File size: 3,191 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 | // 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_normalize : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
3 2
pnnx.Input input 0 1 input
F.normalize op_0 1 1 input out dim=%dim eps=%eps p=%p
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "Normalize";
}
const char* name_str() const
{
return "normalize";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params) const
{
const int batch_index = op->inputs[0]->params["__batch_index"].i;
int axis = captured_params.at("dim").i;
if (axis == batch_index)
{
fprintf(stderr, "normalize along batch axis %d is not supported\n", batch_index);
return;
}
if (axis < 0)
{
int input_rank = op->inputs[0]->shape.size();
axis = input_rank + axis;
}
if (axis > batch_index)
axis -= 1;
float p = 0.f;
if (captured_params.at("p").type == 2)
p = captured_params.at("p").i;
if (captured_params.at("p").type == 3)
p = captured_params.at("p").f;
if (p != 2.f)
{
fprintf(stderr, "unsupported normalize p=%f\n", p);
return;
}
int input_rank = op->inputs[0]->shape.size();
if (batch_index >= 0 && batch_index < input_rank)
input_rank -= 1;
if (input_rank == 2 || axis != 0)
{
fprintf(stderr, "unsupported normalize for %d-rank tensor with axis %d\n", input_rank, axis);
return;
}
if (input_rank == 1 && axis == 0)
{
op->params["0"] = 1; // across_spatial
op->params["4"] = 1; // across_channel
}
if (input_rank == 3 && axis == 0)
{
op->params["0"] = 0; // across_spatial
op->params["4"] = 1; // across_channel
}
op->params["1"] = 1; // channel_shared
op->params["2"] = captured_params.at("eps");
op->params["3"] = 1; // scale_data_size
op->params["9"] = 1; // eps_mode
op->attrs["0"] = Attribute({1}, std::vector<float>(1, 1.f));
}
};
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_normalize, 20)
} // namespace ncnn
} // namespace pnnx
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