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Important note to gradient penalty for wgangp #513

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Important note to gradient penalty for wgangp #513

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1 change: 1 addition & 0 deletions models/networks.py
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Expand Up @@ -281,6 +281,7 @@ def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', const
lambda_gp (float) -- weight for this loss

Returns the gradient penalty loss
NOTE: Strongly advised not to use batch/instance norm with the Discriminator(or Critic) if using gradient penalty!
"""
if lambda_gp > 0.0:
if type == 'real': # either use real images, fake images, or a linear interpolation of two.
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