fdd136466f
* adding tensorrt implementation of superpoint network. * inference output result & todo list added to README
20 lines
505 B
Python
20 lines
505 B
Python
import torch
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import struct
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from model import SuperPointNet
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model_name = "superpoint_v1"
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net = SuperPointNet()
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net.load_state_dict(torch.load("superpoint_v1.pth"))
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net = net.cuda()
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net.eval()
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f = open(model_name + ".wts", "w")
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f.write("{}\n".format(len(net.state_dict().keys())))
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for k, v in net.state_dict().items():
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vr = v.reshape(-1).cpu().numpy()
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f.write("{} {}".format(k, len(vr)))
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for vv in vr:
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f.write(" ")
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f.write(struct.pack(">f", float(vv)).hex())
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f.write("\n") |