent call last):
File "/usr/local/bin/mvNCCompile", line 118, in <module>
create_graph(args.network, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights)
File "/usr/local/bin/mvNCCompile", line 104, in create_graph
net = parse_tensor(args, myriad_config)
File "/usr/local/bin/ncsdk/Controllers/TensorFlowParser.py", line 259, in parse_tensor
input_data = np.random.uniform(0, 1, shape)
File "mtrand.pyx", line 961, in numpy.random.mtrand.RandomState.uniform
File "common.pyx", line 557, in numpy.random.common.cont
TypeError: 'NoneType' object cannot be interpreted as an integer
Related
python : 3.9.13
tensorflow : 2.9.1
I am making a custom dataset with 'tensorflow object detection'
The saved_model.pb file was generated by trainning with the FastRCNN dataset.
I took this file and applied it to Nuclio (Serverless function framework) but failed
It seems to have apply inference graph file type
I find export util pythonf file in models/research/object_detection directory "export_inference_graph.py"
But this file not working .
This is error message
Traceback (most recent call last):
File "export_inference_graph.py", line 211, in <module>
tf.app.run()
File "/home/namu/.local/lib/python3.8/site-packages/tensorflow/python/platform/app.py", line 36, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/home/namu/.local/lib/python3.8/site-packages/absl/app.py", line 308, in run
_run_main(main, args)
File "/home/namu/.local/lib/python3.8/site-packages/absl/app.py", line 254, in _run_main
sys.exit(main(argv))
File "export_inference_graph.py", line 199, in main
exporter.export_inference_graph(
File "/home/namu/myspace/data/models/research/object_detection/exporter.py", line 618, in export_inference_graph
_export_inference_graph(
File "/home/namu/myspace/data/models/research/object_detection/exporter.py", line 521, in _export_inference_graph
profile_inference_graph(tf.get_default_graph())
File "/home/namu/myspace/data/models/research/object_detection/exporter.py", line 649, in profile_inference_graph
contrib_tfprof.model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
NameError: name 'contrib_tfprof' is not defined
I knew from Google that this did not work on tensorflow 2.x
https://medium.com/#sebastingarcaacosta/how-to-export-a-tensorflow-2-x-keras-model-to-a-frozen-and-optimized-graph-39740846d9eb
I am working on it by referring to the above site
But
import tensorflow as tf
from tensorflow import keras
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
import numpy as np
#path of the directory where you want to save your model
frozen_out_path = "/home/namu/myspace/data/models/export_graph"
# name of the .pb file
frozen_model = "frozen_graph"
model = tf.keras.models.load_model('/home/namu/myspace/data/models/train_pb/saved_model') # tf_saved_model load
# model = tf.saved_model.load('/home/namu/myspace/data/models/train_pb/saved_model')
full_model = tf.function(lambda x: model(x))
full_model = full_model.get_concrete_function(tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))
When i execute this code , error occurs
ValueError: Unable to create a Keras model from SavedModel at /home/namu/myspace/data/models/train_pb/saved_model. This SavedModel was exported with `tf.saved_model.save`, and lacks the Keras metadata file. Please save your Keras model by calling `model.save`or `tf.keras.models.save_model`. Note that you can still load this SavedModel with `tf.saved_model.load`.
How can i create inference graph pb file.
I have trained a network(git link below) and saved in the saved model format. And would like to convert it to tflite. I am using python API for tflite coverter(tflite.py in git link below). But I'am not able to do so.
System information:
OS Platform and Distribution: Ubuntu 18.04.3 LTS
TensorFlow version: tensorflow/tensorflow:2.2.0-gpu (docker)
The link to network and save model code.
