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Zero
Running
on
Zero
| from loguru import logger | |
| import torch | |
| import pytorch_lightning as pl | |
| from matplotlib import pyplot as plt | |
| plt.switch_backend('agg') | |
| from src.xoftr import XoFTR_Pretrain | |
| from src.losses.xoftr_loss_pretrain import XoFTRLossPretrain | |
| from src.optimizers import build_optimizer, build_scheduler | |
| from src.utils.plotting import make_mae_figures | |
| from src.utils.comm import all_gather | |
| from src.utils.misc import lower_config, flattenList | |
| from src.utils.profiler import PassThroughProfiler | |
| from src.utils.pretrain_utils import generate_random_masks, get_target | |
| class PL_XoFTR_Pretrain(pl.LightningModule): | |
| def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None): | |
| """ | |
| TODO: | |
| - use the new version of PL logging API. | |
| """ | |
| super().__init__() | |
| # Misc | |
| self.config = config # full config | |
| _config = lower_config(self.config) | |
| self.xoftr_cfg = lower_config(_config['xoftr']) | |
| self.profiler = profiler or PassThroughProfiler() | |
| self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1) | |
| # generator to create the same masks for validation | |
| self.val_seed = self.config.PRETRAIN.VAL_SEED | |
| self.val_generator = torch.Generator(device="cuda").manual_seed(self.val_seed) | |
| self.mae_margins = config.PRETRAIN.MAE_MARGINS | |
| # Matcher: XoFTR | |
| self.matcher = XoFTR_Pretrain(config=_config['xoftr']) | |
| self.loss = XoFTRLossPretrain(_config) | |
| # Pretrained weights | |
| if pretrained_ckpt: | |
| state_dict = torch.load(pretrained_ckpt, map_location='cpu')['state_dict'] | |
| self.matcher.load_state_dict(state_dict, strict=False) | |
| logger.info(f"Load \'{pretrained_ckpt}\' as pretrained checkpoint") | |
| # Testing | |
| self.dump_dir = dump_dir | |
| def configure_optimizers(self): | |
| # FIXME: The scheduler did not work properly when `--resume_from_checkpoint` | |
| optimizer = build_optimizer(self, self.config) | |
| scheduler = build_scheduler(self.config, optimizer) | |
| return [optimizer], [scheduler] | |
| def optimizer_step( | |
| self, epoch, batch_idx, optimizer, optimizer_idx, | |
| optimizer_closure, on_tpu, using_native_amp, using_lbfgs): | |
| # learning rate warm up | |
| warmup_step = self.config.TRAINER.WARMUP_STEP | |
| if self.trainer.global_step < warmup_step: | |
| if self.config.TRAINER.WARMUP_TYPE == 'linear': | |
| base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR | |
| lr = base_lr + \ | |
| (self.trainer.global_step / self.config.TRAINER.WARMUP_STEP) * \ | |
| abs(self.config.TRAINER.TRUE_LR - base_lr) | |
| for pg in optimizer.param_groups: | |
| pg['lr'] = lr | |
| elif self.config.TRAINER.WARMUP_TYPE == 'constant': | |
| pass | |
| else: | |
| raise ValueError(f'Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}') | |
| # update params | |
| optimizer.step(closure=optimizer_closure) | |
| optimizer.zero_grad() | |
| def _trainval_inference(self, batch, generator=None): | |
| generate_random_masks(batch, | |
| patch_size=self.config.PRETRAIN.PATCH_SIZE, | |
| mask_ratio=self.config.PRETRAIN.MASK_RATIO, | |
| generator=generator, | |
| margins=self.mae_margins) | |
| with self.profiler.profile("XoFTR"): | |
| self.matcher(batch) | |
| with self.profiler.profile("Compute losses"): | |
| # Create target pacthes to reconstruct | |
| get_target(batch) | |
| self.loss(batch) | |
| def training_step(self, batch, batch_idx): | |
| self._trainval_inference(batch) | |
| # logging | |
| if self.trainer.global_rank == 0 and self.global_step % self.trainer.log_every_n_steps == 0: | |
| # scalars | |
| for k, v in batch['loss_scalars'].items(): | |
| self.logger[0].experiment.add_scalar(f'train/{k}', v, self.global_step) | |
| if self.config.TRAINER.USE_WANDB: | |
| self.logger[1].log_metrics({f'train/{k}': v}, self.global_step) | |
| if self.config.TRAINER.ENABLE_PLOTTING: | |
| figures = make_mae_figures(batch) | |
| for i, figure in enumerate(figures): | |
| self.logger[0].experiment.add_figure( | |
| f'train_mae/node_{self.trainer.global_rank}-device_{self.device.index}-batch_{i}', | |
| figure, self.global_step) | |
| return {'loss': batch['loss']} | |
| def training_epoch_end(self, outputs): | |
| avg_loss = torch.stack([x['loss'] for x in outputs]).mean() | |
| if self.trainer.global_rank == 0: | |
| self.logger[0].experiment.add_scalar( | |
| 'train/avg_loss_on_epoch', avg_loss, | |
| global_step=self.current_epoch) | |
| if self.config.TRAINER.USE_WANDB: | |
| self.logger[1].log_metrics( | |
| {'train/avg_loss_on_epoch': avg_loss}, | |
| self.current_epoch) | |
| def validation_step(self, batch, batch_idx): | |
| self._trainval_inference(batch, self.val_generator) | |
| val_plot_interval = max(self.trainer.num_val_batches[0] // \ | |
| (self.trainer.num_gpus * self.n_vals_plot), 1) | |
| figures = [] | |
| if batch_idx % val_plot_interval == 0: | |
| figures = make_mae_figures(batch) | |
| return { | |
| 'loss_scalars': batch['loss_scalars'], | |
| 'figures': figures, | |
| } | |
| def validation_epoch_end(self, outputs): | |
| self.val_generator.manual_seed(self.val_seed) | |
| # handle multiple validation sets | |
| multi_outputs = [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs | |
| for valset_idx, outputs in enumerate(multi_outputs): | |
| # since pl performs sanity_check at the very begining of the training | |
| cur_epoch = self.trainer.current_epoch | |
| if not self.trainer.resume_from_checkpoint and self.trainer.running_sanity_check: | |
| cur_epoch = -1 | |
| # 1. loss_scalars: dict of list, on cpu | |
| _loss_scalars = [o['loss_scalars'] for o in outputs] | |
| loss_scalars = {k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars])) for k in _loss_scalars[0]} | |
| _figures = [o['figures'] for o in outputs] | |
| figures = [item for sublist in _figures for item in sublist] | |
| # tensorboard records only on rank 0 | |
| if self.trainer.global_rank == 0: | |
| for k, v in loss_scalars.items(): | |
| mean_v = torch.stack(v).mean() | |
| self.logger[0].experiment.add_scalar(f'val_{valset_idx}/avg_{k}', mean_v, global_step=cur_epoch) | |
| if self.config.TRAINER.USE_WANDB: | |
| self.logger[1].log_metrics({f'val_{valset_idx}/avg_{k}': mean_v}, cur_epoch) | |
| for plot_idx, fig in enumerate(figures): | |
| self.logger[0].experiment.add_figure( | |
| f'val_mae_{valset_idx}/pair-{plot_idx}', fig, cur_epoch, close=True) | |
| plt.close('all') | |