train_bidastereo.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import argparse
import logging
from pathlib import Path
from tqdm import tqdm
import os
import torch
import torch.optim as optim

from munch import DefaultMunch
import json
from pytorch_lightning.lite import LightningLite
from torch.cuda.amp import GradScaler

from bidastereo.train_utils.utils import (
    run_test_eval,
    save_ims_to_tb,
    count_parameters,
)
from bidastereo.train_utils.logger import Logger
from bidastereo.models.core.bidastereo import BiDAStereo

from bidastereo.evaluation.core.evaluator import Evaluator
from bidastereo.train_utils.losses import sequence_loss
import bidastereo.datasets.bidastereo_datasets as datasets


def fetch_optimizer(args, model):
    """Create the optimizer and learning rate scheduler"""
    for name, param in model.named_parameters():
        if any([key in name for key in ['raft']]):
            param.requires_grad_(False)

    optimizer = optim.AdamW(
        model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=1e-8
    )
    scheduler = optim.lr_scheduler.OneCycleLR(
        optimizer,
        args.lr,
        args.num_steps + 100,
        pct_start=0.01,
        cycle_momentum=False,
        anneal_strategy="linear",
    )
    return optimizer, scheduler


def forward_batch(batch, model, args):
    output = {}
    disparities = model(
        batch["img"][:, :, 0],
        batch["img"][:, :, 1],
        iters=args.train_iters,
        test_mode=False,
    )
    num_traj = len(batch["disp"][0])

    for i in range(num_traj):
        seq_loss, metrics = sequence_loss(
            disparities[:, i], batch["disp"][:, i, 0], batch["valid_disp"][:, i, 0]
        )

        output[f"disp_{i}"] = {"loss": seq_loss / num_traj, "metrics": metrics}
    output["disparity"] = {
        "predictions": torch.cat(
            [disparities[-1, i, 0] for i in range(num_traj)], dim=1).detach(),
    }
    return output


class Lite(LightningLite):
    def run(self, args):
        self.seed_everything(0)
        # eval_dataloader_dr = datasets.DynamicReplicaDataset(
        #     split="valid", sample_len=40, only_first_n_samples=1
        # )
        # eval_dataloader_sintel_clean = datasets.SequenceSintelStereo(dstype="clean")
        # eval_dataloader_sintel_final = datasets.SequenceSintelStereo(dstype="final")

        eval_dataloaders = [
            # ("sintel_clean", eval_dataloader_sintel_clean),
            # ("sintel_final", eval_dataloader_sintel_final),
            # ("dynamic_replica", eval_dataloader_dr),
        ]

        evaluator = Evaluator()

        eval_vis_cfg = {
            "visualize_interval": 0,  # Use 0 for no visualization
            "exp_dir": args.ckpt_path,
        }
        eval_vis_cfg = DefaultMunch.fromDict(eval_vis_cfg, object())
        evaluator.setup_visualization(eval_vis_cfg)

        if args.name == 'bidastereo':
            model = BiDAStereo(
                mixed_precision=args.mixed_precision,
            )
        else:
            raise ValueError("Wrong Model!")

        with open(args.ckpt_path + "/meta.json", "w") as file:
            json.dump(vars(args), file, sort_keys=True, indent=4)

        model.cuda()

        train_loader = datasets.fetch_dataloader(args)
        train_loader = self.setup_dataloaders(train_loader, move_to_device=False)

        logging.info(f"Train loader size:  {len(train_loader)}")

        optimizer, scheduler = fetch_optimizer(args, model)
        print("Parameter Count:", {count_parameters(model)})
        logging.info(f"Parameter Count:  {count_parameters(model)}")
        total_steps = 0
        logger = Logger(model, scheduler, args.ckpt_path)

        folder_ckpts = [
            f
            for f in os.listdir(args.ckpt_path)
            if not os.path.isdir(f) and f.endswith(".pth") and not "final" in f
        ]
        if len(folder_ckpts) > 0:
            ckpt_path = sorted(folder_ckpts)[-1]
            ckpt = self.load(os.path.join(args.ckpt_path, ckpt_path))
            logging.info(f"Loading checkpoint {ckpt_path}")
            if "model" in ckpt:
                model.load_state_dict(ckpt["model"])
            else:
                model.load_state_dict(ckpt)
            if "optimizer" in ckpt:
                logging.info("Load optimizer")
                optimizer.load_state_dict(ckpt["optimizer"])
            if "scheduler" in ckpt:
                logging.info("Load scheduler")
                scheduler.load_state_dict(ckpt["scheduler"])
            if "total_steps" in ckpt:
                total_steps = ckpt["total_steps"]
                logging.info(f"Load total_steps {total_steps}")

        elif args.restore_ckpt is not None:
            assert args.restore_ckpt.endswith(".pth") or args.restore_ckpt.endswith(
                ".pt"
            )
            logging.info("Loading checkpoint...")
            strict = True

            state_dict = self.load(args.restore_ckpt)
            if "model" in state_dict:
                state_dict = state_dict["model"]
            if list(state_dict.keys())[0].startswith("module."):
                state_dict = {
                    k.replace("module.", ""): v for k, v in state_dict.items()
                }
            model.load_state_dict(state_dict, strict=strict)

            logging.info(f"Done loading checkpoint")
        model, optimizer = self.setup(model, optimizer, move_to_device=False)
        model.cuda()
        model.train()
        model.module.module.freeze_bn()  # We keep BatchNorm frozen

        save_freq = args.save_freq
        scaler = GradScaler(enabled=args.mixed_precision)

        should_keep_training = True
        global_batch_num = 0
        epoch = -1
        while should_keep_training:
            epoch += 1

