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Fengmoon93 avatar Fengmoon93 commented on July 28, 2024 1

I just resize the cityscapes dataset into 512*1024,I record the time that the network processed the data,the average time is 0.02s,but when it loads the checkpoint flie,the first iteration takes about 0.04s.
Anyway,thanks for your amazing job~ @hellochick

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Anubhav7 avatar Anubhav7 commented on July 28, 2024 1

You beat me to the fix :). Thanks for the help, really appreciate it.
I am getting inference times of around .07s now

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hellochick avatar hellochick commented on July 28, 2024

Hey, it will initialize the model at first iteration, so the time you calculated contain the time of initialization.
You can try to run more images and calculate like following:

for i in range(10):
     start_time = time.time()
     preds = sess.run(pred, feed_dict={x: img})
     end_time = time.time()
     
    print('time: ', end_time - start_time)

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Anubhav7 avatar Anubhav7 commented on July 28, 2024

Hey,

Thanks for the quick reply. I tried your suggestion and now it reports .06s per frame. My analysis says that the function decode_labels is taking the rest of the time. I am thinking of ways to speed that up.. do you have any suggestions?

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hellochick avatar hellochick commented on July 28, 2024

Hey @Anubhav7 ,
I will fix this bottleneck tomorrow, just wait for me, thanks.

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hellochick avatar hellochick commented on July 28, 2024

@Anubhav7, I have change the decode_label function. Can you test on it again?

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StOnEGiggity avatar StOnEGiggity commented on July 28, 2024

Hi, thanks for your excellent work at first.There is one thing confuses me that inference time doesn't include interp layer only when testing.In the official code or paper, it looks like inference time includes this part and it costs only 0.5ms in caffe.

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hellochick avatar hellochick commented on July 28, 2024

Hey @StOnEGiggity. My way to calculate inference time is to subtract the time of processing input data, so it contains interp layer. Does this confuse you, or something else?

Btw, it seems that caffe use caffe time to calculate, and it only contains the time of forwarding in the network.

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StOnEGiggity avatar StOnEGiggity commented on July 28, 2024

Sorry for my unsuitable expression. I mean that the time may contain this line

raw_output_up = tf.image.resize_bilinear(raw_output, size=input_size, align_corners=True)

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hellochick avatar hellochick commented on July 28, 2024

@StOnEGiggity , Ohhh!!!!! I got it, this is my fault. I forgot to calculate this time at evaluate.py, now i understand what you mean. Thanks for your reply, I have just fixed it .

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StOnEGiggity avatar StOnEGiggity commented on July 28, 2024

Hi, I am interested in your new inference time.I have larger one than paper refers to due to GPUs.Would you mind updating it somewhere?Thanks a lot:) @hellochick

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hellochick avatar hellochick commented on July 28, 2024

Hey @StOnEGiggity , the new inference time test on GTX 1080ti shown below:
Inference time excluding loading data ( Calculated as described in paper ): ~0.03s
Inference time including loading data : ~0.04s

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