File size: 4,794 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 | # 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, xg1, xg2, y, yg1, yg2):
# norm to -1 ~ 1
xg1 = xg1 * 2 - 1
xg2 = xg2 * 2 - 1
yg1 = yg1 * 2 - 1
yg2 = yg2 * 2 - 1
x = F.grid_sample(x, xg1, mode='bilinear', padding_mode='zeros', align_corners=False)
x = F.grid_sample(x, xg2, mode='bilinear', padding_mode='border', align_corners=False)
x = F.grid_sample(x, xg1, mode='bilinear', padding_mode='reflection', align_corners=False)
x = F.grid_sample(x, xg2, mode='nearest', padding_mode='zeros', align_corners=False)
x = F.grid_sample(x, xg1, mode='nearest', padding_mode='border', align_corners=False)
x = F.grid_sample(x, xg2, mode='nearest', padding_mode='reflection', align_corners=False)
x = F.grid_sample(x, xg1, mode='bicubic', padding_mode='zeros', align_corners=False)
x = F.grid_sample(x, xg2, mode='bicubic', padding_mode='border', align_corners=False)
x = F.grid_sample(x, xg1, mode='bicubic', padding_mode='reflection', align_corners=False)
x = F.grid_sample(x, xg2, mode='bilinear', padding_mode='zeros', align_corners=True)
x = F.grid_sample(x, xg1, mode='bilinear', padding_mode='border', align_corners=True)
x = F.grid_sample(x, xg2, mode='bilinear', padding_mode='reflection', align_corners=True)
x = F.grid_sample(x, xg1, mode='nearest', padding_mode='zeros', align_corners=True)
x = F.grid_sample(x, xg2, mode='nearest', padding_mode='border', align_corners=True)
x = F.grid_sample(x, xg1, mode='nearest', padding_mode='reflection', align_corners=True)
x = F.grid_sample(x, xg2, mode='bicubic', padding_mode='zeros', align_corners=True)
x = F.grid_sample(x, xg1, mode='bicubic', padding_mode='border', align_corners=True)
x = F.grid_sample(x, xg2, mode='bicubic', padding_mode='reflection', align_corners=True)
y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='zeros', align_corners=False)
y = F.grid_sample(y, yg2, mode='bilinear', padding_mode='border', align_corners=False)
y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='reflection', align_corners=False)
y = F.grid_sample(y, yg2, mode='nearest', padding_mode='zeros', align_corners=False)
y = F.grid_sample(y, yg1, mode='nearest', padding_mode='border', align_corners=False)
y = F.grid_sample(y, yg2, mode='nearest', padding_mode='reflection', align_corners=False)
y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='zeros', align_corners=True)
y = F.grid_sample(y, yg2, mode='bilinear', padding_mode='border', align_corners=True)
y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='reflection', align_corners=True)
y = F.grid_sample(y, yg2, mode='nearest', padding_mode='zeros', align_corners=True)
y = F.grid_sample(y, yg1, mode='nearest', padding_mode='border', align_corners=True)
y = F.grid_sample(y, yg2, mode='nearest', padding_mode='reflection', align_corners=True)
return x, y
def test():
net = Model()
net.eval()
torch.manual_seed(0)
x = torch.rand(1, 3, 12, 16)
xg1 = torch.rand(1, 21, 27, 2)
xg2 = torch.rand(1, 12, 16, 2)
y = torch.rand(1, 5, 10, 12, 16)
yg1 = torch.rand(1, 10, 21, 27, 3)
yg2 = torch.rand(1, 10, 12, 16, 3)
a0, a1 = net(x, xg1, xg2, y, yg1, yg2)
# export torchscript
mod = torch.jit.trace(net, (x, xg1, xg2, y, yg1, yg2))
mod.save("test_F_grid_sample.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_F_grid_sample.pt inputshape=[1,3,12,16],[1,21,27,2],[1,12,16,2],[1,5,10,12,16],[1,10,21,27,3],[1,10,12,16,3]")
# pnnx inference
import test_F_grid_sample_pnnx
b0, b1 = test_F_grid_sample_pnnx.test_inference()
return torch.equal(a0, b0) and torch.equal(a1, b1)
if __name__ == "__main__":
if test():
exit(0)
else:
exit(1)
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