Draft: [Add][WIP] benchmark scripts

Context

The aim is to add an universal system to measure the accuracy and time performances of each Aidge module, wheter they are backend or export modules.

Usage

generate_graph.py

python benchmarks/generate_graph.py --operator-config benchmarks/operator_config/add_config.json --ref benchmarks/results/add_onnxruntime.json --libs benchmarks/results/add_torch.json benchmarks/results/add_aidge_backend_cpu.json

This will create a bar plot comparing onnxruntime, torch and aidge_backend_cpu liraries with onnxruntime performances as a reference

The result JSON files should have the following structure - see this example for the Add operator for module aidge_backend_cpu:

{
    "library": "aidge_backend_cpu",
    "compare": {},
    "time": {
        "dim size": {
            "1": [
                1.1659999999968917e-05,   // <- one iteration time measured
                ...
             ],
            "4": [...],
            "16": [...],
            "64": [...],
            "128": [...]
        },
        "one dim broadcasted (idx)": {
            ...
        },
        "two dims broadcasted (idx)": {
            ...
        },
        "nb missing axis 1st input": {
            ...
        }
    }
}

Extract of operator-config file for Add Operator:

{
    "operator": "Add",
    "opset_version": 21,
    "initializer_rank": 2,
    "base_configuration": {
        "input_shapes": [
	    ["input_0", [64, 64, 64, 64]],
	    ["input_1", [64, 64, 64, 64]]
	],
	    "attributes": {}
    },
    "test_configuration": {
        "main_parameters": {
	    "dim size": [
		1,4,16,64,128
	    ],
            "one dim broadcasted (idx)": [...],
            "two dims broadcasted (idx)": [...],
            "nb missing axis 1st input": [...]
	},
        "other_parameters": {
	    "dim size": {
                "1": {
		    "attributes": {},
		    "input_shapes": [
			["input_0", [1, 1, 1, 1]],
                        ["input_1", [1, 1, 1, 1]]
		    ]
		},
		"4": {
		    "attributes": {},
		    "input_shapes": [
			["input_0", [4, 4, 4, 4]],
                        ["input_1", [4, 4, 4, 4]]
		    ]
		},
...