File size: 3,631 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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | // 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_ncnn.h"
namespace pnnx {
namespace ncnn {
class F_batch_norm : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
5 4
pnnx.Input input 0 1 input
pnnx.Attribute op_mean 0 1 running_mean @data
pnnx.Attribute op_var 0 1 running_var @data
F.batch_norm op_0 3 1 input running_mean running_var out weight=None bias=None eps=%eps
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "BatchNorm";
}
const char* name_str() const
{
return "bn";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
{
Attribute running_mean = captured_attrs.at("op_mean.data");
Attribute running_var = captured_attrs.at("op_var.data");
op->params["0"] = running_mean.shape[0];
op->params["1"] = captured_params.at("eps");
const int channels = running_mean.shape[0];
op->attrs["0"] = Attribute({channels}, std::vector<float>(channels, 1.f));
op->attrs["1"] = running_mean;
op->attrs["2"] = running_var;
op->attrs["3"] = Attribute({channels}, std::vector<float>(channels, 0.f));
}
};
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_batch_norm, 20)
class F_batch_norm_1 : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
7 6
pnnx.Input input 0 1 input
pnnx.Attribute op_mean 0 1 running_mean @data
pnnx.Attribute op_var 0 1 running_var @data
pnnx.Attribute op_weight 0 1 weight @data
pnnx.Attribute op_bias 0 1 bias @data
F.batch_norm op_0 5 1 input running_mean running_var weight bias out eps=%eps
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "BatchNorm";
}
const char* name_str() const
{
return "bn";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
{
Attribute running_mean = captured_attrs.at("op_mean.data");
Attribute running_var = captured_attrs.at("op_var.data");
Attribute weight = captured_attrs.at("op_weight.data");
Attribute bias = captured_attrs.at("op_bias.data");
op->params["0"] = running_mean.shape[0];
op->params["1"] = captured_params.at("eps");
op->attrs["0"] = weight;
op->attrs["1"] = running_mean;
op->attrs["2"] = running_var;
op->attrs["3"] = bias;
}
};
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_batch_norm_1, 20)
} // namespace ncnn
} // namespace pnnx
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