Description
Hello
I need to use this lightweight model as a tfjs model for face detection task.
So I tried to convert the RFB version of model (from 'tf/export_models/RFB') to a tfjs model using the following command:
tensorflowjs_converter \ --input_format=tf_saved_model \ --output_format=tfjs_graph_model \ --signature_name=serving_default \ --saved_model_tags=serve \ /saved_model \ /tfjs_model
the model is converted successfully with a couple of warnings:
2023-05-20 14:19:52.560333: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. WARNING:root:TensorFlow Decision Forests 1.2.0 is compatible with the following TensorFlow Versions: ['2.11.0']. However, TensorFlow 2.11.1 was detected. This can cause issues with the TF API and symbols in the custom C++ ops. See the TF and TF-DF compatibility table at https://github.com/tensorflow/decision-forests/blob/main/documentation/known_issues.md#compatibility-table. ... WARNING:tensorflow:Didn't find expected Conv2D or DepthwiseConv2dNative input to 'StatefulPartitionedCall/functional_1/basenet.7.branch0.2_bn/FusedBatchNormV3' ...
and when trying to use the tfjs model.predict() after loading it as a graph model (model = await tf.loadGraphModel('./model.json')), following error is raised:
Error: This execution contains the node 'StatefulPartitionedCall/functional_1/tf_op_layer_NonMaxSuppressionV3/NonMaxSuppressionV3', which has the dynamic op 'NonMaxSuppressionV3'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [StatefulPartitionedCall/functional_1/tf_op_layer_GatherV2_1/GatherV2_1]
Is there a way to fix this so that i can use the tfjs model?
Thanks