- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Hello.
I am new to openvino and using Openvino 2020.1.023
I use the SSD300 from the link below:
https://github.com/balancap/SSD-Tensorflow
I freeze the model using the code below
with tf.Session(graph=tf.Graph()) as sess: # We import the meta graph in the current default Graph saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices) graph_def = tf.get_default_graph().as_graph_def() node_list=[n.name for n in graph_def.node] # We restore the weights saver.restore(sess, input_checkpoint) # Dddd=output_node_names.split(",") # We use a built-in TF helper to export variables to constants output_graph_def = tf.graph_util.convert_variables_to_constants( sess, # The session is used to retrieve the weights tf.get_default_graph().as_graph_def(), # The graph_def is used to retrieve the nodes node_list # The output node names are used to select the usefull nodes ) # tf.train.write_graph(sess.graph_def, '.', 'tfdroid.pbtxt') # Finally we serialize and dump the output graph to the filesystem with tf.gfile.GFile(output_graph, "wb") as f: f.write(output_graph_def.SerializeToString()) print("%d ops in the final graph." % len(output_graph_def.node)) return output_graph_def
and then use the command line:
python3 mo_tf.py --input_model frozen_model.pb --output_dir /home/ibox/Desktop/ --input_shape [1,300,300,3] --log_level=DEBUG
the debug text is in the attached file(TensorFlow_SSD300(DEBUG))
I have also tried keras ssd 300 from the link below:
https://github.com/pierluigiferrari/ssd_keras
and convert to .pb used the code below.
def export_keras_to_tf(input_model, output_model, num_output): print('Loading Keras model: ', input_model) keras_model = load_model(input_model,custom_objects={'loss': bce_jaccard_loss,'dice_coeff': dice_coeff}) print(keras_model.summary()) predictions = [None] * num_output predrediction_node_names = [None] * num_output for i in range(num_output): predrediction_node_names = 'output_node' + str(i) predictions = tf.identity(keras_model.outputs, name=predrediction_node_names) sess = K.get_session() constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), predrediction_node_names) infer_graph = graph_util.remove_training_nodes(constant_graph) graph_io.write_graph(infer_graph, '.', output_model, as_text=False)
and use the command line:
python3 mo.py --input_model ddd.h5.pb --output_dir /home/ibox/Desktop/ --input_shape [1,300,300,3] --log_level=DEBUG
The debug text is in the attached file(Keras_SSD300). I have check the output shape in debug file, it is the same as what i have got using keras model.summary() method.
Really don't know is going on, really appreciate your help!
Link Copied
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Hello, LAI, PEI YU.
Please carefully check this documentation on how to convert a frozen TensorFlow model - https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html#Convert_From_TF
You might need to specify the following parameters:
--tensorflow_use_custom_operations_config --tensorflow_object_detection_api_pipeline_config
Also, in the following thread, a user converted a SSD mobilenet to IR - https://software.intel.com/en-us/forums/computer-vision/topic/805387
This might be similar to your case.
Thanks.
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Dear Max
Thanks for your reply,
I'm not using the object detection api model, so is it ok with me to use the config file from the object detection api model?
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Hello LAI, PEI YU
Do you use any other API and/or sample application from OpenVINO toolkit distribution? As I'm aware of, SSD models are mostly utilized for object detection purposes.
If your case is similar to this one, please give it a try with the object detection config file to see if it works.
Best regards, Max.
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Dear Max,
Thanks for your reply,
I have added the parameter you suggested, but it doesn't work.
python3 mo_tf.py --input_model /home/ibox/Desktop/BusParkingDetection/frozen_model/frozen_model.pb --output_dir /home/ibox/Desktop/BusParkingDetection/ --input_shape [1,300,300,3] --tensorflow_use_custom_operations_config /opt/intel/openvino_2020.1.023/deployment_tools/model_optimizer/extensions/front/tf/ssd_support.json --tensorflow_object_detection_api_pipeline_config /home/ibox/Downloads/ssd_mobilenet_v1_coco_2018_01_28/pipeline.config
Maybe I should create my own config file? Do you have any suggestion?
Really appreciate your help!
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Hi, LAI, PEI YU.
SSD-300 model that you are using is based on Object Detection API.
For converting the TensorFlow version of this model please try to use one of the following .json and .config file pairs, according to different conditions:
--transformations_config <path_to_subgraph_replacement_configuration_file.json> — A subgraph replacement configuration file with transformations description. For the models downloaded from the TensorFlow* Object Detection API zoo, you can find the configuration files in the <INSTALL_DIR>/deployment_tools/model_optimizer/extensions/front/tf directory. Use:
ssd_v2_support.json — for frozen SSD topologies from the models zoo version up to 1.13.X inclusively
ssd_support_api_v.1.14.json (I suppose this one would be more appropriate for your case) — for frozen SSD topologies trained manually using the TensorFlow* Object Detection API version 1.14 or higher
--tensorflow_object_detection_api_pipeline_config <path_to_pipeline.config> — A special configuration file that describes the topology hyper-parameters and structure of the TensorFlow Object Detection API model. For the models downloaded from the TensorFlow* Object Detection API zoo, the configuration file is named pipeline.config. If you plan to train a model yourself, you can find templates for these files in the models repository - https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs
Also, you could have a chance to try this officially validated Caffe-version of SSD-300 model for OpenVINO toolkit - https://github.com/opencv/open_model_zoo/tree/master/models/public/ssd300
Best regards, Max.
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Dear Max,
Thanks for your reply,
I have added what you suggested, but it didn't work.My ssd model is vgg based, I think it is different from the models in detection model zoo. The error is still the same:
Number of elements in input [ 1 300 300 300 3] and output [300, 300, 3] of reshape node ssd_preprocessing_train/resize_image/Reshape mismatch
I know tensorflow model does not have well-define shapes, so I used keras as well. I have checked the input/output shape, still can not find the problem.
the error is : Can't permute attrs for node pool4/MaxPool, the same as link below:
https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit/topic/844525
So maybe I should wait for intel to fix this? or you have other suggestions ?
Because I don't have an device for training openvino model (lack of intel gpu), so I think converting it from my PC(not intel gpu) is a more appropriate way.
Thanks for your help!
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Hi LAI, PEI YU.
With regards to TensorFlow model, I just recently converted VGG-16 based model. Please see my reply in this topic - https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit/topic/848743
If you face "AssertionError: Bad argument number", you might need to use
pip3 install gast==0.2.2
On Keras model, yes, it has been already reported to developers team.
![](/skins/images/B7BA37F9CE4B52FF7CAE1FB0388839C8/responsive_peak/images/icon_anonymous_message.png)
- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page