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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | // 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.
#include "pass_level1.h"
// #include "../pass_level3/fuse_expression.h"
#include "../utils.h"
namespace pnnx {
class Conv2d : public FuseModulePass
{
public:
const char* match_type_str() const
{
return "__torch__.torch.nn.modules.conv.Conv2d";
}
const char* type_str() const
{
return "nn.Conv2d";
}
void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const
{
// {
// pnnx::Graph pnnx_graph;
//
// pnnx_graph.load(mod, graph);
//
// pnnx::fuse_expression(pnnx_graph);
//
// pnnx_graph.save("tmp.param", "tmp.bin");
// }
const torch::jit::Node* convolution = find_node_by_kind(graph, "aten::_convolution");
const torch::jit::Node* convolution_mode = find_node_by_kind(graph, "aten::_convolution_mode");
const torch::jit::Node* pad = find_node_by_kind(graph, "aten::pad");
const torch::jit::Node* reflection_pad2d = find_node_by_kind(graph, "aten::reflection_pad2d");
const torch::jit::Node* replication_pad2d = find_node_by_kind(graph, "aten::replication_pad2d");
if (convolution_mode)
{
convolution = convolution_mode;
}
const auto& weight = mod.attr("weight").toTensor();
op->params["groups"] = convolution->namedInput("groups");
op->params["in_channels"] = weight.size(1) * op->params["groups"].i;
op->params["out_channels"] = weight.size(0);
op->params["kernel_size"] = Parameter{weight.size(2), weight.size(3)};
op->params["stride"] = convolution->namedInput("stride");
if (pad)
{
op->params["padding_mode"] = pad->namedInput("mode");
op->params["padding"] = pad->namedInput("pad");
std::vector<int>& padding = op->params["padding"].ai;
if (padding.size() == 4)
{
// Conv2d only accepts tuple of two integers
if (padding[0] == padding[1] && padding[1] == padding[2] && padding[2] == padding[3])
{
padding.resize(2);
}
else if (padding[0] == padding[2] && padding[1] == padding[3] && padding[0] != padding[1])
{
padding.resize(0);
op->params["padding"].s = "same";
}
}
}
else if (reflection_pad2d)
{
op->params["padding_mode"] = "reflect";
op->params["padding"] = reflection_pad2d->namedInput("padding");
std::vector<int>& padding = op->params["padding"].ai;
if (padding.size() == 4)
{
// Conv2d only accepts tuple of two integers
if (padding[0] == padding[1] && padding[1] == padding[2] && padding[2] == padding[3])
{
padding.resize(2);
}
else if (padding[0] == padding[2] && padding[1] == padding[3] && padding[0] != padding[1])
{
padding.resize(0);
op->params["padding"].s = "same";
}
}
}
else if (replication_pad2d)
{
op->params["padding_mode"] = "replicate";
op->params["padding"] = replication_pad2d->namedInput("padding");
std::vector<int>& padding = op->params["padding"].ai;
if (padding.size() == 4)
{
// Conv2d only accepts tuple of two integers
if (padding[0] == padding[1] && padding[1] == padding[2] && padding[2] == padding[3])
{
padding.resize(2);
}
else if (padding[0] == padding[2] && padding[1] == padding[3] && padding[0] != padding[1])
{
padding.resize(0);
op->params["padding"].s = "same";
}
}
}
else
{
op->params["padding_mode"] = "zeros";
op->params["padding"] = convolution->namedInput("padding");
}
op->params["dilation"] = convolution->namedInput("dilation");
op->params["bias"] = mod.hasattr("bias");
op->attrs["weight"] = weight;
if (mod.hasattr("bias"))
{
op->attrs["bias"] = mod.attr("bias").toTensor();
}
}
};
REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(Conv2d)
} // namespace pnnx
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