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Implement inception module B

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This commit is contained in:
Matúš Námešný
2024-05-26 16:48:58 +02:00
parent 1e3b2f7d7b
commit d1fc45d9e0
1 changed files with 114 additions and 37 deletions
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151
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 dim2d inputSize, const dim2d inputSize,
const int inputChannels, const int inputChannels,
const int outputChannels, const int outputChannels,
const dim2d kernelSize, const dim2d kernelSize,
const dim2d stride, const dim2d stride,
const dim2d padding, const dim2d padding,
const std::string &prefix const std::string &prefix
) { ) {
// Create the convolution layer // Create the convolution layer
@@ -44,6 +44,10 @@ class BasicConv2d : public CUDANet::Module {
return batchNorm->forward(d_output); return batchNorm->forward(d_output);
} }
dim2d getOutputDims() {
return batchNorm->getOutputDims();
}
private: private:
CUDANet::Layers::Conv2d *conv; CUDANet::Layers::Conv2d *conv;
CUDANet::Layers::BatchNorm2d *batchNorm; CUDANet::Layers::BatchNorm2d *batchNorm;
@@ -52,7 +56,7 @@ class BasicConv2d : public CUDANet::Module {
class InceptionA : public CUDANet::Module { class InceptionA : public CUDANet::Module {
public: public:
InceptionA( InceptionA(
const dim2d inputSize, const dim2d inputSize,
const int inputChannels, const int inputChannels,
const int poolFeatures, const int poolFeatures,
const std::string &prefix const std::string &prefix
@@ -62,13 +66,15 @@ class InceptionA : public CUDANet::Module {
poolFeatures(poolFeatures) { poolFeatures(poolFeatures) {
// Branch 1x1 // Branch 1x1
branch1x1 = new BasicConv2d( branch1x1 = new BasicConv2d(
inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0}, prefix + ".branch1x1" inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch1x1"
); );
addLayer("", branch1x1); addLayer("", branch1x1);
// Branch 5x5 // Branch 5x5
branch5x5_1 = new BasicConv2d( branch5x5_1 = new BasicConv2d(
inputSize, inputChannels, 48, {1, 1}, {1, 1}, {0, 0}, prefix + ".branch5x5_1" inputSize, inputChannels, 48, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch5x5_1"
); );
addLayer("", branch5x5_1); addLayer("", branch5x5_1);
branch5x5_2 = new BasicConv2d( branch5x5_2 = new BasicConv2d(
@@ -77,18 +83,21 @@ class InceptionA : public CUDANet::Module {
addLayer("", branch5x5_2); addLayer("", branch5x5_2);
// Branch 3x3 // Branch 3x3
branch3x3_1 = new BasicConv2d( branch3x3dbl_1 = new BasicConv2d(
inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0}, prefix + ".branch3x3_1" inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch3x3dbl_1"
); );
addLayer("", branch3x3_1); addLayer("", branch3x3dbl_1);
branch3x3_2 = new BasicConv2d( branch3x3dbl_2 = new BasicConv2d(
inputSize, 64, 96, {3, 3}, {1, 1}, {1, 1}, prefix + ".branch3x3_2" inputSize, 64, 96, {3, 3}, {1, 1}, {1, 1},
prefix + ".branch3x3dbl_2"
); );
addLayer("", branch3x3_2); addLayer("", branch3x3dbl_2);
branch3x3_3 = new BasicConv2d( branch3x3dbl_3 = new BasicConv2d(
inputSize, 96, 96, {3, 3}, {1, 1}, {1, 1}, prefix + ".branch3x3_3" inputSize, 96, 96, {3, 3}, {1, 1}, {1, 1},
prefix + ".branch3x3dbl_3"
); );
addLayer("", branch3x3_3); addLayer("", branch3x3dbl_3);
// Branch Pool // Branch Pool
branchPool_1 = new CUDANet::Layers::AvgPooling2d( branchPool_1 = new CUDANet::Layers::AvgPooling2d(
@@ -97,8 +106,8 @@ class InceptionA : public CUDANet::Module {
); );
addLayer("", branchPool_1); addLayer("", branchPool_1);
branchPool_2 = new BasicConv2d( branchPool_2 = new BasicConv2d(
branchPool_1->getOutputDims(), inputChannels, poolFeatures, {1, 1}, {1, 1}, {0, 0}, branchPool_1->getOutputDims(), inputChannels, poolFeatures, {1, 1},
prefix + ".branchPool" {1, 1}, {0, 0}, prefix + ".branchPool"
); );
addLayer("", branchPool_2); addLayer("", branchPool_2);
@@ -107,7 +116,7 @@ class InceptionA : public CUDANet::Module {
branch1x1->getOutputSize(), branch5x5_2->getOutputSize() branch1x1->getOutputSize(), branch5x5_2->getOutputSize()
); );
concat_2 = new CUDANet::Layers::Concat( concat_2 = new CUDANet::Layers::Concat(
concat_1->getOutputSize(), branch3x3_3->getOutputSize() concat_1->getOutputSize(), branch3x3dbl_3->getOutputSize()
