def __init__(self,
output_size=(1, 28, 28),
genome=None,
input_shape=(1, 1, 10, 10),
optimizer_conf=config.gan.generator.optimizer):
super().__init__(output_size=output_size,
genome=genome,
input_shape=input_shape)
self.noise_size = int(np.prod(self.input_shape[1:]))
self.inception_score_mean = 0
self.fid_score = None
self.rmse_score = None
self.optimizer_conf = optimizer_conf
if genome is None:
if config.gan.generator.fixed:
self.genome = Genome(
random=False,
add_layer_prob=0,
rm_layer_prob=0,
gene_mutation_prob=0,
simple_layers=config.gan.generator.simple_layers,
linear_at_end=False)
self.genome.add(
Linear(4 * int(np.prod(output_size)),
activation_type="LeakyReLU",
activation_params={"negative_slope": 0.2}))
if not config.gan.generator.simple_layers:
self.genome.add(
Deconv2d(128,
activation_type="LeakyReLU",
activation_params={"negative_slope": 0.2}))
self.genome.add(
Deconv2d(64,
activation_type="LeakyReLU",
activation_params={"negative_slope": 0.2}))
else:
self.genome = Genome(
random=not config.evolution.sequential_layers,
linear_at_end=False)
self.genome.possible_genes = [
(getattr(evolution, l), {})
for l in config.gan.generator.possible_layers
]
self.genome.add(Linear(4096))
if config.gan.generator.simple_layers:
# self.genome.output_genes = [Deconv2d(output_size[0], activation_type="Tanh")]
self.genome.output_genes = [
Linear(int(np.prod(output_size)),
activation_type="Tanh",
normalize=False)
]
else:
self.genome.output_genes = [
Deconv2d(output_size[0],
activation_type="Tanh",
normalize=False)
]
def test_simple_deconv2d_64(self):
self.phenotype.output_size = (3, 64, 64)
self.genome.linear_at_end = False
self.genome.add(Deconv2d(32))
self.genome.output_genes.append(Deconv2d(3))
x = Variable(torch.randn(5, 100)).view(5, 1, 10, 10)
self.phenotype.create_model(x)
out = self.phenotype.model(x)
self.assertEqual([64, 64], list(out.size()[2:]))
def __init__(self,
output_size=(1, 28, 28),
genome=None,
input_shape=(1, 1, 10, 10)):
super().__init__(output_size=output_size,
genome=genome,
input_shape=input_shape)
self.noise_size = int(np.prod(self.input_shape[1:]))
self.inception_score_mean = 0
self.fid_score = None
self.rmse_score = None
if genome is None:
if config.gan.generator.fixed:
self.genome = Genome(
random=False,
add_layer_prob=0,
rm_layer_prob=0,
gene_mutation_prob=0,
simple_layers=config.gan.generator.simple_layers,
linear_at_end=False)
self.genome.add(
Linear(4 * int(np.prod(output_size)),
activation_type="ReLU"))
# self.genome.add(Linear(4*int(np.prod(output_size)), activation_type="LeakyReLU"))
if not config.gan.generator.simple_layers:
self.genome.add(Deconv2d(128, activation_type="ReLU"))
self.genome.add(Deconv2d(64, activation_type="ReLU"))
self.genome.add(Deconv2d(32, activation_type="ReLU"))
self.genome.add(Deconv2d(16, activation_type="ReLU"))
# self.genome.add(Deconv2d(8, activation_type="ReLU"))
else:
self.genome = Genome(
random=not config.evolution.sequential_layers,
linear_at_end=False)
self.genome.possible_genes = [
g for g in self.genome.possible_genes if g[0] != Conv2d
]
# IMPORTANT: the performance without a liner layer is pretty bad
self.genome.add(Linear(512))
# self.genome.add_random_gene()
if config.gan.generator.simple_layers:
