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[WIP] confusion (#180) 

* skip string arrays for device validation

Signed-off-by: raver119 <raver119@gmail.com>

* confusion_matrix fix

Signed-off-by: raver119 <raver119@gmail.com>
master
raver119 2019-08-27 14:30:37 +03:00 committed by GitHub
parent dff599aa8f
commit 0e523490e9
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 20 additions and 36 deletions
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58
libnd4j/include/ops/declarable/helpers/cuda/confusion.cu
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@ -30,10 +30,10 @@ namespace helpers {
template <typename T>
__global__ static void copyBuffers(Nd4jLong* destination, void const* source, Nd4jLong bufferLength) {
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (int t = tid; t < bufferLength; t += step) {
destination[t] = reinterpret_cast<T const*>(source)[t];
destination[t] = static_cast<Nd4jLong>(reinterpret_cast<T const*>(source)[t]);
}
}
@ -51,38 +51,24 @@ namespace helpers {
}
__syncthreads();
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (int t = tid; t < bufferLength; t += step) {
//auto tX = reinterpret_cast<T*>(inputList[t]);
//auto xShape = reinterpret_cast<Nd4jLong*>(inputShapeList[t]);
auto label = labelsBuffer[t]; //->e<Nd4jLong>(j);
auto pred = predictionBuffer[t]; //->e<Nd4jLong>(j);
auto tZ = z + tadOffsets[label];
T val = (weightsBuffer == nullptr ? (T)1.0f : w[t]);
//for (int e = threadIdx.x; e < arrLen; e += blockDim.x) {
tZ[shape::getIndexOffset(pred, tadShape, arrLen)] = val; //tX[shape::getIndexOffset(e, , arrLen)];
auto idx = shape::getIndexOffset(pred, tadShape, arrLen);
tZ[idx] = val;
}
}
template <typename T>
template <typename X, typename Z>
void _confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) {
// std::unique_ptr<ResultSet> arrs(output->allTensorsAlongDimension({1}));
//
//#pragma omp parallel for if(labels->lengthOf() > Environment::getInstance()->elementwiseThreshold()) schedule(static)
// for (int j = 0; j < labels->lengthOf(); ++j){
// auto label = labels->e<Nd4jLong>(j);
// auto pred = predictions->e<Nd4jLong>(j);
// T value = (weights == nullptr ? (T)1.0f : weights->e<T>(j));
// (*arrs->at(label)).p<T>(pred, value);
// }
int dimension = 1;
auto pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), dimension);
auto stream = context->getCudaStream();
auto pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), 1);
PointersManager manager(context, "helpers::confusion");
@ -90,26 +76,26 @@ namespace helpers {
Nd4jLong* predictionLongBuffer = predictions->dataType() == nd4j::DataType::INT64?(Nd4jLong*)predictions->specialBuffer():nullptr;
if (labelsLongBuffer == nullptr) {
cudaError_t err = cudaMalloc(&labelsLongBuffer, labels->lengthOf() * sizeof(Nd4jLong));
auto err = cudaMalloc(&labelsLongBuffer, labels->lengthOf() * sizeof(Nd4jLong));
if (err != 0)
throw nd4j::cuda_exception::build("Cannot allocate memory for labels long buffer", err);
// copy with type conversion
copyBuffers<T><<<256, 512, 8192>>>(labelsLongBuffer, labels->getSpecialBuffer(), labels->lengthOf());
copyBuffers<X><<<256, 512, 1024, *stream>>>(labelsLongBuffer, labels->getSpecialBuffer(), labels->lengthOf());
}
if (predictionLongBuffer == nullptr) {
cudaError_t err = cudaMalloc(&predictionLongBuffer, predictions->lengthOf() * sizeof(Nd4jLong));
auto err = cudaMalloc(&predictionLongBuffer, predictions->lengthOf() * sizeof(Nd4jLong));
if (err != 0)
throw nd4j::cuda_exception::build("Cannot allocate memory for predictions long buffer", err);
// copy with type conversion
copyBuffers<T><<<256, 512, 8192>>>(predictionLongBuffer, predictions->getSpecialBuffer(), predictions->lengthOf());
copyBuffers<X><<<256, 512, 1024, *stream>>>(predictionLongBuffer, predictions->getSpecialBuffer(), predictions->lengthOf());
}
auto bufferLength = labels->lengthOf();
dim3 launchDims(32, 32, 1024);
auto stream = context->getCudaStream();
confusionFunctorKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(labelsLongBuffer, predictionLongBuffer,
bufferLength, weights != nullptr? weights->getSpecialBuffer():nullptr, output->specialBuffer(), pack.specialShapeInfo(), pack.specialOffsets());
confusionFunctorKernel<Z><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(labelsLongBuffer, predictionLongBuffer, bufferLength, weights != nullptr? weights->getSpecialBuffer():nullptr, output->specialBuffer(), pack.specialShapeInfo(), pack.specialOffsets());
manager.synchronize();
if (predictionLongBuffer != predictions->getSpecialBuffer()) {
cudaError_t err = cudaFree(predictionLongBuffer);
@ -122,17 +108,15 @@ namespace helpers {
if (err != 0)
throw nd4j::cuda_exception::build("Cannot deallocate memory for labels long buffer", err);
}
manager.synchronize();
}
void confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) {
auto xType = output->dataType(); // weights can be null
BUILD_SINGLE_SELECTOR(xType, _confusionFunctor, (context, labels, predictions, weights, output), NUMERIC_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void _confusionFunctor, (nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output);, NUMERIC_TYPES);
auto xType = predictions->dataType();
auto zType = output->dataType(); // weights can be null
NDArray::prepareSpecialUse({output}, {labels, predictions, weights});
BUILD_DOUBLE_SELECTOR(xType, zType, _confusionFunctor, (context, labels, predictions, weights, output), INDEXING_TYPES, NUMERIC_TYPES);
NDArray::registerSpecialUse({output}, {labels, predictions, weights});
}
}
}
}