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#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# training code for DUSt3R
# --------------------------------------------------------
# References:
# MAE: https://github.com/facebookresearch/mae
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import argparse
import datetime
import json
import numpy as np
import os
import sys
import time
import math
from collections import defaultdict
from pathlib import Path
from typing import Sized
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >= 1.12
from dust3r.model import AsymmetricCroCo3DStereo, inf # noqa: F401, needed when loading the model
from dust3r.datasets import get_data_loader # noqa
from dust3r.losses import * # noqa: F401, needed when loading the model
from dust3r.inference import loss_of_one_batch # noqa
import dust3r.utils.path_to_croco # noqa: F401
import croco.utils.misc as misc # noqa
from croco.utils.misc import NativeScalerWithGradNormCount as NativeScaler # noqa
def get_args_parser():
parser = argparse.ArgumentParser('DUST3R training', add_help=False)
# model and criterion
parser.add_argument('--model', default="AsymmetricCroCo3DStereo(patch_embed_cls='ManyAR_PatchEmbed')",
type=str, help="string containing the model to build")
parser.add_argument('--pretrained', default=None, help='path of a starting checkpoint')
parser.add_argument('--train_criterion', default="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)",
type=str, help="train criterion")
parser.add_argument('--test_criterion', default=None, type=str, help="test criterion")
# dataset
parser.add_argument('--train_dataset', required=True, type=str, help="training set")
parser.add_argument('--test_dataset', default='[None]', type=str, help="testing set")
# training
parser.add_argument('--seed', default=0, type=int, help="Random seed")
parser.add_argument('--batch_size', default=64, type=int,
help="Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus")
parser.add_argument('--accum_iter', default=1, type=int,
help="Accumulate gradient iterations (for increasing the effective batch size under memory constraints)")
parser.add_argument('--epochs', default=800, type=int, help="Maximum number of epochs for the scheduler")
parser.add_argument('--weight_decay', type=float, default=0.05, help="weight decay (default: 0.05)")
parser.add_argument('--lr', type=float, default=None, metavar='LR', help='learning rate (absolute lr)')
parser.add_argument('--blr', type=float, default=1.5e-4, metavar='LR',
help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
parser.add_argument('--min_lr', type=float, default=0., metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0')
parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N', help='epochs to warmup LR')
parser.add_argument('--amp', type=int, default=0,
choices=[0, 1], help="Use Automatic Mixed Precision for pretraining")
# others
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
parser.add_argument('--eval_freq', type=int, default=1, help='Test loss evaluation frequency')
parser.add_argument('--save_freq', default=1, type=int,
help='frequence (number of epochs) to save checkpoint in checkpoint-last.pth')
parser.add_argument('--keep_freq', default=20, type=int,
help='frequence (number of epochs) to save checkpoint in checkpoint-%d.pth')
parser.add_argument('--print_freq', default=20, type=int,
help='frequence (number of iterations) to print infos while training')
# output dir
parser.add_argument('--output_dir', default='./output/', type=str, help="path where to save the output")
return parser
def main(args):
misc.init_distributed_mode(args)
global_rank = misc.get_rank()
world_size = misc.get_world_size()
print("output_dir: "+args.output_dir)
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
# auto resume
last_ckpt_fname = os.path.join(args.output_dir, f'checkpoint-last.pth')
args.resume = last_ckpt_fname if os.path.isfile(last_ckpt_fname) else None
print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
print("{}".format(args).replace(', ', ',\n'))
device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
# fix the seed
seed = args.seed + misc.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
# training dataset and loader
print('Building train dataset {:s}'.format(args.train_dataset))
