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- # Tencent is pleased to support the open source community by making ncnn available.
- #
- # Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
- #
- # 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
- #
- # https://opensource.org/licenses/BSD-3-Clause
- #
- # Unless required by applicable law or agreed to in writing, software distributed
- # under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
- # CONDITIONS OF ANY KIND, either express or implied. See the License for the
- # specific language governing permissions and limitations under the License.
-
- 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):
- x = F.relu(x)
- return x
-
- def test_export():
- net = Model()
- net.eval()
-
- torch.manual_seed(0)
- x = torch.rand(1, 16)
- x2 = torch.rand(1, 128)
-
- a0 = net(x)
- a1 = net(x2)
-
- net_pnnx = pnnx.export(net, "test_F_relu_dexport", x, x2)
-
- b0 = net_pnnx(x)
- b1 = net_pnnx(x2)
-
- assert torch.equal(a0, b0) and torch.equal(a1, b1)
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