From 4b730f76454a975b847838858a2b75fc4ddb2cbb Mon Sep 17 00:00:00 2001
From: Tomasz Dwojak <t.dwojak@amu.edu.pl>
Date: Wed, 14 Sep 2016 22:28:45 +0100
Subject: [PATCH] Speed up validation; add batch.

---
 src/validate_mnist.cu | 71 +++++++++++++++++++++++++++++++------------
 1 file changed, 52 insertions(+), 19 deletions(-)

diff --git a/src/validate_mnist.cu b/src/validate_mnist.cu
index a42fa881..1ed69407 100644
--- a/src/validate_mnist.cu
+++ b/src/validate_mnist.cu
@@ -9,11 +9,12 @@ using namespace keywords;
 int main(int argc, char** argv) {
   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::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...";
@@ -48,29 +49,61 @@ int main(int argc, char** argv) {
   auto predict = softmax(scores, axis=1, name="pred");
   std::cerr << "\tDone." << std::endl;
 
-  Tensor xt({numofdata, IMAGE_SIZE});
-  xt.Load(testImages);
-
-  predict.forward(numofdata);
-
-  auto results = predict.val();
+  Tensor xt({BATCH_SIZE, IMAGE_SIZE});
+  /* xt.Load(testImages); */
+  /* x = xt; */
 
   size_t acc = 0;
+  size_t startId = 0;
+  size_t endId = startId + BATCH_SIZE;
 
-  for (size_t i = 0; i < testLabels.size(); i += LABEL_SIZE) {
-    size_t correct = 0;
-    size_t predicted = 0;
-    for (size_t j = 0; j < LABEL_SIZE; ++j) {
-      if (testLabels[i+j]) correct = j;
-      if (results[i + j] > results[i + predicted]) predicted = j;
+  while (endId < numofdata) {
+    std::vector<float> tmp(testImages.begin() + (startId * IMAGE_SIZE),
+                           testImages.begin() + (endId * IMAGE_SIZE));
+    xt.Load(tmp);
+    x = xt;
+
+    predict.forward(BATCH_SIZE);
+
+    thrust::host_vector<float> results(predict.val().begin(), predict.val().begin() + LABEL_SIZE * BATCH_SIZE);
+
+    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);
     }
-    acc += (correct == predicted);
-    std::cerr << "corect: " << correct << " | " << predicted <<  "(";
-    for (size_t j = 0; j < LABEL_SIZE; ++j) {
-      std::cerr << results[i+j] << " ";
-    }
-    std::cerr << std::endl;
+
+    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.Load(tmp);
+      x = xt;
+
+      predict.forward(endId - startId);
+
+      thrust::host_vector<float> results(predict.val().begin(), predict.val().begin() + LABEL_SIZE * (endId - startId));
+
+      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;