81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
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# Tencent is pleased to support the open source community by making ncnn available.
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#
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# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# https://opensource.org/licenses/BSD-3-Clause
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#
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# Unless required by applicable law or agreed to in writing, software distributed
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# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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# CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations under the License.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Model(nn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.pool_0 = nn.MaxPool1d(kernel_size=3)
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self.pool_1 = nn.MaxPool1d(kernel_size=4, stride=2, padding=2, dilation=1)
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self.pool_2 = nn.MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, return_indices=False, ceil_mode=False)
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self.pool_3 = nn.MaxPool1d(kernel_size=5, stride=2, padding=2, dilation=1, return_indices=False, ceil_mode=True)
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self.pool_4 = nn.MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=2, return_indices=False, ceil_mode=False)
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self.pool_5 = nn.MaxPool1d(kernel_size=2, stride=1, padding=0, dilation=1, return_indices=False, ceil_mode=True)
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self.pool_6 = nn.MaxPool1d(kernel_size=5, stride=1, padding=2, dilation=1, return_indices=True, ceil_mode=False)
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def forward(self, x, y):
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x = self.pool_0(x)
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x = self.pool_1(x)
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x = self.pool_2(x)
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x = self.pool_3(x)
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x = self.pool_4(x)
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x = self.pool_5(x)
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x, indices = self.pool_6(x)
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y = self.pool_0(y)
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y = self.pool_1(y)
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y = self.pool_2(y)
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y = self.pool_3(y)
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y = self.pool_4(y)
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y = self.pool_5(y)
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return x, indices, y
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def test():
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net = Model()
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net.eval()
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torch.manual_seed(0)
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x = torch.rand(1, 12, 64)
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y = torch.rand(12, 64)
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a = net(x, y)
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# export torchscript
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mod = torch.jit.trace(net, (x, y))
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mod.save("test_nn_MaxPool1d.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_nn_MaxPool1d.pt inputshape=[1,12,64],[12,64]")
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# pnnx inference
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import test_nn_MaxPool1d_pnnx
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b = test_nn_MaxPool1d_pnnx.test_inference()
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for a0, b0 in zip(a, b):
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if not torch.equal(a0, b0):
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return False
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return True
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if __name__ == "__main__":
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if test():
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exit(0)
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else:
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exit(1)
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