I have the same questions.
Is that refer to the clip layer (which act as minimum and relu layer after quantised)?
That's why I asked whether the final official influence code contain an np.clip function for the output or not?
If yes, we can remove the clip layer and save the time.
Hello
May I know whether in the final official influence code contain an np.clip function for the output or not?
If not, we need the clip layer in our model, and this cost us more 8ms in total.
It may be better to clarify it as it may signifianct affect the runtime.
Thanks a lot.
Yes I do think so, since 360,640 is for testing speed.
Congrats. I saw u achieved a nice score.
May I know how you submit the zip file?
Do you zip the 100 sr images naming 0801.png.etc, and the file is like over 300MB right?
Because I always get an error during the submission and am frustrated...
Do you uplaod the 100 sr pngs and run the evaluation by the platform successfully?
I always have this error.
WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap.
Traceback (most recent call last):
File...
Thanks for reply.
Is that the PSNR value are measured based on the uploaded 100 sr images or influenced once again from the tflite online?
Also, is that the standard range of input images for the model should be 0,255 instead of 0,1?
When I submit the zip file, there is an error.
WARNING: Your...
Hello, for the submission I got a problem.
I notice that the online system has 30MB upload limitation.
How can u submit the full size sr images, which cost 300MB?
Hi Lishen
May I know that do you use the demo https://github.com/aiff22/MAI-2021-Workshop/blob/main/fsrcnn_quantization/fsrcnn.py to develop?
I notice that the quantization part in that demo for the input size is fixed (TFLITE_MODEL_INPUT_SHAPE = [1, 360, 640, 1])?
Do you know how to change it...