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This commit is contained in:
Jan Kowalczyk
2024-06-28 11:36:46 +02:00
parent d33c6b1e16
commit 71f9662022
40 changed files with 2938 additions and 1260 deletions
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113
Deep-SAD-PyTorch/src/DeepSAD.py
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@@ -41,53 +41,80 @@ class DeepSAD(object):
self.ae_optimizer_name = None
self.results = {
'train_time': None,
'test_auc': None,
'test_time': None,
'test_scores': None,
"train_time": None,
"test_auc": None,
"test_time": None,
"test_scores": None,
}
self.ae_results = {
'train_time': None,
'test_auc': None,
'test_time': None
}
self.ae_results = {"train_time": None, "test_auc": None, "test_time": None}
def set_network(self, net_name):
"""Builds the neural network phi."""
self.net_name = net_name
self.net = build_network(net_name)
def train(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 50,
lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda',
n_jobs_dataloader: int = 0):
def train(
self,
dataset: BaseADDataset,
optimizer_name: str = "adam",
lr: float = 0.001,
n_epochs: int = 50,
lr_milestones: tuple = (),
batch_size: int = 128,
weight_decay: float = 1e-6,
device: str = "cuda",
n_jobs_dataloader: int = 0,
):
"""Trains the Deep SAD model on the training data."""
self.optimizer_name = optimizer_name
self.trainer = DeepSADTrainer(self.c, self.eta, optimizer_name=optimizer_name, lr=lr, n_epochs=n_epochs,
lr_milestones=lr_milestones, batch_size=batch_size, weight_decay=weight_decay,
device=device, n_jobs_dataloader=n_jobs_dataloader)
self.trainer = DeepSADTrainer(
self.c,
self.eta,
optimizer_name=optimizer_name,
lr=lr,
n_epochs=n_epochs,
lr_milestones=lr_milestones,
batch_size=batch_size,
weight_decay=weight_decay,
device=device,
n_jobs_dataloader=n_jobs_dataloader,
)
# Get the model
self.net = self.trainer.train(dataset, self.net)
self.results['train_time'] = self.trainer.train_time
self.results["train_time"] = self.trainer.train_time
self.c = self.trainer.c.cpu().data.numpy().tolist() # get as list
def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0):
def test(
self, dataset: BaseADDataset, device: str = "cuda", n_jobs_dataloader: int = 0
):
"""Tests the Deep SAD model on the test data."""
if self.trainer is None:
self.trainer = DeepSADTrainer(self.c, self.eta, device=device, n_jobs_dataloader=n_jobs_dataloader)
self.trainer = DeepSADTrainer(
self.c, self.eta, device=device, n_jobs_dataloader=n_jobs_dataloader
)
self.trainer.test(dataset, self.net)
# Get results
self.results['test_auc'] = self.trainer.test_auc
self.results['test_time'] = self.trainer.test_time
self.results['test_scores'] = self.trainer.test_scores
self.results["test_auc"] = self.trainer.test_auc
self.results["test_time"] = self.trainer.test_time
self.results["test_scores"] = self.trainer.test_scores
def pretrain(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 100,
lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda',
n_jobs_dataloader: int = 0):
def pretrain(
self,
dataset: BaseADDataset,
optimizer_name: str = "adam",
lr: float = 0.001,
n_epochs: int = 100,
lr_milestones: tuple = (),
batch_size: int = 128,
weight_decay: float = 1e-6,
device: str = "cuda",
n_jobs_dataloader: int = 0,
):
"""Pretrains the weights for the Deep SAD network phi via autoencoder."""
# Set autoencoder network
@@ -95,20 +122,27 @@ class DeepSAD(object):
# Train
self.ae_optimizer_name = optimizer_name
self.ae_trainer = AETrainer(optimizer_name, lr=lr, n_epochs=n_epochs, lr_milestones=lr_milestones,
batch_size=batch_size, weight_decay=weight_decay, device=device,
n_jobs_dataloader=n_jobs_dataloader)
self.ae_trainer = AETrainer(
optimizer_name,
lr=lr,
n_epochs=n_epochs,
lr_milestones=lr_milestones,
batch_size=batch_size,
weight_decay=weight_decay,
device=device,
n_jobs_dataloader=n_jobs_dataloader,
)
self.ae_net = self.ae_trainer.train(dataset, self.ae_net)
# Get train results
self.ae_results['train_time'] = self.ae_trainer.train_time
self.ae_results["train_time"] = self.ae_trainer.train_time
# Test
self.ae_trainer.test(dataset, self.ae_net)
# Get test results
self.ae_results['test_auc'] = self.ae_trainer.test_auc
self.ae_results['test_time'] = self.ae_trainer.test_time
self.ae_results["test_auc"] = self.ae_trainer.test_auc
self.ae_results["test_time"] = self.ae_trainer.test_time
# Initialize Deep SAD network weights from pre-trained encoder
self.init_network_weights_from_pretraining()
@@ -132,30 +166,31 @@ class DeepSAD(object):
net_dict = self.net.state_dict()
ae_net_dict = self.ae_net.state_dict() if save_ae else None
torch.save({'c': self.c,
'net_dict': net_dict,
'ae_net_dict': ae_net_dict}, export_model)
torch.save(
{"c": self.c, "net_dict": net_dict, "ae_net_dict": ae_net_dict},
export_model,
)
def load_model(self, model_path, load_ae=False, map_location='cpu'):
def load_model(self, model_path, load_ae=False, map_location="cpu"):
"""Load Deep SAD model from model_path."""
model_dict = torch.load(model_path, map_location=map_location)
self.c = model_dict['c']
self.net.load_state_dict(model_dict['net_dict'])
self.c = model_dict["c"]
self.net.load_state_dict(model_dict["net_dict"])
# load autoencoder parameters if specified
if load_ae:
if self.ae_net is None:
self.ae_net = build_autoencoder(self.net_name)
self.ae_net.load_state_dict(model_dict['ae_net_dict'])
self.ae_net.load_state_dict(model_dict["ae_net_dict"])
def save_results(self, export_json):
"""Save results dict to a JSON-file."""
with open(export_json, 'w') as fp:
with open(export_json, "w") as fp:
json.dump(self.results, fp)
def save_ae_results(self, export_json):
"""Save autoencoder results dict to a JSON-file."""
with open(export_json, 'w') as fp:
with open(export_json, "w") as fp:
json.dump(self.ae_results, fp)