Move ReduceMean operator member function code from header to source file

src/operator/ReduceMean.cpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include "aidge/operator/ReduceMean.hpp"
+
+#include <algorithm>  // std::for_each, std::sort
+#include <cstddef>    // std::size_t
+#include <cstdint>    // std::int32_t
+#include <memory>
+#include <stdexcept>  // std::runtime_error
+#include <string>
+#include <vector>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/utils/ErrorHandling.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+const std::string Aidge::ReduceMean_Op::Type = "ReduceMean";
+
+void Aidge::ReduceMean_Op::computeOutputDims() {
+        if (!getInput(0)) {
+            AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+        }
+        if (!getInput(0)->empty()) {
+            // make Axes attribute positive
+            std::vector<std::int32_t>& axes = this->template getAttr<ReduceMeanAttr::Axes>();
+            std::for_each(axes.begin(), axes.end(), [&] (std::int32_t& val) {
+                if (val < 0)
+                    val+=static_cast<std::int32_t>(getInput(0)->nbDims());
+            });
+            std::sort(axes.begin(), axes.end());
+
+            // build output dimensions
+            std::vector<DimSize_t> outDims = getInput(0)->dims();
+            if (this->template getAttr<ReduceMeanAttr::KeepDims>()) {
+                std::for_each(axes.cbegin(), axes.cend(), [&outDims] (const std::int32_t& val) { outDims[val] = 1; });
+            }
+            else {
+                for (auto it = axes.crbegin(); it != axes.crend(); ++it)
+                    outDims.erase(outDims.begin() + static_cast<std::size_t>(*it));
+            }
+
+            // TODO: change {1} for {} when scalar Tensors are better handled.
+            mOutputs[0]->resize((outDims.size()>0) ? outDims : std::vector<DimSize_t>({1}));
+
+        }
+    }
+
+void Aidge::ReduceMean_Op::setBackend(const std::string& name, Aidge::DeviceIdx_t device) {
+    SET_IMPL_MACRO(ReduceMean_Op, *this, name);
+    mOutputs[0]->setBackend(name, device);
+}
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