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https://github.com/marian-nmt/marian.git
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Merge branch 'master' of github.com:emjotde/marian
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commit
8ea40b587b
@ -29,10 +29,15 @@ cuda_add_executable(
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validate_mnist
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validate_mnist.cu
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)
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cuda_add_executable(
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validate_mnist_batch
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validate_mnist_batch.cu
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)
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target_link_libraries(validate_mnist marian_lib)
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target_link_libraries(validate_mnist_batch marian_lib)
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foreach(exec marian train_mnist validate_mnist)
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foreach(exec marian train_mnist validate_mnist validate_mnist_batch )
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target_link_libraries(${exec} ${EXT_LIBS} cuda cudnn)
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cuda_add_cublas_to_target(${exec})
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set_target_properties(${exec} PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
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113
src/validate_mnist_batch.cu
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113
src/validate_mnist_batch.cu
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@ -0,0 +1,113 @@
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#include "marian.h"
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#include "mnist.h"
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#include "npz_converter.h"
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using namespace marian;
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using namespace keywords;
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int main(int argc, char** argv) {
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cudaSetDevice(0);
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const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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const size_t BATCH_SIZE = 24;
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int numofdata;
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std::cerr << "Loading test set...";
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std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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std::cerr << "\tDone." << std::endl;
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std::cerr << "Loading model params...";
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NpzConverter converter("../scripts/test_model/model.npz");
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std::vector<float> wData;
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Shape wShape;
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converter.Load("weights", wData, wShape);
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std::vector<float> bData;
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Shape bShape;
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converter.Load("bias", bData, bShape);
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auto initW = [wData](Tensor t) {
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t.set(wData);
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};
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auto initB = [bData](Tensor t) {
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t.set(bData);
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};
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std::cerr << "\tDone." << std::endl;
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auto x = input(shape={whatevs, IMAGE_SIZE}, name="X");
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auto y = input(shape={whatevs, LABEL_SIZE}, name="Y");
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auto w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
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auto b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
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std::cerr << "Building model...";
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auto predict = softmax(dot(x, w) + b, axis=1, name="pred");
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std::cerr << "Done." << std::endl;
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Tensor xt({BATCH_SIZE, IMAGE_SIZE});
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size_t acc = 0;
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size_t startId = 0;
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size_t endId = startId + BATCH_SIZE;
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while (endId < numofdata) {
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std::vector<float> tmp(testImages.begin() + (startId * IMAGE_SIZE),
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testImages.begin() + (endId * IMAGE_SIZE));
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xt << tmp;
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x = xt;
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predict.forward(BATCH_SIZE);
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std::vector<float> results(LABEL_SIZE * BATCH_SIZE);
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results << predict.val();
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for (size_t i = 0; i < BATCH_SIZE * LABEL_SIZE; i += LABEL_SIZE) {
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size_t correct = 0;
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size_t predicted = 0;
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for (size_t j = 0; j < LABEL_SIZE; ++j) {
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if (testLabels[startId * LABEL_SIZE + i + j]) correct = j;
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if (results[i + j] > results[i + predicted]) predicted = j;
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}
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acc += (correct == predicted);
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}
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startId += BATCH_SIZE;
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endId += BATCH_SIZE;
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}
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if (endId != numofdata) {
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endId = numofdata;
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if (endId - startId > 0) {
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std::vector<float> tmp(testImages.begin() + (startId * IMAGE_SIZE),
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testImages.begin() + (endId * IMAGE_SIZE));
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xt << tmp;
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x = xt;
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predict.forward(endId - startId);
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std::vector<float> results(LABEL_SIZE * BATCH_SIZE);
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results << predict.val();
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for (size_t i = 0; i < (endId - startId) * LABEL_SIZE; i += LABEL_SIZE) {
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size_t correct = 0;
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size_t predicted = 0;
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for (size_t j = 0; j < LABEL_SIZE; ++j) {
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if (testLabels[startId * LABEL_SIZE + i + j]) correct = j;
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if (results[i + j] > results[i + predicted]) predicted = j;
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}
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acc += (correct == predicted);
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}
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}
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}
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std::cerr << "ACC: " << float(acc)/numofdata << std::endl;
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return 0;
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}
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