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Split inception v3 source file
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764
examples/inception_v3/inception_modules.cpp
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764
examples/inception_v3/inception_modules.cpp
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#include "inception_v3.hpp"
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#include <cudanet.cuh>
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// Inception Basic Convolution 2D
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BasicConv2d::BasicConv2d(
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const shape2d inputShape,
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const int inputChannels,
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const int outputChannels,
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const shape2d kernelSize,
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const shape2d stride,
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const shape2d padding,
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const std::string &prefix
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)
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: outputChannels(outputChannels) {
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// Create the convolution layer
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conv = new CUDANet::Layers::Conv2d(
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inputShape, inputChannels, kernelSize, stride, outputChannels, padding,
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CUDANet::Layers::ActivationType::NONE
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);
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shape2d batchNormSize = conv->getOutputDims();
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batchNorm = new CUDANet::Layers::BatchNorm2d(
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batchNormSize, outputChannels, 1e-3f,
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CUDANet::Layers::ActivationType::RELU
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);
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inputSize = inputShape.first * inputShape.second * inputChannels;
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outputSize = batchNorm->getOutputDims().first *
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batchNorm->getOutputDims().second * outputChannels;
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addLayer(prefix + ".conv", conv);
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addLayer(prefix + ".bn", batchNorm);
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}
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BasicConv2d::~BasicConv2d() {
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delete conv;
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delete batchNorm;
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}
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float *BasicConv2d::forward(const float *d_input) {
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float *d_output = conv->forward(d_input);
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return batchNorm->forward(d_output);
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}
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shape2d BasicConv2d::getOutputDims() {
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return batchNorm->getOutputDims();
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}
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int BasicConv2d::getOutputChannels() {
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return outputChannels;
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}
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// Inception Block A
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InceptionA::InceptionA(
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const shape2d inputShape,
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const int inputChannels,
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const int poolFeatures,
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const std::string &prefix
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)
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: inputShape(inputShape),
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inputChannels(inputChannels),
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poolFeatures(poolFeatures) {
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inputSize = inputShape.first * inputShape.second * inputChannels;
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// Branch 1x1
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branch1x1 = new BasicConv2d(
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inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
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prefix + ".branch1x1"
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);
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addLayer("", branch1x1);
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// Branch 5x5
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branch5x5_1 = new BasicConv2d(
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inputShape, inputChannels, 48, {1, 1}, {1, 1}, {0, 0},
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prefix + ".branch5x5_1"
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);
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addLayer("", branch5x5_1);
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branch5x5_2 = new BasicConv2d(
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branch5x5_1->getOutputDims(), 48, 64, {5, 5}, {1, 1}, {2, 2},
