Time benchmark script

[Dickson Neoh]

Include time benchmark script from here - https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/102-pytorch-to-openvino/102-pytorch-onnx-to-openvino.ipynb

num_images = 100

with torch.no_grad():
    start = time.perf_counter()
    for _ in range(num_images):
        model(torch.as_tensor(input_image).float())
    end = time.perf_counter()
    time_torch = end - start
print(
    f"PyTorch model on CPU: {time_torch/num_images:.3f} seconds per image, "
    f"FPS: {num_images/time_torch:.2f}"
)

compiled_model_onnx = core.compile_model(model=model_onnx, device_name="CPU")
start = time.perf_counter()
for _ in range(num_images):
    compiled_model_onnx([normalized_input_image])
end = time.perf_counter()
time_onnx = end - start
print(
    f"ONNX model in OpenVINO Runtime/CPU: {time_onnx/num_images:.3f} "
    f"seconds per image, FPS: {num_images/time_onnx:.2f}"
)

compiled_model_ir = core.compile_model(model=model_ir, device_name="CPU")
start = time.perf_counter()
for _ in range(num_images):
    compiled_model_ir([input_image])
end = time.perf_counter()
time_ir = end - start
print(
    f"OpenVINO IR model in OpenVINO Runtime/CPU: {time_ir/num_images:.3f} "
    f"seconds per image, FPS: {num_images/time_ir:.2f}"
)

if "GPU" in core.available_devices:
    compiled_model_onnx_gpu = core.compile_model(model=model_onnx, device_name="GPU")
    start = time.perf_counter()
    for _ in range(num_images):
        compiled_model_onnx_gpu([input_image])
    end = time.perf_counter()
    time_onnx_gpu = end - start
    print(
        f"ONNX model in OpenVINO/GPU: {time_onnx_gpu/num_images:.3f} "
        f"seconds per image, FPS: {num_images/time_onnx_gpu:.2f}"
    )

    compiled_model_ir_gpu = core.compile_model(model=model_ir, device_name="GPU")
    start = time.perf_counter()
    for _ in range(num_images):
        compiled_model_ir_gpu([input_image])
    end = time.perf_counter()
    time_ir_gpu = end - start
    print(
        f"IR model in OpenVINO/GPU: {time_ir_gpu/num_images:.3f} "
        f"seconds per image, FPS: {num_images/time_ir_gpu:.2f}"
    )