openvino2tensorflow 0.3.3

This script converts the OpenVINO IR model to Tensorflow's saved_model, tflite, h5 and pb. in (NCHW) format

openvino2tensorflow

This script converts the OpenVINO IR model to Tensorflow's saved_model, tflite, h5 and pb.

Work in progress now.

I'm continuing to add more layers of support and bug fixes on a daily basis. If you have a model that you are having trouble converting, please share the .bin and .xml with the issue. I will try to convert as much as possible.

1. Environment

• TensorFlow v2.3.1
• OpenVINO 2021.1.110
• Python 3.6+

2. Use case

• PyTorch (NCHW) -> ONNX (NCHW) -> OpenVINO (NCHW) ->

  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TFLite (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TFJS (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TF-TRT (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> CoreML (NHWC)

• Caffe (NCHW) -> OpenVINO (NCHW) ->

  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TFLite (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TFJS (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TF-TRT (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> CoreML (NHWC)

• MXNet (NCHW) -> OpenVINO (NCHW) ->

  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TFLite (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TFJS (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TF-TRT (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> CoreML (NHWC)

3. Supported Layers

• Currently, only 4D tensors are supported as input tensors.
• Currently, there are problems with the Reshape operation of 5D Tensor.

No.	OpenVINO Layer	TF Layer	Remarks
1	Parameter	Input	Input (4D tensor only)
2	Const	Constant, Bias
3	Convolution	Conv2D
4	Add	Add
5	ReLU	ReLU
6	PReLU	PReLU	Maximum(0.0,x)+alpha*Minimum(0.0,x)
7	MaxPool	MaxPool2D
8	AvgPool	AveragePooling2D
9	GroupConvolution	DepthwiseConv2D, Conv2D/Split/Concat
10	ConvolutionBackpropData	Conv2DTranspose
11	Concat	Concat
12	Multiply	Multiply
13	Tan	Tan
14	Tanh	Tanh
15	Elu	Elu
16	Sigmoid	Sigmoid
17	HardSigmoid	hard_sigmoid
18	SoftPlus	SoftPlus
19	Swish	Swish	You can replace swish and hard-swish with each other by using the "--replace_swish_and_hardswish" option
20	Interpolate	ResizeNearestNeighbor, ResizeBilinear
21	ShapeOf	Shape
22	Convert	Cast
23	StridedSlice	Strided_Slice
24	Pad	Pad, MirrorPad
25	Clamp	ReLU6, Clip
26	TopK	ArgMax, top_k
27	Transpose	Transpose
28	Squeeze	Squeeze
29	Unsqueeze	Identity, expand_dims	WIP
30	ReduceMean	reduce_mean
31	ReduceMax	reduce_max
32	ReduceMin	reduce_min
33	ReduceSum	reduce_sum
34	ReduceProd	reduce_prod
35	Subtract	Subtract
36	MatMul	MatMul
37	Reshape	Reshape
38	Range	Range	WIP
39	Exp	Exp
40	Abs	Abs
41	SoftMax	SoftMax
42	Negative	Negative
43	Maximum	Maximum	No broadcast
44	Minimum	Minimum	No broadcast
45	Acos	Acos
46	Acosh	Acosh
47	Asin	Asin
48	Asinh	Asinh
49	Atan	Atan
50	Atanh	Atanh
51	Ceiling	Ceil
52	Cos	Cos
53	Cosh	Cosh
54	Sin	Sin
55	Sinh	Sinh
56	Gather	Gather
57	Divide	Divide, FloorDiv
58	Erf	Erf
59	Floor	Floor
60	FloorMod	FloorMod
61	HSwish	HardSwish	x*ReLU6(x+3)*0.16666667, You can replace swish and hard-swish with each other by using the "--replace_swish_and_hardswish" option
62	Log	Log
63	Power	Pow	No broadcast
64	Mish	Mish	x*Tanh(softplus(x))
65	Selu	Selu
66	Equal	equal
67	NotEqual	not_equal
68	Greater	greater
69	GreaterEqual	greater_equal
70	Less	less
71	LessEqual	less_equal
72	Select	Select	No broadcast
73	LogicalAnd	logical_and
74	LogicalNot	logical_not
75	LogicalOr	logical_or
76	LogicalXor	logical_xor
77	Broadcast	broadcast_to, ones, Multiply	numpy / bidirectional mode, WIP
78	Split	Split
79	VariadicSplit	Split
80	Result	Identity	Output

4. Setup

To install using the Python Package Index (PyPI), use the following command.

$ pip3 install openvino2tensorflow --upgrade

To install with the latest source code of the main branch, use the following command.

