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Fix module layer issues

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This commit is contained in:
Matúš Námešný
2024-05-30 13:08:13 +02:00
parent 8ac2da004c
commit 479c1119e7
4 changed files with 116 additions and 95 deletions
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179
examples/inception_v3/inception_v3.cpp
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@@ -4,17 +4,18 @@
class BasicConv2d : public CUDANet::Module {
public:
BasicConv2d(
const shape2d inputSize,
const shape2d inputShape,
const int inputChannels,
const int outputChannels,
const shape2d kernelSize,
const shape2d stride,
const shape2d padding,
const std::string &prefix
) {
)
: outputChannels(outputChannels) {
// Create the convolution layer
conv = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, kernelSize, stride, outputChannels,
inputShape, inputChannels, kernelSize, stride, outputChannels,
padding, CUDANet::Layers::ActivationType::NONE
);
@@ -25,6 +26,10 @@ class BasicConv2d : public CUDANet::Module {
CUDANet::Layers::ActivationType::RELU
);
inputSize = inputShape.first * inputShape.second * inputChannels;
outputSize = batchNorm->getOutputDims().first *
batchNorm->getOutputDims().second * outputChannels;
addLayer(prefix + ".conv", conv);
addLayer(prefix + ".bn", batchNorm);
}
@@ -48,6 +53,8 @@ class BasicConv2d : public CUDANet::Module {
}
private:
int outputChannels;
CUDANet::Layers::Conv2d *conv;
CUDANet::Layers::BatchNorm2d *batchNorm;
};
@@ -55,24 +62,27 @@ class BasicConv2d : public CUDANet::Module {
class InceptionA : public CUDANet::Module {
public:
InceptionA(
const shape2d inputSize,
const shape2d inputShape,
const int inputChannels,
const int poolFeatures,
const std::string &prefix
)
: inputSize(inputSize),
: inputShape(inputShape),
inputChannels(inputChannels),
poolFeatures(poolFeatures) {
inputSize = inputShape.first * inputShape.second * inputChannels;
// Branch 1x1
branch1x1 = new BasicConv2d(
inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch1x1"
);
addLayer("", branch1x1);
// Branch 5x5
branch5x5_1 = new BasicConv2d(
inputSize, inputChannels, 48, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 48, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch5x5_1"
);
addLayer("", branch5x5_1);
@@ -84,7 +94,7 @@ class InceptionA : public CUDANet::Module {
// Branch 3x3
branch3x3dbl_1 = new BasicConv2d(
inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch3x3dbl_1"
);
addLayer("", branch3x3dbl_1);
@@ -101,13 +111,13 @@ class InceptionA : public CUDANet::Module {
// Branch Pool
branchPool_1 = new CUDANet::Layers::AvgPooling2d(
inputSize, inputChannels, {3, 3}, {1, 1}, {1, 1},
inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
CUDANet::Layers::ActivationType::NONE
);
addLayer("", branchPool_1);
addLayer(prefix + ".branch_pool", branchPool_1);
branchPool_2 = new BasicConv2d(
branchPool_1->getOutputDims(), inputChannels, poolFeatures, {1, 1},
{1, 1}, {0, 0}, prefix + ".branchPool"
{1, 1}, {0, 0}, prefix + ".branch_pool"
);
addLayer("", branchPool_2);
@@ -121,6 +131,8 @@ class InceptionA : public CUDANet::Module {
concat_3 = new CUDANet::Layers::Concat(
concat_2->getOutputSize(), branchPool_2->getOutputSize()
);
outputSize = concat_3->getOutputSize();
}
~InceptionA() {
@@ -143,15 +155,15 @@ class InceptionA : public CUDANet::Module {
float *d_branch5x5_out = branch5x5_1->forward(d_input);
d_branch5x5_out = branch5x5_2->forward(d_branch5x5_out);
float *d_branch3x3_out = branch3x3dbl_1->forward(d_input);
d_branch3x3_out = branch3x3dbl_2->forward(d_branch3x3_out);
