ncnn/tools/pnnx/python/tests/test_convert.py

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# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
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# https://opensource.org/licenses/BSD-3-Clause
#
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import pytest
import pnnx

import torch
import torch.nn as nn
import torch.nn.functional as F
from packaging import version

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, x, y, z, w):
        x = F.relu(x)
        y = F.relu(y)
        z = F.relu(z)
        w = F.relu(w)
        return x, y, z, w

def test_convert():
    net = Model()
    net.eval()

    torch.manual_seed(0)
    x = torch.rand(1, 16)
    y = torch.rand(12, 2, 16)
    z = torch.rand(1, 3, 12, 16)
    w = torch.rand(1, 5, 7, 9, 11)

    a0, a1, a2, a3 = net(x, y, z, w)

    # export torchscript
    mod = torch.jit.trace(net, (x, y, z, w))
    mod.save("test_F_relu_convert.pt")

    net2 = pnnx.convert("test_F_relu_convert.pt", (x, y, z, w))

    b0, b1, b2, b3 = net2(x, y, z, w)

    assert torch.equal(a0, b0) and torch.equal(a1, b1) and torch.equal(a2, b2) and torch.equal(a3, b3)