91 lines
4.6 KiB
Python
91 lines
4.6 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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def forward(self, x, xg1, xg2, y, yg1, yg2):
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x = F.grid_sample(x, xg1, mode='bilinear', padding_mode='zeros', align_corners=False)
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x = F.grid_sample(x, xg2, mode='bilinear', padding_mode='border', align_corners=False)
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x = F.grid_sample(x, xg1, mode='bilinear', padding_mode='reflection', align_corners=False)
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x = F.grid_sample(x, xg2, mode='nearest', padding_mode='zeros', align_corners=False)
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x = F.grid_sample(x, xg1, mode='nearest', padding_mode='border', align_corners=False)
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x = F.grid_sample(x, xg2, mode='nearest', padding_mode='reflection', align_corners=False)
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x = F.grid_sample(x, xg1, mode='bicubic', padding_mode='zeros', align_corners=False)
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x = F.grid_sample(x, xg2, mode='bicubic', padding_mode='border', align_corners=False)
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x = F.grid_sample(x, xg1, mode='bicubic', padding_mode='reflection', align_corners=False)
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x = F.grid_sample(x, xg2, mode='bilinear', padding_mode='zeros', align_corners=True)
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x = F.grid_sample(x, xg1, mode='bilinear', padding_mode='border', align_corners=True)
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x = F.grid_sample(x, xg2, mode='bilinear', padding_mode='reflection', align_corners=True)
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x = F.grid_sample(x, xg1, mode='nearest', padding_mode='zeros', align_corners=True)
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x = F.grid_sample(x, xg2, mode='nearest', padding_mode='border', align_corners=True)
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x = F.grid_sample(x, xg1, mode='nearest', padding_mode='reflection', align_corners=True)
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x = F.grid_sample(x, xg2, mode='bicubic', padding_mode='zeros', align_corners=True)
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x = F.grid_sample(x, xg1, mode='bicubic', padding_mode='border', align_corners=True)
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x = F.grid_sample(x, xg2, mode='bicubic', padding_mode='reflection', align_corners=True)
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y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='zeros', align_corners=False)
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y = F.grid_sample(y, yg2, mode='bilinear', padding_mode='border', align_corners=False)
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y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='reflection', align_corners=False)
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y = F.grid_sample(y, yg2, mode='nearest', padding_mode='zeros', align_corners=False)
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y = F.grid_sample(y, yg1, mode='nearest', padding_mode='border', align_corners=False)
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y = F.grid_sample(y, yg2, mode='nearest', padding_mode='reflection', align_corners=False)
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y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='zeros', align_corners=True)
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y = F.grid_sample(y, yg2, mode='bilinear', padding_mode='border', align_corners=True)
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y = F.grid_sample(y, yg1, mode='bilinear', padding_mode='reflection', align_corners=True)
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y = F.grid_sample(y, yg2, mode='nearest', padding_mode='zeros', align_corners=True)
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y = F.grid_sample(y, yg1, mode='nearest', padding_mode='border', align_corners=True)
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y = F.grid_sample(y, yg2, mode='nearest', padding_mode='reflection', align_corners=True)
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return x, y
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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, 12, 16)
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xg1 = torch.rand(1, 21, 27, 2)
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xg2 = torch.rand(1, 12, 16, 2)
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y = torch.rand(1, 5, 10, 12, 16)
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yg1 = torch.rand(1, 10, 21, 27, 3)
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yg2 = torch.rand(1, 10, 12, 16, 3)
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a0, a1 = net(x, xg1, xg2, y, yg1, yg2)
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# export torchscript
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mod = torch.jit.trace(net, (x, xg1, xg2, y, yg1, yg2))
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mod.save("test_F_grid_sample.pt")
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# torchscript to pnnx
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import os
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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]")
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# pnnx inference
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import test_F_grid_sample_pnnx
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b0, b1 = test_F_grid_sample_pnnx.test_inference()
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return torch.equal(a0, b0) and torch.equal(a1, b1)
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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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