# from cellseg_models_pytorch.inference import ResizeInferer
# from cellseg_models_pytorch.models import cellpose_plus
# from cellseg_models_pytorch.utils.latency_benchmark import LatencyBenchmarker


# def test_latency_benchmark(img_dir):
#     model = cellpose_plus(sem_classes=3, type_classes=3, long_skip="unet")

#     inferer = ResizeInferer(
#         model,
#         img_dir,
#         out_activations={"sem": "softmax", "type": "softmax", "cellpose": "tanh"},
#         out_boundary_weights={"sem": False, "type": False, "cellpose": True},
#         resize=(256, 256),
#         padding=80,
#         instance_postproc="hovernet",
#         batch_size=1,
#         save_intermediate=True,
#         device="cpu",
#         parallel=False,
#     )
#     inferer.infer()

#     bm = LatencyBenchmarker(inferer)

#     bm.postproc_latency("inst", reps_per_img=1)
#     bm.postproc_latency("type", reps_per_img=1)
#     bm.postproc_latency("sem", reps_per_img=1)
#     bm.inference_latency(reps=1, warmup_reps=0)
#     bm.inference_postproc_latency(reps=1)
#     # bm.model_latency(input_size=(64, 64), reps=1, warmup_reps=0, device="cpu")
#     # bm.model_throughput(input_size=(64, 64), reps=1, warmup_reps=0, device="cpu")
