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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__()
-
- self.pool_0 = nn.MaxPool2d(kernel_size=3)
- self.pool_1 = nn.MaxPool2d(kernel_size=4, stride=2, padding=2, dilation=1)
- self.pool_2 = nn.MaxPool2d(kernel_size=(1,3), stride=1, padding=(0,1), dilation=1, return_indices=False, ceil_mode=False)
- self.pool_3 = nn.MaxPool2d(kernel_size=(4,5), stride=(1,2), padding=(1,2), dilation=1, return_indices=False, ceil_mode=True)
- self.pool_4 = nn.MaxPool2d(kernel_size=(2,3), stride=1, padding=1, dilation=(1,2), return_indices=False, ceil_mode=False)
- self.pool_5 = nn.MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, return_indices=True, ceil_mode=True)
- self.pool_6 = nn.MaxPool2d(kernel_size=(5,4), stride=1, padding=2, dilation=1, return_indices=True, ceil_mode=False)
-
- def forward(self, x, y):
- x = self.pool_0(x)
- x = self.pool_1(x)
- x = self.pool_2(x)
- x = self.pool_3(x)
- x = self.pool_4(x)
- x, tx = self.pool_5(x)
- x, indices = self.pool_6(x)
-
- y = self.pool_0(y)
- y = self.pool_1(y)
- y = self.pool_2(y)
- y = self.pool_3(y)
- y = self.pool_4(y)
- y, ty = self.pool_5(y)
- return x, indices, y
-
- def test():
- net = Model()
- net.eval()
-
- torch.manual_seed(0)
- x = torch.rand(1, 12, 64, 64)
- y = torch.rand(12, 64, 64)
-
- a = net(x, y)
-
- # export torchscript
- mod = torch.jit.trace(net, (x, y))
- mod.save("test_nn_MaxPool2d.pt")
-
- # torchscript to pnnx
- import os
- os.system("../src/pnnx test_nn_MaxPool2d.pt inputshape=[1,12,64,64],[12,64,64]")
-
- # pnnx inference
- import test_nn_MaxPool2d_pnnx
- b = test_nn_MaxPool2d_pnnx.test_inference()
-
- for a0, b0 in zip(a, b):
- if not torch.equal(a0, b0):
- return False
- return True
-
- if __name__ == "__main__":
- if test():
- exit(0)
- else:
- exit(1)
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