d_branch3x3_out = branch3x3dbl_3->forward(d_branch3x3_out);
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_1->forward(d_input);
d_branchPool_out = branchPool_2->forward(d_branchPool_out);
float *d_output = concat_1->forward(d_branch1x1_out, d_branch5x5_out);
d_output = concat_2->forward(d_output, d_branch3x3_out);
d_output = concat_2->forward(d_output, d_branch3x3dbl_out);
d_output = concat_3->forward(d_output, d_branchPool_out);
return d_output;
@@ -169,7 +181,7 @@ class InceptionA : public CUDANet::Module {
}
private:
shape2d inputSize;
shape2d inputShape;
int inputChannels;
int poolFeatures;
@@ -193,21 +205,24 @@ class InceptionA : public CUDANet::Module {
class InceptionB : public CUDANet::Module {
public:
InceptionB(
const shape2d inputSize,
const shape2d inputShape,
const int inputChannels,
const std::string &prefix
)
: inputSize(inputSize), inputChannels(inputChannels) {
: inputShape(inputShape), inputChannels(inputChannels) {
inputSize = inputShape.first * inputShape.second * inputChannels;
// Branch 3x3
branch3x3 = new BasicConv2d(
inputSize, inputChannels, 384, {3, 3}, {2, 2}, {0, 0},
inputShape, inputChannels, 384, {3, 3}, {2, 2}, {0, 0},
prefix + ".branch1x1"
);
addLayer("", branch3x3);
// Branch 3x3dbl
branch3x3dbl_1 = new BasicConv2d(
inputSize, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch3x3dbl_1"
);
addLayer("", branch3x3dbl_1);
@@ -223,10 +238,10 @@ class InceptionB : public CUDANet::Module {
addLayer("", branch3x3dbl_3);
branchPool = new CUDANet::Layers::MaxPooling2d(
inputSize, inputChannels, {3, 3}, {2, 2}, {0, 0},
inputShape, inputChannels, {3, 3}, {2, 2}, {0, 0},
CUDANet::Layers::ActivationType::NONE
);
addLayer(prefix + ".branchPool", branchPool);
addLayer(prefix + ".branch_pool", branchPool);
concat_1 = new CUDANet::Layers::Concat(
branch3x3->getOutputSize(), branch3x3dbl_3->getOutputSize()
@@ -234,6 +249,8 @@ class InceptionB : public CUDANet::Module {
concat_2 = new CUDANet::Layers::Concat(
concat_1->getOutputSize(), branchPool->getOutputSize()
);
outputSize = concat_2->getOutputSize();
}
~InceptionB() {
@@ -272,7 +289,7 @@ class InceptionB : public CUDANet::Module {
}
private:
shape2d inputSize;
shape2d inputShape;
int inputChannels;
BasicConv2d *branch3x3;
@@ -290,22 +307,25 @@ class InceptionB : public CUDANet::Module {
class InceptionC : public CUDANet::Module {
public:
InceptionC(
const shape2d inputSize,
const shape2d inputShape,
const int inputChannels,
const int nChannels_7x7,
const std::string &prefix
)
: inputSize(inputSize), inputChannels(inputChannels) {
: inputShape(inputShape), inputChannels(inputChannels) {
inputSize = inputShape.first * inputShape.second * inputChannels;
// Branch 1x1
branch1x1 = new BasicConv2d(
inputSize, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch1x1"
);
addLayer("", branch1x1);
// Branch 7x7
branch7x7_1 = new BasicConv2d(
inputSize, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch7x7_1"
);
addLayer("", branch7x7_1);
@@ -322,7 +342,7 @@ class InceptionC : public CUDANet::Module {
// Branch 7x7dbl
branch7x7dbl_1 = new BasicConv2d(
inputSize, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch7x7dbl_1"
);
addLayer("", branch7x7dbl_1);
@@ -349,13 +369,13 @@ class InceptionC : public CUDANet::Module {
// Branch Pool
branchPool_1 = new CUDANet::Layers::AvgPooling2d(
inputSize, inputChannels, {3, 3}, {1, 1}, {1, 1},
inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
CUDANet::Layers::ActivationType::NONE
);
addLayer("", branchPool_1);
addLayer(prefix + ".branch_pool", branchPool_1);
branchPool_2 = new BasicConv2d(
