def create_model(options):
latent_size = 0
discriminator = apply_spectral_norm(Discriminator())
#discriminator = Discriminator()
generator = Generator()
lr_base = 0.0002 #* 10 #* 0.1
model = Gan(
discriminator=discriminator,
generator=generator,
latent_size=latent_size,
optimizer_discriminator_fn=functools.partial(optimizer_fn,
lr=lr_base), # TTUR
optimizer_generator_fn=functools.partial(optimizer_fn, lr=lr_base),
real_image_from_batch_fn=get_image,
image_pool=GanDataPool(100))
model.apply(init_weights)
return model
def create_model():
latent_size = 64
generator = Generator(latent_size=latent_size)
discriminator = Discriminator()
optimizer_fn = functools.partial(torch.optim.Adam,
lr=0.001,
betas=(0.5, 0.999))
model = Gan(discriminator=discriminator,
generator=generator,
latent_size=latent_size,
optimizer_discriminator_fn=optimizer_fn,
optimizer_generator_fn=optimizer_fn,
real_image_from_batch_fn=get_image)
return model
def create_model():
latent_size = 0
#netD = apply_spectral_norm(Discriminator())
discriminator = Discriminator()
generator = Generator()
lr_base = 0.0002
model = Gan(
discriminator=discriminator,
generator=generator,
latent_size=latent_size,
optimizer_discriminator_fn=functools.partial(optimizer_fn,
lr=lr_base), # TTUR
optimizer_generator_fn=functools.partial(optimizer_fn, lr=lr_base),
real_image_from_batch_fn=get_image,
)
return model