twhui / srgan-pytorch Goto Github PK

Hello twhui,

I would like to perform a test phase on the SGRAN.
I downloaded the SGRAN pretrained model at your link : https://drive.google.com/file/d/1vAtPLGbdyt--SZQxUl0YKPRQgu6-kR6v/view, and set il all up on my local env.
While reading the README, i was sure that the bench support testing for SGRAN
but when I inspect the sources, my understanding is that "test mode" is only available
for SRResNet.

In model.py below, It is clear that sr_gan is only loaded for "training mode" , isn't it :

def create_model(opt): 
    if opt.model == 'sr_resnet': 
        from .sr_resnet_model import SRResNetModel 
        model = SRResNetModel() 
    elif opt.model == 'sr_resnet_test': 
        from .sr_resnet_test_model import SRResNetTestModel 
        model = SRResNetTestModel() 
 
    elif opt.model == 'sr_gan': 
        from .sr_gan_model import SRGANModel 
        model = SRGANModel() 
 
    else: 
        raise NotImplementedError('Model [%s] not recognized.' % opt.model) 
    model.initialize(opt) 
    print('Model [%s] is created.' % model.name()) 
    return model 

In addition, when I execute the cmd below, I got a crash that tells me again that test mode is not supported got SRGAN :

CUDA_VISIBLE_DEVICES=0 python ./test.py --option ./options/test/SRGAN_x4.json

Traceback (most recent call last):
  File "./test.py", line 45, in <module>
    model = create_model(opt)
  File "/home/dgerin/SRGAN-PyTorch/models/models.py", line 17, in create_model
    model.initialize(opt)
  File "/home/dgerin/SRGAN-PyTorch/models/sr_gan_model.py", line 20, in initialize
    assert opt.is_train

Could you give more information about that ?

Thanks,
Dimitri

Hello twhui,
I would like to train my SRGAN using your code. And the pretrained model you provided have a great performance during testing, so can you tell me the batchsize , epochs, traing time and machine configuration of your pretrained model, thank you very much.

Hi,
I guess there may be some problem with the comment in this line.
If the input range is [-1, 1], the mean will be:

# [2*0.485-1, 2*0.456-1, 2*0.406-1] 

rather than:

# [0.485-1, 0.456-1, 0.406-1]