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prefix + ".branch5x5_2"
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);
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addLayer("", branch5x5_2);
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// Branch 3x3
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branch3x3dbl_1 = new BasicConv2d(
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inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
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prefix + ".branch3x3dbl_1"
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);
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addLayer("", branch3x3dbl_1);
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branch3x3dbl_2 = new BasicConv2d(
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branch3x3dbl_1->getOutputDims(), 64, 96, {3, 3}, {1, 1}, {1, 1},
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prefix + ".branch3x3dbl_2"
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);
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addLayer("", branch3x3dbl_2);
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branch3x3dbl_3 = new BasicConv2d(
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branch3x3dbl_2->getOutputDims(), 96, 96, {3, 3}, {1, 1}, {1, 1},
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prefix + ".branch3x3dbl_3"
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);
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addLayer("", branch3x3dbl_3);
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// Branch Pool
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branchPool_1 = new CUDANet::Layers::AvgPooling2d(
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inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
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CUDANet::Layers::ActivationType::NONE
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);
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addLayer(prefix + ".branch_pool", branchPool_1);
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branchPool_2 = new BasicConv2d(
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branchPool_1->getOutputDims(), inputChannels, poolFeatures, {1, 1},
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{1, 1}, {0, 0}, prefix + ".branch_pool"
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);
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addLayer("", branchPool_2);
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// Concat
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concat_1 = new CUDANet::Layers::Concat(
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branch1x1->getOutputSize(), branch5x5_2->getOutputSize()
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);
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concat_2 = new CUDANet::Layers::Concat(
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concat_1->getOutputSize(), branch3x3dbl_3->getOutputSize()
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);
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concat_3 = new CUDANet::Layers::Concat(
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concat_2->getOutputSize(), branchPool_2->getOutputSize()
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);
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outputSize = concat_3->getOutputSize();
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}
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InceptionA::~InceptionA() {
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delete branch1x1;
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delete branch5x5_1;
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delete branch5x5_2;
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delete branch3x3dbl_1;
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delete branch3x3dbl_2;
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delete branch3x3dbl_3;
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delete branchPool_1;
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delete branchPool_2;
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delete concat_1;
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delete concat_2;
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delete concat_3;
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}
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float *InceptionA::forward(const float *d_input) {
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float *d_branch1x1_out = branch1x1->forward(d_input);
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float *d_branch5x5_out = branch5x5_1->forward(d_input);
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d_branch5x5_out = branch5x5_2->forward(d_branch5x5_out);
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float *d_branch3x3dbl_out = branch3x3dbl_1->forward(d_input);
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d_branch3x3dbl_out = branch3x3dbl_2->forward(d_branch3x3dbl_out);
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d_branch3x3dbl_out = branch3x3dbl_3->forward(d_branch3x3dbl_out);
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float *d_branchPool_out = branchPool_1->forward(d_input);
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d_branchPool_out = branchPool_2->forward(d_branchPool_out);
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float *d_output = concat_1->forward(d_branch1x1_out, d_branch5x5_out);
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d_output = concat_2->forward(d_output, d_branch3x3dbl_out);
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d_output = concat_3->forward(d_output, d_branchPool_out);
