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ncnn/tools/pnnx/python/examples/export.py
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Python

# Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2020 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 pnnx
import torch
import torch.nn as nn
import torch.nn.functional as F
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
if __name__ == "__main__":
net = Model()
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)
pnnx.export(net, "test_F_relu", (x, y, z, w))