main.py

import determinism  # noqa
determinism.i_do_nothing_but_dont_remove_me_otherwise_things_break()  # noqa

import argparse
import bisect
import copy
import os
import sys
import time
from argparse import ArgumentParser

import torch
import wandb
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.engine import PeriodicCheckpointer
from detectron2.engine import launch
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

import config
import losses
import utils
from eval_utils import eval_unsupmf, get_unsup_image_viz, get_vis_header
from mask_former_trainer import setup, Trainer


logger = utils.log.getLogger('gwm')

def freeze(module, set=False):
    for param in module.parameters():
        param.requires_grad = set


def main(args):
    cfg = setup(args)
    logger.info(f"Called as {' '.join(sys.argv)}")
    logger.info(f'Output dir {cfg.OUTPUT_DIR}')

    print('Using seed:', cfg.SEED)
    random_state = utils.random_state.PytorchRNGState(seed=cfg.SEED).to(torch.device(cfg.MODEL.DEVICE))
    random_state.seed_everything()
    utils.log.checkpoint_code(cfg.OUTPUT_DIR)

    if not cfg.SKIP_TB:
        writer = SummaryWriter(log_dir=cfg.OUTPUT_DIR)
    else:
        writer = None

    # initialize model 
    model = Trainer.build_model(cfg)
    optimizer = Trainer.build_optimizer(cfg, model)
    scheduler = Trainer.build_lr_scheduler(cfg, optimizer)

    logger.info(f'Optimiser is {type(optimizer)}')


    checkpointer = DetectionCheckpointer(model,
                                         save_dir=os.path.join(cfg.OUTPUT_DIR, 'checkpoints'),
                                         random_state=random_state,
                                         optimizer=optimizer,
                                         scheduler=scheduler)
    periodic_checkpointer = PeriodicCheckpointer(checkpointer=checkpointer,
                                                 period=cfg.SOLVER.CHECKPOINT_PERIOD,
                                                 max_iter=cfg.SOLVER.MAX_ITER,
                                                 max_to_keep=None if cfg.FLAGS.KEEP_ALL else 5,
                                                 file_prefix='checkpoint')
    checkpoint = checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=args.resume_path is not None)
    iteration = 0 if args.resume_path is None else checkpoint['iteration']

    train_loader, val_loader = config.loaders(cfg)
    criterion = losses.BCELoss(cfg, model)

    if args.eval_only:
        if len(val_loader.dataset) == 0:
            logger.error("Validation dataset: empty")
            sys.exit(0)
        model.eval()
        iou = eval_unsupmf(cfg=cfg, val_loader=val_loader, model=model, criterion=criterion, writer=writer,
                           writer_iteration=iteration)
        logger.info(f"Results: iteration: {iteration} IOU = {iou}")
        return
    if len(train_loader.dataset) == 0:
        logger.error("Training dataset: empty")
        sys.exit(0)

    logger.info(
        f'Start of training: dataset {cfg.GWM.DATASET},'
        f' train {len(train_loader.dataset)}, val {len(val_loader.dataset)}'
        f' device {model.device}, keys {cfg.GWM.SAMPLE_KEYS}, '
        f'multiple flows {cfg.GWM.USE_MULT_FLOW}')

    iou_best = 0
    timestart = time.time()
    dilate_kernel = torch.ones((2, 2), device=model.device)

    total_iter = cfg.TOTAL_ITER if cfg.TOTAL_ITER else cfg.SOLVER.MAX_ITER  # early stop
    with torch.autograd.set_detect_anomaly(cfg.DEBUG) and \
         tqdm(initial=iteration, total=total_iter, disable=utils.environment.is_slurm()) as pbar:
        while iteration < total_iter:
            for sample in train_loader:
                if cfg.MODEL.META_ARCHITECTURE != 'UNET' and cfg.FLAGS.UNFREEZE_AT:
                    if hasattr(model.backbone, 'frozen_stages'):
                        assert cfg.MODEL.BACKBONE.FREEZE_AT == -1, f"MODEL initial parameters forced frozen"
                        stages = [s for s, m in cfg.FLAGS.UNFREEZE_AT]
                        milest = [m for s, m in cfg.FLAGS.UNFREEZE_AT]
                        pos = bisect.bisect_right(milest, iteration) - 1
                        if pos >= 0:
                            curr_setting = model.backbone.frozen_stages
                            if curr_setting != stages[pos]:
                                logger.info(f"Updating backbone freezing stages from {curr_setting} to {stages[pos]}")
                                model.backbone.frozen_stages = stages[pos]
                                model.train()
                    else:
                        assert cfg.MODEL.BACKBONE.FREEZE_AT == -1, f"MODEL initial parameters forced frozen"
                        stages = [s for s, m in cfg.FLAGS.UNFREEZE_AT]
                        milest = [m for s, m in cfg.FLAGS.UNFREEZE_AT]
                        pos = bisect.bisect_right(milest, iteration) - 1
                        freeze(model, set=False)
                        freeze(model.sem_seg_head.predictor, set=True)
                        if pos >= 0:
                            stage = stages[pos]
                            if stage <= 2:
                                freeze(model.sem_seg_head, set=True)
                            if stage <= 1:
                                freeze(model.backbone, set=True)
                        model.train()