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return d_output;
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}
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shape2d InceptionA::getOutputDims() {
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return branch1x1->getOutputDims();
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}
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int InceptionA::getOutputChannels() {
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return branch1x1->getOutputChannels() + branch5x5_2->getOutputChannels() +
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branch3x3dbl_3->getOutputChannels() +
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branchPool_2->getOutputChannels();
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}
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// Inception Block B
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InceptionB::InceptionB(
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const shape2d inputShape,
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const int inputChannels,
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const std::string &prefix
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)
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: inputShape(inputShape), inputChannels(inputChannels) {
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inputSize = inputShape.first * inputShape.second * inputChannels;
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// Branch 3x3
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branch3x3 = new BasicConv2d(
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inputShape, inputChannels, 384, {3, 3}, {2, 2}, {0, 0},
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prefix + ".branch3x3"
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);
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addLayer("", branch3x3);
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// Branch 3x3dbl
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branch3x3dbl_1 = new BasicConv2d(
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inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
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prefix + ".branch3x3dbl_1"
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);
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addLayer("", branch3x3dbl_1);
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branch3x3dbl_2 = new BasicConv2d(
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branch3x3dbl_1->getOutputDims(), 64, 96, {3, 3}, {1, 1}, {1, 1},
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prefix + ".branch3x3dbl_2"
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);
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addLayer("", branch3x3dbl_2);
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branch3x3dbl_3 = new BasicConv2d(
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branch3x3dbl_2->getOutputDims(), 96, 96, {3, 3}, {2, 2}, {1, 1},
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prefix + ".branch3x3dbl_3"
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);
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addLayer("", branch3x3dbl_3);
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branchPool = new CUDANet::Layers::MaxPooling2d(
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inputShape, inputChannels, {3, 3}, {2, 2}, {0, 0},
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CUDANet::Layers::ActivationType::NONE
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);
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addLayer(prefix + ".branch_pool", branchPool);
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concat_1 = new CUDANet::Layers::Concat(
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branch3x3->getOutputSize(), branch3x3dbl_3->getOutputSize()
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);
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concat_2 = new CUDANet::Layers::Concat(
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concat_1->getOutputSize(), branchPool->getOutputSize()
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);
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outputSize = concat_2->getOutputSize();
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}
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InceptionB::~InceptionB() {
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delete branch3x3;
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delete branch3x3dbl_1;
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delete branch3x3dbl_2;
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delete branch3x3dbl_3;
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delete branchPool;
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delete concat_1;
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delete concat_2;
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}
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float *InceptionB::forward(const float *d_input) {
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float *d_branch3x3_out = branch3x3->forward(d_input);
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float *d_branch3x3dbl_out = branch3x3dbl_1->forward(d_input);
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d_branch3x3dbl_out = branch3x3dbl_2->forward(d_branch3x3dbl_out);
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d_branch3x3dbl_out = branch3x3dbl_3->forward(d_branch3x3dbl_out);
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float *d_branchPool_out = branchPool->forward(d_input);