branchPool_1->getOutputDims(), inputChannels, 192, {1, 1}, {1, 1},
{0, 0}, prefix + ".branchPool_2"
{0, 0}, prefix + ".branch_pool"
);
addLayer("", branchPool_2);
@@ -369,6 +389,8 @@ class InceptionC : public CUDANet::Module {
concat_3 = new CUDANet::Layers::Concat(
concat_2->getOutputSize(), branchPool_2->getOutputSize()
);
outputSize = concat_3->getOutputSize();
}
~InceptionC() {
@@ -423,7 +445,7 @@ class InceptionC : public CUDANet::Module {
}
private:
shape2d inputSize;
shape2d inputShape;
int inputChannels;
BasicConv2d *branch1x1;
@@ -449,50 +471,53 @@ class InceptionC : public CUDANet::Module {
class InceptionD : public CUDANet::Module {
public:
InceptionD(
const shape2d inputSize,
const shape2d inputShape,
const int inputChannels,
const std::string &prefix
)
: inputSize(inputSize), inputChannels(inputChannels) {
: inputShape(inputShape), inputChannels(inputChannels) {
inputSize = inputShape.first * inputShape.second * inputChannels;
// Branch 3x3
branch3x3_1 = new BasicConv2d(
inputSize, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch3x3"
);
addLayer("", branch3x3_1);
branch3x3_2 = new BasicConv2d(
inputSize, 192, 320, {3, 3}, {2, 2}, {0, 0}, prefix + ".branch3x3_2"
inputShape, 192, 320, {3, 3}, {2, 2}, {0, 0}, prefix + ".branch3x3_2"
);
addLayer("", branch3x3_2);
// Branch 7x7x3
branch7x7x3_1 = new BasicConv2d(
inputSize, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch7x7x3_1"
);
addLayer("", branch7x7x3_1);
branch7x7x3_2 = new BasicConv2d(
inputSize, 192, 192, {1, 7}, {1, 1}, {0, 3},
inputShape, 192, 192, {1, 7}, {1, 1}, {0, 3},
prefix + ".branch7x7x3_2"
);
addLayer("", branch7x7x3_2);
branch7x7x3_3 = new BasicConv2d(
inputSize, 192, 192, {7, 1}, {1, 1}, {3, 0},
inputShape, 192, 192, {7, 1}, {1, 1}, {3, 0},
prefix + ".branch7x7x3_3"
);
addLayer("", branch7x7x3_3);
branch7x7x3_4 = new BasicConv2d(
inputSize, 192, 192, {3, 3}, {2, 2}, {0, 0},
inputShape, 192, 192, {3, 3}, {2, 2}, {0, 0},
prefix + ".branch7x7x3_4"
);
addLayer("", branch7x7x3_4);
// Branch Pool
branchPool = new CUDANet::Layers::MaxPooling2d(
inputSize, 192, {3, 3}, {2, 2}, {0, 0},
inputShape, 192, {3, 3}, {2, 2}, {0, 0},
CUDANet::Layers::ActivationType::NONE
);
addLayer("", branchPool);
addLayer(prefix + ".branch_pool", branchPool);
// Concat
concat_1 = new CUDANet::Layers::Concat(
@@ -501,6 +526,8 @@ class InceptionD : public CUDANet::Module {
concat_2 = new CUDANet::Layers::Concat(
concat_1->getOutputSize(), branchPool->getOutputSize()
);
outputSize = concat_2->getOutputSize();
}
~InceptionD() {
@@ -542,7 +569,7 @@ class InceptionD : public CUDANet::Module {
}
private:
shape2d inputSize;
shape2d inputShape;
int inputChannels;
BasicConv2d *branch3x3_1;
@@ -562,72 +589,79 @@ class InceptionD : public CUDANet::Module {
class InceptionE : public CUDANet::Module {
public:
InceptionE(
const shape2d inputSize,
const shape2d inputShape,
const int inputChannels,
const std::string &prefix
)
: inputSize(inputSize), inputChannels(inputChannels) {
: inputShape(inputShape), inputChannels(inputChannels) {
inputSize = inputShape.first * inputShape.second * inputChannels;
// Branch 1x1
branch1x1 = new BasicConv2d(
inputSize, inputChannels, 320, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 320, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch1x1"
);
addLayer("", branch1x1);
// Branch 3x3
branch3x3_1 = new BasicConv2d(
inputSize, inputChannels, 384, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 384, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch3x3_1"
);
addLayer("", branch3x3_1);
branch3x3_2a = new BasicConv2d(
