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Implement Inception block E

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This commit is contained in:
Matúš Námešný
2024-05-27 22:02:55 +02:00
parent 2909147afe
commit 387990edbe
1 changed files with 163 additions and 24 deletions
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187
examples/inception_v3/inception_v3.cpp
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@@ -14,12 +14,12 @@ int main(int argc, const char *const argv[]) {
class BasicConv2d : public CUDANet::Module { class BasicConv2d : public CUDANet::Module {
public: public:
BasicConv2d( BasicConv2d(
const shape2d inputSize, const shape2d inputSize,
const int inputChannels, const int inputChannels,
const int outputChannels, const int outputChannels,
const shape2d kernelSize, const shape2d kernelSize,
const shape2d stride, const shape2d stride,
const shape2d padding, const shape2d padding,
const std::string &prefix const std::string &prefix
) { ) {
// Create the convolution layer // Create the convolution layer
@@ -61,7 +61,7 @@ class BasicConv2d : public CUDANet::Module {
class InceptionA : public CUDANet::Module { class InceptionA : public CUDANet::Module {
public: public:
InceptionA( InceptionA(
const shape2d inputSize, const shape2d inputSize,
const int inputChannels, const int inputChannels,
const int poolFeatures, const int poolFeatures,
const std::string &prefix const std::string &prefix
@@ -164,8 +164,8 @@ class InceptionA : public CUDANet::Module {
private: private:
shape2d inputSize; shape2d inputSize;
int inputChannels; int inputChannels;
int poolFeatures; int poolFeatures;
BasicConv2d *branch1x1; BasicConv2d *branch1x1;
@@ -187,7 +187,7 @@ class InceptionA : public CUDANet::Module {
class InceptionB : public CUDANet::Module { class InceptionB : public CUDANet::Module {
public: public:
InceptionB( InceptionB(
const shape2d inputSize, const shape2d inputSize,
const int inputChannels, const int inputChannels,
const std::string &prefix const std::string &prefix
) )
@@ -257,7 +257,7 @@ class InceptionB : public CUDANet::Module {
private: private:
shape2d inputSize; shape2d inputSize;
int inputChannels; int inputChannels;
BasicConv2d *branch3x3; BasicConv2d *branch3x3;
@@ -274,7 +274,7 @@ class InceptionB : public CUDANet::Module {
class InceptionC : public CUDANet::Module { class InceptionC : public CUDANet::Module {
public: public:
InceptionC( InceptionC(
const shape2d inputSize, const shape2d inputSize,
const int inputChannels, const int inputChannels,
const int nChannels_7x7, const int nChannels_7x7,
const std::string &prefix const std::string &prefix
@@ -396,7 +396,7 @@ class InceptionC : public CUDANet::Module {
private: private:
shape2d inputSize; shape2d inputSize;
int inputChannels; int inputChannels;
BasicConv2d *branch1x1; BasicConv2d *branch1x1;
@@ -421,7 +421,7 @@ class InceptionC : public CUDANet::Module {
class InceptionD : public CUDANet::Module { class InceptionD : public CUDANet::Module {
public: public:
InceptionD( InceptionD(
const shape2d inputSize, const shape2d inputSize,
const int inputChannels, const int inputChannels,
const std::string &prefix const std::string &prefix
) )
@@ -433,8 +433,7 @@ class InceptionD : public CUDANet::Module {
); );