The output from the converter invocation:
File "tflite.py", line 22, in convert_model
tflite_model = converter.convert()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/lite.py", line 459, in convert
self._funcs[0], lower_control_flow=False))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/convert_to_constants.py", line 706, in convert_variables_to_constants_v2_as_graph
func, lower_control_flow, aggressive_inlining)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/convert_to_constants.py", line 457, in _convert_variables_to_constants_v2_impl
tensor_data = _get_tensor_data(func)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/convert_to_constants.py", line 217, in _get_tensor_data
data = val_tensor.numpy()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 961, in numpy
maybe_arr = self._numpy() # pylint: disable=protected-access
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 929, in _numpy
six.raise_from(core._status_to_exception(e.code, e.message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot convert a Tensor of dtype resource to a NumPy array.
The link to the saved model
I have an error in exporting an inference graph and I searched for hours but can't find a solution; this is my error:
Command:
python export_inference_graph.py --input_type image_tensor --pipeline_config_path training faster_rcnn_inception_v2_pets.config --trained_checkpoint_prefix training "model.ckpt-2950" --output_directory export
Error Traceback:
Traceback (most recent call last):
File "export_inference_graph.py", line 151, in
tf.app.run()
File "C:\Users\OctaNet\Miniconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "export_inference_graph.py", line 135, in main
text_format.Merge(f.read(), pipeline_config)
File "C:\Users\OctaNet\Miniconda3\envs\tensorflow\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 125, in read
self._preread_check()
File "C:\Users\OctaNet\Miniconda3\envs\tensorflow\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 85, in _preread_check
compat.as_bytes(self.__name), 1024 * 512, status)
File "C:\Users\OctaNet\Miniconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 528, in exit
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.UnknownError: NewRandomAccessFile failed to Create/Open: training : Acc\udce8s refus\udce9.
; Input/output error
I guess you need remove : from 'training : Acc\udce8s refus\udce9'
The --pipeline_config_path flag has been set to training faster_rcnn_inception_v2_pets.config which is not a valid path due to the space character following the word training.
Perhaps user11095295 intended to write training/faster_rcnn_inception_v2_pets.config.
I'm using TF version 1.12 with conda and python 3.
My question concerns the model_dir value of tf.contrib.factorization.KMeansClustering : how to use a string placeholder for the model_dir value?
Here is the context: I have pretrained KMeans in different situation, checkpoints are in different model_dir.
I want to use predictions of these pretrained models inside a graph, depending on each situation, so I need to put in this graph the KMeansClustering which can accept different model_dirs.
In the graph I defined :
ckpt_ph = tf.placeholder(tf.string)
...
kmeans = KMeansClustering(5, model_dir=ckpt_ph,distance_metric='cosine')
def input_fn():
return tf.train.limit_epochs(tf.convert_to_tensor(x, dtype=tf.float32), num_epochs=1)
centers_idx = list(kmeans.predict(input_fn,predict_keys='cluster_index',checkpoint_path=ckpt_ph,yield_single_examples=False))[0]['cluster_index']
centers_val = kmeans.cluster_centers()
...
And I run it with:
...
for ind in range(nb_cases):
...
sess.run([...], feed_dict={..., ckpt_ph: km_ckpt[ind]})
...
Where km_ckpt is the list of pretrained KMeansClustering checkpoints pathes that I want to use for each situations.
The error that I get is:
Traceback (most recent call last):
File "main.py", line 28, in <module>
tf.app.run()
File "C:\Users\Denis\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "main.py", line 23, in main
launch_training()
File "main.py", line 14, in launch_training
train_mnist.train_model()
File "C:\Users\Denis\ML\ScatteringReconstruction\src\model\train_mnist.py", line 355, in train_model
X_r = SR(X_tensor)
File "C:\Users\Denis\ML\ScatteringReconstruction\src\model\train_mnist.py", line 316, in __call__
kmeans = KMeansClustering(FLAGS.km_k, model_dir=ckpt_ph, distance_metric='cosine')
File "C:\Users\Denis\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\factorization\python\ops\kmeans.py", line 423, in __init__
config=config)
File "C:\Users\Denis\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 189, in __init__
model_dir)
File "C:\Users\Denis\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1665, in maybe_overwrite_model_dir_and_session_config
if model_dir:
File "C:\Users\Denis\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 671, in __bool__
raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.
