-// Copyright (C) 2018 Intel Corporation
+// Copyright (C) 2018-2019 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <builders/ie_deconvolution_layer.hpp>
-#include <details/caseless.hpp>
+#include <ie_cnn_layer_builder.h>
+#include <limits>
+#include <vector>
#include <string>
using namespace InferenceEngine;
Builder::DeconvolutionLayer::DeconvolutionLayer(const std::string& name): ConvolutionLayer(name) {
- getLayer().setType("Deconvolution");
+ getLayer()->setType("Deconvolution");
}
-Builder::DeconvolutionLayer::DeconvolutionLayer(Layer& genLayer): ConvolutionLayer(genLayer.getName()) {
- getLayer().setName("");
- getLayer().setType("");
- getLayer() = genLayer;
- if (!details::CaselessEq<std::string>()(getLayer().getType(), "Deconvolution"))
- THROW_IE_EXCEPTION << "Cannot create DeconvolutionLayer decorator for layer " << getLayer().getType();
+Builder::DeconvolutionLayer::DeconvolutionLayer(const Layer::Ptr& layer): ConvolutionLayer(layer->getName()) {
+ this->getLayer() = layer;
+ checkType("Deconvolution");
}
+Builder::DeconvolutionLayer::DeconvolutionLayer(const Layer::CPtr& layer): ConvolutionLayer(layer->getName()) {
+ this->getLayer().reset();
+ cLayer = layer;
+ checkType("Deconvolution");
+}
+
+REG_VALIDATOR_FOR(Deconvolution, [] (const InferenceEngine::Builder::Layer::CPtr& layer, bool partial) {
+ // WA for old IRs
+ if (layer->getParameters().find("kernel") == layer->getParameters().end() &&
+ layer->getParameters().find("kernel-x") != layer->getParameters().end() &&
+ layer->getParameters().find("kernel-y") != layer->getParameters().end())
+ return;
+ Builder::DeconvolutionLayer deconvBuilder(layer);
+ std::vector<size_t> l_kernel = deconvBuilder.getKernel();
+ std::vector<size_t> l_dilation = deconvBuilder.getDilation();
+ std::vector<size_t> l_paddingBegin = deconvBuilder.getPaddingsBegin();
+ std::vector<size_t> l_paddingEnd = deconvBuilder.getPaddingsEnd();
+ std::vector<size_t> l_strides = deconvBuilder.getStrides();
+
+ if (l_paddingBegin.empty() && !l_kernel.empty())
+ l_paddingBegin.resize(l_kernel.size(), 0);
+ if (l_paddingEnd.empty() && !l_kernel.empty())
+ l_paddingEnd.resize(l_kernel.size(), 0);
+ if (l_dilation.empty() && !l_kernel.empty())
+ l_dilation.resize(l_kernel.size(), 1);
+ if (l_strides.empty() && !l_kernel.empty())
+ l_strides.resize(l_kernel.size(), 1);
+
+ if (l_kernel.empty()) {
+ THROW_IE_EXCEPTION << "Kernel is empty!";
+ }
+
+ if (l_paddingBegin.size() != l_paddingEnd.size()) {
+ THROW_IE_EXCEPTION << "Padding_begin dimension is not equal to padding_end dimension";
+ }
+
+ if (!l_paddingBegin.empty() && l_kernel.size() != l_paddingBegin.size()) {
+ THROW_IE_EXCEPTION << "Padding dimension is not equal to kernel dimension";
+ }
+
+ if (l_kernel.size() != l_strides.size()) {
+ THROW_IE_EXCEPTION << "Stride dimension is not equal to kernel dimension";
+ }
+
+ if (!l_dilation.empty() && l_kernel.size() != l_dilation.size()) {
+ THROW_IE_EXCEPTION << "Dilation dimension is not equal to kernel dimension";
+ }
+
+ if (deconvBuilder.getOutDepth() == 0) {
+ THROW_IE_EXCEPTION << "OutDepth parameter should be more than 0";
+ }
+
