CONVERT YOUR MODEL TO IR FORMAT
INSTALLATION
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OpenVino Toolkit
Download openvinotoolkit 2021.3 version : Link
Instruction of installation : Link
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Pytorch
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Torchvision
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Blobconverter
PYTORCH2ONNX
Create file pytorch2onnx.py and edit follow below :
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Import packages :
import sys import torch.onnx import torch import argparse from torch.autograd import Variable sys.path.append(".") import os
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Define image shape :
dummy_input = Variable(torch.randn(1, num_channels, h_image_input, w_image_input))
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Load model :
state_dict = torch.load(model_path,map_location='cpu' or 'gpu')
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Export model :
model.load_state_dict(state_dict) torch.onnx.export(model, # model being run dummy_input, # model input (or a tuple for multiple inputs) "model_name_output.onnx", # where to save the model (can be a file or file-like object) #export_params=True, # store the trained parameter weights inside the model file opset_version=12, # the ONNX version to export the model to #do_constant_folding=True, # whether to execute constant folding for optimization #input_names = ['data'], # the model's input names #output_names = ['output'], # the model's output names #dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes # 'output' : {0 : 'batch_size'}}) )
Detail for Instruction : Link
ONNX2IR
Complete installation openvinotoolkit, Cd ../openvino_2021/deployment_tools/model_optimizer and run :
python mo.py --input_model <path your Model.onnx> --input_shape [1, num_channels, h_image_input, w_image_input] --output_dir <path folder for your Model.bin and Model.xml>
IR2BLOB
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Using online converter : Link
Choose openvino version (default 2021.3 version), next step choose OpenVino Model then upload Model.bin and Model.xml
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Or using package blobconverter : Link