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_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_dualgansystem(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 = DualGANsystem(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_lapgan3d(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, 1]
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']['upscaling'] = 2
X = np.random.rand(101, 8, 8, 8)
dataset = Dataset(X)
wgan = GANsystem(LapWGAN, params)
wgan.train(dataset)
X_down = np.random.rand(500, 4, 4, 4, 1)
img = wgan.generate(N=2, X_down=X_down[:2])
assert (len(img) == 2)
assert (img.shape[1:] == (8, 8, 8, 1))
img = wgan.generate(N=500, X_down=X_down[:500])
assert (len(img) == 500)