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float *d_output = concat_1->forward(d_branch3x3_out, d_branch3x3dbl_out);
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d_output = concat_2->forward(d_output, d_branchPool_out);
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return d_output;
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}
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shape2d InceptionB::getOutputDims() {
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return branch3x3->getOutputDims();
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}
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int InceptionB::getOutputChannels() {
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return branch3x3->getOutputChannels() +
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branch3x3dbl_3->getOutputChannels() + inputChannels;
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}
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// Inception Block C
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InceptionC::InceptionC(
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const shape2d inputShape,
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const int inputChannels,
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const int nChannels_7x7,
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const std::string &prefix
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)
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: inputShape(inputShape), inputChannels(inputChannels) {
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inputSize = inputShape.first * inputShape.second * inputChannels;
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// Branch 1x1
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branch1x1 = new BasicConv2d(
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inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
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prefix + ".branch1x1"
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);
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addLayer("", branch1x1);
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// Branch 7x7
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branch7x7_1 = new BasicConv2d(
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inputShape, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
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prefix + ".branch7x7_1"
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);
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addLayer("", branch7x7_1);
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branch7x7_2 = new BasicConv2d(
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branch7x7_1->getOutputDims(), nChannels_7x7, nChannels_7x7, {1, 7},
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{1, 1}, {0, 3}, prefix + ".branch7x7_2"
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);
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addLayer("", branch7x7_2);
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branch7x7_3 = new BasicConv2d(
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branch7x7_2->getOutputDims(), nChannels_7x7, 192, {7, 1}, {1, 1},
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{3, 0}, prefix + ".branch7x7_3"
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);
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addLayer("", branch7x7_3);
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// Branch 7x7dbl
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branch7x7dbl_1 = new BasicConv2d(
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inputShape, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
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prefix + ".branch7x7dbl_1"
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);
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addLayer("", branch7x7dbl_1);
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branch7x7dbl_2 = new BasicConv2d(
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branch7x7dbl_1->getOutputDims(), nChannels_7x7, nChannels_7x7, {7, 1},
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{1, 1}, {3, 0}, prefix + ".branch7x7dbl_2"
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);
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addLayer("", branch7x7dbl_2);
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branch7x7dbl_3 = new BasicConv2d(
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branch7x7dbl_2->getOutputDims(), nChannels_7x7, nChannels_7x7, {1, 7},
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{1, 1}, {0, 3}, prefix + ".branch7x7dbl_3"
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);
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addLayer("", branch7x7dbl_3);
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branch7x7dbl_4 = new BasicConv2d(
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branch7x7dbl_3->getOutputDims(), nChannels_7x7, nChannels_7x7, {7, 1},
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{1, 1}, {3, 0}, prefix + ".branch7x7dbl_4"
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);
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addLayer("", branch7x7dbl_4);
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branch7x7dbl_5 = new BasicConv2d(
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branch7x7dbl_4->getOutputDims(), nChannels_7x7, 192, {1, 7}, {1, 1},
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{0, 3}, prefix + ".branch7x7dbl_5"
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);
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addLayer("", branch7x7dbl_5);
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// Branch Pool