+ for (size_t kernel_dim : l_kernel) {
+ if (kernel_dim == 0) {
+ THROW_IE_EXCEPTION << "Kernel dimensions should be more than 0";
+ }
+ }
+
+ for (size_t i_stride : l_strides) {
+ if (i_stride == 0) {
+ THROW_IE_EXCEPTION << "Strides should be more than 0";
+ }
+ }
+
+ for (size_t dil : l_dilation) {
+ if (dil == 0)
+ THROW_IE_EXCEPTION << "Dilation should be more than 0";
+ }
+
+ if (!deconvBuilder.getGroup())
+ THROW_IE_EXCEPTION << "Group should be more than 0";
+
+ if (deconvBuilder.getInputPort().shape().empty())
+ return;
+
+ const size_t IC = deconvBuilder.getInputPort().shape()[1];
+ if (IC % deconvBuilder.getGroup())
+ THROW_IE_EXCEPTION << "Number of input channels (" << IC <<
+ ") is not divided by group number (" << deconvBuilder.getGroup() << ")";
+
+ size_t weight_size = deconvBuilder.getOutDepth() * IC / deconvBuilder.getGroup();
+ for (size_t kernel_dim : l_kernel) {
+ if (static_cast<double>(weight_size) * kernel_dim > std::numeric_limits<size_t>::max()) {
+ THROW_IE_EXCEPTION << "Weight size exceeds the size_t max";
+ }
+ weight_size *= kernel_dim;
+ }
+
+ if (partial)
+ return;
+
+ const auto weights = layer->getInputPorts()[1].getData()->getData();
+ if (weights->size() != weight_size) {
+ THROW_IE_EXCEPTION << "Weight size is not correct!";
+ }
+
+ const auto biases = layer->getInputPorts()[2].getData()->getData();
+ if (biases && biases->cbuffer() && biases->size() != deconvBuilder.getOutDepth())
+ THROW_IE_EXCEPTION << "Biases size is incorrect!";
+});
+
+REG_CONVERTER_FOR(Deconvolution, [](const CNNLayerPtr& cnnLayer, Builder::Layer& layer) {
+ // WA for old IRs
+ if (cnnLayer->params.find("kernel") == cnnLayer->params.end() &&
+ cnnLayer->params.find("kernel-x") != cnnLayer->params.end() &&
+ cnnLayer->params.find("kernel-y") != cnnLayer->params.end())
+ return;
+ std::vector<unsigned int> tmp = cnnLayer->GetParamAsUInts("kernel");
+ std::vector<size_t> cur(tmp.size());
+ for (size_t i = 0; i < tmp.size(); ++i) {
+ cur[i] = static_cast<size_t>(tmp[i]);
+ }
+ layer.getParameters()["kernel"] = cur;
+
+ tmp = cnnLayer->GetParamAsUInts("strides");
+ cur.resize(tmp.size());
+ for (size_t i = 0; i < tmp.size(); ++i) {
+ cur[i] = static_cast<size_t>(tmp[i]);
+ }
+ layer.getParameters()["strides"] = cur;
+
+ tmp = cnnLayer->GetParamAsUInts("dilations");
+ cur.resize(tmp.size());
+ for (size_t i = 0; i < tmp.size(); ++i) {
+ cur[i] = static_cast<size_t>(tmp[i]);
+ }
+ layer.getParameters()["dilations"] = cur;
+
+ tmp = cnnLayer->GetParamAsUInts("pads_begin");
+ cur.resize(tmp.size());
+ for (size_t i = 0; i < tmp.size(); ++i) {
+ cur[i] = static_cast<size_t>(tmp[i]);
+ }
+ layer.getParameters()["pads_begin"] = cur;
+
+ tmp = cnnLayer->GetParamAsUInts("pads_end");
+ cur.resize(tmp.size());
+ for (size_t i = 0; i < tmp.size(); ++i) {
+ cur[i] = static_cast<size_t>(tmp[i]);
+ }
+ layer.getParameters()["pads_end"] = cur;
+
+ layer.getParameters()["group"] = static_cast<size_t>(cnnLayer->GetParamAsUInt("group"));
+ layer.getParameters()["output"] = static_cast<size_t>(cnnLayer->GetParamAsUInt("output"));
+});
\ No newline at end of file
projects / platform / upstream / dldt.git / blobdiff