tsingqguo / exposure-fusion-shadow-removal

We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.
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Details on Training RefineNet #30

Open yx-chan131 opened 2 years ago

yx-chan131 commented 2 years ago

In the paper's Implementation Details section it is stated that the RefineNet is trained with exposure estimation and FusionNet together but freezing the latter two. I already trained the FusionNet. But I didn't see in the code that RefineNet training process used my pre-trained FusionNet, The RefineNet just defined new netG and netM models.

I'm a bit confused about this training process. If the trained FusionNet is never used, then what's the point of training a FusionNet?

fl82hope commented 2 years ago

There is some bugs for loading I think, previously, refineNet is bulid in a second branch, in the merging into current branch, the loading have some bugs.

if opt.load_dir and opt.load_dir != 'None': print('load fusion net from:', opt.load_dir) model.load_networks('latest', opt.load_dir)

here for loading fusion network weights, please have a check the if statement.

Season-Chan commented 2 years ago

This problem confuses me a lot too! Have you solved it yet? Could you provide the solution? Thanks.

xuhangc commented 1 year ago

This problem confuses me a lot too! Have you solved it yet? Could you provide the solution? Thanks.

In this line, add a check after the for loop https://github.com/tsingqguo/exposure-fusion-shadow-removal/blob/531c79b4307ff63a76d062967c3fcdcb73f18e5b/models/base_model.py#L175

if name == "R":
    continue
HarryHsing commented 2 weeks ago
if name == "R":
    continue

thanks bruh