160 lines
5.4 KiB
Python
160 lines
5.4 KiB
Python
import json
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import torch
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from base.base_dataset import BaseADDataset
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from networks.main import build_network, build_autoencoder
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from optim import SemiDeepGenerativeTrainer, VAETrainer
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class SemiDeepGenerativeModel(object):
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"""A class for the Semi-Supervised Deep Generative model (M1+M2 model).
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Paper: Kingma et al. (2014). Semi-supervised learning with deep generative models. In NIPS (pp. 3581-3589).
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Link: https://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf
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Attributes:
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net_name: A string indicating the name of the neural network to use.
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net: The neural network.
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trainer: SemiDeepGenerativeTrainer to train a Semi-Supervised Deep Generative model.
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optimizer_name: A string indicating the optimizer to use for training.
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results: A dictionary to save the results.
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"""
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def __init__(self, alpha: float = 0.1):
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"""Inits SemiDeepGenerativeModel."""
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self.alpha = alpha
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self.net_name = None
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self.net = None
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self.trainer = None
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self.optimizer_name = None
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self.vae_net = None # variational autoencoder network for pretraining
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self.vae_trainer = None
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self.vae_optimizer_name = None
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self.results = {
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"train_time": None,
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"test_auc": None,
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"test_time": None,
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"test_scores": None,
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}
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self.vae_results = {"train_time": None, "test_auc": None, "test_time": None}
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def set_vae(self, net_name):
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"""Builds the variational autoencoder network for pretraining."""
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self.net_name = net_name
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self.vae_net = build_autoencoder(self.net_name) # VAE for pretraining
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def set_network(self, net_name):
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"""Builds the neural network."""
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self.net_name = net_name
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self.net = build_network(net_name, ae_net=self.vae_net) # full M1+M2 model
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def train(
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self,
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dataset: BaseADDataset,
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optimizer_name: str = "adam",
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lr: float = 0.001,
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n_epochs: int = 50,
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lr_milestones: tuple = (),
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batch_size: int = 128,
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weight_decay: float = 1e-6,
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device: str = "cuda",
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n_jobs_dataloader: int = 0,
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):
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"""Trains the Semi-Supervised Deep Generative model on the training data."""
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self.optimizer_name = optimizer_name
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self.trainer = SemiDeepGenerativeTrainer(
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alpha=self.alpha,
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optimizer_name=optimizer_name,
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lr=lr,
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n_epochs=n_epochs,
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lr_milestones=lr_milestones,
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batch_size=batch_size,
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weight_decay=weight_decay,
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device=device,
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n_jobs_dataloader=n_jobs_dataloader,
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)
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self.net = self.trainer.train(dataset, self.net)
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self.results["train_time"] = self.trainer.train_time
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def test(
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self, dataset: BaseADDataset, device: str = "cuda", n_jobs_dataloader: int = 0
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):
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"""Tests the Semi-Supervised Deep Generative model on the test data."""
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if self.trainer is None:
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self.trainer = SemiDeepGenerativeTrainer(
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alpha=self.alpha, device=device, n_jobs_dataloader=n_jobs_dataloader
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)
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self.trainer.test(dataset, self.net)
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# Get results
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self.results["test_auc"] = self.trainer.test_auc
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self.results["test_time"] = self.trainer.test_time
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self.results["test_scores"] = self.trainer.test_scores
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def pretrain(
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self,
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dataset: BaseADDataset,
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optimizer_name: str = "adam",
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lr: float = 0.001,
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n_epochs: int = 100,
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lr_milestones: tuple = (),
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batch_size: int = 128,
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weight_decay: float = 1e-6,
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device: str = "cuda",
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n_jobs_dataloader: int = 0,
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):
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"""Pretrains a variational autoencoder (M1) for the Semi-Supervised Deep Generative model."""
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# Train
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self.vae_optimizer_name = optimizer_name
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self.vae_trainer = VAETrainer(
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optimizer_name=optimizer_name,
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lr=lr,
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n_epochs=n_epochs,
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lr_milestones=lr_milestones,
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batch_size=batch_size,
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weight_decay=weight_decay,
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device=device,
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n_jobs_dataloader=n_jobs_dataloader,
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)
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self.vae_net = self.vae_trainer.train(dataset, self.vae_net)
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# Get train results
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self.vae_results["train_time"] = self.vae_trainer.train_time
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# Test
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self.vae_trainer.test(dataset, self.vae_net)
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# Get test results
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self.vae_results["test_auc"] = self.vae_trainer.test_auc
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self.vae_results["test_time"] = self.vae_trainer.test_time
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def save_model(self, export_model):
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"""Save a Semi-Supervised Deep Generative model to export_model."""
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net_dict = self.net.state_dict()
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torch.save({"net_dict": net_dict}, export_model)
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def load_model(self, model_path):
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"""Load a Semi-Supervised Deep Generative model from model_path."""
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model_dict = torch.load(model_path)
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self.net.load_state_dict(model_dict["net_dict"])
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def save_results(self, export_json):
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"""Save results dict to a JSON-file."""
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with open(export_json, "w") as fp:
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json.dump(self.results, fp)
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def save_vae_results(self, export_json):
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"""Save variational autoencoder results dict to a JSON-file."""
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with open(export_json, "w") as fp:
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json.dump(self.vae_results, fp)
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