Add Resize operator with python binding

include/aidge/operator/Resize.hpp

+/********************************************************************************
+ * 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
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CORE_OPERATOR_Resize_H_
+#define AIDGE_CORE_OPERATOR_Resize_H_
+
+#include <cassert>
+#include <memory>
+#include <vector>
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/graph/Node.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/StaticAttributes.hpp"
+#include "aidge/utils/Types.h"
+
+namespace Aidge {
+
+class Resize_Op : public OperatorTensor,
+                   public Registrable<Resize_Op, std::string, std::unique_ptr<OperatorImpl>(const Resize_Op&)>
+                   {
+
+public:
+    static const std::string Type;  // du
+
+    Resize_Op() : OperatorTensor(Type, 2, 0, 1) {}
+    //  4 input 0 attribute 1 output
+    // "data_input", "roi", "scales", "sizes"
+    
+    /**
+     * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
+     * @param op Operator to copy. 
+     */
+    Resize_Op(const Resize_Op& op)
+        : OperatorTensor(op)
+    {
+        //  copy an operator
+        if (!op.backend().empty()) {
+            SET_IMPL_MACRO(Resize_Op, *this, op.backend());
+        }
+        else {
+            mImpl = nullptr;
+        }
+    }
+
+    /**
+     * @brief Clone the operator using its copy-constructor.
+     * @see Operator::Resize_Op
+     */
+    std::shared_ptr<Operator> clone() const override {
+        return std::make_shared<Resize_Op>(*this);
+    }
+
+    // function see inputs
+    bool forwardDims(bool allowDataDependency = false) override final;
+
+    void setBackend(const std::string& name, DeviceIdx_t device = 0) override final{
+    //     // to select which backend CUDA / CPU?
+    //     mImpl = Registrar<Resize_Op>::create(name)(*this);
+    //     mOutputs[0]->setBackend(name, device);
+    }
+
+    static const std::vector<std::string> getInputsName(){
+        return {"data_input", "roi ", "scales", "sizes"};
+    }
+    static const std::vector<std::string> getOutputsName(){
+        return {"data_output"};
+    }
+};
+
+//  WTF
+inline std::shared_ptr<Node> Resize(const std::string &name = "") {
+    // FIXME: properly handle default w&b initialization in every cases
+    return std::make_shared<Node>(std::make_shared<Resize_Op>(), name);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CORE_OPERATOR_Resize_H_ */

python_binding/operator/pybind_Resize.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 <pybind11/pybind11.h>
+
+#include "aidge/operator/Resize.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
+
+namespace py = pybind11;
+namespace Aidge {
+
+void init_Resize(py::module& m) {
+    py::class_<Resize_Op, std::shared_ptr<Resize_Op>, OperatorTensor>(m, "ResizeOp", py::multiple_inheritance())
+    .def("get_inputs_name", &Resize_Op::getInputsName)
+    .def("get_outputs_name", &Resize_Op::getOutputsName);
+
+    m.def("Resize", &Resize, py::arg("name") = "");
+}
+}  // namespace Aidge

src/operator/Resize.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/Resize.hpp"
+
+#include <cstddef>    // std::size_t
+#include <cstdint>    // std::int64_t
+#include <stdexcept>  // std::runtime_error
+#include <string>
+#include <vector>
+#include <fmt/core.h>
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/data/Tensor.hpp"
+#include "aidge/utils/ErrorHandling.hpp"
+#include "aidge/utils/Types.h"
+
+
+const std::string Aidge::Resize_Op::Type = "Resize";
+
+bool Aidge::Resize_Op::forwardDims(bool /*allowDataDependency*/) {
+
+    // check input ("data_input","roi", "scales", "data_input","sizes") has been associated
+    for (size_t i = 0; i < 4; ++i) {
+        if (!getInput(i)) {
+            AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: input #{} not provided", type(), i);
+        }
+    }
+
+    // roi not implemented
+
+    // require scales of the input to have dims defined
+
+    if ((!getInput(0)->empty()) && (getInput(1)->empty() && !getInput(2)->empty() && getInput(3)->empty()) ) {
+        // condition 1.  input data_input tensor + scales
+      
+
+        std::vector<DimSize_t> outDims = getInput(0)->dims();
+        fmt::print("input dims 0 = {}\n", outDims);
+        fmt::print("input dims 1 = {}\n", getInput(1)->dims());
+        fmt::print("input dims 2 = {}\n", getInput(2)->dims());
+        fmt::print("input dims 3 = {}\n", getInput(3)->dims());        
+
+        printf("Scales Input(1)");
+
+        float* scales = static_cast<float*>(getInput(1)->getImpl()->rawPtr()); //TODO cast according to tensor type
+
+        // std::vector<DimSize_t> outDims[  width_out = width_input_tensor*scales[3], \ 
+                                        // height_out = heigh_input_tensor*scales[2], \
+                                        // depth_input_tensor = depth_input_tensor*scales[1] \ 
+                                        // batch_input_tensor = batch_input_tensor*scales[0] ];
+
+        for (std::size_t dim=0; dim < getInput(1)->size(); ++dim) {
+            // todo verify if batch and depth is not 1 !
+            printf("{} {} \n", dim, scales[dim] );
+            outDims[dim] *= scales[dim];
+        }
+
+        mOutputs[0]->resize(outDims);
+        fmt::print("output dims 0 = {}\n", outDims);
+        
+        return true;
+    }
+
+    if ((!getInput(0)->empty()) && (getInput(1)->empty()) && !getInput(2)->empty()) {
+        // condition 2.  input tensor + sizes
+        // to verify 2 arg
+        // "Input 0 and 2 must be provided and input 1 must not be provided." 
+        // "data_input" and "sizes"
+        
+        std::vector<DimSize_t> outDims = getInput(0)->dims();
+        fmt::print("input dims 0 = {}\n", outDims);
+        fmt::print("input dims 1 = {}\n", getInput(1)->dims());
+        fmt::print("input dims 2 = {}\n", getInput(2)->dims());
+        fmt::print("input dims 3 = {}\n", getInput(3)->dims());        
+
+        printf("Sizes Input(2)");
+        
+        float* sizes = static_cast<float*>(getInput(2)->getImpl()->rawPtr()); //TODO cast according to tensor type
+        
+        // std::vector<DimSize_t> outDims[  width_out = sizes[0], \ 
+                                        // height_out = sizes[1], \
+                                        // depth_input_tensor, \ 
+                                        // batch_input_tensor];
+
+        for (std::size_t dim=0; dim < getInput(1)->size(); ++dim) {
+            // todo verify if batch and depth is not 1 !
+            outDims[dim] = sizes[dim];
+            printf("{} {} \n", dim, sizes[dim] );
+        }
+        mOutputs[0]->resize(outDims);
+        fmt::print("output dims 0 = {}\n", outDims);
+        return true;
+    }
+
+    return false;
+}
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