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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | // 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 "fuse_convtranspose2d_batchnorm2d.h"
#include "pass_level2.h"
#include <math.h>
#include <string.h>
namespace pnnx {
class fuse_convtranspose2d_batchnorm2d_pass : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
4 3
pnnx.Input input 0 1 input
nn.ConvTranspose2d op_0 1 1 input a in_channels=%in_channels out_channels=%out_channels kernel_size=%kernel_size stride=%stride output_padding=%output_padding padding=%padding dilation=%dilation groups=%groups bias=%bias @weight @bias
nn.BatchNorm2d op_1 1 1 a out num_features=%num_features eps=%eps affine=%affine @running_mean @running_var @weight @bias
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* type_str() const
{
return "nn.ConvTranspose2d";
}
const char* name_str() const
{
return "convtransposebn2d";
}
void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
{
op->params["in_channels"] = captured_params.at("in_channels");
op->params["out_channels"] = captured_params.at("out_channels");
op->params["kernel_size"] = captured_params.at("kernel_size");
op->params["stride"] = captured_params.at("stride");
op->params["output_padding"] = captured_params.at("output_padding");
op->params["padding"] = captured_params.at("padding");
op->params["dilation"] = captured_params.at("dilation");
op->params["groups"] = captured_params.at("groups");
op->params["bias"] = true;
// resolve merged convtranspose2d weight and bias
int channels = captured_params.at("num_features").i;
float bn_eps = captured_params.at("eps").f;
bool has_bn_affine = captured_params.at("affine").b;
bool has_convtranspose_bias = captured_params.at("bias").b;
auto bn_running_mean = captured_attrs.at("op_1.running_mean").get_float32_data();
auto bn_running_var = captured_attrs.at("op_1.running_var").get_float32_data();
auto bn_weight = has_bn_affine ? captured_attrs.at("op_1.weight").get_float32_data() : std::vector<float>();
auto bn_bias = has_bn_affine ? captured_attrs.at("op_1.bias").get_float32_data() : std::vector<float>();
// a = bias - slope * mean / sqrt(var + eps)
// b = slope / sqrt(var + eps)
// value = value * b + a
std::vector<float> a(channels);
std::vector<float> b(channels);
for (int i = 0; i < channels; i++)
{
double sqrt_var = sqrt(bn_running_var[i] + bn_eps);
if (has_bn_affine)
{
a[i] = (float)(bn_bias[i] - bn_weight[i] * bn_running_mean[i] / sqrt_var);
b[i] = (float)(bn_weight[i] / sqrt_var);
}
else
{
a[i] = (float)(-bn_running_mean[i] / sqrt_var);
b[i] = (float)(1.f / sqrt_var);
}
}
op->attrs["weight"] = captured_attrs.at("op_0.weight");
if (has_convtranspose_bias)
{
op->attrs["bias"] = captured_attrs.at("op_0.bias");
}
else
{
// init bias as zero
op->attrs["bias"] = Attribute();
op->attrs["bias"].type = op->attrs["weight"].type;
op->attrs["bias"].shape = {channels};
op->attrs["bias"].set_float32_data(std::vector<float>(channels, 0.f));
}
auto conv_weight = op->attrs["weight"].get_float32_data();
auto conv_bias = op->attrs["bias"].get_float32_data();
// group-inch/group-outch/group-kh-kw
const int inch = captured_params.at("in_channels").i;
const int outch = captured_params.at("out_channels").i;
const int groups = captured_params.at("groups").i;
const int kh = captured_params.at("kernel_size").ai[0];
const int kw = captured_params.at("kernel_size").ai[1];
const int outch_g = outch / groups;
const int inch_g = inch / groups;
const int maxk = kh * kw;
for (int g = 0; g < groups; g++)
{
float* wg = (float*)conv_weight.data() + g * inch_g * outch_g * maxk;
for (int i = 0; i < inch_g; i++)
{
for (int j = 0; j < outch_g; j++)
{
for (int k = 0; k < maxk; k++)
{
wg[(i * outch_g + j) * maxk + k] *= b[g * outch_g + j];
}
}
}
}
for (int i = 0; i < channels; i++)
{
conv_bias[i] = conv_bias[i] * b[i] + a[i];
}
op->attrs["weight"].set_float32_data(conv_weight);
op->attrs["bias"].set_float32_data(conv_bias);
}
};
void fuse_convtranspose2d_batchnorm2d(Graph& graph)
{
fuse_convtranspose2d_batchnorm2d_pass a;
int opindex = 0;
pnnx_graph_rewrite(graph, &a, opindex);
}
} // namespace pnnx
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