inputSize, 384, 384, {1, 3}, {1, 1}, {0, 1},
inputShape, 384, 384, {1, 3}, {1, 1}, {0, 1},
prefix + ".branch3x3_2a"
);
addLayer("", branch3x3_2a);
branch3x3_2b = new BasicConv2d(
inputSize, 384, 384, {3, 1}, {1, 1}, {1, 0},
inputShape, 384, 384, {3, 1}, {1, 1}, {1, 0},
prefix + ".branch3x3_2b"
);
addLayer("", branch3x3_2b);
branch_3x3_2_concat = new CUDANet::Layers::Concat(
branch3x3_2a->getOutputSize(), branch3x3_2b->getOutputSize()
);
std::cout << "branch_3x3_2_concat: "
<< branch_3x3_2_concat->getOutputSize() << std::endl;
// Branch 3x3dbl
branch3x3dbl_1 = new BasicConv2d(
inputSize, inputChannels, 448, {1, 1}, {1, 1}, {0, 0},
inputShape, inputChannels, 448, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch3x3dbl_1"
);
addLayer("", branch3x3dbl_1);
branch3x3dbl_2 = new BasicConv2d(
inputSize, 448, 384, {3, 3}, {1, 1}, {1, 1},
inputShape, 448, 384, {3, 3}, {1, 1}, {1, 1},
prefix + ".branch3x3dbl_2"
);
addLayer("", branch3x3dbl_2);
branch3x3dbl_3a = new BasicConv2d(
inputSize, 384, 384, {1, 3}, {1, 1}, {0, 1},
inputShape, 384, 384, {1, 3}, {1, 1}, {0, 1},
prefix + ".branch3x3dbl_3a"
);
addLayer("", branch3x3dbl_3a);
branch3x3dbl_3b = new BasicConv2d(
inputSize, 384, 384, {3, 1}, {1, 1}, {1, 0},
inputShape, 384, 384, {3, 1}, {1, 1}, {1, 0},
prefix + ".branch3x3dbl_3b"
);
addLayer("", branch3x3dbl_3b);
branch_3x3dbl_3_concat = new CUDANet::Layers::Concat(
branch3x3dbl_3a->getOutputSize(), branch3x3dbl_3b->getOutputSize()
);
std::cout << "branch_3x3dbl_3_concat: "
<< branch_3x3dbl_3_concat->getOutputSize() << std::endl;
// Branch Pool
branchPool_1 = new CUDANet::Layers::AvgPooling2d(
inputSize, inputChannels, {3, 3}, {1, 1}, {1, 1},
inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
CUDANet::Layers::ActivationType::NONE
);
addLayer("", branchPool_1);
addLayer(prefix + ".branch_pool", branchPool_1);
branchPool_2 = new BasicConv2d(
inputSize, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
prefix + ".branchPool_2"
inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
prefix + ".branch_pool"
);
addLayer("", branchPool_2);
@@ -641,6 +675,8 @@ class InceptionE : public CUDANet::Module {
concat_3 = new CUDANet::Layers::Concat(
concat_2->getOutputSize(), branchPool_2->getOutputSize()
);
outputSize = concat_3->getOutputSize();
}
~InceptionE() {
@@ -704,7 +740,7 @@ class InceptionE : public CUDANet::Module {
}
private:
shape2d inputSize;
shape2d inputShape;
int inputChannels;
BasicConv2d *branch1x1;
@@ -731,24 +767,24 @@ class InceptionE : public CUDANet::Module {
class InceptionV3 : public CUDANet::Model {
public:
InceptionV3(
const shape2d inputSize,
const shape2d inputShape,
const int inputChannels,
const int outputSize
)
: CUDANet::Model(inputSize, inputChannels, outputSize) {
: CUDANet::Model(inputShape, inputChannels, outputSize) {
conv2d_1a_3x3 = new BasicConv2d(
inputSize, inputChannels, 32, {3, 3}, {2, 2}, {0, 0},
"conv2d_1a_3x3"
inputShape, inputChannels, 32, {3, 3}, {2, 2}, {0, 0},
"Conv2d_1a_3x3"
);
addLayer("", conv2d_1a_3x3);
conv2d_2a_3x3 = new BasicConv2d(
conv2d_1a_3x3->getOutputDims(), 32, 32, {3, 3}, {1, 1}, {0, 0},
"conv2d_2a_3x3"
"Conv2d_2a_3x3"
);
addLayer("", conv2d_2a_3x3);
conv2d_2b_3x3 = new BasicConv2d(
conv2d_2a_3x3->getOutputDims(), 32, 64, {3, 3}, {1, 1}, {1, 1},
"conv2d_2b_3x3"
"Conv2d_2b_3x3"
);
addLayer("", conv2d_2b_3x3);
@@ -756,16 +792,16 @@ class InceptionV3 : public CUDANet::Model {
conv2d_2b_3x3->getOutputDims(), 64, {3, 3}, {2, 2}, {0, 0},
CUDANet::Layers::ActivationType::NONE
);
addLayer("maxpool1", maxpool1);
addLayer("Maxpool1", maxpool1);
conv2d_3b_1x1 = new BasicConv2d(
