146 lines
5.7 KiB
Python
146 lines
5.7 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.up_1d_0_0 = nn.Upsample(size=16)
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self.up_1d_0_1 = nn.Upsample(scale_factor=2, mode='nearest')
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self.up_1d_0_2 = nn.Upsample(size=(20), mode='nearest')
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self.up_1d_0_3 = nn.Upsample(scale_factor=(4), mode='nearest')
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self.up_1d_1_0 = nn.Upsample(size=16, mode='linear')
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self.up_1d_1_1 = nn.Upsample(scale_factor=2, mode='linear')
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self.up_1d_1_2 = nn.Upsample(size=(24), mode='linear', align_corners=True)
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self.up_1d_1_3 = nn.Upsample(scale_factor=(3), mode='linear', align_corners=True)
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self.up_2d_0_0 = nn.Upsample(size=16)
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self.up_2d_0_1 = nn.Upsample(scale_factor=2, mode='nearest')
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self.up_2d_0_2 = nn.Upsample(size=(20,20), mode='nearest')
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self.up_2d_0_3 = nn.Upsample(scale_factor=(4,4), mode='nearest')
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self.up_2d_0_4 = nn.Upsample(size=(16,24), mode='nearest')
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self.up_2d_0_5 = nn.Upsample(scale_factor=(2,3), mode='nearest')
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self.up_2d_1_0 = nn.Upsample(size=16, mode='bilinear')
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self.up_2d_1_1 = nn.Upsample(scale_factor=2, mode='bilinear')
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self.up_2d_1_2 = nn.Upsample(size=(20,20), mode='bilinear', align_corners=False)
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self.up_2d_1_3 = nn.Upsample(scale_factor=(4,4), mode='bilinear', align_corners=False)
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self.up_2d_1_4 = nn.Upsample(size=(16,24), mode='bilinear', align_corners=True)
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self.up_2d_1_5 = nn.Upsample(scale_factor=(2,3), mode='bilinear', align_corners=True)
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self.up_2d_2_0 = nn.Upsample(size=16, mode='bicubic')
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self.up_2d_2_1 = nn.Upsample(scale_factor=2, mode='bicubic')
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self.up_2d_2_2 = nn.Upsample(size=(20,20), mode='bicubic', align_corners=False)
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self.up_2d_2_3 = nn.Upsample(scale_factor=(4,4), mode='bicubic', align_corners=False)
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self.up_2d_2_4 = nn.Upsample(size=(16,24), mode='bicubic', align_corners=True)
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self.up_2d_2_5 = nn.Upsample(scale_factor=(2,3), mode='bicubic', align_corners=True)
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self.up_3d_0_0 = nn.Upsample(size=16)
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self.up_3d_0_1 = nn.Upsample(scale_factor=2, mode='nearest')
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self.up_3d_0_2 = nn.Upsample(size=(20,20,20), mode='nearest')
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self.up_3d_0_3 = nn.Upsample(scale_factor=(4,4,4), mode='nearest')
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self.up_3d_0_4 = nn.Upsample(size=(16,24,20), mode='nearest')
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self.up_3d_0_5 = nn.Upsample(scale_factor=(2,3,4), mode='nearest')
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self.up_3d_1_0 = nn.Upsample(size=16, mode='trilinear')
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self.up_3d_1_1 = nn.Upsample(scale_factor=2, mode='trilinear')
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self.up_3d_1_2 = nn.Upsample(size=(20,20,20), mode='trilinear', align_corners=False)
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self.up_3d_1_3 = nn.Upsample(scale_factor=(4,4,4), mode='trilinear', align_corners=False)
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self.up_3d_1_4 = nn.Upsample(size=(16,24,20), mode='trilinear', align_corners=True)
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self.up_3d_1_5 = nn.Upsample(scale_factor=(2,3,4), mode='trilinear', align_corners=True)
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self.up_w = nn.Upsample(scale_factor=(1.499,1.499), mode='nearest')
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def forward(self, x, y, z, w):
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x = self.up_1d_0_0(x)
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x = self.up_1d_0_1(x)
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x = self.up_1d_0_2(x)
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x = self.up_1d_0_3(x)
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x = self.up_1d_1_0(x)
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x = self.up_1d_1_1(x)
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x = self.up_1d_1_2(x)
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x = self.up_1d_1_3(x)
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y = self.up_2d_0_0(y)
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y = self.up_2d_0_1(y)
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y = self.up_2d_0_2(y)
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y = self.up_2d_0_3(y)
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y = self.up_2d_0_4(y)
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y = self.up_2d_0_5(y)
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y = self.up_2d_1_0(y)
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y = self.up_2d_1_1(y)
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y = self.up_2d_1_2(y)
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y = self.up_2d_1_3(y)
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y = self.up_2d_1_4(y)
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y = self.up_2d_1_5(y)
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y = self.up_2d_2_0(y)
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y = self.up_2d_2_1(y)
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y = self.up_2d_2_2(y)
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y = self.up_2d_2_3(y)
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y = self.up_2d_2_4(y)
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y = self.up_2d_2_5(y)
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z = self.up_3d_0_0(z)
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z = self.up_3d_0_1(z)
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z = self.up_3d_0_2(z)
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z = self.up_3d_0_3(z)
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z = self.up_3d_0_4(z)
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z = self.up_3d_0_5(z)
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z = self.up_3d_1_0(z)
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z = self.up_3d_1_1(z)
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z = self.up_3d_1_2(z)
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z = self.up_3d_1_3(z)
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z = self.up_3d_1_4(z)
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z = self.up_3d_1_5(z)
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w = self.up_w(w)
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return x, y, z, w
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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, 3, 32)
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y = torch.rand(1, 3, 32, 32)
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z = torch.rand(1, 3, 32, 32, 32)
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w = torch.rand(1, 8, 12, 12)
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a = net(x, y, z, w)
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# export torchscript
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mod = torch.jit.trace(net, (x, y, z, w))
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mod.save("test_nn_Upsample.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_nn_Upsample.pt inputshape=[1,3,32],[1,3,32,32],[1,3,32,32,32],[1,8,12,12]")
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# pnnx inference
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import test_nn_Upsample_pnnx
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b = test_nn_Upsample_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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