File size: 2,481 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 | // 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 "../utils.h"
namespace pnnx {
class ConvTranspose2d : public FuseModulePass
{
public:
const char* match_type_str() const
{
return "__torch__.torch.nn.modules.conv.ConvTranspose2d";
}
const char* type_str() const
{
return "nn.ConvTranspose2d";
}
void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const
{
const torch::jit::Node* convolution = find_node_by_kind(graph, "aten::_convolution");
const auto& weight = mod.attr("weight").toTensor();
op->params["groups"] = convolution->namedInput("groups");
op->params["in_channels"] = weight.size(0);
op->params["out_channels"] = weight.size(1) * op->params["groups"].i;
op->params["kernel_size"] = Parameter{weight.size(2), weight.size(3)};
op->params["stride"] = convolution->namedInput("stride");
op->params["padding"] = convolution->namedInput("padding");
op->params["output_padding"] = convolution->namedInput("output_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();
}
if (op->inputs.size() > 1)
{
fprintf(stderr, "ConvTranspose2d arg output_size detected and dropped !\n");
for (size_t i = 1; i < op->inputs.size(); i++)
{
op->inputs[i]->remove_consumer(op);
}
op->inputs.resize(1);
}
}
};
REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(ConvTranspose2d)
} // namespace pnnx
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