steffen/audio_anomaly_detection
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delete LCAE, adjust training

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This commit is contained in:
Robert Müller 2020-03-18 17:13:00 +01:00
parent 3d7dbf222c
commit 55402d219c
5 changed files with 30 additions and 84 deletions
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4
cfg.py
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@ -2,8 +2,8 @@ from pathlib import Path
import torch
BATCH_SIZE = 128
NUM_EPOCHS = 50
NUM_WORKERS = 4
NUM_EPOCHS = 1
NUM_WORKERS = 0
NUM_SEGMENTS = 5
NUM_SEGMENT_HOPS = 2
SEEDS = [42, 1337]

24
main.py
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@ -2,7 +2,7 @@ import numpy as np
from tqdm import tqdm
from cfg import *
from mimii import MIMII
from models.ae import AE, LCAE
from models.ae import AE
import torch.nn as nn
import torch.optim as optim
import random
@ -12,10 +12,11 @@ torch.cuda.manual_seed(42)
np.random.seed(42)
random.seed(42)
dataset_path = ALL_DATASET_PATHS[5]
dataset_path = ALL_DATASET_PATHS[0]
print(f'Training on {dataset_path.name}')
mimii = MIMII(dataset_path=ALL_DATASET_PATHS[5], machine_id=0)
mimii.preprocess(n_fft=1024, hop_length=512, n_mels=64, center=False, power=2.0)
mimii = MIMII(dataset_path=dataset_path, machine_id=0)
mimii.to(DEVICE)
#mimii.preprocess(n_fft=1024, hop_length=256, n_mels=80, center=False, power=2.0)
dl = mimii.train_dataloader(
segment_len=NUM_SEGMENTS,
@ -26,7 +27,7 @@ dl = mimii.train_dataloader(
)
model = LCAE(320).to(DEVICE)
model = AE(400).to(DEVICE)
model.init_weights()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
@ -38,24 +39,19 @@ beta_2 = 0.0
for epoch in range(NUM_EPOCHS):
print(f'EPOCH #{epoch+1}')
losses = []
entropies = []
l1s = []
for batch in tqdm(dl):
data, labels = batch
data = data.to(DEVICE)
data = data.view(data.shape[0], -1)
preds, entropy, diversity = model(data)
loss = criterion(preds, data) + beta_1*entropy.mean() + beta_2*diversity
y_hat, y = model(data)
loss = criterion(y_hat, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
#print(reconstruction.shape)
losses.append(loss.item())
entropies.append(entropy.mean().item())
l1s.append(diversity.item())
print(f'Loss: {np.mean(losses)}; Entropy: {np.mean(entropies)}; l1:{np.mean(l1s)}')
print(f'Loss: {np.mean(losses)}')
auc = mimii.evaluate_model(model, NUM_SEGMENTS, NUM_SEGMENTS)
print(f'AUC: {auc}')

14
mimii.py
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@ -32,6 +32,12 @@ class MIMII(object):
self.train_paths = train[len(test):]
self.test_paths = normal_test + test
self.device = 'cpu'
def to(self, device):
self.device = device
return self
def _normalize(self, S):
return np.clip((S - self.min_level_db) / -self.min_level_db, 0, 1)
@ -52,6 +58,7 @@ class MIMII(object):
mel_spec_norm = self._normalize(mel_spec_db)
m, n = mel_spec_norm.shape
np.save(folder/(file.stem + f'_{m}_{n}.npy'), mel_spec_norm)
return self
def train_dataloader(self, segment_len=20, hop_len=5, **kwargs):
# return both!!!
@ -83,11 +90,10 @@ class MIMII(object):
file_preds = []
for batch in loader:
data, labels = batch
data = data.to('cuda')
data = data.view(data.shape[0], -1)
data = data.to(self.device)
y_hat, entropy, diversity = f(data)
preds = torch.sum((y_hat - data) ** 2, dim=tuple(range(1, y_hat.dim())))
y_hat, y = f(data)
preds = torch.sum((y_hat - y) ** 2, dim=tuple(range(1, y_hat.dim())))
file_preds += preds.cpu().data.tolist()
y_true.append(labels.max().item())

64
models/ae.py
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@ -3,7 +3,7 @@ import torch.nn as nn
import torch.functional as F
class AE(nn.Module):
def __init__(self, in_dim=320):
def __init__(self, in_dim=400):
super(AE, self).__init__()
self.net = nn.Sequential(
nn.Linear(in_dim, 64),
@ -16,69 +16,13 @@ class AE(nn.Module):
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 320),
nn.Linear(64, in_dim),
nn.ReLU(),
)
def forward(self, data):
return self.net(data)
def init_weights(self):
def _weight_init(m):
if hasattr(m, 'weight'):
if isinstance(m.weight, torch.Tensor):
torch.nn.init.xavier_uniform_(m.weight,
gain=nn.init.calculate_gain('relu'))
if hasattr(m, 'bias'):
if isinstance(m.bias, torch.Tensor):
m.bias.data.fill_(0.01)
self.apply(_weight_init)
class LCAE(nn.Module):
def __init__(self, in_dim=320):
super(LCAE, self).__init__()
num_mem = 10
mem_size= 8
self.num_mem = num_mem
self.encode = nn.Sequential(
nn.Linear(in_dim, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, num_mem),
nn.Softmax(-1)
)
self.decode = nn.Sequential(
nn.Linear(mem_size, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 320),
nn.ReLU(),
)
self.M = nn.Parameter(
torch.randn(num_mem, mem_size)
)
def forward(self, data):
alphas = self.encode(data).unsqueeze(-1)
entropy_alphas = (alphas * -alphas.log()).sum(1)
M = self.M.expand(data.shape[0], *self.M.shape)
#print(M.shape, alphas.shape) # torch.Size([128, 4, 8]) torch.Size([128, 4, 1])
elu = nn.ELU()
weighted = alphas * (1+elu(M+1e-13))
#print(weighted.shape)
summed = weighted.sum(1)
#print(summed.shape)
decoded = self.decode(summed)
diversity = (alphas.sum(dim=0)/data.shape[0]).max()
#print(alphas[0])
return decoded, entropy_alphas, diversity
x = data.view(data.shape[0], -1)
return self.net(x), x
def init_weights(self):
def _weight_init(m):

8
models/layers.py
View File

@ -21,8 +21,8 @@ class Subspectrogram(object):
if __name__ == '__main__':
import numpy as np
sub_spec_tnfm = Subspectrogram(20, 10)
X = np.random.rand(1, 60, 40)
sub_spec_tnfm = Subspectrogram(20, 20)
X = np.random.rand(1, 80, 40)
Y = sub_spec_tnfm(X)
print(f'\t Sub-Spectrogram transformation from shape {X.shape} to {Y.shape}')
print('Done ...')
print(f'\tSub-Spectrogram transformation from shape {X.shape} to {Y.shape}')
print('\tDone ...')