File size: 7,870 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 | // 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_conv1d_4 : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
4 3
pnnx.Input input 0 1 input
pnnx.Input weight 0 1 weight
F.conv1d op_0 2 1 input weight out bias=None stride=%stride padding=%padding dilation=%dilation groups=1
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "Convolution1D";
}
const char* name_str() const
{
return "conv1d";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& /*captured_attrs*/) const
{
std::vector<int> weight_shape = op->inputs[1]->shape;
if (weight_shape.empty())
{
weight_shape = {0, 0, 0, 0};
}
op->params["0"] = weight_shape[0];
op->params["1"] = weight_shape[2];
op->params["2"] = captured_params.at("dilation").ai[0];
op->params["3"] = captured_params.at("stride").ai[0];
if (captured_params.at("padding").type == 4)
{
if (captured_params.at("padding").s == "same")
op->params["4"] = -233;
else if (captured_params.at("padding").s == "valid")
op->params["4"] = 0;
}
else
{
op->params["4"] = captured_params.at("padding").ai[0];
}
op->params["5"] = 0;
op->params["6"] = (int)(weight_shape[0] * weight_shape[1] * weight_shape[2]);
op->params["19"] = 1; // dynamic weight
}
};
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_conv1d_4, 22)
class F_conv1d_5 : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
5 4
pnnx.Input input 0 1 input
pnnx.Input weight 0 1 weight
pnnx.Input bias 0 1 bias
F.conv1d op_0 3 1 input weight bias out stride=%stride padding=%padding dilation=%dilation groups=1
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "Convolution1D";
}
const char* name_str() const
{
return "conv1d";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& /*captured_attrs*/) const
{
std::vector<int> weight_shape = op->inputs[1]->shape;
if (weight_shape.empty())
{
weight_shape = {0, 0, 0, 0};
}
op->params["0"] = weight_shape[0];
op->params["1"] = weight_shape[2];
op->params["2"] = captured_params.at("dilation").ai[0];
op->params["3"] = captured_params.at("stride").ai[0];
if (captured_params.at("padding").type == 4)
{
if (captured_params.at("padding").s == "same")
op->params["4"] = -233;
else if (captured_params.at("padding").s == "valid")
op->params["4"] = 0;
}
else
{
op->params["4"] = captured_params.at("padding").ai[0];
}
op->params["5"] = 1;
op->params["6"] = (int)(weight_shape[0] * weight_shape[1] * weight_shape[2]);
op->params["19"] = 1; // dynamic weight
}
};
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_conv1d_5, 22)
class F_conv1d_6 : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
4 3
pnnx.Input input 0 1 input
pnnx.Input weight 0 1 weight
F.conv1d op_0 2 1 input weight out bias=None stride=%stride padding=%padding dilation=%dilation groups=%groups
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "ConvolutionDepthWise1D";
}
const char* name_str() const
{
return "convdw1d";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& /*captured_attrs*/) const
{
std::vector<int> weight_shape = op->inputs[1]->shape;
if (weight_shape.empty())
{
weight_shape = {0, 0, 0, 0};
}
op->params["0"] = weight_shape[0];
op->params["1"] = weight_shape[2];
op->params["2"] = captured_params.at("dilation").ai[0];
op->params["3"] = captured_params.at("stride").ai[0];
if (captured_params.at("padding").type == 4)
{
if (captured_params.at("padding").s == "same")
op->params["4"] = -233;
else if (captured_params.at("padding").s == "valid")
op->params["4"] = 0;
}
else
{
op->params["4"] = captured_params.at("padding").ai[0];
}
op->params["5"] = 0;
op->params["6"] = (int)(weight_shape[0] * weight_shape[1] * weight_shape[2]);
op->params["7"] = captured_params.at("groups");
op->params["19"] = 1; // dynamic weight
}
};
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_conv1d_6, 23)
class F_conv1d_7 : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
5 4
pnnx.Input input 0 1 input
pnnx.Input weight 0 1 weight
pnnx.Input bias 0 1 bias
F.conv1d op_0 3 1 input weight bias out stride=%stride padding=%padding dilation=%dilation groups=%groups
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "ConvolutionDepthWise1D";
}
const char* name_str() const
{
return "convdw1d";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& /*captured_attrs*/) const
{
std::vector<int> weight_shape = op->inputs[1]->shape;
if (weight_shape.empty())
{
weight_shape = {0, 0, 0, 0};
}
op->params["0"] = weight_shape[0];
op->params["1"] = weight_shape[2];
op->params["2"] = captured_params.at("dilation").ai[0];
op->params["3"] = captured_params.at("stride").ai[0];
if (captured_params.at("padding").type == 4)
{
if (captured_params.at("padding").s == "same")
op->params["4"] = -233;
else if (captured_params.at("padding").s == "valid")
op->params["4"] = 0;
}
else
{
op->params["4"] = captured_params.at("padding").ai[0];
}
op->params["5"] = 1;
op->params["6"] = (int)(weight_shape[0] * weight_shape[1] * weight_shape[2]);
op->params["7"] = captured_params.at("groups");
op->params["19"] = 1; // dynamic weight
}
};
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_conv1d_7, 23)
} // namespace ncnn
} // namespace pnnx
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