train.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 dynamic_stereo.train_utils.utils import (
    run_test_eval,
    save_ims_to_tb,
    count_parameters,
)
from dynamic_stereo.train_utils.logger import Logger
from dynamic_stereo.models.core.dynamic_stereo import DynamicStereo
from dynamic_stereo.evaluation.core.evaluator import Evaluator
from dynamic_stereo.train_utils.losses import sequence_loss
import dynamic_stereo.datasets.dynamic_stereo_datasets as datasets


def fetch_optimizer(args, model):
    """Create the optimizer and learning rate scheduler"""
    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": 1,  # 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)

        model = DynamicStereo(
            max_disp=256,
            mixed_precision=args.mixed_precision,
            num_frames=args.sample_len,
            attention_type=args.attention_type,
            use_3d_update_block=args.update_block_3d,
            different_update_blocks=args.different_update_blocks,
        )

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

        model.cuda()

        logging.info(f"Parameter Count:  {count_parameters(model)}")

        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)

        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 total_steps % save_freq == save_freq - 1:
                        save_ims_to_tb(logger.writer, batch, output, total_steps)
                    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)
                scheduler.step()
                scaler.update()
                total_steps += 1

                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)

                        if epoch % args.evaluate_every_n_epoch == 0:
                            logging.info(f"Evaluation at epoch {epoch}")
                            run_test_eval(
                                args.ckpt_path,
                                "valid",
                                evaluator,
                                model,
                                eval_dataloaders,
                                logger.writer,
                                total_steps,
                            )

                            model.train()
                            model.module.module.freeze_bn()

                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="dynamic-stereo", 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=0.0002, help="max learning rate.")

    parser.add_argument(
        "--num_steps", type=int, default=100000, 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=16,
        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=2, 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.",
    )
    # Architecure choices
    parser.add_argument(
        "--different_update_blocks",
        action="store_true",
        help="use different update blocks for each resolution",
    )
    parser.add_argument(
        "--attention_type",
        type=str,
        help="attention type of the SST and update blocks. \
            Any combination of 'self_stereo', 'temporal', 'update_time', 'update_space' connected by an underscore.",
    )
    parser.add_argument(
        "--update_block_3d", action="store_true", help="use Conv3D update block"
    )
    # 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()

    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
    )

    Path(args.ckpt_path).mkdir(exist_ok=True, parents=True)
    from pytorch_lightning.strategies import DDPStrategy

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