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

examples/mt/download.sh

@@ -0,0 +1,4 @@
+
+wget http://data.statmt.org/wmt16/translation-task/dev.tgz
+tar xvf dev.tgz
+

src/CMakeLists.txt

@@ -9,6 +9,7 @@ cuda_add_library(marian_lib
   tensor.cu
   tensor_operators.cu
   expression_operators.cu
+  vocab.cpp
 )
 
 target_link_libraries(marian_lib)
@@ -35,11 +36,16 @@ cuda_add_executable(
   validate_mnist_batch
   validate_mnist_batch.cu
 )
+cuda_add_executable(
+  validate_encoder_decoder
+  validate_encoder_decoder.cu
+)
 
 target_link_libraries(validate_mnist marian_lib)
 target_link_libraries(validate_mnist_batch marian_lib)
+target_link_libraries(validate_encoder_decoder marian_lib)
 
-foreach(exec marian train_mnist validate_mnist validate_mnist_batch )
+foreach(exec marian train_mnist validate_mnist validate_mnist_batch validate_encoder_decoder)
   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/test.cu

@@ -1,13 +1,17 @@
-
+#include <fstream>
 #include "marian.h"
 #include "mnist.h"
+#include "vocab.h"
 
 int main(int argc, char** argv) {
   cudaSetDevice(0);
 
+  using namespace std;
   using namespace marian;
   using namespace keywords;
 
+  Vocab sourceVocab, targetVocab;
+
   int input_size = 10;
   int output_size = 2;
   int batch_size = 25;
@@ -30,6 +34,18 @@ int main(int argc, char** argv) {
   Expr bh = g.param(shape={1, hidden_size}, init=uniform(), name="bh");
   Expr h0 = g.param(shape={1, hidden_size}, init=uniform(), name="h0");
 
+  // read parallel corpus from file
+  std::fstream sourceFile("../examples/mt/dev/newstest2013.de");
+  std::fstream targetFile("../examples/mt/dev/newstest2013.en");
+
+  string sourceLine, targetLine;
+  while (getline(sourceFile, sourceLine)) {
+	  getline(targetFile, targetLine);
+
+	  std::vector<size_t> sourceIds = sourceVocab.ProcessSentence(sourceLine);
+	  std::vector<size_t> targetIds = sourceVocab.ProcessSentence(targetLine);
+  }
+
   std::cerr << "Building RNN..." << std::endl;
   H.emplace_back(tanh(dot(X[0], Wxh) + dot(h0, Whh) + bh));
   for (int t = 1; t < num_inputs; ++t) {

src/vocab.cpp

@@ -0,0 +1,53 @@
+#include "vocab.h"
+
+using namespace std;
+
+////////////////////////////////////////////////////////
+inline std::vector<std::string> Tokenize(const std::string& str,
+    const std::string& delimiters = " \t")
+{
+  std::vector<std::string> tokens;
+  // Skip delimiters at beginning.
+  std::string::size_type lastPos = str.find_first_not_of(delimiters, 0);
+  // Find first "non-delimiter".
+  std::string::size_type pos = str.find_first_of(delimiters, lastPos);
+
+  while (std::string::npos != pos || std::string::npos != lastPos) {
+    // Found a token, add it to the vector.
+    tokens.push_back(str.substr(lastPos, pos - lastPos));
+    // Skip delimiters.  Note the "not_of"
+    lastPos = str.find_first_not_of(delimiters, pos);
+    // Find next "non-delimiter"
+    pos = str.find_first_of(delimiters, lastPos);
+  }
+
+  return tokens;
+}
+////////////////////////////////////////////////////////
+
+size_t Vocab::GetOrCreate(const std::string &word)
+{
+	size_t id;
+	Coll::const_iterator iter = coll_.find(word);
+	if (iter == coll_.end()) {
+		id = coll_.size();
+		coll_[word] = id;
+	}
+	else {
+		id = iter->second;
+	}
+	return id;
+}
+
+std::vector<size_t> Vocab::ProcessSentence(const std::string &sentence)
+{
+	vector<string> toks = Tokenize(sentence);
+	vector<size_t> ret(toks.size());
+
+	for (size_t i = 0; i < toks.size(); ++i) {
+		size_t id = GetOrCreate(toks[i]);
+		ret[i] = id;
+	}
+
+	return ret;
+}

src/vocab.h

@@ -0,0 +1,17 @@
+#pragma once
+
+#include <unordered_map>
+#include <string>
+#include <vector>
+
+class Vocab
+{
+public:
+	size_t GetOrCreate(const std::string &word);
+	std::vector<size_t> ProcessSentence(const std::string &sentence);
+
+protected:
+	typedef std::unordered_map<std::string, size_t> Coll;
+	Coll coll_;
+};
+