addLayer("", branch3x3_1); addLayer("", branch3x3_1);
branch3x3_2 = new BasicConv2d( branch3x3_2 = new BasicConv2d(
inputSize, 192, 320, {3, 3}, {2, 2}, {0, 0}, inputSize, 192, 320, {3, 3}, {2, 2}, {0, 0}, prefix + "branch3x3_2"
prefix + "branch3x3_2"
); );
addLayer("", branch3x3_2); addLayer("", branch3x3_2);
@@ -469,12 +468,10 @@ class InceptionD : public CUDANet::Module {
// Concat // Concat
concat_1 = new CUDANet::Layers::Concat( concat_1 = new CUDANet::Layers::Concat(
branch3x3_2->getOutputSize(), branch3x3_2->getOutputSize(), branch7x7x3_4->getOutputSize()
branch7x7x3_4->getOutputSize()
); );
concat_2 = new CUDANet::Layers::Concat( concat_2 = new CUDANet::Layers::Concat(
concat_1->getOutputSize(), concat_1->getOutputSize(), branchPool->getOutputSize()
branchPool->getOutputSize()
); );
} }
@@ -492,24 +489,24 @@ class InceptionD : public CUDANet::Module {
float *forward(float *d_input) { float *forward(float *d_input) {
float *branch1x1_output = branch3x3_1->forward(d_input); float *branch1x1_output = branch3x3_1->forward(d_input);
branch1x1_output = branch3x3_2->forward(branch1x1_output); branch1x1_output = branch3x3_2->forward(branch1x1_output);
float *branch7x7_output = branch7x7x3_1->forward(d_input); float *branch7x7_output = branch7x7x3_1->forward(d_input);
branch7x7_output = branch7x7x3_2->forward(branch7x7_output); branch7x7_output = branch7x7x3_2->forward(branch7x7_output);
branch7x7_output = branch7x7x3_3->forward(branch7x7_output); branch7x7_output = branch7x7x3_3->forward(branch7x7_output);
branch7x7_output = branch7x7x3_4->forward(branch7x7_output); branch7x7_output = branch7x7x3_4->forward(branch7x7_output);
float *branchPool_output = branchPool->forward(d_input); float *branchPool_output = branchPool->forward(d_input);
float *d_output = concat_1->forward(branch1x1_output, branch7x7_output); float *d_output = concat_1->forward(branch1x1_output, branch7x7_output);
d_output = concat_2->forward(d_output, branchPool_output); d_output = concat_2->forward(d_output, branchPool_output);
return d_output; return d_output;
} }
private: private:
shape2d inputSize; shape2d inputSize;
int inputChannels; int inputChannels;
BasicConv2d *branch3x3_1; BasicConv2d *branch3x3_1;
BasicConv2d *branch3x3_2; BasicConv2d *branch3x3_2;
@@ -523,5 +520,147 @@ class InceptionD : public CUDANet::Module {
CUDANet::Layers::Concat *concat_1; CUDANet::Layers::Concat *concat_1;
CUDANet::Layers::Concat *concat_2; CUDANet::Layers::Concat *concat_2;
};
class InceptionE : public CUDANet::Module {
public:
InceptionE(shape2d inputSize, int inputChannels)
: inputSize(inputSize), inputChannels(inputChannels) {
// Branch 1x1
branch1x1 = new BasicConv2d(
inputSize, inputChannels, 320, {1, 1}, {1, 1}, {0, 0}, "branch1x1"
);
addLayer("", branch1x1);
// Branch 3x3
branch3x3_1 = new BasicConv2d(
inputSize, inputChannels, 384, {1, 1}, {1, 1}, {0, 0}, "branch3x3_1"
);
addLayer("", branch3x3_1);
branch3x3_2a = new BasicConv2d(
inputSize, 384, 384, {1, 3}, {1, 1}, {0, 1}, "branch3x3_2a"
);
addLayer("", branch3x3_2a);
branch3x3_2b = new BasicConv2d(
inputSize, 384, 384, {3, 1}, {1, 1}, {1, 0}, "branch3x3_2b"
);
addLayer("", branch3x3_2b);
branch_3x3_2_concat = new CUDANet::Layers::Concat(
branch3x3_2a->getOutputSize(), branch3x3_2b->getOutputSize()
);
// Branch 3x3dbl
