D = mahalanobis(xx, yy, IV)
except:
D = 0.0
results.append(D)
return results
if __name__ == '__main__':
metaCollector = MetaFeaturesCollector(16, 64)
metaCollector.train(f"../processed_data/processed_16_64_2/")
lambdas = LambdaFeaturesCollector(16, 64)
loader = get_loader(f"../processed_data/test/",
16,
64,
2,
metaCollector,
lambdas,
100,
5,
train_meta=False)
generator = Generator(16, 64, 2, metaCollector.getLength(), 100)
generator.load_state_dict(torch.load(f'./models/generator-16_64_2-75.pkl'))
generator.eval()
generator.cuda()
results = []
for i, data in enumerate(loader):
print(i)
metas = to_variable(data[1])
batch_size = data[0].size(0)
noise = torch.randn(batch_size, 100)
noise = noise.view((noise.size(0), noise.size(1), 1, 1))
from scipy.spatial.distance import mahalanobis
V = np.cov(np.array([x_meta, y_meta]).T)
V[np.diag_indices_from(V)] += 0.1
IV = np.linalg.inv(V)
D = mahalanobis(x_meta, y_meta, IV)
return D
if __name__ == '__main__':
datasize = 64
z_size = 100
batch_size = 1
workers = 5
lambdas = LambdaFeaturesCollector(16, 64)
metas = MetaFeaturesCollector(16, 64)
dataloader = get_loader(f"../processed_data/processed_16_64_2/", 16, 64, 2,
metas, lambdas, batch_size, workers)
datatest = get_loader(f"../processed_data/test/",
16,
64,
2,
metas,
lambdas,
batch_size,
workers,
train_meta=False)
meta_list = []
lambdas_list = []
for i, (data, meta, lambda_l) in tqdm(enumerate(dataloader)):
meta_o = meta[:, :].numpy()
meta_o = meta_o.ravel()
def __init__(self,
num_epochs: int = 20,
cuda: bool = True,
continue_from: int = 0):
self.features = 16
self.instances = 64
self.classes = 2
self.z_size = 100
self.batch_size = 100
self.workers = 4
self.num_epochs = num_epochs
self.cuda = cuda
self.log_step = 10
self.log_step_print = 50
self.save_period = 5
self.continue_from = continue_from
self.models_path = "./cnn1206"
self.lambdas = LambdaFeaturesCollector(self.features, self.instances)
self.metas = MetaZerosCollector(self.features, self.instances)
self.data_loader = get_loader(
f"../processed_data/processed_{self.features}_{self.instances}_{self.classes}/",
self.features, self.instances, self.classes, self.metas,
self.lambdas, self.batch_size, self.workers)
self.test_loader = get_loader(f"../processed_data/test/",
16,
64,
2,
self.metas,
self.lambdas,
147,
self.workers,
train_meta=False)
if continue_from == 0:
self.discriminator = Discriminator(self.features, self.instances,
self.classes,
self.metas.getLength(),
self.lambdas.getLength())
else:
self.discriminator = Discriminator(self.features, self.instances,
self.classes,
self.metas.getLength(),
self.lambdas.getLength())
self.discriminator.load_state_dict(
torch.load(
f'{self.models_path}/discriminator-{self.features}_{self.instances}_{self.classes}-{continue_from}.pkl'
))
self.discriminator.eval()
if self.cuda:
self.discriminator.cuda()
self.lr = 0.0002
self.beta1 = 0.5
self.beta2 = 0.999
self.d_optimizer = optim.Adam(self.discriminator.parameters(), self.lr,
[self.beta1, self.beta2])
self.cross_entropy = BCEWithLogitsLoss()
if self.cuda:
self.cross_entropy.cuda()
self.mse = MSELoss()
if self.cuda:
self.mse.cuda()