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convert_to_onnx.py
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convert_to_onnx.py
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import argparse
import torch
from models.with_mobilenet import PoseEstimationWithMobileNet
from modules.load_state import load_state
def convert_to_onnx(net, output_name):
input = torch.randn(1, 3, 256, 456)
input_names = ['data']
output_names = ['stage_0_output_1_heatmaps', 'stage_0_output_0_pafs',
'stage_1_output_1_heatmaps', 'stage_1_output_0_pafs']
torch.onnx.export(net, input, output_name, verbose=True, input_names=input_names, output_names=output_names)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint-path', type=str, required=True, help='path to the checkpoint')
parser.add_argument('--output-name', type=str, default='human-pose-estimation.onnx',
help='name of output model in ONNX format')
args = parser.parse_args()
net = PoseEstimationWithMobileNet()
checkpoint = torch.load(args.checkpoint_path,map_location=torch.device('cpu'))
load_state(net, checkpoint)
convert_to_onnx(net, args.output_name)