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/*
* Copyright (c) 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_VALIDATE_HELPERS_H__
#define __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_VALIDATE_HELPERS_H__
#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/Tensor.h"
#include "arm_compute/graph/Types.h"
#include "arm_compute/graph/nodes/Nodes.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensorInfo.h"
namespace arm_compute
{
namespace graph
{
namespace backends
{
namespace detail
{
/** Returns backing tensor info of a given tensor
*
* @param[in] tensor Tensor to extract the backing tensor from
*
* @return Backing tensor tensor info if present else nullptr
*/
inline arm_compute::ITensorInfo *get_backing_tensor_info(arm_compute::graph::Tensor *tensor)
{
return ((tensor == nullptr) || (tensor->handle() == nullptr)) ? nullptr : tensor->handle()->tensor().info();
}
/** Validates a Channel Shuffle layer node
*
* @tparam ChannelShuffleLayer Channel Shuffle layer function type
*
* @param[in] node Node to validate
*
* @return Status
*/
template <typename ChannelShuffleLayer>
Status validate_channel_shuffle_layer(ChannelShuffleLayerNode &node)
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Validating ChannelShuffle node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_inputs() != 1);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_outputs() != 1);
// Extract IO and info
arm_compute::ITensorInfo *input = get_backing_tensor_info(node.input(0));
arm_compute::ITensorInfo *output = get_backing_tensor_info(node.output(0));
const unsigned int num_groups = node.num_groups();
return ChannelShuffleLayer::validate(input, output, num_groups);
}
/** Validates a Convolution layer node
*
* @tparam ConvolutionLayer Default Convolution layer function type
* @tparam DirectConvolutionLayer Direct Convolution layer function type
* @tparam GEMMConvolutionLayer GEMM Convolution layer function type
* @tparam WinogradConvolutionLayer Winograd Convolution layer function type
*
* @param[in] node Node to validate
*
* @return Status
*/
template <typename ConvolutionLayer, typename DirectConvolutionLayer, typename GEMMConvolutionLayer, typename WinogradConvolutionLayer>
Status validate_convolution_layer(ConvolutionLayerNode &node)
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Validating ConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_inputs() != 3);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_outputs() != 1);
// Extract IO and info
arm_compute::ITensorInfo *input = get_backing_tensor_info(node.input(0));
arm_compute::ITensorInfo *weights = get_backing_tensor_info(node.input(1));
arm_compute::ITensorInfo *biases = get_backing_tensor_info(node.input(2));
arm_compute::ITensorInfo *output = get_backing_tensor_info(node.output(0));
if(is_data_type_quantized_asymmetric(input->data_type()))
{
biases->set_data_type(DataType::S32);
}
const PadStrideInfo conv_info = node.convolution_info();
const ConvolutionMethod conv_algorithm = node.convolution_method();
const bool fast_math = node.fast_math_hint() == FastMathHint::Enabled;
const unsigned int num_groups = node.num_groups();
// Validate function
Status status{};
switch(conv_algorithm)
{
case ConvolutionMethod::Direct:
ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups != 1, "DirectConvolutionLayer does not support grouping!");
status = DirectConvolutionLayer::validate(input, weights, biases, output, conv_info);
break;
case ConvolutionMethod::GEMM:
status = GEMMConvolutionLayer::validate(input, weights, biases, output, conv_info,
WeightsInfo(), Size2D(1, 1), ActivationLayerInfo(), num_groups);
break;
case ConvolutionMethod::Winograd:
ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups != 1, "WinogradConvolutionLayer does not support grouping!");
status = WinogradConvolutionLayer::validate(input, weights, biases, output, conv_info, ActivationLayerInfo(), fast_math);
break;
case ConvolutionMethod::Default:
status = ConvolutionLayer::validate(input, weights, biases, output, conv_info,
WeightsInfo(), Size2D(1, 1), ActivationLayerInfo(), fast_math, num_groups);
break;
default:
ARM_COMPUTE_RETURN_ERROR_MSG("Unsupported convolution method");
}
return status;
}
/** Validates a Depthwise Convolution layer node
*
* @tparam DepthwiseConvolutionLayer Default Depthwise Convolution layer type
* @tparam DepthwiseConvolutionLayer3x3 Optimized 3x3 Depthwise Convolution layer type
*
* @param[in] node Node to validate
*
* @return Status
*/
template <typename DepthwiseConvolutionLayer, typename DepthwiseConvolutionLayer3x3>
Status validate_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node)
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Validating DepthwiseConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_inputs() != 3);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_outputs() != 1);
// Extract IO and info
arm_compute::ITensorInfo *input = detail::get_backing_tensor_info(node.input(0));
arm_compute::ITensorInfo *weights = detail::get_backing_tensor_info(node.input(1));
arm_compute::ITensorInfo *biases = get_backing_tensor_info(node.input(2));
arm_compute::ITensorInfo *output = get_backing_tensor_info(node.output(0));
const PadStrideInfo conv_info = node.convolution_info();
const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method();
// Validate function
Status status{};
switch(dwc_algorithm)
{
case DepthwiseConvolutionMethod::Default:
case DepthwiseConvolutionMethod::GEMV:
status = DepthwiseConvolutionLayer::validate(input, weights, biases, output, conv_info);
break;
case DepthwiseConvolutionMethod::Optimized3x3:
status = DepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info);
break;
default:
ARM_COMPUTE_RETURN_ERROR_MSG("Unsupported depthwise convolution method");
}
return status;
}
/** Validates a permute layer node
*
* @tparam PermuteLayer Permute layer type
*
* @param[in] node Node to validate
*
* @return Status
*/
template <typename PermuteLayer>
Status validate_permute_layer(PermuteLayerNode &node)
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Validating PermuteLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_inputs() != 1);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_outputs() != 1);
// Extract IO and info
arm_compute::ITensorInfo *input = get_backing_tensor_info(node.input(0));
arm_compute::ITensorInfo *output = get_backing_tensor_info(node.output(0));
const PermutationVector &perm = node.permutation_vector();
return PermuteLayer::validate(input, output, perm);
}
/** Validates a Reorg layer node
*
* @tparam ReorgLayer Reorg layer type
*
* @param[in] node Node to validate
*
* @return Status
*/
template <typename ReorgLayer>
Status validate_reorg_layer(ReorgLayerNode &node)
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Validating ReorgLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_inputs() != 1);
ARM_COMPUTE_RETURN_ERROR_ON(node.num_outputs() != 1);
// Extract input and output
arm_compute::ITensorInfo *input = detail::get_backing_tensor_info(node.input(0));
arm_compute::ITensorInfo *output = get_backing_tensor_info(node.output(0));
// Validate function
return ReorgLayer::validate(input, output, node.stride());
}
} // namespace detail
} // namespace backends
} // namespace graph
} // namespace arm_compute
#endif /* __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_VALIDATE_HELPERS_H__ */
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