I was trying to import a frozen (and optimized using tensorflow.python.tools.optimize_for_inference ) pb "optimized.pb" file using
cv2.dnn.readNetFromTensorflow("optimized.pb")
This resulted in the following error:
Traceback (most recent call last):
File "opencv.py", line 4, in <module>
net = cv2.dnn.readNetFromTensorflow("optimized.pb")
cv2.error: OpenCV(3.4.3) /io/opencv/modules/dnn/src/tensorflow/tf_importer.cpp:1380: error: (-215:Assertion failed) scaleMat.type() == CV_32FC1 in function 'populateNet'
The model included transpose convolution layers. However the error disappears when I do not include any deconvolution layers.
Can anyone help me understand and correct this error?
I've solved this error in my network by replace
up8 = UpSampling2D(size=(2, 2), interpolation='bilinear')(conv7)
to
up8 = UpSampling2D(size=(2, 2))(conv7)
It looks like my opencv(version 3.4.6) is not supported bilinear interpolation in UpSampling2D layer.
Related
I'm having an ONNX model, which I can successfully convert to TF with TF 2.4.1. But when it comes to the conversion of that saved model to TFLite an error happens.
The code:
import onnx
import tensorflow as tf
from onnx_tf.backend import prepare
print(tf.__version__)
# Convert model.onnx to Tensorflow
onnx_model = onnx.load('model.onnx')
onnx.checker.check_model(onnx_model)
tf_rep = prepare(onnx_model)
tf_rep.export_graph('model')
# Convert saved model to tflite
converter = tf.lite.TFLiteConverter.from_saved_model('model')
tf_lite_model = converter.convert()
open('model.tflite', 'wb').write(tf_lite_model)
Everything goes OK until the conversion step, which ends like so:
>>> tf_lite_model = converter.convert()
2021-04-22 18:18:14.715046: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-04-22 18:18:14.715072: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-04-22 18:18:14.715078: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:325] Ignored change_concat_input_ranges.
2021-04-22 18:18:14.716044: I tensorflow/cc/saved_model/reader.cc:32] Reading SavedModel from: model
2021-04-22 18:18:14.778050: I tensorflow/cc/saved_model/reader.cc:55] Reading meta graph with tags { serve }
2021-04-22 18:18:14.778083: I tensorflow/cc/saved_model/reader.cc:93] Reading SavedModel debug info (if present) from: model
2021-04-22 18:18:14.998062: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2021-04-22 18:18:15.043862: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.
2021-04-22 18:18:15.438804: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: model
2021-04-22 18:18:15.809851: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 1093808 microseconds.
2021-04-22 18:18:18.757257: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:194] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(callsite(callsite("Pad_16#__inference___call___16503" at "PartitionedCall#__inference_signature_wrapper_16752") at "PartitionedCall")): error: operand #0 does not dominate this use
Traceback (most recent call last):
File "/Users/decades/anaconda3/envs/py38/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 210, in toco_convert_protos
model_str = wrap_toco.wrapped_toco_convert(model_flags_str,
File "/Users/decades/anaconda3/envs/py38/lib/python3.8/site-packages/tensorflow/lite/python/wrap_toco.py", line 32, in wrapped_toco_convert
return _pywrap_toco_api.TocoConvert(
Exception: <unknown>:0: error: loc(callsite(callsite("Pad_16#__inference___call___16503" at "PartitionedCall#__inference_signature_wrapper_16752") at "PartitionedCall")): operand #0 does not dominate this use
<unknown>:0: note: loc("PartitionedCall"): called from
<unknown>:0: note: loc(callsite(callsite("Pad_16#__inference___call___16503" at "PartitionedCall#__inference_signature_wrapper_16752") at "PartitionedCall")): operand defined here
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/decades/anaconda3/envs/py38/lib/python3.8/site-packages/tensorflow/lite/python/lite.py", line 739, in convert
result = _convert_saved_model(**converter_kwargs)
File "/Users/decades/anaconda3/envs/py38/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 632, in convert_saved_model
data = toco_convert_protos(
File "/Users/decades/anaconda3/envs/py38/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 216, in toco_convert_protos
raise ConverterError(str(e))
tensorflow.lite.python.convert.ConverterError: <unknown>:0: error: loc(callsite(callsite("Pad_16#__inference___call___16503" at "PartitionedCall#__inference_signature_wrapper_16752") at "PartitionedCall")): operand #0 does not dominate this use
<unknown>:0: note: loc("PartitionedCall"): called from
<unknown>:0: note: loc(callsite(callsite("Pad_16#__inference___call___16503" at "PartitionedCall#__inference_signature_wrapper_16752") at "PartitionedCall")): operand defined here
I have no idea, what this message means, but if I switch to TF 2.2 the conversion passes w/o errors. The bad thing is, that due to another problem now the initial ONNX to TF conversion fails.
Anybody having an idea, what this message means and what could be done with it?
TIA
Is it possible to share your the saved model directory to me? I can help debugging.
The general advise is that, there are two possibilities that
(1) TF Lite converter may not handle the saved model correctly.
(2) onnx conversion tool may not create a valid TF saved model.
Using the recent TF version (2.5 or tf-nightly) might help resolve this problem in the (1) case but it's not guaranteed.
