train_stereo_rt.py

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'

import argparse
import logging
import numpy as np
from pathlib import Path
from tqdm import tqdm

from torch.utils.tensorboard import SummaryWriter
import torch
import torch.nn as nn
import torch.optim as optim
from core_rt.rt_igev_stereo import IGEVStereo
from evaluate_stereo_rt import *
import core.stereo_datasets as datasets
import torch.nn.functional as F

try:
    from torch.cuda.amp import GradScaler
except:
    class GradScaler:
        def __init__(self):
            pass
        def scale(self, loss):
            return loss
        def unscale_(self, optimizer):
            pass
        def step(self, optimizer):
            optimizer.step()
        def update(self):
            pass

def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

def sequence_loss(agg_pred, iter_preds, disp_gt, valid, loss_gamma=0.9):
    """ Loss function defined over sequence of flow predictions """

    n_predictions = len(iter_preds)
    assert n_predictions >= 1
    disp_loss = 0.0
    mag = torch.sum(disp_gt**2, dim=1).sqrt()
    valid = ((valid >= 0.5) & (mag < 192)).unsqueeze(1)
    assert valid.shape == disp_gt.shape, [valid.shape, disp_gt.shape]
    assert not torch.isinf(disp_gt[valid.bool()]).any()


    disp_loss += 1.0 * F.smooth_l1_loss(agg_pred[valid.bool()], disp_gt[valid.bool()], reduction='mean')
    for i in range(n_predictions):
        adjusted_loss_gamma = loss_gamma**(15/(n_predictions - 1))
        i_weight = adjusted_loss_gamma**(n_predictions - i - 1)
        i_loss = (iter_preds[i] - disp_gt).abs()
        assert i_loss.shape == valid.shape, [i_loss.shape, valid.shape, disp_gt.shape, iter_preds[i].shape]
        disp_loss += i_weight * i_loss[valid.bool()].mean()

    epe = torch.sum((iter_preds[-1] - disp_gt)**2, dim=1).sqrt()
    epe = epe.view(-1)[valid.view(-1)]

    metrics = {
        'epe': epe.mean().item(),
        '1px': (epe < 1).float().mean().item(),
        '3px': (epe < 3).float().mean().item(),
        '5px': (epe < 5).float().mean().item(),
    }
    return disp_loss, metrics

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


class Logger:
    SUM_FREQ = 100
    def __init__(self, model, scheduler, logdir):
        self.model = model
        self.scheduler = scheduler
        self.total_steps = 0
        self.running_loss = {}
        self.logdir = logdir
        self.writer = SummaryWriter(log_dir=self.logdir)

    def _print_training_status(self):
        metrics_data = [self.running_loss[k]/Logger.SUM_FREQ for k in sorted(self.running_loss.keys())]
        training_str = "[{:6d}, {:10.7f}] ".format(self.total_steps+1, self.scheduler.get_last_lr()[0])
        metrics_str = ("{:10.4f}, "*len(metrics_data)).format(*metrics_data)
        
        # print the training status
        logging.info(f"Training Metrics ({self.total_steps}): {training_str + metrics_str}")

        if self.writer is None:
            self.writer = SummaryWriter(log_dir=self.logdir)

        for k in self.running_loss:
            self.writer.add_scalar(k, self.running_loss[k]/Logger.SUM_FREQ, self.total_steps)
            self.running_loss[k] = 0.0

    def push(self, metrics):
        self.total_steps += 1

        for key in metrics:
            if key not in self.running_loss:
                self.running_loss[key] = 0.0

            self.running_loss[key] += metrics[key]

        if self.total_steps % Logger.SUM_FREQ == Logger.SUM_FREQ-1:
            self._print_training_status()
            self.running_loss = {}

    def write_dict(self, results):
        if self.writer is None:
            self.writer = SummaryWriter(log_dir=self.logdir)

        for key in results:
            self.writer.add_scalar(key, results[key], self.total_steps)

    def close(self):
        self.writer.close()


def train(args):

    model = nn.DataParallel(IGEVStereo(args))
    print("Parameter Count: %d" % count_parameters(model))

    train_loader = datasets.fetch_dataloader(args)
    optimizer, scheduler = fetch_optimizer(args, model)
    total_steps = 0
    logger = Logger(model, scheduler, args.logdir)

    if args.restore_ckpt is not None:
        assert args.restore_ckpt.endswith(".pth")
        logging.info("Loading checkpoint...")
        checkpoint = torch.load(args.restore_ckpt)
        model.load_state_dict(checkpoint, strict=True)
        logging.info(f"Done loading checkpoint")
    model.cuda()
    model.train()
    model.module.freeze_bn() # We keep BatchNorm frozen

