| import lightning.pytorch as pl |
| from lightning.pytorch import Callback |
|
|
|
|
| import os.path |
| import numpy |
| from typing import Sequence, Any, Dict |
| from concurrent.futures import ThreadPoolExecutor |
|
|
| from lightning.pytorch.utilities.types import STEP_OUTPUT |
| from lightning_utilities.core.rank_zero import rank_zero_info |
|
|
|
|
|
|
| class SaveImagesHook(Callback): |
| def __init__(self, save_dir="val", save_compressed=False): |
| self.save_dir = save_dir |
| self.save_compressed = save_compressed |
|
|
| def save_start(self, target_dir): |
| self.samples = [] |
| self.target_dir = target_dir |
| self.executor_pool = ThreadPoolExecutor(max_workers=8) |
| if not os.path.exists(self.target_dir): |
| os.makedirs(self.target_dir, exist_ok=True) |
| |
| |
| |
| rank_zero_info(f"Save images to {self.target_dir}") |
| self._saved_num = 0 |
|
|
| def save_image(self, trainer, pl_module, images, metadatas,): |
| images = images.permute(0, 2, 3, 1).cpu().numpy() |
| for sample, metadata in zip(images, metadatas): |
| save_fn = metadata.pop("save_fn", None) |
| self.executor_pool.submit(save_fn, sample, metadata, self.target_dir) |
|
|
| def process_batch( |
| self, |
| trainer: "pl.Trainer", |
| pl_module: "pl.LightningModule", |
| samples: STEP_OUTPUT, |
| batch: Any, |
| ) -> None: |
| xT, y, metadata = batch |
| b, c, h, w = samples.shape |
| if not self.save_compressed or self._saved_num < 10: |
| self._saved_num += b |
| self.save_image(trainer, pl_module, samples, metadata) |
|
|
| all_samples = pl_module.all_gather(samples).view(-1, c, h, w) |
| if trainer.is_global_zero: |
| all_samples = all_samples.permute(0, 2, 3, 1).cpu().numpy() |
| self.samples.append(all_samples) |
|
|
| def save_end(self): |
| if self.save_compressed and len(self.samples) > 0: |
| samples = numpy.concatenate(self.samples) |
| numpy.savez(f'{self.target_dir}/output.npz', arr_0=samples) |
| self.executor_pool.shutdown(wait=True) |
| self.target_dir = None |
| self.executor_pool = None |
| self.samples = [] |
|
|
| def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: |
| target_dir = os.path.join(trainer.default_root_dir, self.save_dir, f"iter_{trainer.global_step}") |
| self.save_start(target_dir) |
|
|
| def on_validation_batch_end( |
| self, |
| trainer: "pl.Trainer", |
| pl_module: "pl.LightningModule", |
| outputs: STEP_OUTPUT, |
| batch: Any, |
| batch_idx: int, |
| dataloader_idx: int = 0, |
| ) -> None: |
| return self.process_batch(trainer, pl_module, outputs, batch) |
|
|
| def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: |
| self.save_end() |
|
|
| def on_predict_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: |
| target_dir = os.path.join(trainer.default_root_dir, self.save_dir, "predict") |
| self.save_start(target_dir) |
|
|
| def on_predict_batch_end( |
| self, |
| trainer: "pl.Trainer", |
| pl_module: "pl.LightningModule", |
| samples: Any, |
| batch: Any, |
| batch_idx: int, |
| dataloader_idx: int = 0, |
| ) -> None: |
| return self.process_batch(trainer, pl_module, samples, batch) |
|
|
| def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: |
| self.save_end() |
|
|
| def state_dict(self) -> Dict[str, Any]: |
| return dict() |
