-// Copyright (C) 2018 Intel Corporation
+// Copyright (C) 2018-2019 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
namespace Extensions {
namespace Cpu {
-inline void clipping(int *idx, const int min, const int max) {
- (*idx) = ((*idx) > min) ? (*idx) : min;
- (*idx) = ((*idx) < max) ? (*idx) : (max - 1);
- return;
-}
-
-class GatherImpl: public ILayerExecImpl {
+class GatherImpl: public ExtLayerBase {
public:
- StatusCode init(LayerConfig& config, ResponseDesc *resp) noexcept override {
- for (auto& input : config.inConfs) {
- for (auto& offset : input.desc.getBlockingDesc().getOffsetPaddingToData()) {
- if (offset) {
- return GENERAL_ERROR;
- }
- }
- }
- for (auto& output : config.outConfs) {
- for (auto& offset : output.desc.getBlockingDesc().getOffsetPaddingToData()) {
- if (offset) {
- return GENERAL_ERROR;
- }
- }
- }
-
- // Check for holes in tensors
- SizeVector dictionary_dims = config.inConfs[GATHER_DICTIONARY].desc.getDims();
- SizeVector indexes_dims = config.inConfs[GATHER_INDEXES].desc.getDims();
- SizeVector out_dims = config.outConfs[0].desc.getDims();
- size_t idx_size = 1;
- for (auto dims : indexes_dims)
- idx_size *= dims;
-
- size_t dct_size = 1;
- for (auto dims : dictionary_dims)
- dct_size *= dims;
-
- size_t out_size = 1;
- for (auto dims : out_dims)
- out_size *= dims;
-
- size_t dctSV = config.inConfs[GATHER_DICTIONARY].desc.getBlockingDesc().getStrides()[0];
- size_t dctDV = config.inConfs[GATHER_DICTIONARY].desc.getBlockingDesc().getBlockDims()[0];
- size_t idxSV = config.inConfs[GATHER_INDEXES].desc.getBlockingDesc().getStrides()[0];
- size_t idxDV = config.inConfs[GATHER_INDEXES].desc.getBlockingDesc().getBlockDims()[0];
- size_t outSV = config.outConfs[0].desc.getBlockingDesc().getStrides()[0];
- size_t outDV = config.outConfs[0].desc.getBlockingDesc().getBlockDims()[0];
- if (outSV * outDV == out_size && idxSV * idxDV == idx_size && dctSV * dctDV == dct_size)
- withHoles = NONE;
- else if (outSV * outDV != out_size && idxSV * idxDV == idx_size && dctSV * dctDV == dct_size)
- withHoles = OUTPUT;
-
- return OK;
- };
-
- StatusCode getSupportedConfigurations(std::vector<LayerConfig>& conf, ResponseDesc *resp) noexcept override {
- if (!errorMsg.empty()) {
- if (resp) {
- errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
- }
- return GENERAL_ERROR;
- }
- conf = confs;
- return OK;
- };
-
explicit GatherImpl(const CNNLayer* layer) {
try {
if (layer->insData.size() != 2 || layer->outData.empty())
- THROW_IE_EXCEPTION << "Incorrect number of input/output edges!";
+ THROW_IE_EXCEPTION << layer->name << " Incorrect number of input/output edges!";
Precision inIdxPrecision = layer->insData[GATHER_INDEXES].lock()->getTensorDesc().getPrecision();
- if (inIdxPrecision != Precision::FP32 &&
- inIdxPrecision != Precision::I32 &&
- inIdxPrecision != Precision::U16 &&
- inIdxPrecision != Precision::I16 &&
- inIdxPrecision != Precision::U8 &&
- inIdxPrecision != Precision::I8)
- THROW_IE_EXCEPTION << "Incorrect input precision. Only FP32|I32|U16|I16|U8|I8 are supported!";
+ if (inIdxPrecision != Precision::FP32 && inIdxPrecision != Precision::I32)
+ THROW_IE_EXCEPTION << layer->name << " Incorrect input precision. Only FP32 or I32 are supported!";
// Remove redundant dimensions
const SizeVector& dictionary_dims = layer->insData[GATHER_DICTIONARY].lock()->getTensorDesc().getDims();
- size_t actualAxis = 0;
SizeVector dims_actual;
for (size_t i = 0; i < dictionary_dims.size(); i++) {
if (dictionary_dims[i] > 1) {
}
if (dims_actual.size() == 0)
- THROW_IE_EXCEPTION << "Incorrect input parameters dimension!";
+ THROW_IE_EXCEPTION << layer->name << " Incorrect input parameters dimension!";
axis = static_cast<int>(layer->GetParamAsInt("axis"));
// Dictionary must be at least rank axis + 1
- if (axis > 0 && (dims_actual.size() - axis) < 1)
- THROW_IE_EXCEPTION << "Incorrect input parameters dimensions and axis number!";
+ if (axis > 0 && static_cast<int>(dims_actual.size()) < (1 + axis))
+ THROW_IE_EXCEPTION << layer->name << " Incorrect input parameters dimensions and axis number!";
else if (axis < 0 && (static_cast<int>(dims_actual.size()) + axis) < 0)
- THROW_IE_EXCEPTION << "Incorrect input parameters dimensions and axis number!";