I think that the problem is that in KMeansClustering and KMeansClustering.predict, model_dir is expecting a Python bool or string, and I'm giving him a Tensor, but then I don't see hos to use pretrained KMeansClustering inside a graph.
Thanks in advance for the help!
I am trying to convert a saved model tf to a tflite mode. I am getting following error in tensorflow c++ while conversion:-
tensorflow/contrib/lite/toco/tooling_util.cc:981] Check failed: name.substr(colon_pos + 1).find_first_not_of("0123456789") == string::npos (1 vs. 18446744073709551615)Array name must only have digits after colon\n'
Here is the source code :-
def convert_to_tflite_util(model_dir, output_dir):
tag_constants = set(["train"])
converter = tf.contrib.lite.TocoConverter.from_saved_model(model_dir, tag_set=tag_constants)
#converter = tf.contrib.lite.TocoConverter.from_saved_model(model_dir)
tflite_model = converter.convert()
Logs :-
2018-11-09 11:44:11.410085: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
INFO:tensorflow:Restoring parameters from /Users/deosingh/code/mldev/sensei-on-device/converters/deeplasso_converter/model/variables/variables
INFO:tensorflow:The given SavedModel MetaGraphDef contains SignatureDefs with the following keys: {'serving_default'}
INFO:tensorflow:input tensors info:
INFO:tensorflow:Tensor's key in saved_model's tensor_map: input
INFO:tensorflow: tensor name: data:0, shape: (-1, 320, 320, 4), type: DT_FLOAT
INFO:tensorflow:output tensors info:
INFO:tensorflow:Tensor's key in saved_model's tensor_map: output
INFO:tensorflow: tensor name: prob:0, shape: (1, 320, 320, 2), type: DT_FLOAT
INFO:tensorflow:Restoring parameters from /Users/deosingh/code/mldev/model/variables/variables
INFO:tensorflow:Froze 387 variables.
INFO:tensorflow:Converted 387 variables to const ops.
Traceback (most recent call last):
File "/Applications/PyCharm CE.app/Contents/helpers/pydev/pydevd.py", line 1664, in <module>
main()
File "/Applications/PyCharm CE.app/Contents/helpers/pydev/pydevd.py", line 1658, in main
globals = debugger.run(setup['file'], None, None, is_module)
File "/Applications/PyCharm CE.app/Contents/helpers/pydev/pydevd.py", line 1068, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/Applications/PyCharm CE.app/Contents/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/Users/deosingh/code/mldev/src/tf_to_tflite.py", line 112, in <module>
main()
File "/Users/deosingh/code/mldev/src/tf_to_tflite.py", line 108, in main
convert_to_tflite_util(args.src_dir, args.dest_dir)
File "/Users/deosingh/code/mldev/src/tf_to_tflite.py", line 91, in convert_to_tflite_util
tflite_model = converter.convert()
File "/Users/deosingh/code/mldev/venv/lib/python3.6/site-packages/tensorflow/contrib/lite/python/lite.py", line 439, in convert
**converter_kwargs)
File "/Users/deosingh/code/mldev/venv/lib/python3.6/site-packages/tensorflow/contrib/lite/python/convert.py", line 309, in toco_convert_impl
input_data.SerializeToString())
File "/Users/deosingh/code/mldev/venv/lib/python3.6/site-packages/tensorflow/contrib/lite/python/convert.py", line 109, in toco_convert_protos
(stdout, stderr))
RuntimeError: TOCO failed see console for info.
b'2018-11-09 11:44:18.328373: F tensorflow/contrib/lite/toco/tooling_util.cc:981] Check failed: name.substr(colon_pos + 1).find_first_not_of("0123456789") == string::npos (1 vs. 18446744073709551615)Array name must only have digits after colon\n'
None
Link to tensorflow source which is throwing this error.
The original model was in caffe , then was converted to tensorflow using mmdnn . Converter is complaining about name not being in the correct format i.e doesn't expect _ after : as per the tf source. Please let me know how to resolve/debug this error. Since the tf source which isthrowing this error is in c++ and I am invoking it through python I am not able to debug it through pycharm on macosx.