Merge branch 'master' of github.com:emjotde/marian

src/CMakeLists.txt

@@ -29,10 +29,15 @@ cuda_add_executable(
   validate_mnist
   validate_mnist.cu
 )
+cuda_add_executable(
+  validate_mnist_batch
+  validate_mnist_batch.cu
+)
 
 target_link_libraries(validate_mnist marian_lib)
+target_link_libraries(validate_mnist_batch marian_lib)
 
-foreach(exec marian train_mnist validate_mnist)
+foreach(exec marian train_mnist validate_mnist validate_mnist_batch )
   target_link_libraries(${exec} ${EXT_LIBS} cuda cudnn)
   cuda_add_cublas_to_target(${exec})
   set_target_properties(${exec} PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")

src/validate_mnist_batch.cu

@@ -0,0 +1,113 @@
+
+#include "marian.h"
+#include "mnist.h"
+#include "npz_converter.h"
+
+using namespace marian;
+using namespace keywords;
+
+int main(int argc, char** argv) {
+  
+  cudaSetDevice(0);
+  
+  const size_t IMAGE_SIZE = 784;
+  const size_t LABEL_SIZE = 10;
+  const size_t BATCH_SIZE = 24;
+  int numofdata;
+
+  std::cerr << "Loading test set...";
+  std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
+  std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
+  std::cerr << "\tDone." << std::endl;
+
+  std::cerr << "Loading model params...";
+  NpzConverter converter("../scripts/test_model/model.npz");
+
+  std::vector<float> wData;
+  Shape wShape;
+  converter.Load("weights", wData, wShape);
+
+  std::vector<float> bData;
+  Shape bShape;
+  converter.Load("bias", bData, bShape);
+
+  auto initW = [wData](Tensor t) {
+    t.set(wData);
+  };
+
+  auto initB = [bData](Tensor t) {
+    t.set(bData);
+  };
+
+  std::cerr << "\tDone." << std::endl;
+
+
+  auto x = input(shape={whatevs, IMAGE_SIZE}, name="X");
+  auto y = input(shape={whatevs, LABEL_SIZE}, name="Y");
+
+  auto w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
+  auto b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
+
+  std::cerr << "Building model...";
+  auto predict = softmax(dot(x, w) + b, axis=1, name="pred");
+
+  std::cerr << "Done." << std::endl;
+
+  Tensor xt({BATCH_SIZE, IMAGE_SIZE});
+
+  size_t acc = 0;
+  size_t startId = 0;
+  size_t endId = startId + BATCH_SIZE;
+
+  while (endId < numofdata) {
+    std::vector<float> tmp(testImages.begin() + (startId * IMAGE_SIZE),
+                           testImages.begin() + (endId * IMAGE_SIZE));
+    xt << tmp;
+    x = xt;
+
+    predict.forward(BATCH_SIZE);
+
+    std::vector<float> results(LABEL_SIZE * BATCH_SIZE);
+    results << predict.val();
+
+    for (size_t i = 0; i < BATCH_SIZE * LABEL_SIZE; i += LABEL_SIZE) {
+      size_t correct = 0;
+      size_t predicted = 0;
+      for (size_t j = 0; j < LABEL_SIZE; ++j) {
+        if (testLabels[startId * LABEL_SIZE + i + j]) correct = j;
+        if (results[i + j] > results[i + predicted]) predicted = j;
+      }
+      acc += (correct == predicted);
+    }
+
+    startId += BATCH_SIZE;
+    endId += BATCH_SIZE;
+  }
+  if (endId != numofdata) {
+    endId = numofdata;
+    if (endId - startId > 0) {
+      std::vector<float> tmp(testImages.begin() + (startId * IMAGE_SIZE),
+                             testImages.begin() + (endId * IMAGE_SIZE));
+      xt << tmp;
+      x = xt;
+
+      predict.forward(endId - startId);
+
+      std::vector<float> results(LABEL_SIZE * BATCH_SIZE);
+      results << predict.val();
+
+      for (size_t i = 0; i < (endId - startId) * LABEL_SIZE; i += LABEL_SIZE) {
+        size_t correct = 0;
+        size_t predicted = 0;
+        for (size_t j = 0; j < LABEL_SIZE; ++j) {
+          if (testLabels[startId * LABEL_SIZE + i + j]) correct = j;
+          if (results[i + j] > results[i + predicted]) predicted = j;
+        }
+        acc += (correct == predicted);
+      }
+    }
+  }
+  std::cerr << "ACC: " << float(acc)/numofdata << std::endl;
+
+  return 0;
+}