maxpool1->getOutputDims(), 64, 80, {1, 1}, {1, 1}, {0, 0},
"conv2d_3b_1x1"
"Conv2d_3b_1x1"
);
addLayer("", conv2d_3b_1x1);
conv2d_4a_3x3 = new BasicConv2d(
conv2d_3b_1x1->getOutputDims(), 80, 192, {3, 3}, {1, 1}, {0, 0},
"conv2d_4a_3x3"
"Conv2d_4a_3x3"
);
addLayer("", conv2d_4a_3x3);
@@ -773,7 +809,7 @@ class InceptionV3 : public CUDANet::Model {
conv2d_4a_3x3->getOutputDims(), 192, {3, 3}, {2, 2}, {0, 0},
CUDANet::Layers::ActivationType::NONE
);
addLayer("maxpool2", maxpool2);
addLayer("Maxpool2", maxpool2);
Mixed_5b =
new InceptionA(maxpool2->getOutputDims(), 192, 32, "Mixed_5b");
@@ -816,7 +852,7 @@ class InceptionV3 : public CUDANet::Model {
addLayer("fc", fc);
}
float* predict(const float* input) {
float *predict(const float *input) {
float *d_x = inputLayer->forward(input);
d_x = conv2d_1a_3x3->forward(d_x);
@@ -839,7 +875,7 @@ class InceptionV3 : public CUDANet::Model {
d_x = Mixed_7c->forward(d_x);
d_x = fc->forward(d_x);
float* output = outputLayer->forward(d_x);
float *output = outputLayer->forward(d_x);
return output;
}
@@ -865,7 +901,6 @@ class InceptionV3 : public CUDANet::Model {
delete fc;
}
private:
BasicConv2d *conv2d_1a_3x3;
BasicConv2d *conv2d_2a_3x3;
@@ -898,12 +933,10 @@ class InceptionV3 : public CUDANet::Model {
};
int main(int argc, const char *const argv[]) {
InceptionV3 *inception_v3 = new InceptionV3({299, 299}, 3, 1000);
inception_v3->printSummary();
if (argc != 3) {
std::cerr << "Usage: " << argv[0] << "<model_weights_path> <image_path>"
<< std::endl;

11
include/model/module.hpp
View File

@@ -17,19 +17,14 @@ class Module : public Layers::SequentialLayer {
int getInputSize();
void addLayer(const std::string& name, Layers::SequentialLayer* layer);
Layers::SequentialLayer* getLayer(const std::string& name);
const std::unordered_map<std::string, Layers::SequentialLayer*>& getLayers() const;
const std::vector<std::pair<std::string, Layers::SequentialLayer*>>& getLayers() const;
protected:
int inputSize;
int inputChannels;
std::vector<std::pair<std::string, Layers::SequentialLayer*>> layers;
int outputSize;
int outputChannels;
std::vector<std::pair<std::string, Layers::SequentialLayer*>> layers;
std::unordered_map<std::string, Layers::SequentialLayer*> layerMap;
int inputSize;
};
} // namespace CUDANet

1
src/model/model.cpp
View File

@@ -67,7 +67,6 @@ void Model::addLayer(const std::string& name, Layers::SequentialLayer* layer) {
return;
}
layers.push_back({name, layer});
layerMap[name] = layer;
}

18
src/model/module.cpp
View File

@@ -1,5 +1,7 @@
#include "module.hpp"
#include <algorithm>
#include "cuda_helper.cuh"
using namespace CUDANet;
@@ -9,7 +11,6 @@ void Module::addLayer(const std::string& name, Layers::SequentialLayer* layer) {
if (module != nullptr) {
for (const auto& moduleLayer : module->getLayers()) {
layerMap[moduleLayer.first] = moduleLayer.second;
layers.push_back({moduleLayer.first, moduleLayer.second});
}
@@ -17,24 +18,17 @@ void Module::addLayer(const std::string& name, Layers::SequentialLayer* layer) {
}
layers.push_back({name, layer});
layerMap[name] = layer;
// std::cout << "Wat?! - module" << name << std::endl;
}
Layers::SequentialLayer* Module::getLayer(const std::string& name) {
return layerMap[name];
}
const std::unordered_map<std::string, Layers::SequentialLayer*>&
const std::vector<std::pair<std::string, Layers::SequentialLayer*>>&
Module::getLayers() const {
return layerMap;
return layers;
}
int Module::getInputSize() {
return inputSize * inputSize * inputChannels;
return inputSize;
}
int Module::getOutputSize() {
return outputSize * outputSize * outputChannels;
return outputSize;
}