def test_1d(self):
bn = False
params = dict()
params['optimization'] = dict()
params['optimization']['epoch'] = 1
params['summary_every'] = 4
params['save_every'] = 5
params['print_every'] = 3
params['net'] = dict()
params['net']['shape'] = [16, 1] # Shape of the image
params['net']['generator'] = dict()
params['net']['generator']['latent_dim'] = 100
params['net']['generator']['full'] = [2 * 8]
params['net']['generator']['nfilter'] = [2, 32, 32, 1]
params['net']['generator']['batch_norm'] = [bn, bn, bn]
params['net']['generator']['shape'] = [[5], [5], [5], [5]]
params['net']['generator']['stride'] = [1, 2, 1, 1]
params['net']['generator']['data_size'] = 1
params['net']['discriminator'] = dict()
params['net']['discriminator']['full'] = [32]
params['net']['discriminator']['nfilter'] = [16, 32, 32, 32]
params['net']['discriminator']['batch_norm'] = [bn, bn, bn, bn]
params['net']['discriminator']['shape'] = [[5], [5], [5], [3]]
params['net']['discriminator']['stride'] = [2, 2, 2, 1]
params['net']['discriminator']['data_size'] = 1
X = np.random.rand(101, 16)
dataset = Dataset(X)
wgan = GANsystem(WGAN, params)
wgan.train(dataset)
img = wgan.generate(2)
assert (len(img) == 2)
assert (img.shape[1:] == (16, 1))
img = wgan.generate(500)
assert (len(img) == 500)
def test_generate(self):
params = dict()
params['optimization'] = dict()
params['optimization']['epoch'] = 1
params['summary_every'] = 4
params['save_every'] = 5
params['print_every'] = 3
params['net']= dict()
params['net']['generator'] = dict()
params['net']['generator']['latent_dim'] = 5
X = np.random.rand(101,16,16)
dataset = Dataset(X)
wgan = GANsystem(WGAN, params)
wgan.train(dataset)
img = wgan.generate(2)
img = wgan.generate(500)
assert(len(img)==500)
z = np.random.randn(133,5)
img1 = wgan.generate(z=z)
img2 = np.zeros(shape=img1.shape)
for i in range(133):
img2[i] = wgan.generate(z=np.reshape(z[i], [1,5]))
np.testing.assert_almost_equal(img1, img2, decimal=6)
def test_gansystem(self):
params = dict()
params['optimization'] = dict()
params['optimization']['epoch'] = 1
params['summary_every'] = 4
params['save_every'] = 5
params['print_every'] = 3
X = np.random.rand(101,16,16)
dataset = Dataset(X)
wgan = GANsystem(WGAN, params)
wgan.train(dataset)
img = wgan.generate(2)
img = wgan.generate(500)
assert(len(img)==500)
def test_patchupscalegan1dborder(self):
bn = False
params = dict()
params['optimization'] = dict()
params['optimization']['epoch'] = 1
params['summary_every'] = 4
params['save_every'] = 5
params['print_every'] = 3
params['net'] = dict()
params['net']['shape'] = [8, 2]
params['net']['generator'] = dict()
params['net']['generator']['latent_dim'] = 8
params['net']['generator']['full'] = [16]
params['net']['generator']['nfilter'] = [8, 32, 1]
params['net']['generator']['batch_norm'] = [bn, bn]
params['net']['generator']['shape'] = [[3], [3], [3]]
params['net']['generator']['stride'] = [1, 1, 1]
params['net']['generator']['data_size'] = 1
params['net']['generator']['borders'] = dict()
params['net']['generator']['borders']['width_full'] = None
params['net']['generator']['borders']['nfilter'] = [4, 1]
params['net']['generator']['borders']['batch_norm'] = [bn, bn]
params['net']['generator']['borders']['shape'] = [[5], [3]]
params['net']['generator']['borders']['stride'] = [2, 2]
params['net']['generator']['borders']['data_size'] = 1
params['net']['generator']['borders']['width_full'] = 2
params['net']['discriminator'] = dict()
params['net']['discriminator']['full'] = [32]
