How to use it for GANs

[naoki7090624]

Thank you for sharing your great work!

Can actNN be used for GAN models? I used actNN with the following GAN architecture, but I got the error. https://github.com/knazeri/edge-connect

Traceback (most recent call last):
  File "train.py", line 3, in <module>
    main(mode=1)
  File "***\edge-connect\main.py", line 50, in main
    model = EdgeConnect(config)
  File "***\edge-connect\src\edge_connect.py", line 27, in __init__
    self.edge_model = EdgeModel(config).to(config.DEVICE)
  File "***\edge-connect\src\models.py", line 67, in __init__
    generator = actnn.QModule(generator)
  File "***\actnn\actnn\actnn\module.py", line 18, in __init__
    QModule.convert_layers(model)
  File "***\actnn\actnn\actnn\module.py", line 76, in convert_layers
    QModule.convert_layers(child)
  File "***\actnn\actnn\actnn\module.py", line 48, in convert_layers
    child.groups, child.bias, child.dilation, child.padding_mode))
  File "***\actnn\actnn\actnn\layers.py", line 137, in __init__
    padding, output_padding, groups, bias, dilation, padding_mode)
  File "***\Python37\lib\site-packages\torch\nn\modules\conv.py", line 904, in __init__
    True, output_padding, groups, bias, padding_mode, **factory_kwargs)
  File "***\Python37\lib\site-packages\torch\nn\modules\conv.py", line 602, in __init__
    groups, bias, padding_mode, **factory_kwargs)
  File "***\Python37\lib\site-packages\torch\nn\modules\conv.py", line 133, in __init__
    if bias:
RuntimeError: Boolean value of Tensor with more than one value is ambiguous

I have add the following code to model.py#L61

        generator = EdgeGenerator(use_spectral_norm=True)
        discriminator = Discriminator(in_channels=2, use_sigmoid=config.GAN_LOSS != 'hinge')
        generator = actnn.QModule(generator)
        print(generator)
        exit()

I would like to seek your advice on this problem. Thanks

[cjf00000]

This might be a bug in the converter function. @merrymercy could you take a look?

A quicker fix is to disable the bias in conv layers. (set bias=None)