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#pragma once
#include "layers/generic.h"
#include "marian.h"
#include <string>
namespace marian {
#ifdef CUDNN
class Convolution : public Factory {
protected:
Ptr<Options> getOptions() { return options_; }
public:
Convolution(Ptr<ExpressionGraph> graph);
Expr apply(Expr x);
virtual Expr apply(const std::vector<Expr>&);
};
typedef Accumulator<Convolution> convolution;
class CharConvPooling {
public:
CharConvPooling(const std::string& prefix,
int kernelHeight,
std::vector<int> kernelWidths,
std::vector<int> kernelNums,
int stride)
: name_(prefix),
size_((int)kernelNums.size()),
kernelHeight_(kernelHeight),
kernelWidths_(kernelWidths),
kernelNums_(kernelNums),
stride_(stride) {}
Expr operator()(Expr x, Expr mask) {
auto graph = x->graph();
auto masked = x * mask;
auto xNCHW = convert2cudnnFormat(masked);
auto maskNCHW = convert2cudnnFormat(mask);
Expr input = xNCHW;
std::vector<Expr> outputs;
for(int i = 0; i < size_; ++i) {
int kernelWidth = kernelWidths_[i];
int kernelNum = kernelNums_[i];
int padWidth = kernelWidth / 2;
auto output
= convolution(graph) //
("prefix",
name_ + "_width_" + std::to_string(kernelWidth)) //
("kernel-dims", std::make_pair(kernelWidth, x->shape()[-1])) //
("kernel-num", kernelNum) //
("paddings", std::make_pair(padWidth, 0))
.apply(input);
auto relued = relu(output);
auto output2 = pooling_with_masking(
relued, maskNCHW, stride_, kernelWidth % 2 == 0);
output2 = reshape(
output2,
{output2->shape()[-1], output2->shape()[0], output2->shape()[1]});
outputs.push_back(output2);
}
auto concated = concatenate(outputs, -1);
return concated;
}
protected:
std::string name_;
int size_;
int kernelHeight_;
std::vector<int> kernelWidths_;
std::vector<int> kernelNums_;
int stride_;
};
#endif
} // namespace marian
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