); );
concat_3 = new CUDANet::Layers::Concat( concat_3 = new CUDANet::Layers::Concat(
concat_2->getOutputSize(), branchPool_2->getOutputSize() concat_2->getOutputSize(), branchPool_2->getOutputSize()
@@ -120,9 +129,9 @@ class InceptionA : public CUDANet::Module {
float *d_branch5x5_out = branch5x5_1->forward(d_input); float *d_branch5x5_out = branch5x5_1->forward(d_input);
d_branch5x5_out = branch5x5_2->forward(d_branch5x5_out); d_branch5x5_out = branch5x5_2->forward(d_branch5x5_out);
float *d_branch3x3_out = branch3x3_1->forward(d_input); float *d_branch3x3_out = branch3x3dbl_1->forward(d_input);
d_branch3x3_out = branch3x3_2->forward(d_branch3x3_out); d_branch3x3_out = branch3x3dbl_2->forward(d_branch3x3_out);
d_branch3x3_out = branch3x3_3->forward(d_branch3x3_out); d_branch3x3_out = branch3x3dbl_3->forward(d_branch3x3_out);
float *d_branchPool_out = branchPool_1->forward(d_input); float *d_branchPool_out = branchPool_1->forward(d_input);
d_branchPool_out = branchPool_2->forward(d_branchPool_out); d_branchPool_out = branchPool_2->forward(d_branchPool_out);
@@ -136,17 +145,20 @@ class InceptionA : public CUDANet::Module {
private: private:
dim2d inputSize; dim2d inputSize;
int inputChannels; int inputChannels;
int poolFeatures; int poolFeatures;
BasicConv2d *branch1x1;
BasicConv2d *branch5x5_1;
BasicConv2d *branch5x5_2;
BasicConv2d *branch3x3dbl_1;
BasicConv2d *branch3x3dbl_2;
BasicConv2d *branch3x3dbl_3;
CUDANet::Module *branch1x1;
CUDANet::Module *branch5x5_1;
CUDANet::Module *branch5x5_2;
CUDANet::Module *branch3x3_1;
CUDANet::Module *branch3x3_2;
CUDANet::Module *branch3x3_3;
CUDANet::Layers::AvgPooling2d *branchPool_1; CUDANet::Layers::AvgPooling2d *branchPool_1;
CUDANet::Module *branchPool_2; BasicConv2d *branchPool_2;
CUDANet::Layers::Concat *concat_1; CUDANet::Layers::Concat *concat_1;
CUDANet::Layers::Concat *concat_2; CUDANet::Layers::Concat *concat_2;
@@ -155,12 +167,77 @@ class InceptionA : public CUDANet::Module {
class InceptionB : public CUDANet::Module { class InceptionB : public CUDANet::Module {
public: public:
InceptionB(const int inputSize, const int inputChannels) InceptionB(
const dim2d inputSize,
const int inputChannels,
const std::string &prefix
)
: inputSize(inputSize), inputChannels(inputChannels) { : inputSize(inputSize), inputChannels(inputChannels) {
// Branch 1x1 // Branch 3x3
branch1x1 = new BasicConv2d( branch3x3 = new BasicConv2d(
inputSize, inputChannels, 128, 1, 1, 0, "branch1x1" inputSize, inputChannels, 384, {3, 3}, {2, 2}, {0, 0}, "branch1x1"
);
addLayer("", branch3x3);
// Branch 3x3dbl
branch3x3dbl_1 = new BasicConv2d(
inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
"branch3x3dbl_1"
);
addLayer("", branch3x3dbl_1);
branch3x3dbl_2 = new BasicConv2d(
branch3x3dbl_1->getOutputDims(), 96, 96, {3, 3}, {1, 1}, {1, 1},
"branch3x3dbl_2"
);
addLayer("", branch3x3dbl_2);
branch3x3dbl_3 = new BasicConv2d(
branch3x3dbl_2->getOutputDims(), 96, 96, {3, 3}, {2, 2}, {1, 1},
"branch3x3dbl_3"
);
addLayer("", branch3x3dbl_3);
branchPool = new CUDANet::Layers::MaxPooling2d(
inputSize, inputChannels, {3, 3}, {2, 2},
CUDANet::Layers::ActivationType::NONE
);
addLayer(prefix + ".branchPool", branchPool);
concat_1 = new CUDANet::Layers::Concat(
branch3x3->getOutputSize(), branch3x3dbl_3->getOutputSize()
);
concat_2 = new CUDANet::Layers::Concat(
concat_1->getOutputSize(), branchPool->getOutputSize()
); );
addLayer("", branch1x1);
} }
}
float *forward(const float *d_input) {
float *d_branch3x3_out = branch3x3->forward(d_input);
float *d_branch3x3dbl_out = branch3x3dbl_1->forward(d_input);
d_branch3x3dbl_out = branch3x3dbl_2->forward(d_branch3x3dbl_out);
d_branch3x3dbl_out = branch3x3dbl_3->forward(d_branch3x3dbl_out);
float *d_branchPool_out = branchPool->forward(d_input);
float *d_output =
concat_1->forward(d_branch3x3_out, d_branch3x3dbl_out);
d_output = concat_2->forward(d_output, d_branchPool_out);
return d_output;
}
private:
dim2d inputSize;
int inputChannels;
BasicConv2d *branch3x3;
BasicConv2d *branch3x3dbl_1;
BasicConv2d *branch3x3dbl_2;
BasicConv2d *branch3x3dbl_3;
CUDANet::Layers::MaxPooling2d *branchPool;
CUDANet::Layers::Concat *concat_1;
CUDANet::Layers::Concat *concat_2;
};