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microsoft / MMdnn

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
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Tensorflow to IR rep not working - `TensorflowEmitter has not supported operator [BiasAdd] with name [dense/BiasAdd].` #610

Open aruna-ram opened 5 years ago

aruna-ram commented 5 years ago

Platform (like ubuntu 16.04/win10): kubuntu 18.10

Python version: 3.6

Source framework with version (like Tensorflow 1.4.1 with GPU): tensorflow 1.12.0 with CPU

Destination framework with version (like CNTK 2.3 with GPU): ONNX with CPU

Pre-trained model path (webpath or webdisk path): Using own simple feed forward model [relu_MNIST_forissue.txt](https://github.com/Microsoft/MMdnn/files/2945398/relu_MNIST_forissue.

OUTLINE OF ISSUE: Conversion from tensorflow to IR rep is not working.

I have made a simple feed forward network will Relu activations to classify MNIST, with only weights no biases (see relu_MNIST_forissue.txt). I saved as ckpt, ckpt.meta files.

I had previously used mmdnn to convert from tensorflow ckpt using the following: mmconvert -sf tensorflow -in /home/aruna/projects_code/relu_MNIST.ckpt.meta -iw /home/aruna/projects_code/relu_MNIST.ckpt --dstNodeName dense_3/Relu -df onnx -om /home/aruna/projects_code/relu_MNIST.onnx

This worked absolutely fine, the terminal output for the conversion (that worked):

/projects_code/relu_MNIST_ir Parse file [/home/aruna/projects_code/relu_MNIST.ckpt.meta] with binary format successfully. Tensorflow model file [/home/aruna/projects_code/relu_MNIST.ckpt.meta] loaded successfully. Tensorflow checkpoint file [/home/aruna/projects_code/relu_MNIST.ckpt] loaded successfully. [17] variables loaded. Tensorflow has not supported operator [VarHandleOp] with name [dense/kernel]. Tensorflow has not supported operator [VarHandleOp] with name [dense/bias]. Tensorflow has not supported operator [VarHandleOp] with name [dense_1/kernel]. Tensorflow has not supported operator [VarHandleOp] with name [dense_1/bias]. Tensorflow has not supported operator [VarHandleOp] with name [dense_2/kernel]. Tensorflow has not supported operator [VarHandleOp] with name [dense_2/bias]. Tensorflow has not supported operator [VarHandleOp] with name [dense_3/kernel]. Tensorflow has not supported operator [VarHandleOp] with name [dense_3/bias]. Tensorflow has not supported operator [ReadVariableOp] with name [dense/MatMul/ReadVariableOp]. Tensorflow has not supported operator [ReadVariableOp] with name [dense/BiasAdd/ReadVariableOp]. Tensorflow has not supported operator [ReadVariableOp] with name [dense_1/MatMul/ReadVariableOp]. Tensorflow has not supported operator [ReadVariableOp] with name [dense_1/BiasAdd/ReadVariableOp]. Tensorflow has not supported operator [ReadVariableOp] with name [dense_2/MatMul/ReadVariableOp]. Tensorflow has not supported operator [ReadVariableOp] with name [dense_2/BiasAdd/ReadVariableOp]. Tensorflow has not supported operator [ReadVariableOp] with name [dense_3/MatMul/ReadVariableOp]. // Tensorflow has not supported operator [ReadVariableOp] with name [dense_3/BiasAdd/ReadVariableOp]. IR network structure is saved as [/home/aruna/projects_code/relu_MNIST_ir.json]. IR network structure is saved as [/home/aruna/projects_code/relu_MNIST_ir.pb]. IR weights are saved as [/home/aruna/projects_code/relu_MNIST_ir.npy].

However trying the above again, with a different version of the checkpoint files doesn't extract the structure of the network correctly - it leaves out connections between layers. The only thing I think has changed between the versions is that the node names have capitals - e.g. dense/Relu in this graph rather than dense/relu for the above graph.

This is the output for the incorrect IR conversion:

Parse file [/home/aruna/projects_code/relu_MNIST_V4.ckpt.meta] with binary format successfully. Tensorflow model file [/home/aruna/projects_code/relu_MNIST_V4.ckpt.meta] loaded successfully. Tensorflow checkpoint file [/home/aruna/projects_code/relu_MNIST.ckpt] loaded successfully. [17] variables loaded. 2019-03-08 10:01:09.742964: I tensorflow/tools/graph_transforms/transform_graph.cc:317] Applying fold_constants 2019-03-08 10:01:09.776952: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-03-08 10:01:09.781310: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense/kernel]. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense/bias]. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense_1/kernel]. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense_1/bias]. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense_2/kernel]. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense_2/bias]. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense_3/kernel]. TensorflowEmitter has not supported operator [VarHandleOp] with name [dense_3/bias]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense/MatMul/ReadVariableOp]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense/BiasAdd/ReadVariableOp]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense_1/MatMul/ReadVariableOp]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense_1/BiasAdd/ReadVariableOp]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense_2/MatMul/ReadVariableOp]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense_2/BiasAdd/ReadVariableOp]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense_3/MatMul/ReadVariableOp]. TensorflowEmitter has not supported operator [ReadVariableOp] with name [dense_3/BiasAdd/ReadVariableOp]. TensorflowEmitter has not supported operator [BiasAdd] with name [dense/BiasAdd]. TensorflowEmitter has not supported operator [BiasAdd] with name [dense_1/BiasAdd]. TensorflowEmitter has not supported operator [BiasAdd] with name [dense_2/BiasAdd]. TensorflowEmitter has not supported operator [BiasAdd] with name [dense_3/BiasAdd]. IR network structure is saved as [/home/aruna/projects_code/relu_MNIST_ir_V4.json]. IR network structure is saved as [/home/aruna/projects_code/relu_MNIST_ir_V4.pb]. IR weights are saved as [/home/aruna/projects_code/relu_MNIST_ir_V4.npy].

Clearly the issue is due to: TensorflowEmitter has not supported operator [BiasAdd] with name [dense/BiasAdd].

But i cannot understand why the conversion worked one time and is now not working for a very simple operation (BiasAdd)?

The IR representations (the correct and incorrect) (which can be visualised using the mmdnn tool) can be compared - these are the files:

  1. CORRECT IR REP - relu_MNIST_ir.tar.gz

  2. INCORRECT IR REP - relu_MNIST_ir_V4.tar.gz

Thank you for your time! If you require further information do ask.

lswgh commented 4 years ago

Hi,aruna-ram. did you know how to see the IR representations (the correct and incorrect) ?can you give me some tricks of it？ Thanks.