$ pip3 install git+https://github.com/PINTO0309/openvino2tensorflow --upgrade

5. Usage

usage: openvino2tensorflow [-h] --model_path MODEL_PATH
                           [--model_output_path MODEL_OUTPUT_PATH]
                           [--output_saved_model OUTPUT_SAVED_MODEL]
                           [--output_h5 OUTPUT_H5]
                           [--output_weight_and_json OUTPUT_WEIGHT_AND_JSON]
                           [--output_pb OUTPUT_PB]
                           [--output_no_quant_float32_tflite OUTPUT_NO_QUANT_FLOAT32_TFLITE]
                           [--output_weight_quant_tflite OUTPUT_WEIGHT_QUANT_TFLITE]
                           [--output_float16_quant_tflite OUTPUT_FLOAT16_QUANT_TFLITE]
                           [--replace_swish_and_hardswish REPLACE_SWISH_AND_HARDSWISH]
                           [--debug]
                           [--debug_layer_number DEBUG_LAYER_NUMBER]

optional arguments:
  -h, --help            show this help message and exit
  --model_path MODEL_PATH
                        input IR model path (.xml)
  --model_output_path MODEL_OUTPUT_PATH
                        The output folder path of the converted model file
  --output_saved_model OUTPUT_SAVED_MODEL
                        saved_model output switch
  --output_h5 OUTPUT_H5
                        .h5 output switch
  --output_weight_and_json OUTPUT_WEIGHT_AND_JSON
                        weight of h5 and json output switch
  --output_pb OUTPUT_PB
                        .pb output switch
  --output_no_quant_float32_tflite OUTPUT_NO_QUANT_FLOAT32_TFLITE
                        float32 tflite output switch
  --output_weight_quant_tflite OUTPUT_WEIGHT_QUANT_TFLITE
                        weight quant tflite output switch
  --output_float16_quant_tflite OUTPUT_FLOAT16_QUANT_TFLITE
                        float16 quant tflite output switch
  --replace_swish_and_hardswish REPLACE_SWISH_AND_HARDSWISH
                        Replace swish and hard-swish with each other.
  --debug               debug mode switch
  --debug_layer_number DEBUG_LAYER_NUMBER
                        The last layer number to output when debugging. Used
                        only when --debug=True.

usage: pb_to_saved_model [-h] --pb_file_path PB_FILE_PATH
                         --inputs INPUTS
                         --outputs OUTPUTS
                         [--model_output_path MODEL_OUTPUT_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --pb_file_path PB_FILE_PATH
                        Input .pb file path (.pb)
  --inputs INPUTS       (e.g.1) input:0,input:1,input:2 / (e.g.2)
                        images:0,input:0,param:0
  --outputs OUTPUTS     (e.g.1) output:0,output:1,output:2 / (e.g.2)
                        Identity:0,Identity:1,output:0
  --model_output_path MODEL_OUTPUT_PATH
                        The output folder path of the converted model file

usage: pb_to_tflite.py [-h] --pb_file_path PB_FILE_PATH --inputs INPUTS
                       --outputs OUTPUTS
                       [--model_output_path MODEL_OUTPUT_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --pb_file_path PB_FILE_PATH
                        Input .pb file path (.pb)
  --inputs INPUTS       (e.g.1) input,input_1,input_2 / (e.g.2)
                        images,input,param
  --outputs OUTPUTS     (e.g.1) output,output_1,output_2 / (e.g.2)
                        Identity,Identity_1,output
  --model_output_path MODEL_OUTPUT_PATH
                        The output folder path of the converted model file

usage: ir_weight_extractor.py [-h] -m MODEL -o OUTPUT_PATH

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        input IR model path
  -o OUTPUT_PATH, --output_path OUTPUT_PATH
                        weights output folder path

6. Execution sample

6-1. Conversion of OpenVINO IR to Tensorflow models

OutOfMemory may occur when converting to saved_model or h5 when the file size of the original model is large, please try the conversion to a pb file alone.

$ openvino2tensorflow \
  --model_path=openvino/448x448/FP32/Resnet34_3inputs_448x448_20200609.xml \
  --output_saved_model True \
  --output_pb True \
  --output_weight_quant_tflite True \
  --output_float16_quant_tflite True \
  --output_no_quant_float32_tflite True

6-2. Convert Protocol Buffer (.pb) to saved_model

This tool is useful if you want to check the internal structure of pb files, tflite files, .h5 files, coreml files and IR (.xml) files. https://lutzroeder.github.io/netron/

$ pb_to_saved_model \
  --pb_file_path model_float32.pb \
  --inputs inputs:0 \
  --outputs Identity:0

6-3. Convert Protocol Buffer (.pb) to tflite

$ pb_to_tflite \
  --pb_file_path model_float32.pb \
  --inputs inputs \
  --outputs Identity,Identity_1,Identity_2

6-4. Converts saved_model to OpenVINO IR

$ python3 ${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/mo_tf.py \
  --saved_model_dir saved_model \
  --output_dir openvino/reverse

6-5. Checking the structure of saved_model

$ saved_model_cli show \
  --dir saved_model \
  --tag_set serve \
  --signature_def serving_default

7. Output sample

8. Model Structure

https://digital-standard.com/threedpose/models/Resnet34_3inputs_448x448_20200609.onnx

ONNX	OpenVINO	TFLite

9. My article

[English] Converting PyTorch, ONNX, Caffe, and OpenVINO (NCHW) models to Tensorflow / TensorflowLite (NHWC) in a snap