add: function to get inference time and output of some GraphView

aidge_backend_cpu/benchmark.py

+import time
+
+import numpy as np
+
+import aidge_core
+
+def prepare_model_scheduler_inputs(model: aidge_core.GraphView, input_data: list[str, np.ndarray]) -> tuple[aidge_core.GraphView, aidge_core.SequentialScheduler]:
+    # update model and inputs backend
+    model.set_backend("cpu")
+    ordered_inputs = [aidge_core.Tensor(i[1]) for i in input_data]
+    for ordered_input in ordered_inputs:
+        ordered_input.set_backend("cpu")
+
+    scheduler = aidge_core.SequentialScheduler(model)
+    scheduler.generate_scheduling()
+
+    return model, scheduler, ordered_inputs
+
+
+def measure_inference_time(model: aidge_core.GraphView, input_data: list[str, np.ndarray], nb_warmup: int = 10, nb_iterations: int = 50) -> list[float]:
+    model, scheduler, ordered_inputs = prepare_model_scheduler_inputs(model, input_data)
+
+    timings = []
+    # Warm-up runs.
+    for i in range(nb_warmup + nb_iterations):
+        if i < nb_warmup:
+            scheduler.forward(forward_dims=False, data=ordered_inputs)
+        else:
+            start = time.process_time()
+            scheduler.forward(forward_dims=False, data=ordered_inputs)
+            end = time.process_time()
+            timings.append((end - start))
+    return timings
+
+def compute_output(model: aidge_core.GraphView, input_data: list[str, np.ndarray]) -> list[np.ndarray]:
+    model, scheduler, ordered_inputs = prepare_model_scheduler_inputs(model, input_data)
+
+    scheduler.forward(forward_dims=False, data=ordered_inputs)
+
+    return [np.array(t[0].get_operator().get_output(t[1])) for t in model.get_ordered_outputs()]
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