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branchPool_1 = new CUDANet::Layers::AvgPooling2d(
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inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
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CUDANet::Layers::ActivationType::NONE
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);
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addLayer(prefix + ".branch_pool", branchPool_1);
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branchPool_2 = new BasicConv2d(
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branchPool_1->getOutputDims(), inputChannels, 192, {1, 1}, {1, 1},
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{0, 0}, prefix + ".branch_pool"
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);
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addLayer("", branchPool_2);
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// Concat
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concat_1 = new CUDANet::Layers::Concat(
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branch1x1->getOutputSize(), branch7x7_3->getOutputSize()
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);
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concat_2 = new CUDANet::Layers::Concat(
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concat_1->getOutputSize(), branch7x7dbl_5->getOutputSize()
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);
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concat_3 = new CUDANet::Layers::Concat(
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concat_2->getOutputSize(), branchPool_2->getOutputSize()
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);
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outputSize = concat_3->getOutputSize();
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}
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InceptionC::~InceptionC() {
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delete branch1x1;
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delete branch7x7_1;
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delete branch7x7_2;
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delete branch7x7_3;
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delete branch7x7dbl_1;
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delete branch7x7dbl_2;
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delete branch7x7dbl_3;
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delete branch7x7dbl_4;
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delete branch7x7dbl_5;
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delete branchPool_1;
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delete branchPool_2;
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delete concat_1;
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delete concat_2;
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delete concat_3;
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}
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float *InceptionC::forward(const float *d_input) {
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float *branch1x1_output = branch1x1->forward(d_input);
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float *branch7x7_output = branch7x7_1->forward(d_input);
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branch7x7_output = branch7x7_2->forward(branch7x7_output);
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branch7x7_output = branch7x7_3->forward(branch7x7_output);
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float *branch7x7dbl_output = branch7x7dbl_1->forward(d_input);
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branch7x7dbl_output = branch7x7dbl_2->forward(branch7x7dbl_output);
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branch7x7dbl_output = branch7x7dbl_3->forward(branch7x7dbl_output);
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branch7x7dbl_output = branch7x7dbl_4->forward(branch7x7dbl_output);
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branch7x7dbl_output = branch7x7dbl_5->forward(branch7x7dbl_output);
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float *branchPool_output = branchPool_1->forward(d_input);
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branchPool_output = branchPool_2->forward(branchPool_output);
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float *d_output = concat_1->forward(branch1x1_output, branch7x7_output);
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d_output = concat_2->forward(d_output, branch7x7dbl_output);
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d_output = concat_3->forward(d_output, branchPool_output);
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return d_output;
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}
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shape2d InceptionC::getOutputDims() {
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return branch1x1->getOutputDims();
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}
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int InceptionC::getOutputChannels() {
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return branch1x1->getOutputChannels() + branch7x7_3->getOutputChannels() +
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branch7x7dbl_5->getOutputChannels() +
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branchPool_2->getOutputChannels();
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}
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// Inception Block D
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InceptionD::InceptionD(
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const shape2d inputShape,
|
