def test_freeze_weight(self):
config.gan.generator.optimizer.type = "RMSprop"
self.genome.add(Conv2d(16))
self.genome.add(Linear(32))
self.genome.add(Linear(16))
self.genome.output_genes.append(Linear(out_features=1, activation_type='Sigmoid'))
x = Variable(torch.randn(5, 64)).view(5, 1, 8, 8)
self.phenotype.create_model(x)
self.train_step(self.phenotype, x)
weight = self.genome.genes[0].module.weight.clone()
self.train_step(self.phenotype, x)
self.assertFalse(weight.equal(self.genome.genes[0].module.weight))
config.evolution.freeze_when_change = True
for gene in self.genome.genes:
weight, bias = gene.module.weight.clone(), gene.module.bias.clone()
gene.freeze()
self.train_step(self.phenotype, x)
self.assertTrue(weight.equal(gene.module.weight))
self.assertTrue(bias.equal(gene.module.bias))
gene.unfreeze()
self.train_step(self.phenotype, x)
self.assertFalse(weight.equal(gene.module.weight))
self.assertFalse(bias.equal(gene.module.bias))
def test_complex_graph2(self):
self.genome.add(Conv2d(1))
self.genome.add(Linear(128))
self.evaluate_model([5, 1, 28, 28])
def test_zero_output(self):
self.genome.add(Conv2d(1, kernel_size=3))
self.genome.add(Conv2d(6, kernel_size=3))
self.genome.add(Conv2d(8, kernel_size=3))
self.genome.add(Conv2d(5, kernel_size=3))
self.evaluate_model([5, 1, 5, 5])
def test_first_linear_after_conv2d(self):
self.genome.add(Linear(32))
self.genome.add(Conv2d(3))
self.assertEqual([Conv2d, Linear], [gene.__class__ for gene in self.genome.genes])
self.evaluate_model([5, 3, 5, 5])