branch3x3dbl_1 = new BasicConv2d(
inputSize, inputChannels, 448, {1, 1}, {1, 1}, {0, 0},
"branch3x3dbl_1"
);
addLayer("", branch3x3dbl_1);
branch3x3dbl_2 = new BasicConv2d(
inputSize, 448, 384, {3, 3}, {1, 1}, {1, 1}, "branch3x3dbl_2"
);
addLayer("", branch3x3dbl_2);
branch3x3dbl_3a = new BasicConv2d(
inputSize, 384, 384, {1, 3}, {1, 1}, {0, 1}, "branch3x3dbl_3a"
);
addLayer("", branch3x3dbl_3a);
branch3x3dbl_3b = new BasicConv2d(
inputSize, 384, 384, {3, 1}, {1, 1}, {1, 0}, "branch3x3dbl_3b"
);
addLayer("", branch3x3dbl_3b);
branch_3x3dbl_3_concat = new CUDANet::Layers::Concat(
branch3x3dbl_3a->getOutputSize(), branch3x3dbl_3b->getOutputSize()
);
// Branch Pool
branchPool_1 = new CUDANet::Layers::AvgPooling2d(
inputSize, inputChannels, {3, 3}, {1, 1}, {1, 1}, CUDANet::Layers::ActivationType::NONE
);
addLayer("", branchPool_1);
branchPool_2 = new BasicConv2d(
inputSize, inputChannels, 192, {1, 1}, {1, 1}, {0, 0}, "branchPool_2"
);
addLayer("", branchPool_2);
// Concat
concat_1 = new CUDANet::Layers::Concat(
branch1x1->getOutputSize(), branch_3x3_2_concat->getOutputSize()
);
concat_2 = new CUDANet::Layers::Concat(
concat_1->getOutputSize(), branch_3x3dbl_3_concat->getOutputSize()
);
concat_3 = new CUDANet::Layers::Concat(
concat_2->getOutputSize(), branchPool_2->getOutputSize()
);
}
~InceptionE() {
delete branch1x1;
delete branch3x3_1;
delete branch3x3_2a;
delete branch3x3_2b;
delete branch_3x3_2_concat;
delete branch3x3dbl_1;
delete branch3x3dbl_2;
delete branch3x3dbl_3a;
delete branch3x3dbl_3b;
delete branch_3x3dbl_3_concat;
delete branchPool_1;
delete branchPool_2;
delete concat_1;
delete concat_2;
delete concat_3;
}
float *forward(const float *d_input) {
float *branch1x1_output = branch1x1->forward(d_input);
float *branch3x3_output = branch3x3_1->forward(d_input);
float *branch3x3_2a_output = branch3x3_2a->forward(branch3x3_output);
float *branch3x3_2b_output = branch3x3_2b->forward(branch3x3_output);
branch3x3_output = branch_3x3_2_concat->forward(
branch3x3_2a_output, branch3x3_2b_output
);
float *branch3x3dbl_output = branch3x3dbl_1->forward(d_input);
branch3x3dbl_output = branch3x3dbl_2->forward(branch3x3dbl_output);
float *branch3x3dbl_3a_output = branch3x3dbl_3a->forward(branch3x3dbl_output);
float *branch3x3dbl_3b_output = branch3x3dbl_3b->forward(branch3x3dbl_output);
branch3x3dbl_output = branch_3x3dbl_3_concat->forward(
branch3x3dbl_3a_output, branch3x3dbl_3b_output
);
float *branchPool_output = branchPool_1->forward(d_input);
branchPool_output = branchPool_2->forward(branchPool_output);
float *d_output = concat_1->forward(branch1x1_output, branch3x3_output);
d_output = concat_2->forward(d_output, branch3x3dbl_output);
d_output = concat_3->forward(d_output, branchPool_output);
return d_output;
}
private:
shape2d inputSize;
int inputChannels;
BasicConv2d *branch1x1;
BasicConv2d *branch3x3_1;
BasicConv2d *branch3x3_2a;
BasicConv2d *branch3x3_2b;
CUDANet::Layers::Concat *branch_3x3_2_concat;
BasicConv2d *branch3x3dbl_1;
BasicConv2d *branch3x3dbl_2;
BasicConv2d *branch3x3dbl_3a;
BasicConv2d *branch3x3dbl_3b;
CUDANet::Layers::Concat *branch_3x3dbl_3_concat;
CUDANet::Layers::AvgPooling2d *branchPool_1;
BasicConv2d *branchPool_2;
CUDANet::Layers::Concat *concat_1;
CUDANet::Layers::Concat *concat_2;
CUDANet::Layers::Concat *concat_3;
}; };