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const int inputChannels,
|
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const std::string &prefix
|
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)
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: inputShape(inputShape), inputChannels(inputChannels) {
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inputSize = inputShape.first * inputShape.second * inputChannels;
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// Branch 3x3
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branch3x3_1 = new BasicConv2d(
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inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
|
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prefix + ".branch3x3_1"
|
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);
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addLayer("", branch3x3_1);
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branch3x3_2 = new BasicConv2d(
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inputShape, 192, 320, {3, 3}, {2, 2}, {0, 0}, prefix + ".branch3x3_2"
|
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);
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addLayer("", branch3x3_2);
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// Branch 7x7x3
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branch7x7x3_1 = new BasicConv2d(
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inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
|
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prefix + ".branch7x7x3_1"
|
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);
|
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addLayer("", branch7x7x3_1);
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branch7x7x3_2 = new BasicConv2d(
|
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inputShape, 192, 192, {1, 7}, {1, 1}, {0, 3}, prefix + ".branch7x7x3_2"
|
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);
|
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addLayer("", branch7x7x3_2);
|
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branch7x7x3_3 = new BasicConv2d(
|
||||
inputShape, 192, 192, {7, 1}, {1, 1}, {3, 0}, prefix + ".branch7x7x3_3"
|
||||
);
|
||||
addLayer("", branch7x7x3_3);
|
||||
branch7x7x3_4 = new BasicConv2d(
|
||||
inputShape, 192, 192, {3, 3}, {2, 2}, {0, 0}, prefix + ".branch7x7x3_4"
|
||||
);
|
||||
addLayer("", branch7x7x3_4);
|
||||
|
||||
// Branch Pool
|
||||
branchPool = new CUDANet::Layers::MaxPooling2d(
|
||||
inputShape, 192, {3, 3}, {2, 2}, {0, 0},
|
||||
CUDANet::Layers::ActivationType::NONE
|
||||
);
|
||||
addLayer(prefix + ".branch_pool", branchPool);
|
||||
|
||||
// Concat
|
||||
concat_1 = new CUDANet::Layers::Concat(
|
||||
branch3x3_2->getOutputSize(), branch7x7x3_4->getOutputSize()
|
||||
);
|
||||
concat_2 = new CUDANet::Layers::Concat(
|
||||
concat_1->getOutputSize(), branchPool->getOutputSize()
|
||||
);
|
||||
|
||||
outputSize = concat_2->getOutputSize();
|
||||
}
|
||||
|
||||
InceptionD::~InceptionD() {
|
||||
delete branch3x3_1;
|
||||
delete branch3x3_2;
|
||||
delete branch7x7x3_1;
|
||||
delete branch7x7x3_2;
|
||||
delete branch7x7x3_3;
|
||||
delete branch7x7x3_4;
|
||||
delete branchPool;
|
||||
delete concat_1;
|
||||
delete concat_2;
|
||||
}
|
||||
|
||||
float *InceptionD::forward(const float *d_input) {
|
||||
float *branch3x3_output = branch3x3_1->forward(d_input);
|
||||
branch3x3_output = branch3x3_2->forward(branch3x3_output);
|
||||
|
||||
float *branch7x7_output = branch7x7x3_1->forward(d_input);
|
||||
branch7x7_output = branch7x7x3_2->forward(branch7x7_output);
|
||||
branch7x7_output = branch7x7x3_3->forward(branch7x7_output);
|
||||
branch7x7_output = branch7x7x3_4->forward(branch7x7_output);
|
||||
|
||||
float *branchPool_output = branchPool->forward(d_input);
|
||||
|
||||
float *d_output = concat_1->forward(branch3x3_output, branch7x7_output);
|
||||
d_output = concat_2->forward(d_output, branchPool_output);
|
||||
|
||||
return d_output;
|
||||
}
|
||||
|
||||
shape2d InceptionD::getOutputDims() {
|
||||
return branch3x3_2->getOutputDims();
|
||||
}
|
||||
|
||||
int InceptionD::getOutputChannels() {
|
||||
return branch3x3_2->getOutputChannels() +
|
||||
branch7x7x3_4->getOutputChannels() + inputChannels;
|
||||
}
|
||||
|
||||
// Inception Block E
|
||||
InceptionE::InceptionE(
|
||||
const shape2d inputShape,
|
||||
const int inputChannels,
|
||||
const std::string &prefix
|
||||
)
|
||||
: inputShape(inputShape), inputChannels(inputChannels) {
|
||||
inputSize = inputShape.first * inputShape.second * inputChannels;
|
||||
|
||||
// Branch 1x1
|
||||
branch1x1 = new BasicConv2d(
|
||||
inputShape, inputChannels, 320, {1, 1}, {1, 1}, {0, 0},
|
||||
prefix + ".branch1x1"
|
||||
);
|
||||
addLayer("", branch1x1);
|
||||
|
||||
// Branch 3x3
|
||||
branch3x3_1 = new BasicConv2d(
|
||||
inputShape, inputChannels, 384, {1, 1}, {1, 1}, {0, 0},
|
||||
prefix + ".branch3x3_1"
|
||||
);
|
||||
addLayer("", branch3x3_1);
|
||||
branch3x3_2a = new BasicConv2d(
|
||||
inputShape, 384, 384, {1, 3}, {1, 1}, {0, 1}, prefix + ".branch3x3_2a"
|
||||
);
|
||||
addLayer("", branch3x3_2a);
|
||||
branch3x3_2b = new BasicConv2d(
|
||||
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()
|
||||
);
|
||||
|
||||
// Branch 3x3dbl
|
||||
branch3x3dbl_1 = new BasicConv2d(
|
||||
inputShape, inputChannels, 448, {1, 1}, {1, 1}, {0, 0},
|
||||
prefix + ".branch3x3dbl_1"
|
||||
);
|
||||
addLayer("", branch3x3dbl_1);
|
||||
branch3x3dbl_2 = new BasicConv2d(
|
||||
inputShape, 448, 384, {3, 3}, {1, 1}, {1, 1}, prefix + ".branch3x3dbl_2"
|
||||
);
|
||||
addLayer("", branch3x3dbl_2);
|
||||
branch3x3dbl_3a = new BasicConv2d(
|
||||
inputShape, 384, 384, {1, 3}, {1, 1}, {0, 1},