# self.genome.output_genes = [Deconv2d(output_size[0], activation_type="Tanh")]
self.genome.output_genes = [
Linear(int(np.prod(output_size)),
activation_type="Tanh",
normalize=False)
]
else:
self.genome.output_genes = [
Deconv2d(output_size[0],
activation_type="Tanh",
normalize=False)
]
def test_multiple_deconv2d(self):
self.phenotype.output_size = (1, 28, 28)
self.genome.linear_at_end = False
self.genome.add(Linear(1568))
self.genome.add(Deconv2d(32, kernel_size=3))
self.genome.add(Deconv2d(32, kernel_size=3))
self.genome.add(Deconv2d(32, kernel_size=3))
self.genome.output_genes.append(Deconv2d(1))
x = Variable(torch.randn(5, 100)).view(5, 1, 10, 10)
self.phenotype.create_model(x)
out = self.phenotype.model(x)
self.assertEqual([28, 28], list(out.size()[2:]))
def test_2d_after_linear(self):
self.phenotype.output_size = (1, 32, 32)
self.genome.linear_at_end = False
self.genome.add(Linear(32*32))
self.genome.add(Deconv2d(1))
self.genome.add(Linear(32*32*3))
self.genome.add(Deconv2d(4))
self.assertEqual([Linear, Linear, Deconv2d, Deconv2d], [gene.__class__ for gene in self.genome.genes])
self.evaluate_model([8, 1, 32, 32])
x = Variable(torch.randn(8, 32*32))
self.phenotype.create_model(x)
self.train_step(self.phenotype, x)
def test_deconv_output_channels(self):
self.phenotype.output_size = (1, 28, 28)
self.genome.linear_at_end = False
self.genome.add(Linear())
self.genome.add(Deconv2d(32))
self.genome.add(Deconv2d(16))
self.genome.add(Deconv2d(8))
self.genome.add(Deconv2d(4))
x = Variable(torch.randn(1, 100))
model = self.phenotype.transform_genotype(x)
print(model)
out = model(x)
self.assertEqual([1, 1, 28, 28], list(out.size()))
def test_multiple_deconv2d_outchannels(self):
self.phenotype.output_size = (1, 28, 28)
self.genome.linear_at_end = False
self.genome.add(Linear(576))
self.genome.add(Deconv2d(64, kernel_size=3))
self.genome.add(Deconv2d(32, kernel_size=3))
self.genome.output_genes.append(Deconv2d(1))
x = Variable(torch.randn(5, 100)).view(5, 1, 10, 10)
self.phenotype.create_model(x)
self.genome.add(Deconv2d())
config.layer.conv2d.random_out_channels = False
model = self.phenotype.transform_genotype(x)
out = model(x)
self.assertEqual(self.genome.genes[-2].out_channels//2, self.genome.genes[-1].out_channels)
self.assertEqual([28, 28], list(out.size()[2:]))
def test_add_deconv_not_random_out(self):
config.layer.conv2d.random_out_channels = False
self.phenotype.output_size = (1, 28, 28)
self.genome.linear_at_end = False
self.genome.add(Linear())
self.genome.add(Deconv2d())
x = Variable(torch.randn(8, 100))
model = self.phenotype.transform_genotype(x)
print(model)
out = model(x)
self.assertEqual([8, 1, 28, 28], list(out.size()))
self.genome.add(Deconv2d())
model = self.phenotype.transform_genotype(x)
print(model)
out = model(x)
self.assertEqual([8, 1, 28, 28], list(out.size()))
def test_invalid_graph(self):
self.phenotype.output_size = (1, 28, 28)
self.genome.linear_at_end = False
self.genome.add(Linear(1568))
self.genome.add(Deconv2d(32))
self.genome.add(Deconv2d(32))
self.genome.add(Deconv2d(32))
self.genome.add(Deconv2d(32))
self.genome.add(Deconv2d(32))
self.genome.output_genes.append(Deconv2d(1))
x = Variable(torch.randn(5, 100)).view(5, 1, 10, 10)
self.assertRaises(Exception, self.phenotype.create_model, x)