Comments (12)
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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You beat me to the fix :). Thanks for the help, really appreciate it.
I am getting inference times of around .07s now
from icnet-tensorflow.
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)
from icnet-tensorflow.
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?
from icnet-tensorflow.
Hey @Anubhav7 ,
I will fix this bottleneck tomorrow, just wait for me, thanks.
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@Anubhav7, I have change the decode_label function. Can you test on it again?
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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.
from icnet-tensorflow.
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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Sorry for my unsuitable expression. I mean that the time may contain this line
Line 100 in 4aeb400
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@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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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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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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Related Issues (20)
- Errors in restoring the session evalucation.py and network.py
- ValueError: Shape must be rank 4 but is rank 3 for 'data_sub2' (op: 'ResizeBilinear') with input shapes: [720,720,3], [2]. HOT 3
- Why is it suddenly 'killed' run train.py? HOT 1
- How predict the result to use my training model ckpt.meta?
- Dimension not equal HOT 1
- ValueError:Variable conv does not exist
- ValueError when using own dataset HOT 2
- Training own dataset
- Inference time is too high(about 3.5x as supposed to be ~0.04s)
- multi GPU training?
- tensorflow's version HOT 1
- How to use the pre-trained modle of ade20k provided by author,I use the code in demo.ipynb,but it can't open the file,the cityscapes works well.
- Training over-fitting after every epochs
- same classification result with every pixel HOT 1
- can get correct result
- bad results of voc2012
- The update stops and the loss does not drop HOT 4
- Assign requires shapes of both tensors to match. lhs shape= [13] rhs shape= [150]
- 上一个项目
- 关于ade20k的分割结果,颜色和标签有对应关系吗?
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