测试模型在GPU上的运行时间_the correct way to measure inference time of deep

出处：https://deci.ai/the-correct-way-to-measure-inference-time-of-deep-neural-networks/?utm_referrer=https%3A%2F%2Ftowardsdatascience.com%2Fa-new-tool-for-analysing-neural-network-inference-performance-13cc21d2efea

向大佬致敬

核心代码：

model = EfficientNet.from_pretrained('efficientnet-b0')
device = torch.device("cuda")
model.to(device)
dummy_input = torch.randn(1, 3,224,224, dtype=torch.float).to(device)

# INIT LOGGERS
starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
repetitions = 300
timings=np.zeros((repetitions,1))
#GPU-WARM-UP
for _ in range(10):
    _ = model(dummy_input)
# MEASURE PERFORMANCE
with torch.no_grad():
    for rep in range(repetitions):
        starter.record()
        _ = model(dummy_input)
        ender.record()
        # WAIT FOR GPU SYNC
        torch.cuda.synchronize()
        curr_time = starter.elapsed_time(ender)
        timings[rep] = curr_time

mean_syn = np.sum(timings) / repetitions
std_syn = np.std(timings)
print(mean_syn)