# dataset and loader
data_loader_train = build_dataset(args.train_dataset, args.batch_size, args.num_workers, test=False)
print('Building test dataset {:s}'.format(args.train_dataset))
data_loader_test = {dataset.split('(')[0]: build_dataset(dataset, args.batch_size, args.num_workers, test=True)
for dataset in args.test_dataset.split('+')}
# model
print('Loading model: {:s}'.format(args.model))
model = eval(args.model)
print(f'>> Creating train criterion = {args.train_criterion}')
train_criterion = eval(args.train_criterion).to(device)
print(f'>> Creating test criterion = {args.test_criterion or args.train_criterion}')
test_criterion = eval(args.test_criterion or args.criterion).to(device)
model.to(device)
model_without_ddp = model
print("Model = %s" % str(model_without_ddp))
if args.pretrained and not args.resume:
print('Loading pretrained: ', args.pretrained)
ckpt = torch.load(args.pretrained, map_location=device)
print(model.load_state_dict(ckpt['model'], strict=False))
del ckpt # in case it occupies memory
eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()
if args.lr is None: # only base_lr is specified
args.lr = args.blr * eff_batch_size / 256
print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
print("actual lr: %.2e" % args.lr)
print("accumulate grad iterations: %d" % args.accum_iter)
print("effective batch size: %d" % eff_batch_size)
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True, static_graph=True)
model_without_ddp = model.module
# following timm: set wd as 0 for bias and norm layers
param_groups = misc.get_parameter_groups(model_without_ddp, args.weight_decay)
optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))
print(optimizer)
loss_scaler = NativeScaler()
def write_log_stats(epoch, train_stats, test_stats):
if misc.is_main_process():
if log_writer is not None:
log_writer.flush()
log_stats = dict(epoch=epoch, **{f'train_{k}': v for k, v in train_stats.items()})
for test_name in data_loader_test:
if test_name not in test_stats:
continue
log_stats.update({test_name+'_'+k: v for k, v in test_stats[test_name].items()})
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
def save_model(epoch, fname, best_so_far):
misc.save_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch, fname=fname, best_so_far=best_so_far)
best_so_far = misc.load_model(args=args, model_without_ddp=model_without_ddp,
optimizer=optimizer, loss_scaler=loss_scaler)
if best_so_far is None:
best_so_far = float('inf')
if global_rank == 0 and args.output_dir is not None:
log_writer = SummaryWriter(log_dir=args.output_dir)
else:
log_writer = None
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
train_stats = test_stats = {}
for epoch in range(args.start_epoch, args.epochs+1):
# Save immediately the last checkpoint
if epoch > args.start_epoch:
if args.save_freq and epoch % args.save_freq == 0 or epoch == args.epochs:
save_model(epoch-1, 'last', best_so_far)
# Test on multiple datasets
new_best = False
if (epoch > 0 and args.eval_freq > 0 and epoch % args.eval_freq == 0):
test_stats = {}
for test_name, testset in data_loader_test.items():
stats = test_one_epoch(model, test_criterion, testset,
device, epoch, log_writer=log_writer, args=args, prefix=test_name)
test_stats[test_name] = stats
# Save best of all
if stats['loss_med'] < best_so_far:
best_so_far = stats['loss_med']
new_best = True
# Save more stuff
write_log_stats(epoch, train_stats, test_stats)
if epoch > args.start_epoch:
if args.keep_freq and epoch % args.keep_freq == 0:
save_model(epoch-1, str(epoch), best_so_far)
if new_best:
save_model(epoch-1, 'best', best_so_far)
if epoch >= args.epochs:
break # exit after writing last test to disk
# Train
train_stats = train_one_epoch(
model, train_criterion, data_loader_train,
optimizer, device, epoch, loss_scaler,
log_writer=log_writer,
args=args)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
save_final_model(args, args.epochs, model_without_ddp, best_so_far=best_so_far)
def save_final_model(args, epoch, model_without_ddp, best_so_far=None):
output_dir = Path(args.output_dir)
checkpoint_path = output_dir / 'checkpoint-final.pth'
to_save = {
'args': args,
'model': model_without_ddp if isinstance(model_without_ddp, dict) else model_without_ddp.cpu().state_dict(),
'epoch': epoch
}
if best_so_far is not None:
to_save['best_so_far'] = best_so_far
print(f'>> Saving model to {checkpoint_path} ...')
