File size: 5,603 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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # 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, z, w):
x = F.interpolate(x, size=16)
x = F.interpolate(x, scale_factor=2, mode='nearest')
x = F.interpolate(x, size=(20), mode='nearest')
x = F.interpolate(x, scale_factor=(4), mode='nearest')
x = F.interpolate(x, size=16, mode='linear')
x = F.interpolate(x, scale_factor=2, mode='linear')
x = F.interpolate(x, size=(24), mode='linear', align_corners=True)
x = F.interpolate(x, scale_factor=(3), mode='linear', align_corners=True)
x = F.interpolate(x, scale_factor=1.5, mode='nearest', recompute_scale_factor=True)
x = F.interpolate(x, scale_factor=1.2, mode='linear', align_corners=False, recompute_scale_factor=True)
x = F.interpolate(x, scale_factor=0.8, mode='linear', align_corners=True, recompute_scale_factor=True)
y = F.interpolate(y, size=16)
y = F.interpolate(y, scale_factor=2, mode='nearest')
y = F.interpolate(y, size=(20,20), mode='nearest')
y = F.interpolate(y, scale_factor=(4,4), mode='nearest')
y = F.interpolate(y, size=(16,24), mode='nearest')
y = F.interpolate(y, scale_factor=(2,3), mode='nearest')
y = F.interpolate(y, size=16, mode='bilinear')
y = F.interpolate(y, scale_factor=2, mode='bilinear')
y = F.interpolate(y, size=(20,20), mode='bilinear', align_corners=False)
y = F.interpolate(y, scale_factor=(4,4), mode='bilinear', align_corners=False)
y = F.interpolate(y, size=(16,24), mode='bilinear', align_corners=True)
y = F.interpolate(y, scale_factor=(2,3), mode='bilinear', align_corners=True)
y = F.interpolate(y, size=16, mode='bicubic')
y = F.interpolate(y, scale_factor=2, mode='bicubic')
y = F.interpolate(y, size=(20,20), mode='bicubic', align_corners=False)
y = F.interpolate(y, scale_factor=(4,4), mode='bicubic', align_corners=False)
y = F.interpolate(y, size=(16,24), mode='bicubic', align_corners=True)
y = F.interpolate(y, scale_factor=(2,3), mode='bicubic', align_corners=True)
y = F.interpolate(y, scale_factor=(1.6,2), mode='nearest', recompute_scale_factor=True)
y = F.interpolate(y, scale_factor=(2,1.2), mode='bilinear', align_corners=False, recompute_scale_factor=True)
y = F.interpolate(y, scale_factor=(0.5,0.4), mode='bilinear', align_corners=True, recompute_scale_factor=True)
y = F.interpolate(y, scale_factor=(0.8,0.9), mode='bicubic', align_corners=False, recompute_scale_factor=True)
y = F.interpolate(y, scale_factor=(1.1,0.5), mode='bicubic', align_corners=True, recompute_scale_factor=True)
z = F.interpolate(z, size=16)
z = F.interpolate(z, scale_factor=2, mode='nearest')
z = F.interpolate(z, size=(20,20,20), mode='nearest')
z = F.interpolate(z, scale_factor=(4,4,4), mode='nearest')
z = F.interpolate(z, size=(16,24,20), mode='nearest')
z = F.interpolate(z, scale_factor=(2,3,4), mode='nearest')
z = F.interpolate(z, size=16, mode='trilinear')
z = F.interpolate(z, scale_factor=2, mode='trilinear')
z = F.interpolate(z, size=(20,20,20), mode='trilinear', align_corners=False)
z = F.interpolate(z, scale_factor=(4,4,4), mode='trilinear', align_corners=False)
z = F.interpolate(z, size=(16,24,20), mode='trilinear', align_corners=True)
z = F.interpolate(z, scale_factor=(2,3,4), mode='trilinear', align_corners=True)
z = F.interpolate(z, scale_factor=(1.5,2.5,2), mode='nearest', recompute_scale_factor=True)
z = F.interpolate(z, scale_factor=(0.7,0.5,1), mode='trilinear', align_corners=False, recompute_scale_factor=True)
z = F.interpolate(z, scale_factor=(0.9,0.8,1.2), mode='trilinear', align_corners=True, recompute_scale_factor=True)
w = F.interpolate(w, scale_factor=(2.976744,2.976744), mode='nearest', recompute_scale_factor=False)
return x, y, z, w
def test():
net = Model()
net.eval()
torch.manual_seed(0)
x = torch.rand(1, 3, 32)
y = torch.rand(1, 3, 32, 32)
z = torch.rand(1, 3, 32, 32, 32)
w = torch.rand(1, 8, 86, 86)
a = net(x, y, z, w)
# export torchscript
mod = torch.jit.trace(net, (x, y, z, w))
mod.save("test_F_interpolate.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_F_interpolate.pt inputshape=[1,3,32],[1,3,32,32],[1,3,32,32,32],[1,8,86,86]")
# pnnx inference
import test_F_interpolate_pnnx
b = test_F_interpolate_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)
|