third_party/ASpanFormer/src/lightning/lightning_aspanformer.py

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.ASpanFormer.aspanformer import ASpanFormer
from src.ASpanFormer.utils.supervision import (
    compute_supervision_coarse,
    compute_supervision_fine,
)
from src.losses.aspan_loss import ASpanLoss
from src.optimizers import build_optimizer, build_scheduler
from src.utils.metrics import (
    compute_symmetrical_epipolar_errors,
    compute_symmetrical_epipolar_errors_offset_bidirectional,
    compute_pose_errors,
    aggregate_metrics,
)
from src.utils.plotting import make_matching_figures, make_matching_figures_offset
from src.utils.comm import gather, all_gather
from src.utils.misc import lower_config, flattenList
from src.utils.profiler import PassThroughProfiler


class PL_ASpanFormer(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["aspan"])
        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 = ASpanFormer(config=_config["aspan"])
        self.loss = ASpanLoss(_config)

        # Pretrained weights
        print(pretrained_ckpt)
        if pretrained_ckpt:
            print("load")
            state_dict = torch.load(pretrained_ckpt, map_location="cpu")["state_dict"]
            msg = self.matcher.load_state_dict(state_dict, strict=False)
            print(msg)
            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("LoFTR"):
            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_symmetrical_epipolar_errors_offset_bidirectional(
                batch
            )  # compute epi_errs for offset 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)
                ],
                "epi_errs_offset": [
                    batch["epi_errs_offset_left"][batch["offset_bids_left"] == b]
                    .cpu()
                    .numpy()
                    for b in range(bs)
                ],  # only consider left side
                "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():
                if not k.startswith("loss_flow") and not k.startswith("conf_"):
                    self.logger.experiment.add_scalar(f"train/{k}", v, self.global_step)

            # log offset_loss and conf for each layer and level
            layer_num = self.loftr_cfg["coarse"]["layer_num"]
            for layer_index in range(layer_num):
                log_title = "layer_" + str(layer_index)
                self.logger.experiment.add_scalar(
                    log_title + "/offset_loss",
                    batch["loss_scalars"]["loss_flow_" + str(layer_index)],
                    self.global_step,
                )
                self.logger.experiment.add_scalar(
                    log_title + "/conf_",
                    batch["loss_scalars"]["conf_" + str(layer_index)],
                    self.global_step,
                )

            # net-params
            if self.config.ASPAN.MATCH_COARSE.MATCH_TYPE == "sinkhorn":
                self.logger.experiment.add_scalar(
                    f"skh_bin_score",
                    self.matcher.coarse_matching.bin_score.clone().detach().cpu().data,
                    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
                    )

                # plot offset
                if self.global_step % 200 == 0:
                    compute_symmetrical_epipolar_errors_offset_bidirectional(batch)
                    figures_left = make_matching_figures_offset(
                        batch, self.config, self.config.TRAINER.PLOT_MODE, side="_left"
                    )
                    figures_right = make_matching_figures_offset(
                        batch, self.config, self.config.TRAINER.PLOT_MODE, side="_right"
                    )
                    for k, v in figures_left.items():
                        self.logger.experiment.add_figure(
                            f"train_offset/{k}" + "_left", v, self.global_step
                        )
                    figures = make_matching_figures_offset(
                        batch, self.config, self.config.TRAINER.PLOT_MODE, side="_right"
                    )
                    for k, v in figures_right.items():
                        self.logger.experiment.add_figure(
                            f"train_offset/{k}" + "_right", 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
        )  # this func also compute the epi_errors

        val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1)
        figures = {self.config.TRAINER.PLOT_MODE: []}
        figures_offset = {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
            )
            figures_offset = make_matching_figures_offset(
                batch, self.config, self.config.TRAINER.PLOT_MODE, "_left"
            )
        return {
            **ret_dict,
            "loss_scalars": batch["loss_scalars"],
            "figures": figures,
            "figures_offset_left": figures_offset,
        }

    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("LoFTR"):
            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) / "LoFTR_pred_eval", dumps)