misc.save_on_master(to_save, checkpoint_path)
def build_dataset(dataset, batch_size, num_workers, test=False):
split = ['Train', 'Test'][test]
print(f'Building {split} Data loader for dataset: ', dataset)
loader = get_data_loader(dataset,
batch_size=batch_size,
num_workers=num_workers,
pin_mem=True,
shuffle=not (test),
drop_last=not (test))
print(f"{split} dataset length: ", len(loader))
return loader
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Sized, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler,
args,
log_writer=None):
assert torch.backends.cuda.matmul.allow_tf32 == True
model.train(True)
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
accum_iter = args.accum_iter
if log_writer is not None:
print('log_dir: {}'.format(log_writer.log_dir))
if hasattr(data_loader, 'dataset') and hasattr(data_loader.dataset, 'set_epoch'):
data_loader.dataset.set_epoch(epoch)
if hasattr(data_loader, 'sampler') and hasattr(data_loader.sampler, 'set_epoch'):
data_loader.sampler.set_epoch(epoch)
optimizer.zero_grad()
for data_iter_step, batch in enumerate(metric_logger.log_every(data_loader, args.print_freq, header)):
epoch_f = epoch + data_iter_step / len(data_loader)
# we use a per iteration (instead of per epoch) lr scheduler
if data_iter_step % accum_iter == 0:
misc.adjust_learning_rate(optimizer, epoch_f, args)
loss_tuple = loss_of_one_batch(batch, model, criterion, device,
symmetrize_batch=True,
use_amp=bool(args.amp), ret='loss')
loss, loss_details = loss_tuple # criterion returns two values
loss_value = float(loss)
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value), force=True)
sys.exit(1)
loss /= accum_iter
loss_scaler(loss, optimizer, parameters=model.parameters(),
update_grad=(data_iter_step + 1) % accum_iter == 0)
if (data_iter_step + 1) % accum_iter == 0:
optimizer.zero_grad()
del loss
del batch
lr = optimizer.param_groups[0]["lr"]
metric_logger.update(epoch=epoch_f)
metric_logger.update(lr=lr)
metric_logger.update(loss=loss_value, **loss_details)
if (data_iter_step + 1) % accum_iter == 0 and ((data_iter_step + 1) % (accum_iter * args.print_freq)) == 0:
loss_value_reduce = misc.all_reduce_mean(loss_value) # MUST BE EXECUTED BY ALL NODES
if log_writer is None:
continue
""" We use epoch_1000x as the x-axis in tensorboard.
This calibrates different curves when batch size changes.
"""
epoch_1000x = int(epoch_f * 1000)
log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x)
log_writer.add_scalar('train_lr', lr, epoch_1000x)
log_writer.add_scalar('train_iter', epoch_1000x, epoch_1000x)
for name, val in loss_details.items():
log_writer.add_scalar('train_'+name, val, epoch_1000x)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def test_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Sized, device: torch.device, epoch: int,
args, log_writer=None, prefix='test'):
model.eval()
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.meters = defaultdict(lambda: misc.SmoothedValue(window_size=9**9))
header = 'Test Epoch: [{}]'.format(epoch)
if log_writer is not None:
print('log_dir: {}'.format(log_writer.log_dir))
if hasattr(data_loader, 'dataset') and hasattr(data_loader.dataset, 'set_epoch'):
data_loader.dataset.set_epoch(epoch)
if hasattr(data_loader, 'sampler') and hasattr(data_loader.sampler, 'set_epoch'):
data_loader.sampler.set_epoch(epoch)
for _, batch in enumerate(metric_logger.log_every(data_loader, args.print_freq, header)):
loss_tuple = loss_of_one_batch(batch, model, criterion, device,
symmetrize_batch=True,
use_amp=bool(args.amp), ret='loss')
loss_value, loss_details = loss_tuple # criterion returns two values
metric_logger.update(loss=float(loss_value), **loss_details)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
aggs = [('avg', 'global_avg'), ('med', 'median')]
results = {f'{k}_{tag}': getattr(meter, attr) for k, meter in metric_logger.meters.items() for tag, attr in aggs}
if log_writer is not None:
for name, val in results.items():
log_writer.add_scalar(prefix+'_'+name, val, 1000*epoch)
return results
if __name__ == '__main__':
args = get_args_parser()
args = args.parse_args()
main(args)