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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.loftr import LoFTR
# from src.loftr.utils.supervision import compute_supervision_coarse, compute_supervision_fine
# from src.losses.loftr_loss import LoFTRLoss
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
from torch.profiler import profile
def reparameter(matcher):
module = matcher.backbone.layer0
if hasattr(module, 'switch_to_deploy'):
module.switch_to_deploy()
for modules in [matcher.backbone.layer1, matcher.backbone.layer2, matcher.backbone.layer3]:
for module in modules:
if hasattr(module, 'switch_to_deploy'):
module.switch_to_deploy()
for modules in [matcher.fine_preprocess.layer2_outconv2, matcher.fine_preprocess.layer1_outconv2]:
for module in modules:
if hasattr(module, 'switch_to_deploy'):
module.switch_to_deploy()
return matcher
class PL_LoFTR(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.loftr_cfg = lower_config(_config['loftr'])
self.profiler = profiler or PassThroughProfiler()
self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1)
# Matcher: LoFTR
self.matcher = LoFTR(config=_config['loftr'], profiler=self.profiler)
# self.loss = LoFTRLoss(_config)
# Pretrained weights
if pretrained_ckpt:
state_dict = torch.load(pretrained_ckpt, map_location='cpu')['state_dict']
msg=self.matcher.load_state_dict(state_dict, strict=False)
logger.info(f"Load \'{pretrained_ckpt}\' as pretrained checkpoint")
# Testing
self.warmup = False
self.reparameter = False
self.start_event = torch.cuda.Event(enable_timing=True)
self.end_event = torch.cuda.Event(enable_timing=True)
self.total_ms = 0
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"):
with torch.autocast(enabled=False, device_type='cuda'):
compute_supervision_coarse(batch, self.config)
with self.profiler.profile("LoFTR"):
with torch.autocast(enabled=self.config.LOFTR.MP, device_type='cuda'):
self.matcher(batch)
with self.profiler.profile("Compute fine supervision"):
with torch.autocast(enabled=False, device_type='cuda'):
compute_supervision_fine(batch, self.config, self.logger)
with self.profiler.profile("Compute losses"):
with torch.autocast(enabled=self.config.LOFTR.MP, device_type='cuda'):
self.loss(batch)
def _compute_metrics(self, batch):
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'].reshape(-1,1))[batch['m_bids'] == b].reshape(-1).cpu().numpy() for b in range(bs)],
'R_errs': batch['R_errs'],
't_errs': batch['t_errs'],
'inliers': batch['inliers'],
'num_matches': [batch['mconf'].shape[0]], # batch size = 1 only
}
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, config=self.config)
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):
if (self.config.LOFTR.BACKBONE_TYPE == 'RepVGG') and not self.reparameter:
self.matcher = reparameter(self.matcher)
if self.config.LOFTR.HALF:
self.matcher = self.matcher.eval().half()
self.reparameter = True
if not self.warmup:
if self.config.LOFTR.HALF:
for i in range(50):
self.matcher(batch)
else:
with torch.autocast(enabled=self.config.LOFTR.MP, device_type='cuda'):
for i in range(50):
self.matcher(batch)
self.warmup = True
torch.cuda.synchronize()
if self.config.LOFTR.HALF:
self.start_event.record()
self.matcher(batch)
self.end_event.record()
torch.cuda.synchronize()
self.total_ms += self.start_event.elapsed_time(self.end_event)
else:
with torch.autocast(enabled=self.config.LOFTR.MP, device_type='cuda'):
self.start_event.record()
self.matcher(batch)
self.end_event.record()
torch.cuda.synchronize()
self.total_ms += self.start_event.elapsed_time(self.end_event)
ret_dict, rel_pair_names = self._compute_metrics(batch)
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.trainer.global_rank == 0:
print('Averaged Matching time over 1500 pairs: {:.2f} ms'.format(self.total_ms / 1500))
val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR, config=self.config)
logger.info('\n' + pprint.pformat(val_metrics_4tb)) |