params['net']['discriminator']['nfilter'] = [16, 32]
params['net']['discriminator']['batch_norm'] = [bn, bn]
params['net']['discriminator']['shape'] = [[5], [3]]
params['net']['discriminator']['stride'] = [2, 2]
params['net']['discriminator']['data_size'] = 1
params['net']['upscaling'] = 2
X = np.random.rand(101, 8, 2)
dataset = Dataset(X)
wgan = GANsystem(UpscalePatchWGAN, params)
wgan.train(dataset)
borders = np.random.rand(500, 8, 1)
X_smooth = np.random.rand(500, 8, 1)
print(borders[:2].shape)
img = wgan.generate(N=2, X_smooth=X_smooth[:2], borders=borders[:2])
assert (len(img) == 2)
assert (img.shape[1:] == (8, 1))
img = wgan.generate(N=500,
X_smooth=X_smooth[:500],
borders=borders[:500])
assert (len(img) == 500)
def test_patchupscalegan3d(self):
bn = False
params = dict()
params['optimization'] = dict()
params['optimization']['epoch'] = 1
params['summary_every'] = 4
params['save_every'] = 5
params['print_every'] = 3
params['net'] = dict()
params['net']['shape'] = [8, 8, 8, 8]
params['net']['generator'] = dict()
params['net']['generator']['latent_dim'] = 8 * 8 * 8
params['net']['generator']['full'] = []
params['net']['generator']['nfilter'] = [8, 32, 1]
params['net']['generator']['batch_norm'] = [bn, bn]
params['net']['generator']['shape'] = [[3, 3, 3], [3, 3, 3], [3, 3, 3]]
params['net']['generator']['stride'] = [1, 1, 1]
params['net']['generator']['data_size'] = 3
params['net']['discriminator'] = dict()
params['net']['discriminator']['full'] = [32]
params['net']['discriminator']['nfilter'] = [16, 32]
params['net']['discriminator']['batch_norm'] = [bn, bn]
params['net']['discriminator']['shape'] = [[3, 3, 3], [3, 3, 3]]
params['net']['discriminator']['stride'] = [2, 2]
params['net']['discriminator']['data_size'] = 3
params['net']['upsampling'] = 2
X = np.random.rand(101, 8, 8, 8, 8)
dataset = Dataset(X)
class UgradedGAN(UpscalePatchWGAN, CosmoWGAN):
pass
wgan = GANsystem(UgradedGAN, params)
wgan.train(dataset)
borders = np.random.rand(500, 8, 8, 8, 7)
X_down = np.random.rand(500, 4, 4, 4, 1)
img = wgan.generate(N=2, X_down=X_down[:2], borders=borders[:2])
assert (len(img) == 2)
assert (img.shape[1:] == (8, 8, 8, 1))
img = wgan.generate(N=500, X_down=X_down[:500], borders=borders[:500])
assert (len(img) == 500)
def test_lapgan2d(self):
bn = False
params = dict()
params['optimization'] = dict()
params['optimization']['epoch'] = 1
params['summary_every'] = 4
params['save_every'] = 5
params['print_every'] = 3
params['net'] = dict()
params['net']['shape'] = [16, 16, 1]
params['net']['generator'] = dict()
params['net']['generator']['latent_dim'] = 16 * 16
params['net']['generator']['full'] = []
params['net']['generator']['nfilter'] = [8, 32, 1]
params['net']['generator']['batch_norm'] = [bn, bn]
params['net']['generator']['shape'] = [[5, 5], [5, 5], [5, 5]]
params['net']['generator']['stride'] = [1, 1, 1]
params['net']['generator']['data_size'] = 2
params['net']['discriminator'] = dict()
params['net']['discriminator']['full'] = [32]
params['net']['discriminator']['nfilter'] = [16, 32]
params['net']['discriminator']['batch_norm'] = [bn, bn]
params['net']['discriminator']['shape'] = [[5, 5], [3, 3]]
params['net']['discriminator']['stride'] = [2, 2]
params['net']['discriminator']['data_size'] = 2
params['net']['upscaling'] = 2
X = np.random.rand(101, 16, 16)
dataset = Dataset(X)
wgan = GANsystem(LapWGAN, params)
wgan.train(dataset)
X_down = np.random.rand(500, 8, 8, 1)
img = wgan.generate(N=2, X_down=X_down[:2])
assert (len(img) == 2)
assert (img.shape[1:] == (16, 16, 1))
