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| from collections import defaultdict | |
| import pprint | |
| from loguru import logger | |
| from pathlib import Path | |
| import torch | |
| import numpy as np | |
| import pytorch_lightning as pl | |
| from matplotlib import pyplot as plt | |
| from src.models import TopicFM | |
| from src.models.utils.supervision import compute_supervision_coarse, compute_supervision_fine | |
| from src.losses.loss import TopicFMLoss | |
| from src.optimizers import build_optimizer, build_scheduler | |
| from src.utils.metrics import ( | |
| compute_symmetrical_epipolar_errors, | |
| compute_pose_errors, | |
| aggregate_metrics | |
| ) | |
| from src.utils.plotting import make_matching_figures | |
| from src.utils.comm import gather, all_gather | |
| from src.utils.misc import lower_config, flattenList | |
| from src.utils.profiler import PassThroughProfiler | |
| class PL_Trainer(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.model_cfg = lower_config(_config['model']) | |
| self.profiler = profiler or PassThroughProfiler() | |
| self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1) | |
| # Matcher: TopicFM | |
| self.matcher = TopicFM(config=_config['model']) | |
| self.loss = TopicFMLoss(_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=True) | |
| 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): | |
| with self.profiler.profile("Compute coarse supervision"): | |
| compute_supervision_coarse(batch, self.config) | |
| with self.profiler.profile("TopicFM"): | |
| self.matcher(batch) | |
| with self.profiler.profile("Compute fine supervision"): | |
| compute_supervision_fine(batch, self.config) | |
| with self.profiler.profile("Compute losses"): | |
| self.loss(batch) | |
| def _compute_metrics(self, batch): | |
| with self.profiler.profile("Copmute metrics"): | |
| compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match | |
| compute_pose_errors(batch, self.config) # compute R_errs, t_errs, pose_errs for each pair | |
| rel_pair_names = list(zip(*batch['pair_names'])) | |
| bs = batch['image0'].size(0) | |
| metrics = { | |
| # to filter duplicate pairs caused by DistributedSampler | |
| 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], | |
| 'epi_errs': [batch['epi_errs'][batch['m_bids'] == b].cpu().numpy() for b in range(bs)], | |
| 'R_errs': batch['R_errs'], | |
| 't_errs': batch['t_errs'], | |
| 'inliers': batch['inliers']} | |
| ret_dict = {'metrics': metrics} | |
| return ret_dict, rel_pair_names | |
| 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.experiment.add_scalar(f'train/{k}', v, self.global_step) | |
| # figures | |
| if self.config.TRAINER.ENABLE_PLOTTING: | |
| compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match | |
| figures = make_matching_figures(batch, self.config, self.config.TRAINER.PLOT_MODE) | |
| for k, v in figures.items(): | |
| self.logger.experiment.add_figure(f'train_match/{k}', v, 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.experiment.add_scalar( | |
| 'train/avg_loss_on_epoch', avg_loss, | |
| global_step=self.current_epoch) | |
| def validation_step(self, batch, batch_idx): | |
| self._trainval_inference(batch) | |
| ret_dict, _ = self._compute_metrics(batch) | |
| val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1) | |
| figures = {self.config.TRAINER.PLOT_MODE: []} | |
| if batch_idx % val_plot_interval == 0: | |
| figures = make_matching_figures(batch, self.config, mode=self.config.TRAINER.PLOT_MODE) | |
| return { | |
| **ret_dict, | |
| 'loss_scalars': batch['loss_scalars'], | |
| 'figures': figures, | |
| } | |
| def validation_epoch_end(self, outputs): | |
| # handle multiple validation sets | |
| multi_outputs = [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs | |
| multi_val_metrics = defaultdict(list) | |
| 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]} | |
| # 2. val metrics: dict of list, numpy | |
| _metrics = [o['metrics'] for o in outputs] | |
| metrics = {k: flattenList(all_gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} | |
| # NOTE: all ranks need to `aggregate_merics`, but only log at rank-0 | |
| val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) | |
| for thr in [5, 10, 20]: | |
| multi_val_metrics[f'auc@{thr}'].append(val_metrics_4tb[f'auc@{thr}']) | |
| # 3. figures | |
| _figures = [o['figures'] for o in outputs] | |
| figures = {k: flattenList(gather(flattenList([_me[k] for _me in _figures]))) for k in _figures[0]} | |
| # 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.experiment.add_scalar(f'val_{valset_idx}/avg_{k}', mean_v, global_step=cur_epoch) | |
| for k, v in val_metrics_4tb.items(): | |
| self.logger.experiment.add_scalar(f"metrics_{valset_idx}/{k}", v, global_step=cur_epoch) | |
| for k, v in figures.items(): | |
| if self.trainer.global_rank == 0: | |
| for plot_idx, fig in enumerate(v): | |
| self.logger.experiment.add_figure( | |
| f'val_match_{valset_idx}/{k}/pair-{plot_idx}', fig, cur_epoch, close=True) | |
| plt.close('all') | |
| for thr in [5, 10, 20]: | |
| # log on all ranks for ModelCheckpoint callback to work properly | |
| self.log(f'auc@{thr}', torch.tensor(np.mean(multi_val_metrics[f'auc@{thr}']))) # ckpt monitors on this | |
| def test_step(self, batch, batch_idx): | |
| with self.profiler.profile("TopicFM"): | |
| self.matcher(batch) | |
| ret_dict, rel_pair_names = self._compute_metrics(batch) | |
| with self.profiler.profile("dump_results"): | |
| if self.dump_dir is not None: | |
| # dump results for further analysis | |
| keys_to_save = {'mkpts0_f', 'mkpts1_f', 'mconf', 'epi_errs'} | |
| pair_names = list(zip(*batch['pair_names'])) | |
| bs = batch['image0'].shape[0] | |
| dumps = [] | |
| for b_id in range(bs): | |
| item = {} | |
| mask = batch['m_bids'] == b_id | |
| item['pair_names'] = pair_names[b_id] | |
| item['identifier'] = '#'.join(rel_pair_names[b_id]) | |
| for key in keys_to_save: | |
| item[key] = batch[key][mask].cpu().numpy() | |
| for key in ['R_errs', 't_errs', 'inliers']: | |
| item[key] = batch[key][b_id] | |
| dumps.append(item) | |
| ret_dict['dumps'] = dumps | |
| return ret_dict | |
| def test_epoch_end(self, outputs): | |
| # metrics: dict of list, numpy | |
| _metrics = [o['metrics'] for o in outputs] | |
| metrics = {k: flattenList(gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} | |
| # [{key: [{...}, *#bs]}, *#batch] | |
| if self.dump_dir is not None: | |
| Path(self.dump_dir).mkdir(parents=True, exist_ok=True) | |
| _dumps = flattenList([o['dumps'] for o in outputs]) # [{...}, #bs*#batch] | |
| dumps = flattenList(gather(_dumps)) # [{...}, #proc*#bs*#batch] | |
| logger.info(f'Prediction and evaluation results will be saved to: {self.dump_dir}') | |
| if self.trainer.global_rank == 0: | |
| print(self.profiler.summary()) | |
| val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) | |
| logger.info('\n' + pprint.pformat(val_metrics_4tb)) | |
| if self.dump_dir is not None: | |
| np.save(Path(self.dump_dir) / 'TopicFM_pred_eval', dumps) | |