+ THROW_IE_EXCEPTION << layer->name << " Incorrect input parameters dimensions and axis number!";
if (axis < 0)
axis += dims_actual.size();
// Find number of dictionaries, index range and data length
- for (size_t i = 0; i < axis; i++)
+ for (int i = 0; i < axis; i++)
numDictionaries *= dims_actual[i];
indexRange = dims_actual[axis];
for (size_t i = axis + 1; i < dims_actual.size(); i++)
dataLength *= dims_actual[i];
if (dataLength == 0)
- THROW_IE_EXCEPTION << "Incorrect input parameters dimension!";
-
- LayerConfig config;
- DataConfig dataConfigIdx, dataConfigDct;
- const SizeVector& indexes_dims = layer->insData[GATHER_INDEXES].lock()->getTensorDesc().getDims();
- dataConfigDct.desc = TensorDesc(InferenceEngine::Precision(InferenceEngine::Precision::FP32), dictionary_dims, InferenceEngine::Layout::ANY);
- dataConfigIdx.desc = TensorDesc(inIdxPrecision, indexes_dims, InferenceEngine::Layout::ANY);
- if (GATHER_DICTIONARY == 0) {
- config.inConfs.push_back(dataConfigDct);
- config.inConfs.push_back(dataConfigIdx);
- } else {
- config.inConfs.push_back(dataConfigIdx);
- config.inConfs.push_back(dataConfigDct);
- }
+ THROW_IE_EXCEPTION << layer->name << " Incorrect input parameters dimension!";
- DataConfig dataConfigOut;
- const SizeVector& out_dims = layer->outData[0]->getTensorDesc().getDims();
- SizeVector blocks = out_dims;
- SizeVector order(blocks.size());
- SizeVector dimOffsets(blocks.size());
- SizeVector strides(blocks.size());
- size_t offset(std::numeric_limits<size_t>::max());
- for (size_t i = 0; i < order.size(); i++) {
- strides[i] = std::numeric_limits<size_t>::max();
- dimOffsets[i] = 0;
- order[i] = i;
- }
- dataConfigOut.desc = TensorDesc(InferenceEngine::Precision(InferenceEngine::Precision::FP32), out_dims,
- { blocks, order, offset, dimOffsets, strides });
- config.outConfs.push_back(dataConfigOut);
- config.dynBatchSupport = false;
- confs.push_back(config);
+ addConfig(layer, { DataConfigurator(ConfLayout::PLN), DataConfigurator(ConfLayout::PLN) },
+ { DataConfigurator(ConfLayout::PLN) });
} catch (InferenceEngine::details::InferenceEngineException &ex) {
errorMsg = ex.what();
}
}
- StatusCode execute(std::vector<Blob::Ptr>& inputs, std::vector<Blob::Ptr>& outputs,
- ResponseDesc *resp) noexcept override {
+ StatusCode execute(std::vector<Blob::Ptr>& inputs, std::vector<Blob::Ptr>& outputs, ResponseDesc *resp) noexcept override {
switch (inputs[GATHER_INDEXES]->precision()) {
case Precision::FP32:
- gather(inputs[GATHER_INDEXES]->cbuffer().as<const float *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0], withHoles);
+ gather(inputs[GATHER_INDEXES]->cbuffer().as<const float *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0]);
break;
case Precision::I32:
- gather(inputs[GATHER_INDEXES]->cbuffer().as<const int32_t *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0], withHoles);
- break;
- case Precision::U16:
- gather(inputs[GATHER_INDEXES]->cbuffer().as<const uint16_t *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0], withHoles);
- break;
- case Precision::I16:
- gather(inputs[GATHER_INDEXES]->cbuffer().as<const int16_t *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0], withHoles);
- break;
- case Precision::U8:
- gather(inputs[GATHER_INDEXES]->cbuffer().as<const uint8_t *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0], withHoles);
- break;
- case Precision::I8:
- gather(inputs[GATHER_INDEXES]->cbuffer().as<const int8_t *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0], withHoles);
+ gather(inputs[GATHER_INDEXES]->cbuffer().as<const int32_t *>(), inputs[GATHER_INDEXES], inputs[GATHER_DICTIONARY], outputs[0]);
break;
default:
return GENERAL_ERROR;
return OK;
}
-protected:
- enum class ConfLayout { ANY, PLN, BLK8, BLK16 };
- std::string errorMsg;
- std::vector<LayerConfig> confs;
-
private:
- enum HolesMode {
- NONE = 0,
- OUTPUT = 1,
- ALL = 2
- };
-
template <typename data_t>
- void gather(data_t *src_dataIdx, Blob::Ptr indexes, Blob::Ptr dictionary, Blob::Ptr output, bool withHoles);
+ void gather(data_t *src_dataIdx, Blob::Ptr indexes, Blob::Ptr dictionary, Blob::Ptr output);
int axis = 0;
size_t numDictionaries = 1;
size_t dataLength = 1;
