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Add device and base class usage to Deepsvdd #597

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Add device and base class usage to Deepsvdd #597

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cdcb283
device for deep_svdd.py
yqin43 Jul 4, 2024
b97f6f2
deep_svdd base
yqin43 Jul 4, 2024
64242b3
Merge branch 'development' into deepsvdd
yzhao062 Jul 22, 2024
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19 changes: 11 additions & 8 deletions pyod/models/deep_svdd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,9 +22,10 @@
from torch.utils.data import DataLoader, TensorDataset

from .base import BaseDetector
from ..utils.torch_utility import get_activation_by_name
from ..utils.torch_utility import get_activation_by_name, get_optimizer_by_name
from ..utils.utility import check_parameter


optimizer_dict = {
'sgd': optim.SGD,
'adam': optim.Adam,
Expand Down Expand Up @@ -242,7 +243,7 @@ def __init__(self, n_features, c=None, use_ae=False, hidden_neurons=None,
batch_size=32,
dropout_rate=0.2, l2_regularizer=0.1, validation_size=0.1,
preprocessing=True,
verbose=1, random_state=None, contamination=0.1):
verbose=1, random_state=None, contamination=0.1, device=None):
super(DeepSVDD, self).__init__(contamination=contamination)

self.n_features = n_features
Expand All @@ -262,6 +263,7 @@ def __init__(self, n_features, c=None, use_ae=False, hidden_neurons=None,
self.random_state = random_state
self.model_ = None
self.best_model_dict = None
self.device = device

if self.random_state is not None:
torch.manual_seed(self.random_state)
Expand Down Expand Up @@ -314,10 +316,11 @@ def fit(self, X, y=None):
output_activation=self.output_activation,
dropout_rate=self.dropout_rate,
l2_regularizer=self.l2_regularizer)
self.model_.to(self.device)
X_norm = torch.tensor(X_norm, dtype=torch.float32)
if self.c is None:
self.c = 0.0
self.model_._init_c(X_norm)
self.model_._init_c(X_norm.to(self.device))

# Predict on X itself and calculate the reconstruction error as
# the outlier scores. Noted X_norm was shuffled has to recreate
Expand All @@ -326,23 +329,23 @@ def fit(self, X, y=None):
else:
X_norm = np.copy(X)

X_norm = torch.tensor(X_norm, dtype=torch.float32)
X_norm = torch.tensor(X_norm, dtype=torch.float32).to(self.device)
dataset = TensorDataset(X_norm, X_norm)
dataloader = DataLoader(dataset, batch_size=self.batch_size,
shuffle=True)

best_loss = float('inf')
best_model_dict = None

optimizer = optimizer_dict[self.optimizer](self.model_.parameters(),
weight_decay=self.l2_regularizer)
optimizer = get_optimizer_by_name(self.model_, self.optimizer, weight_decay=self.l2_regularizer)
w_d = 1e-6 * sum(
[torch.linalg.norm(w) for w in self.model_.parameters()])

for epoch in range(self.epochs):
self.model_.train()
epoch_loss = 0
for batch_x, _ in dataloader:
batch_x = batch_x.to(self.device)
optimizer.zero_grad()
outputs = self.model_(batch_x)
dist = torch.sum((outputs - self.c) ** 2, dim=-1)
Expand Down Expand Up @@ -390,10 +393,10 @@ def decision_function(self, X):
X_norm = self.scaler_.transform(X)
else:
X_norm = np.copy(X)
X_norm = torch.tensor(X_norm, dtype=torch.float32)
X_norm = torch.tensor(X_norm, dtype=torch.float32).to(self.device)
self.model_.eval()
with torch.no_grad():
outputs = self.model_(X_norm)
dist = torch.sum((outputs - self.c) ** 2, dim=-1)
anomaly_scores = dist.numpy()
anomaly_scores = dist.cpu().numpy()
return anomaly_scores
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