About the inference time of the network

[wulalalalalalala]

Thanks for your contribution.

But the inference time I calculated is quite different from it mentioned in the paper. It takes more than 100ms for a 4K image. The code I use is as follows.
'''
model = B_transformer().cuda()
a = torch.randn(1, 3, 1024, 1024).cuda()
starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
repetitions = 100
timings = np.zeros((repetitions, 1))
# GPU-WARM-UP
for _ in range(50):
enhanced_image = model(a)
# MEASURE PERFORMANCE
with torch.no_grad():
for rep in range(repetitions):
torch.cuda.synchronize()
starter.record()
enhanced_image = model(a)
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)
'''
Is this right?

[zzr-idam]

Our model is quick for inference and may have something to do with your cold start.

[wulalalalalalala]

Thank you for your reply. But my code contains the GPU warm-up step.
If you don't mind, could you provide the correct code for measuring the inference time?

[zhangn77]

Our model is quick for inference and may have something to do with your cold start.

I have the same question. The running time test on the Telsa V100 GPU for a 1024X1024 image comes to 100ms, which is quiet different from the data in your paper (9ms for a 4k image). Can you share the code for measuring the inference time?