const size_t GATHER_DICTIONARY = 0;
const size_t GATHER_INDEXES = 1;
- HolesMode withHoles = ALL;
};
template <typename data_t>
-void GatherImpl::gather(data_t *src_dataIdx, Blob::Ptr indexes, Blob::Ptr dictionary, Blob::Ptr output, bool withHoles) {
+void GatherImpl::gather(data_t *src_dataIdx, Blob::Ptr indexes, Blob::Ptr dictionary, Blob::Ptr output) {
size_t src_dataIdxSize = indexes->size();
- size_t dataSize = sizeof(float) * dataLength;
-
- if (withHoles == GatherImpl::NONE) { // No holes in tensors
- const float *src_dataDict = dictionary->cbuffer().as<const float *>() + dictionary->getTensorDesc().getBlockingDesc().getOffsetPadding();
- float* dst_data = output->cbuffer().as<float *>() + output->getTensorDesc().getBlockingDesc().getOffsetPadding();
- src_dataIdx += indexes->getTensorDesc().getBlockingDesc().getOffsetPadding();
+ const float *src_dataDict = dictionary->cbuffer().as<const float *>() + dictionary->getTensorDesc().getBlockingDesc().getOffsetPadding();
+ float* dst_data = output->cbuffer().as<float *>() + output->getTensorDesc().getBlockingDesc().getOffsetPadding();
+ src_dataIdx += indexes->getTensorDesc().getBlockingDesc().getOffsetPadding();
- if (axis == 0) {
- parallel_for(src_dataIdxSize, [&](size_t i) {
- int idx = static_cast<int>(src_dataIdx[i]);
-
- // Index clipping
- clipping(&idx, 0, indexRange);
+ if (axis == 0) {
+ parallel_for(src_dataIdxSize, [&](size_t i) {
+ unsigned int idx = static_cast<unsigned int>(src_dataIdx[i]);
+ // Index clipping
+ if (idx < indexRange) {
// Copying data to destination from Dictionary
- simple_copy(&dst_data[dataLength * i],
+ simple_copy(&dst_data[i * dataLength],
output->byteSize() - (dataLength * i),
&src_dataDict[dataLength * idx],
- dataSize);
- });
- } else {
- parallel_for(src_dataIdxSize, [&](size_t i) {
- int idx = static_cast<int>(src_dataIdx[i]);
-
- // Index clipping
- clipping(&idx, 0, indexRange);
+ sizeof(float) * dataLength);
+ } else {
+ std::fill_n(&dst_data[i * dataLength], dataLength, 0.f);
+ }
+ });
+ } else {
+ parallel_for(src_dataIdxSize, [&](size_t i) {
+ unsigned int idx = static_cast<unsigned int>(src_dataIdx[i]);
+ // Index clipping
+ if (idx < indexRange) {
// Copying data to destination from Dictionary
for (size_t j = 0; j < numDictionaries; j++) {
simple_copy(&dst_data[dataLength * (i + j * src_dataIdxSize)],
output->byteSize() - (dataLength * (i + j * src_dataIdxSize)),
&src_dataDict[dataLength * (idx + j * indexRange)],
- dataSize);
+ sizeof(float) * dataLength);
}
- });
- }
- } else if (withHoles == GatherImpl::OUTPUT) { // If only output tensor have holes
- const float *src_dataDict = dictionary->cbuffer().as<const float *>() + dictionary->getTensorDesc().getBlockingDesc().getOffsetPadding();
- float* dst_data = output->cbuffer().as<float *>();
- src_dataIdx += indexes->getTensorDesc().getBlockingDesc().getOffsetPadding();
-
- parallel_for(src_dataIdxSize, [&](size_t i) {
- int idx = static_cast<int>(src_dataIdx[i]);
-
- // Index clipping
- clipping(&idx, 0, indexRange);
-
- // Copying data to destination from Dictionary
- for (size_t j = 0; j < numDictionaries; j++) {
- for (size_t k = 0; k < dataLength; k++) {
- dst_data[output->getTensorDesc().offset(k + dataLength * (i + j * src_dataIdxSize))] =
- src_dataDict[k + dataLength * (idx + j * indexRange)];
- }
- }
- });
- } else { // If input and oupput tensors have holes
- const float *src_dataDict = dictionary->cbuffer().as<const float *>();
- float* dst_data = output->cbuffer().as<float *>();
-
- parallel_for(src_dataIdxSize, [&](size_t i) {
- int idx = static_cast<int>(src_dataIdx[indexes->getTensorDesc().offset(i)]);
-
- // Index clipping
- clipping(&idx, 0, indexRange);
-
- // Copying data to destination from Dictionary
- for (size_t j = 0; j < numDictionaries; j++) {
- for (size_t k = 0; k < dataLength; k++) {
- dst_data[output->getTensorDesc().offset(k + dataLength * (i + j * src_dataIdxSize))] =
- src_dataDict[dictionary->getTensorDesc().offset(k + dataLength * (idx + j * indexRange))];
+ } else {
+ for (size_t j = 0; j < numDictionaries; j++) {
+ std::fill_n(&dst_data[dataLength * (i + j * src_dataIdxSize)], dataLength, 0.f);
}
}
});
projects / platform / upstream / dldt.git / blobdiff