            for i_batch, batch in enumerate(tqdm(train_loader)):
                optimizer.zero_grad()
                if batch is None:
                    print("batch is None")
                    continue
                for k, v in batch.items():
                    batch[k] = v.cuda()

                assert model.training

                output = forward_batch(batch, model, args)

                loss = 0
                logger.update()
                for k, v in output.items():
                    if "loss" in v:
                        loss += v["loss"]
                        logger.writer.add_scalar(
                            f"live_{k}_loss", v["loss"].item(), total_steps
                        )
                    if "metrics" in v:
                        logger.push(v["metrics"], k)

                if self.global_rank == 0:
                    if len(output) > 1:
                        logger.writer.add_scalar(
                            f"live_total_loss", loss.item(), total_steps
                        )
                    logger.writer.add_scalar(
                        f"learning_rate", optimizer.param_groups[0]["lr"], total_steps
                    )
                    global_batch_num += 1
                self.barrier()
                self.backward(scaler.scale(loss))
                scaler.unscale_(optimizer)
                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

                scaler.step(optimizer)
                if total_steps < args.num_steps:
                    scheduler.step()
                scaler.update()
                total_steps += 1
                if total_steps == args.finetune_step:
                    logging.info("All trainable parameters!")
                    for name, param in model.named_parameters():
                        param.requires_grad_(True)

                if self.global_rank == 0:

                    if (i_batch >= len(train_loader) - 1) or (
                        total_steps == 1 and args.validate_at_start
                    ):
                        ckpt_iter = "0" * (6 - len(str(total_steps))) + str(total_steps)
                        save_path = Path(
                            f"{args.ckpt_path}/model_{args.name}_{ckpt_iter}.pth"
                        )

                        save_dict = {
                            "model": model.module.module.state_dict(),
                            "optimizer": optimizer.state_dict(),
                            "scheduler": scheduler.state_dict(),
                            "total_steps": total_steps,
                        }

                        logging.info(f"Saving file {save_path}")
                        self.save(save_dict, save_path)

                self.barrier()
                if total_steps > args.num_steps:
                    should_keep_training = False
                    break

        logger.close()
        PATH = f"{args.ckpt_path}/{args.name}_final.pth"
        torch.save(model.module.module.state_dict(), PATH)

        test_dataloader_dr = datasets.DynamicStereoDataset(
            split="test", sample_len=150, only_first_n_samples=1
        )
        test_dataloaders = [
            ("sintel_clean", eval_dataloader_sintel_clean),
            ("sintel_final", eval_dataloader_sintel_final),
            ("dynamic_replica", test_dataloader_dr),
        ]
        run_test_eval(
            args.ckpt_path,
            "test",
            evaluator,
            model,
            test_dataloaders,
            logger.writer,
            total_steps,
        )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--name", default="bdastereo", help="name your experiment")
    parser.add_argument("--restore_ckpt", help="restore checkpoint")
    parser.add_argument("--ckpt_path", help="path to save checkpoints")
    parser.add_argument(
        "--mixed_precision", action="store_true", help="use mixed precision"
    )

    # Training parameters
    parser.add_argument(
        "--batch_size", type=int, default=6, help="batch size used during training."
    )
    parser.add_argument(
        "--train_datasets",
        nargs="+",
        default=["things", "monkaa", "driving"],
        help="training datasets.",
    )
    parser.add_argument("--lr", type=float, default=0.0004, help="max learning rate.")

    parser.add_argument(
        "--num_steps", type=int, default=80000, help="length of training schedule."
    )
    parser.add_argument(
        "--finetune_step", type=int, default=40000, help="length of training schedule."
    )
    parser.add_argument(
        "--image_size",
        type=int,
        nargs="+",
        default=[320, 720],
        help="size of the random image crops used during training.",
    )
    parser.add_argument(
        "--train_iters",
        type=int,
        default=10,
        help="number of updates to the disparity field in each forward pass.",
    )
    parser.add_argument(
        "--wdecay", type=float, default=0.00001, help="Weight decay in optimizer."
    )

    parser.add_argument(
        "--sample_len", type=int, default=5, help="length of training video samples"
    )
    parser.add_argument(
        "--validate_at_start", action="store_true", help="validate the model at start"
    )
    parser.add_argument("--save_freq", type=int, default=100, help="save frequency")

    parser.add_argument(
        "--evaluate_every_n_epoch",
        type=int,
        default=1,
        help="evaluate every n epoch",
    )

    parser.add_argument(
        "--num_workers", type=int, default=6, help="number of dataloader workers."
    )
    # Validation parameters
    parser.add_argument(
        "--valid_iters",
        type=int,
        default=32,
        help="number of updates to the disparity field in each forward pass during validation.",
    )
    # Data augmentation
    parser.add_argument(
        "--img_gamma", type=float, nargs="+", default=None, help="gamma range"
    )
    parser.add_argument(
        "--saturation_range",
        type=float,
        nargs="+",
        default=None,
        help="color saturation",
    )
    parser.add_argument(
        "--do_flip",
        default=False,
        choices=["h", "v"],
        help="flip the images horizontally or vertically",
    )
    parser.add_argument(
        "--spatial_scale",
        type=float,
        nargs="+",
        default=[0, 0],
        help="re-scale the images randomly",
    )
    parser.add_argument(
        "--noyjitter",
        action="store_true",
        help="don't simulate imperfect rectification",
    )
    args = parser.parse_args()

    Path(args.ckpt_path).mkdir(exist_ok=True, parents=True)

    logging.basicConfig(
        level=logging.INFO,
        filename=args.ckpt_path + '/' + args.name + '.log',
        filemode='a',
        format="%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
    )

    from pytorch_lightning.strategies import DDPStrategy

    Lite(
        strategy=DDPStrategy(find_unused_parameters=True),
        devices="auto",
        accelerator="gpu",
        precision=32,
    ).run(args)