img = wgan.generate(N=500, X_down=X_down[:500])
assert (len(img) == 500)
params_optimization['discriminator'] = dict()
params_optimization['discriminator']['optimizer'] = 'adam'
params_optimization['discriminator']['kwargs'] = {'beta1': 0.5, 'beta2': 0.9}
params_optimization['discriminator']['learning_rate'] = 1e-4
# all parameters
params = dict()
params['net'] = dict() # All the parameters for the model
params['net']['generator'] = params_generator
params['net']['discriminator'] = params_discriminator
params['net']['prior_distribution'] = 'gaussian'
params['net']['shape'] = [128, 128, 1] # Shape of the image
params['net']['gamma_gp'] = 10 # Gradient penalty
params['net']['fs'] = 16000 // downscale
params['net']['loss_type'] = 'wasserstein'
params['optimization'] = params_optimization
params['summary_every'] = 100 # Tensorboard summaries every ** iterations
params['print_every'] = 50 # Console summaries every ** iterations
params['save_every'] = 1000 # Save the model every ** iterations
params['summary_dir'] = os.path.join(global_path, name + '_summary/')
params['save_dir'] = os.path.join(global_path, name + '_checkpoints/')
params['Nstats'] = 500
resume, params = utils.test_resume(True, params)
params['optimization']['epoch'] = 10000
wgan = GANsystem(SpectrogramGAN, params)
wgan.train(dataset, resume=resume)
def test_conditionalwgan(self):
bn = False
ns = 16
# Parameters for the generator
params_generator = dict()
params_generator['latent_dim'] = 126
params_generator['stride'] = [1, 2, 1, 1]
params_generator['nfilter'] = [32, 64, 32, 1]
params_generator['shape'] = [[5, 5], [5, 5], [5, 5], [5, 5]]
params_generator['batch_norm'] = [bn, bn, bn]
params_generator['full'] = [8 * 8 * 32]
params_generator['non_lin'] = tf.nn.relu
params_generator['in_conv_shape'] = [8, 8]
# Parameters for the discriminator
params_discriminator = dict()
params_discriminator['stride'] = [2, 2, 1]
params_discriminator['nfilter'] = [16, 64, 32]
params_discriminator['shape'] = [[5, 5], [5, 5], [3, 3]]
params_discriminator['batch_norm'] = [bn, bn, bn]
params_discriminator['full'] = [512, 128, 32]
params_discriminator['minibatch_reg'] = False
# Optimization parameters
d_opt = dict()
d_opt['optimizer'] = "rmsprop"
d_opt['learning_rate'] = 3e-5
params_optimization = dict()
params_optimization['n_critic'] = 5
params_optimization['batch_size'] = 8
params_optimization['epoch'] = 1
# all parameters
params = dict()
params['net'] = dict() # All the parameters for the model
params['net']['generator'] = params_generator
params['net']['discriminator'] = params_discriminator
params['net']['shape'] = [ns, ns, 1] # Shape of the image
params['net']['gamma_gp'] = 10 # Gradient penalty
# Conditional params
params['net']['prior_normalization'] = False
params['net']['cond_params'] = 2
params['net']['init_range'] = [[0, 1], [0, 1]]
params['net']['prior_distribution'] = "gaussian_length"
params['net']['final_range'] = [
0.1 * np.sqrt(params_generator['latent_dim']),
1 * np.sqrt(params_generator['latent_dim'])
]
params['optimization'] = params_optimization
params['summary_every'] = 4
params['save_every'] = 5
params['print_every'] = 3
X = np.random.rand(101, ns, ns)
parameters = np.random.rand(101, 2)
dataset = Dataset_parameters(X, parameters)
wgan = GANsystem(ConditionalParamWGAN, params)
wgan.train(dataset)
img = wgan.generate(N=16,
**{
'z':
wgan.net.sample_latent(bs=16,
params=np.random.rand(
16, 2))
})
assert (img.shape[0] == 16 and img.shape[1] == ns
and img.shape[2] == ns and img.shape[3] == 1)