/* * Copyright (C) 2019 The Android Open Source Project * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ // Contains the implementation of the operations. #define LOG_TAG "Operations" #include "Squeeze.h" #include <vector> #include "OperationResolver.h" #include "Operations.h" #include "Tracing.h" namespace android { namespace nn { namespace squeeze { #ifdef NN_INCLUDE_CPU_IMPLEMENTATION bool prepare(IOperationExecutionContext* context) { // Only the squeeze dims tensor can be omitted. NN_RET_CHECK(!context->isOmittedInput(kInputTensor)); NN_RET_CHECK(!context->isOmittedOutput(kOutputTensor)); const int32_t* squeezeDims = context->getInputBuffer<int32_t>(kSqueezeDims); const Shape inputShape = context->getInputShape(kInputTensor); const Shape squeezeDimsShape = context->getInputShape(kSqueezeDims); int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape)); NN_RET_CHECK_LE(getNumberOfDimensions(inputShape), 4u); // squeezeDims need to be provided as a 1-D int32 tensor. NN_OPS_CHECK(squeezeDimsShape.type == OperandType::TENSOR_INT32); NN_OPS_CHECK(getNumberOfDimensions(squeezeDimsShape) == 1); std::vector<bool> shouldSqueeze(numInputDims, false); int32_t numDimsSqueezed = 0; if (context->isOmittedInput(kSqueezeDims)) { // If squeezeDims is omitted, all dims with value 1 will be squeezed. for (int32_t idx = 0; idx < numInputDims; ++idx) { if (getSizeOfDimension(inputShape, idx) == 1) { shouldSqueeze[idx] = true; ++numDimsSqueezed; } } } else { int32_t squeezeDimsSize = static_cast<int32_t>(getSizeOfDimension(squeezeDimsShape, 0)); for (int32_t idx = 0; idx < squeezeDimsSize; ++idx) { int32_t current = squeezeDims[idx] < 0 ? squeezeDims[idx] + numInputDims : squeezeDims[idx]; NN_OPS_CHECK(current >= 0 && current < numInputDims && getSizeOfDimension(inputShape, current) == 1); if (!shouldSqueeze[current]) ++numDimsSqueezed; shouldSqueeze[current] = true; } } // Sets output dimensions. std::vector<uint32_t> outDims(numInputDims - numDimsSqueezed); if (numInputDims == numDimsSqueezed) { // Handle edge case where squeeze removes all dimensions. outDims.push_back(1); } else { for (int32_t inIdx = 0, outIdx = 0; inIdx < numInputDims; ++inIdx) { if (!shouldSqueeze[inIdx]) { outDims[outIdx++] = getSizeOfDimension(inputShape, inIdx); } } } Shape outputShape(inputShape); outputShape.dimensions = outDims; return context->setOutputShape(kOutputTensor, outputShape); } bool execute(IOperationExecutionContext* context) { switch (context->getInputType(kInputTensor)) { case OperandType::TENSOR_FLOAT16: case OperandType::TENSOR_FLOAT32: case OperandType::TENSOR_QUANT8_ASYMM: case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: return copyData(context->getInputBuffer(kInputTensor), context->getInputShape(kInputTensor), context->getOutputBuffer(kOutputTensor), context->getOutputShape(kOutputTensor)); default: NN_RET_CHECK_FAIL() << "Unsupported tensor type for SQUEEZE op."; } } #endif // NN_INCLUDE_CPU_IMPLEMENTATION } // namespace squeeze NN_REGISTER_OPERATION_DEFAULT_VALIDATION(SQUEEZE, squeeze::prepare, squeeze::execute, .allowOmittedOperand = true); } // namespace nn } // namespace android