72 lines
2.9 KiB
Python
72 lines
2.9 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.deconv_0 = nn.ConvTranspose2d(in_channels=12, out_channels=16, kernel_size=3)
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self.deconv_1 = nn.ConvTranspose2d(in_channels=16, out_channels=20, kernel_size=(2,4), stride=(2,1), padding=2, output_padding=0)
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self.deconv_2 = nn.ConvTranspose2d(in_channels=20, out_channels=24, kernel_size=(1,3), stride=1, padding=(2,4), output_padding=(0,0), dilation=1, groups=1, bias=False)
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self.deconv_3 = nn.ConvTranspose2d(in_channels=24, out_channels=28, kernel_size=(5,4), stride=2, padding=0, output_padding=(0,1), dilation=1, groups=4, bias=True)
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self.deconv_4 = nn.ConvTranspose2d(in_channels=28, out_channels=32, kernel_size=3, stride=1, padding=1, output_padding=0, dilation=(1,2), groups=2, bias=False)
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self.deconv_5 = nn.ConvTranspose2d(in_channels=32, out_channels=32, kernel_size=2, stride=2, padding=3, output_padding=1, dilation=1, groups=32, bias=True)
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self.deconv_6 = nn.ConvTranspose2d(in_channels=32, out_channels=28, kernel_size=2, stride=1, padding=2, output_padding=0, dilation=1, groups=1, bias=False)
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self.deconv_7 = nn.ConvTranspose2d(in_channels=28, out_channels=24, kernel_size=3, stride=2, padding=(5,6), output_padding=(1,0), dilation=2, groups=1, bias=True)
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def forward(self, x):
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x = self.deconv_0(x)
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x = self.deconv_1(x)
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x = self.deconv_2(x)
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x = self.deconv_3(x)
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x = self.deconv_4(x)
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x = self.deconv_5(x)
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x = self.deconv_6(x)
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x = self.deconv_7(x)
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return x
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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, 10, 10)
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a = net(x)
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# export torchscript
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mod = torch.jit.trace(net, x)
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mod.save("test_nn_ConvTranspose2d.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_nn_ConvTranspose2d.pt inputshape=[1,12,10,10]")
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# pnnx inference
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import test_nn_ConvTranspose2d_pnnx
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b = test_nn_ConvTranspose2d_pnnx.test_inference()
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return torch.equal(a, b)
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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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