I confirmed that the tf-nightly version could convert the attached saved model without any issue:
converter = tf.lite.TFLiteConverter.from_saved_model(
"/tmp/onnx_model")
tflite_model = converter.convert()
with open("/tmp/onnx.tflite", "wb") as f:
f.write(tflite_model)
I am using Tensorflow 2 on Windows 10 and I download a model from TensorFlow Detection Model Zoo.
The model I am using is ssd.mobilenetv2.oid4
The model details are:
[<tf.Tensor 'image_tensor:0' shape=(None, None, None, 3) dtype=uint8>]
Note: I also have the frozen_inference_graph.pb available along with config file and checkpoint.
I used the TensorFlowLiteConverter Snippet to convert a saved_model.pb file to .tflite with custom shape:
import tensorflow as tf
input_dir = "D:\\Models\\ssd_mobilenet_v2_oid_v4_2018_12_12\\saved_model"
model = tf.saved_model.load(input_dir)
concrete_func = model.signatures[
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
concrete_func.inputs[0].set_shape([None, None, None, 3])
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
tflite_model = converter.convert()
I get the following error:
Traceback (most recent call last):
File "C:\Users\Bhavin\Desktop\TensorFlow_pb_converter.py", line 10, in <module>
tflite_model = converter.convert()
File "C:\Users\Bhavin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_core\lite\python\lite.py", line 428, in convert
"invalid shape '{1}'.".format(_get_tensor_name(tensor), shape_list))
ValueError: None is only supported in the 1st dimension. Tensor 'image_tensor' has invalid shape '[None, None, None, 3]'
I tried using toco and tflite_convert but I got the same error.
What am I doing wrong and how do I convert this pb file to tflite file?
Currently tensorflow Lite doesn't support converting dynamic shape except for first dimension.
Consider setting the exact shape instead of 'None'
I downloaded pretrained mobilenetV2 models from tensorflow models,and try to restore the graph,but got unexpected error.
Codes to reproduce the error is pretty concise:
import tensorflow as tf
meta_path = 'path/to/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224.ckpt.meta'
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver = tf.train.import_meta_graph(meta_path)
then the last line raises error:
Traceback (most recent call last):
File "/home/CVAR/study/codes/languages/python/pycharm/learn_tensorflow/train_mobileNet_v2/test_of_functions/saver_test.py", line 21, in <module>
saver = tf.train.import_meta_graph(meta_path)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1960, in import_meta_graph
**kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/meta_graph.py", line 744, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 432, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py", line 391, in import_graph_def
_RemoveDefaultAttrs(op_dict, producer_op_list, graph_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py", line 158, in _RemoveDefaultAttrs
op_def = op_dict[node.op]
KeyError: 'InfeedEnqueueTuple'
My system information is :
ubuntu 16.04
python 3.5
tensorflow-gpu 1.9
Any idea?
I recently also met such a problem. It seems like the reason is that the TensorFlow version you use to train the model is different from the version you use to read the graph description proto. What you need to do is to reinstall the TensorFlow to your training version. Otherwise, retraining the model would work.
FYI, the TensorFlow version I used to train is 1.12.0, by contrast, the version I use to load the graph is 1.13.1. Reinstallation solves the problem.
There are some ops not defined. from conv_blocks import * will fix this bug but I got another problem "ValueError: NodeDef expected inputs 'float, int32' do not match 1 inputs specified;". Still debugging, but hope this tip solves your problem.
I got the following error:
anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/gradients.py:90: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Traceback (most recent call last):
trainstep = tf.train.AdamOptimizer(0.0001).minimize(lossobj)
File "anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 196, in minimize
grad_loss=grad_loss)
File "anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 253, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/gradients.py", line 469, in gradients
in_grads = _AsList(grad_fn(op, *out_grads))
File "anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/array_grad.py", line 504, in _ExtractImagePatchesGrad
rows_out = int(ceil(rows_in / stride_r))
TypeError: unsupported operand type(s) for /: 'NoneType' and 'long'
there is look like gather ops is wrong.
I see that this is an old issue, but I have found a quick work-around for some cases of this. Chances are, you are feeding your input using a placeholder and one of the dimensions of the placeholder shape is "None". If you set that dimension to your batch size, it will no longer be an unknown shape.
I am trying to generate the graph for MNIST beginner tutorial but is getting the following error. For some reason, merged_summary_op object is None.
Traceback (most recent call last):
File "mnist1.py", line 48, in <module>
summary_str = sess.run(merged_summary_op)
File "/home/vagrant/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 307, in run
% (subfetch, fetch, type(subfetch), e.message))
TypeError: Fetch argument None of None has invalid type <type 'NoneType'>, must be a string or Tensor. (Can not convert a NoneType into a Tensor or Operation.)
I think I am missing a step here. I launched the session first and then running the statement:
merged_summary_op = tf.merge_all_summaries()
I had the same error.
In my case, adding at least one tf.scalar_summary() before calling tf.merge_all_summaries() solved the problem.
For example,
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
tf.scalar_summary("cross_entropy", cross_entropy)
merged_summary_op = tf.merge_all_summaries()
I hope this snippet helps you.