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- # Copyright 2021 Tencent
- # SPDX-License-Identifier: BSD-3-Clause
-
- 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):
- x = F.dropout2d(x, training=False)
- z = F.dropout2d(y, p=0.6, training=False)
- return x, y
-
- def test():
- net = Model()
- net.eval()
-
- torch.manual_seed(0)
- x = torch.rand(1, 12, 2, 16)
- y = torch.rand(1, 3, 12, 16)
-
- a0, a1 = net(x, y)
-
- # export torchscript
- mod = torch.jit.trace(net, (x, y))
- mod.save("test_F_dropout2d.pt")
-
- # torchscript to pnnx
- import os
- os.system("../src/pnnx test_F_dropout2d.pt inputshape=[1,12,2,16],[1,3,12,16]")
-
- # pnnx inference
- import test_F_dropout2d_pnnx
- b0, b1 = test_F_dropout2d_pnnx.test_inference()
-
- return torch.equal(a0, b0) and torch.equal(a1, b1)
-
- if __name__ == "__main__":
- if test():
- exit(0)
- else:
- exit(1)
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