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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,48 @@ | ||
| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2022 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. | ||
|
|
||
| #include "pass_level1.h" | ||
|
|
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| #include "../utils.h" | ||
|
|
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| namespace pnnx { | ||
|
|
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| class Fold : public FuseModulePass | ||
| { | ||
| public: | ||
| const char* match_type_str() const | ||
| { | ||
| return "__torch__.torch.nn.modules.fold.Fold"; | ||
| } | ||
|
|
||
| const char* type_str() const | ||
| { | ||
| return "nn.Fold"; | ||
| } | ||
|
|
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| void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const | ||
| { | ||
| const torch::jit::Node* col2im = find_node_by_kind(graph, "aten::col2im"); | ||
|
|
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| op->params["output_size"] = col2im->namedInput("output_size"); | ||
| op->params["kernel_size"] = col2im->namedInput("kernel_size"); | ||
| op->params["stride"] = col2im->namedInput("stride"); | ||
| op->params["padding"] = col2im->namedInput("padding"); | ||
| op->params["dilation"] = col2im->namedInput("dilation"); | ||
| } | ||
| }; | ||
|
|
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| REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(Fold) | ||
|
|
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| } // namespace pnnx |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2022 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. | ||
|
|
||
| #include "pass_level1.h" | ||
|
|
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| #include "../utils.h" | ||
|
|
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| namespace pnnx { | ||
|
|
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| class Unfold : public FuseModulePass | ||
| { | ||
| public: | ||
| const char* match_type_str() const | ||
| { | ||
| return "__torch__.torch.nn.modules.fold.Unfold"; | ||
| } | ||
|
|
||
| const char* type_str() const | ||
| { | ||
| return "nn.Unfold"; | ||
| } | ||
|
|
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| void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const | ||
| { | ||
| const torch::jit::Node* im2col = find_node_by_kind(graph, "aten::im2col"); | ||
|
|
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| op->params["kernel_size"] = im2col->namedInput("kernel_size"); | ||
| op->params["stride"] = im2col->namedInput("stride"); | ||
| op->params["padding"] = im2col->namedInput("padding"); | ||
| op->params["dilation"] = im2col->namedInput("dilation"); | ||
| } | ||
| }; | ||
|
|
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| REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(Unfold) | ||
|
|
||
| } // namespace pnnx |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,45 @@ | ||
| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2022 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. | ||
|
|
||
| #include "pass_level2.h" | ||
|
|
||
| namespace pnnx { | ||
|
|
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| class F_fold : public GraphRewriterPass | ||
| { | ||
| public: | ||
| const char* match_pattern_graph() const | ||
| { | ||
| return R"PNNXIR(7767517 | ||
| 8 7 | ||
| pnnx.Input input_0 0 1 input | ||
| pnnx.Input input_1 0 1 output_size | ||
| pnnx.Input input_2 0 1 kernel_size | ||
| pnnx.Input input_3 0 1 dilation | ||
| pnnx.Input input_4 0 1 padding | ||
| pnnx.Input input_5 0 1 stride | ||
| aten::col2im op_0 6 1 input output_size kernel_size dilation padding stride out | ||
| pnnx.Output output 1 0 out | ||
| )PNNXIR"; | ||
| } | ||
|
|
||
| const char* type_str() const | ||
| { | ||
| return "F.fold"; | ||
| } | ||
| }; | ||
|
|
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| REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_fold, 10) | ||
|
|
||
| } // namespace pnnx |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,44 @@ | ||
| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2022 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. | ||
|
|
||
| #include "pass_level2.h" | ||
|
|
||
| namespace pnnx { | ||
|
|
||
| class F_unfold : public GraphRewriterPass | ||
| { | ||
| public: | ||
| const char* match_pattern_graph() const | ||
| { | ||
| return R"PNNXIR(7767517 | ||
| 7 6 | ||
| pnnx.Input input_0 0 1 input | ||
| pnnx.Input input_1 0 1 kernel_size | ||
| pnnx.Input input_2 0 1 dilation | ||
| pnnx.Input input_3 0 1 padding | ||
| pnnx.Input input_4 0 1 stride | ||
| aten::im2col op_0 5 1 input kernel_size dilation padding stride out | ||
| pnnx.Output output 1 0 out | ||
| )PNNXIR"; | ||
| } | ||
|
|
||
| const char* type_str() const | ||
| { | ||
| return "F.unfold"; | ||
| } | ||
| }; | ||
|
|
||
| REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_unfold, 10) | ||
|
|
||
| } // namespace pnnx |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,60 @@ | ||
| # Tencent is pleased to support the open source community by making ncnn available. | ||
| # | ||
| # Copyright (C) 2022 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 | ||
| from packaging import version | ||
|
|
||
| class Model(nn.Module): | ||
| def __init__(self): | ||
| super(Model, self).__init__() | ||
|
|
||
| def forward(self, x, y, z): | ||
| x = F.fold(x, output_size=22, kernel_size=3) | ||
| y = F.fold(y, output_size=(17,18), kernel_size=(2,4), stride=(2,1), padding=2, dilation=1) | ||
| z = F.fold(z, output_size=(5,11), kernel_size=(1,3), stride=1, padding=(2,4), dilation=1) | ||
|
|
||
| return x, y, z | ||
|
|
||
| def test(): | ||
| net = Model() | ||
| net.eval() | ||
|
|
||
| torch.manual_seed(0) | ||
| x = torch.rand(1, 108, 400) | ||
| y = torch.rand(1, 96, 190) | ||
| z = torch.rand(1, 33, 153) | ||
|
|
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| a0, a1, a2 = net(x, y, z) | ||
|
|
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| # export torchscript | ||
| mod = torch.jit.trace(net, (x, y, z)) | ||
| mod.save("test_F_fold.pt") | ||
|
|
||
| # torchscript to pnnx | ||
| import os | ||
| os.system("../src/pnnx test_F_fold.pt inputshape=[1,108,400],[1,96,190],[1,33,153]") | ||
|
|
||
| # pnnx inference | ||
| import test_F_fold_pnnx | ||
| b0, b1, b2 = test_F_fold_pnnx.test_inference() | ||
|
|
||
| return torch.equal(a0, b0) and torch.equal(a1, b1) and torch.equal(a2, b2) | ||
|
|
||
| if __name__ == "__main__": | ||
| if test(): | ||
| exit(0) | ||
| else: | ||
| exit(1) |
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