File size: 3,063 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | # Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software distributed
# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.
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.max_pool2d(x, kernel_size=3)
x = F.max_pool2d(x, kernel_size=4, stride=2, padding=2, dilation=1)
x = F.max_pool2d(x, kernel_size=(1,3), stride=1, padding=(0,1), dilation=1, return_indices=False, ceil_mode=False)
x = F.max_pool2d(x, kernel_size=(4,5), stride=(1,2), padding=(1,2), dilation=1, return_indices=False, ceil_mode=True)
x = F.max_pool2d(x, kernel_size=(2,3), stride=1, padding=1, dilation=(1,2), return_indices=False, ceil_mode=False)
x = F.max_pool2d(x, kernel_size=2, stride=1, padding=0, dilation=1, return_indices=False, ceil_mode=True)
x, indices1 = F.max_pool2d(x, kernel_size=2, padding=1, dilation=1, return_indices=True, ceil_mode=False)
x, indices2 = F.max_pool2d(x, kernel_size=(5,4), stride=1, padding=2, dilation=1, return_indices=True, ceil_mode=False)
y = F.max_pool2d(y, kernel_size=3)
y = F.max_pool2d(y, kernel_size=4, stride=2, padding=2, dilation=1)
y = F.max_pool2d(y, kernel_size=(1,3), stride=1, padding=(0,1), dilation=1, return_indices=False, ceil_mode=False)
y = F.max_pool2d(y, kernel_size=(4,5), stride=(1,2), padding=(1,2), dilation=1, return_indices=False, ceil_mode=True)
y = F.max_pool2d(y, kernel_size=(2,3), stride=1, padding=1, dilation=(1,2), return_indices=False, ceil_mode=False)
y = F.max_pool2d(y, kernel_size=2, stride=1, padding=0, dilation=1, return_indices=False, ceil_mode=True)
return x, indices1, indices2, y
def test():
net = Model()
net.eval()
torch.manual_seed(0)
x = torch.rand(1, 12, 128, 127)
y = torch.rand(12, 128, 127)
a = net(x, y)
# export torchscript
mod = torch.jit.trace(net, (x, y))
mod.save("test_F_max_pool2d.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_F_max_pool2d.pt inputshape=[1,12,128,127],[12,128,127]")
# pnnx inference
import test_F_max_pool2d_pnnx
b = test_F_max_pool2d_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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