nnfp/test.py

from towhee import ops

import warnings

import torch
import numpy
import onnx
import onnxruntime

device = 'cuda' if torch.cuda.is_available() else 'cpu'

def to_numpy(tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()


# decode = ops.audio_decode.ffmpeg()
# audio = [x[0] for x in decode('path/to/audio.wav')]
audio = torch.rand(10, 256, 32).to(device)

op = ops.audio_embedding.nnfp()
out0 = op.get_op().model(audio)
# print(out0)

# Test Pytorch
op.get_op().save_model(format='pytorch')
op = ops.audio_embedding.nnfp(model_path='./saved/pytorch/nnfp.pt')
out1 = op.get_op().model(audio)
assert ((out0 == out1).all())

# Test Torchscript
op.get_op().save_model(format='torchscript')
op = ops.audio_embedding.nnfp(model_path='./saved/torchscript/nnfp.pt')
out2 = op.get_op().model(audio)
assert ((out0 == out2).all())

# Test ONNX
op.get_op().save_model(format='onnx')
op = ops.audio_embedding.nnfp()
onnx_model = onnx.load('./saved/onnx/nnfp.onnx')
onnx.checker.check_model(onnx_model)

ort_session = onnxruntime.InferenceSession(
'./saved/onnx/nnfp.onnx',
providers=['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'])

ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(audio)}
ort_outs = ort_session.run(None, ort_inputs)
out3 = ort_outs[0]
# print(out3)
assert (numpy.allclose(to_numpy(out0), out3, rtol=1e-03, atol=1e-05))