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import pytorch_lightning as pl | ||
import torch | ||
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from fdiff.dataloaders.datamodules import Datamodule | ||
from fdiff.models.score_models import ScoreModule | ||
from fdiff.sampling.metrics import Metric, MetricCollection | ||
from fdiff.sampling.sampler import DiffusionSampler | ||
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from .fourier import idft | ||
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class SamplingCallback(pl.Callback): | ||
def __init__( | ||
self, | ||
every_n_epochs: int, | ||
sample_batch_size: int, | ||
num_samples: int, | ||
num_diffusion_steps: int, | ||
metrics: list[Metric], | ||
) -> None: | ||
super().__init__() | ||
self.every_n_epochs = every_n_epochs | ||
self.sample_batch_size = sample_batch_size | ||
self.num_samples = num_samples | ||
self.num_diffusion_steps = num_diffusion_steps | ||
self.metrics = metrics | ||
self.datamodule_initialized = False | ||
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def setup_datamodule(self, datamodule: Datamodule) -> None: | ||
# Exract the necessary information from the datamodule | ||
self.standardize = datamodule.standardize | ||
self.fourier_transform = datamodule.fourier_transform | ||
self.feature_mean, self.feature_std = datamodule.feature_mean_and_std | ||
self.metric_collection = MetricCollection( | ||
metrics=self.metrics, | ||
original_samples=datamodule.X_train, | ||
include_baselines=False, | ||
) | ||
self.datamodule_initialized = True | ||
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def on_train_start(self, trainer: pl.Trainer, pl_module: ScoreModule) -> None: | ||
# Initialize the sampler with the score model | ||
self.sampler = DiffusionSampler( | ||
score_model=pl_module, | ||
sample_batch_size=self.sample_batch_size, | ||
) | ||
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def on_train_epoch_end( | ||
self, trainer: pl.Trainer, pl_module: pl.LightningModule | ||
) -> None: | ||
if trainer.current_epoch % self.every_n_epochs == 0: | ||
# Sample from score model | ||
X = self.sample() | ||
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# Compute metrics | ||
results = self.metric_collection(X) | ||
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# Add a metrics/ suffix to the keys in results | ||
results = {f"metrics/{key}": value for key, value in results.items()} | ||
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# Log metrics | ||
pl_module.log_dict(results, on_step=False, on_epoch=True) | ||
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def sample(self) -> torch.Tensor: | ||
# Check that the dqtqmodule is initialized | ||
assert self.datamodule_initialized, ( | ||
"The datamodule has not been initialized. " | ||
"Please call `setup_datamodule` before sampling." | ||
) | ||
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# Sample from score model | ||
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X = self.sampler.sample( | ||
num_samples=self.num_samples, | ||
num_diffusion_steps=self.num_diffusion_steps, | ||
) | ||
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# Map to the original scale if the input was standardized | ||
if self.standardize: | ||
X = X * self.feature_std + self.feature_mean | ||
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# If sampling in frequency domain, bring back the sample to time domain | ||
if self.fourier_transform: | ||
X = idft(X) | ||
assert isinstance(X, torch.Tensor) | ||
return X |