    validation_frequency = 10000

    scaler = GradScaler(enabled=args.mixed_precision)

    should_keep_training = True
    global_batch_num = 0
    while should_keep_training:
        for i_batch, (_, *data_blob) in enumerate(tqdm(train_loader)):
            optimizer.zero_grad()
            image1, image2, disp_gt, valid = [x.cuda() for x in data_blob]

            assert model.training
            agg_pred, iter_preds = model(image1, image2, iters=args.train_iters)
            assert model.training

            loss, metrics = sequence_loss(agg_pred, iter_preds, disp_gt, valid)
            logger.writer.add_scalar("live_loss", loss.item(), global_batch_num)
            logger.writer.add_scalar(f'learning_rate', optimizer.param_groups[0]['lr'], global_batch_num)
            global_batch_num += 1
            scaler.scale(loss).backward()
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

            scaler.step(optimizer)
            scheduler.step()
            scaler.update()
            logger.push(metrics)

            if total_steps % validation_frequency == validation_frequency - 1:
                save_path = Path(args.logdir + '/%d_%s.pth' % (total_steps + 1, args.name))
                logging.info(f"Saving file {save_path.absolute()}")
                torch.save(model.state_dict(), save_path)
                if 'sceneflow' in args.train_datasets:
                    results = validate_sceneflow(model.module, iters=args.valid_iters)
                elif 'kitti' in args.train_datasets:
                    results = validate_kitti(model.module, iters=args.valid_iters)
                elif 'middlebury' in args.train_datasets:
                    results = validate_middlebury(model.module, iters=args.valid_iters)
                elif 'eth3d' in args.train_datasets:
                    results = validate_eth3d(model.module, iters=args.valid_iters)
                else:
                    print(f"Val dataset is not supported.")
                # logger.write_dict(results)
                model.train()
                model.module.freeze_bn()

            total_steps += 1

            if total_steps > args.num_steps:
                should_keep_training = False
                break

    print("FINISHED TRAINING")
    logger.close()
    PATH = args.logdir + '/%s.pth' % args.name
    torch.save(model.state_dict(), PATH)

    return PATH


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--name', default='rt-igev-stereo', help="name your experiment")
    parser.add_argument('--restore_ckpt', default=None, help='load the weights from a specific checkpoint')
    parser.add_argument('--logdir', default='./checkpoints_rt', help='the directory to save logs and checkpoints')
    parser.add_argument('--mixed_precision', default=True, action='store_true', help='use mixed precision')

    # Training parameters
    parser.add_argument('--batch_size', type=int, default=8, help="batch size used during training.")
    parser.add_argument('--train_datasets', default='sceneflow', choices=['sceneflow', 'kitti'], help="training datasets.")
    parser.add_argument('--lr', type=float, default=0.0002, help="max learning rate.")
    parser.add_argument('--num_steps', type=int, default=200000, help="length of training schedule.")
    parser.add_argument('--image_size', type=int, nargs='+', default=[320, 768], help="size of the random image crops used during training.")
    parser.add_argument('--train_iters', type=int, default=22, help="number of updates to the disparity field in each forward pass.")
    parser.add_argument('--wdecay', type=float, default=.00001, help="Weight decay in optimizer.")

    # Validation parameters
    parser.add_argument('--valid_iters', type=int, default=32, help='number of flow-field updates during validation forward pass')

    # Architecure choices
    parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid")
    parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid")
    parser.add_argument('--n_downsample', type=int, default=2, help="resolution of the disparity field (1/2^K)")
    parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently")
    parser.add_argument('--n_gru_layers', type=int, default=1, help="number of hidden GRU levels")
    parser.add_argument('--hidden_dim', nargs='+', type=int, default=96, help="hidden state and context dimensions")
    parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume")

    # Data augmentation
    parser.add_argument('--img_gamma', type=float, nargs='+', default=None, help="gamma range")
    parser.add_argument('--saturation_range', type=float, nargs='+', default=[0, 1.4], 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=[-0.4, 0.8], help='re-scale the images randomly')
    parser.add_argument('--noyjitter', action='store_true', help='don\'t simulate imperfect rectification')
    args = parser.parse_args()

    torch.manual_seed(666)
    np.random.seed(666)
    
    logging.basicConfig(level=logging.INFO,
                        format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s')

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

    train(args)