                else:
                    logger.debug_once(f'Unfreezing disabled schedule: {cfg.FLAGS.UNFREEZE_AT}')

                raw_sem_seg = False
                if cfg.GWM.FLOW_RES is not None:
                    raw_sem_seg = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME == 'MegaBigPixelDecoder'

                preds = model.forward_base(sample, keys=cfg.GWM.SAMPLE_KEYS, get_eval=True, raw_sem_seg=raw_sem_seg)
                masks_raw = torch.stack([x['sem_seg'] for x in preds], 0)
                logger.debug_once(f'mask shape: {masks_raw.shape}')
                masks_softmaxed_list = [torch.sigmoid(masks_raw)]
                
                total_losses = []
                for mask_idx, masks_softmaxed in enumerate(masks_softmaxed_list):
                    pseudo_gt = torch.stack([x["pseudo_gt"].to(model.device) for x in sample], dim=0)
                    loss = criterion(masks_softmaxed, pseudo_gt)
                    total_losses.append(loss)

                loss = total_losses[0]

                train_log_dict = {}
                train_log_dict['train/learning_rate'] = optimizer.param_groups[-1]['lr']
                train_log_dict['train/loss_total'] = loss.item()

                optimizer.zero_grad()

                loss.backward()
                optimizer.step()
                scheduler.step()

                pbar.set_postfix(loss=loss.item())
                pbar.update()

                # Sanity check for RNG state
                if (iteration + 1) % 1000 == 0 or iteration + 1 in {1, 50}:
                    logger.info(
                        f'Iteration {iteration + 1}. RNG outputs {utils.random_state.get_randstate_magic_numbers(model.device)}')

                if cfg.DEBUG or (iteration + 1) % 100 == 0:
                    logger.info(
                        f'Iteration: {iteration + 1}, time: {time.time() - timestart:.01f}s, loss: {loss.item():.02f}.')

                    for k, v in train_log_dict.items():
                        if writer:
                            writer.add_scalar(k, v, iteration + 1)

                    if cfg.WANDB.ENABLE:
                        wandb.log(train_log_dict, step=iteration + 1)

                if (iteration + 1) % cfg.LOG_FREQ == 0 or (iteration + 1) in [1]:   # or (iteration + 1) in [1, 50, 500]
                    model.eval()
                    if cfg.WANDB.ENABLE and (iteration + 1) % 2500 == 0:
                        image_viz = get_unsup_image_viz(model, cfg, sample)
                        wandb.log({'train/viz': wandb.Image(image_viz.float())}, step=iteration + 1)

                    if iou := eval_unsupmf(cfg=cfg, val_loader=val_loader, model=model, criterion=criterion,
                                           writer=writer, writer_iteration=iteration + 1, use_wandb=cfg.WANDB.ENABLE):
                        if cfg.SOLVER.CHECKPOINT_PERIOD:
                            if iou > iou_best:
                                iou_best = iou
                                if not args.wandb_sweep_mode:
                                    checkpointer.save(name='checkpoint_best', iteration=iteration + 1, loss=loss,
                                                    iou=iou_best)
                                logger.info(f'New best IoU {iou_best:.02f} after iteration {iteration + 1}')
                            else:
                                logger.info(f'Current best IoU: {iou_best}')
                        if cfg.WANDB.ENABLE:
                            wandb.log({'eval/IoU_best': iou_best}, step=iteration + 1)
                        if writer:
                            writer.add_scalar('eval/IoU_best', iou_best, iteration + 1)
                    model.train()

                periodic_checkpointer.step(iteration=iteration + 1, loss=loss)
                iteration += 1
                timestart = time.time()


def get_argparse_args():
    parser = ArgumentParser()
    parser.add_argument('--resume_path', type=str, default=None)    # default='../outputs/fbms/20230521_145239/checkpoints/checkpoint_best.pth'
    parser.add_argument('--use_wandb', dest='wandb_sweep_mode', action='store_true')  # for sweep
    parser.add_argument('--config-file', type=str,
                        default='configs/maskformer/maskformer_R50_bs16_160k_dino.yaml')
    parser.add_argument('--eval_only', action='store_true')
    parser.add_argument(
        "opts",
        help="Modify config options by adding 'KEY VALUE' pairs at the end of the command. "
             "See config references at "
             "https://detectron2.readthedocs.io/modules/config.html#config-references",
        default=None,
        nargs=argparse.REMAINDER,
    )
    return parser


if __name__ == "__main__":
    args = get_argparse_args().parse_args()
    if args.resume_path:
        args.config_file = "/".join(args.resume_path.split('/')[:-2]) + '/config.yaml'
    print('Using config from:', args.config_file)

    main(args)