|
||||
prefix + ".branch3x3dbl_3a"
|
||||
);
|
||||
addLayer("", branch3x3dbl_3a);
|
||||
branch3x3dbl_3b = new BasicConv2d(
|
||||
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()
|
||||
);
|
||||
|
||||
// Branch Pool
|
||||
branchPool_1 = new CUDANet::Layers::AvgPooling2d(
|
||||
inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
|
||||
CUDANet::Layers::ActivationType::NONE
|
||||
);
|
||||
addLayer(prefix + ".branch_pool", branchPool_1);
|
||||
branchPool_2 = new BasicConv2d(
|
||||
inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
|
||||
prefix + ".branch_pool"
|
||||
);
|
||||
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()
|
||||
);
|
||||
|
||||
outputSize = concat_3->getOutputSize();
|
||||
}
|
||||
|
||||
InceptionE::~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 *InceptionE::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;
|
||||
}
|
||||
|
||||
shape2d InceptionE::getOutputDims() {
|
||||
return branch3x3_2a->getOutputDims();
|
||||
}
|
||||
|
||||
int InceptionE::getOutputChannels() {
|
||||
return branch1x1->getOutputChannels() + branch3x3_2a->getOutputChannels() +
|
||||
branch3x3_2b->getOutputChannels() +
|
||||
branch3x3dbl_3a->getOutputChannels() +
|
||||
branch3x3dbl_3b->getOutputChannels() +
|
||||
branchPool_2->getOutputChannels();
|
||||
}
|
||||
|
||||
// InceptionV3 Model
|
||||
InceptionV3::InceptionV3(
|
||||
const shape2d inputShape,
|
||||
const int inputChannels,
|
||||
const int outputSize
|
||||
)
|
||||
: CUDANet::Model(inputShape, inputChannels, outputSize) {
|
||||
conv2d_1a_3x3 = new BasicConv2d(
|
||||
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"
|
||||
);
|
||||
addLayer("", conv2d_2a_3x3);
|
||||
conv2d_2b_3x3 = new BasicConv2d(
|
||||
conv2d_2a_3x3->getOutputDims(), 32, 64, {3, 3}, {1, 1}, {1, 1},
|
||||
"Conv2d_2b_3x3"
|
||||
);
|
||||
addLayer("", conv2d_2b_3x3);
|
||||
|
||||
maxpool1 = new CUDANet::Layers::MaxPooling2d(
|
||||
conv2d_2b_3x3->getOutputDims(), 64, {3, 3}, {2, 2}, {0, 0},
|
||||
CUDANet::Layers::ActivationType::NONE
|
||||
);
|
||||
addLayer("Maxpool1", maxpool1);
|
||||
|
||||
conv2d_3b_1x1 = new BasicConv2d(
|
||||
maxpool1->getOutputDims(), 64, 80, {1, 1}, {1, 1}, {0, 0},
|
||||
"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"
|
||||
);
|
||||
addLayer("", conv2d_4a_3x3);
|
||||
|
||||
maxpool2 = new CUDANet::Layers::MaxPooling2d(
|
||||
conv2d_4a_3x3->getOutputDims(), 192, {3, 3}, {2, 2}, {0, 0},
|
||||
CUDANet::Layers::ActivationType::NONE
|
||||
);
|
||||
addLayer("Maxpool2", maxpool2);
|
||||
|
||||
Mixed_5b = new InceptionA(maxpool2->getOutputDims(), 192, 32, "Mixed_5b");
|
||||
addLayer("", Mixed_5b);
|
||||
Mixed_5c = new InceptionA(Mixed_5b->getOutputDims(), 256, 64, "Mixed_5c");
|
||||
addLayer("", Mixed_5c);
|
||||
Mixed_5d = new InceptionA(Mixed_5c->getOutputDims(), 288, 64, "Mixed_5d");
|
||||
addLayer("", Mixed_5d);
|
||||
|
||||
Mixed_6a = new InceptionB(Mixed_5d->getOutputDims(), 288, "Mixed_6a");
|
||||
addLayer("", Mixed_6a);
|
||||
|
||||
Mixed_6b = new InceptionC(Mixed_6a->getOutputDims(), 768, 128, "Mixed_6b");
|
||||
addLayer("", Mixed_6b);
|
||||
Mixed_6c = new InceptionC(Mixed_6b->getOutputDims(), 768, 160, "Mixed_6c");
|
||||
addLayer("", Mixed_6c);
|
||||
Mixed_6d = new InceptionC(Mixed_6c->getOutputDims(), 768, 160, "Mixed_6d");
|
||||
addLayer("", Mixed_6d);
|
||||
Mixed_6e = new InceptionC(Mixed_6d->getOutputDims(), 768, 192, "Mixed_6e");
|
||||
addLayer("", Mixed_6e);
|
||||
|
||||
Mixed_7a = new InceptionD(Mixed_6e->getOutputDims(), 768, "Mixed_7a");
|
||||
addLayer("", Mixed_7a);
|
||||
|
||||
Mixed_7b = new InceptionE(Mixed_7a->getOutputDims(), 1280, "Mixed_7b");
|
||||
addLayer("", Mixed_7b);
|
||||
Mixed_7c = new InceptionE(Mixed_7b->getOutputDims(), 2048, "Mixed_7c");
|
||||
addLayer("", Mixed_7c);
|
||||
|
||||
avgpool = new CUDANet::Layers::AdaptiveAvgPooling2d(
|
||||
Mixed_7c->getOutputDims(), Mixed_7c->getOutputChannels(), {1, 1},
|
||||
CUDANet::Layers::ActivationType::NONE
|
||||
);
|
||||
addLayer("AveragePool", avgpool);
|
||||
|
||||
fc = new CUDANet::Layers::Dense(
|
||||
avgpool->getOutputSize(), 1000, CUDANet::Layers::ActivationType::SOFTMAX
|
||||
);
|
||||
addLayer("fc", fc);
|
||||
}
|
||||
|
||||
float *InceptionV3::predict(const float *input) {
|
||||
float *d_x = inputLayer->forward(input);
|
||||
|
||||
d_x = conv2d_1a_3x3->forward(d_x);
|
||||
d_x = conv2d_2a_3x3->forward(d_x);
|
||||
d_x = conv2d_2b_3x3->forward(d_x);
|
||||
d_x = maxpool1->forward(d_x);
|
||||
d_x = conv2d_3b_1x1->forward(d_x);
|
||||
d_x = conv2d_4a_3x3->forward(d_x);
|
||||
d_x = maxpool2->forward(d_x);
|
||||
d_x = Mixed_5b->forward(d_x);
|
||||
d_x = Mixed_5c->forward(d_x);
|
||||
d_x = Mixed_5d->forward(d_x);
|
||||
d_x = Mixed_6a->forward(d_x);
|
||||
d_x = Mixed_6b->forward(d_x);
|
||||
d_x = Mixed_6c->forward(d_x);
|
||||
d_x = Mixed_6d->forward(d_x);
|
||||
d_x = Mixed_6e->forward(d_x);
|
||||
d_x = Mixed_7a->forward(d_x);
|
||||
d_x = Mixed_7b->forward(d_x);
|
||||
d_x = Mixed_7c->forward(d_x);
|
||||
d_x = avgpool->forward(d_x);
|
||||
d_x = fc->forward(d_x);
|
||||
|
||||
float *output = outputLayer->forward(d_x);
|
||||
return output;
|
||||
}
|
||||
|
||||
InceptionV3::~InceptionV3() {
|
||||
delete conv2d_1a_3x3;
|
||||
delete conv2d_2a_3x3;
|
||||
delete conv2d_2b_3x3;
|
||||
delete maxpool1;
|
||||
delete conv2d_3b_1x1;
|
||||
delete conv2d_4a_3x3;
|
||||
delete maxpool2;
|
||||
delete Mixed_5b;
|
||||
delete Mixed_5c;
|
||||
delete Mixed_5d;
|
||||
delete Mixed_6a;
|
||||
delete Mixed_6b;
|
||||
delete Mixed_6c;
|
||||
delete Mixed_6d;
|
||||
delete Mixed_6e;
|
||||
delete Mixed_7a;
|
||||
delete Mixed_7b;
|
||||
delete Mixed_7c;
|
||||
delete avgpool;
|
||||
delete fc;
|
||||
}
|
||||
Reference in New Issue
Block a user