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# 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.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# MAE: https://github.com/facebookresearch/mae
# --------------------------------------------------------
import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import timm.optim.optim_factory as optim_factory
import util.misc as misc
import mcc_model
from util.misc import NativeScalerWithGradNormCount as NativeScaler
from util.hypersim_dataset import HyperSimDataset, hypersim_collate_fn
from util.co3d_dataset import CO3DV2Dataset, co3dv2_collate_fn
from engine_mcc import train_one_epoch, run_viz, eval_one_epoch
from util.co3d_utils import get_all_dataset_maps
def get_args_parser():
parser = argparse.ArgumentParser('MCC', add_help=False)
# Model
parser.add_argument('--input_size', default=224, type=int,
help='Images input size')
parser.add_argument('--occupancy_weight', default=1.0, type=float,
help='A constant to weight the occupancy loss')
parser.add_argument('--rgb_weight', default=0.01, type=float,
help='A constant to weight the color prediction loss')
parser.add_argument('--n_queries', default=550, type=int,
help='Number of queries used in decoder.')
parser.add_argument('--drop_path', default=0.1, type=float,
help='drop_path probability')
parser.add_argument('--regress_color', action='store_true',
help='If true, regress color with MSE. Otherwise, 256-way classification for each channel.')
# Training
parser.add_argument('--batch_size', default=16, type=int,
help='Batch size per GPU for training (effective batch size is batch_size * accum_iter * # gpus')
parser.add_argument('--eval_batch_size', default=2, type=int,
help='Batch size per GPU for evaluation (effective batch size is batch_size * accum_iter * # gpus')
parser.add_argument('--epochs', default=100, type=int)
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('--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=1e-4, metavar='LR',
help='Base learning rate: absolute_lr = base_lr * total_batch_size / 512')
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=5, metavar='N',
help='Epochs to warmup LR')
parser.add_argument('--clip_grad', type=float, default=1.0,
help='Clip gradient at the specified norm')
# Job
parser.add_argument('--job_dir', default='',
help='Path to where to save, empty for no saving')
parser.add_argument('--output_dir', default='./output_dir',
help='Path to where to save, empty for no saving')
parser.add_argument('--device', default='cuda',
help='Device to use for training / testing')
parser.add_argument('--seed', default=0, type=int,
help='Random seed.')
parser.add_argument('--resume', default='weights/co3dv2_all_categories.pth',
help='Resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='Start epoch')
parser.add_argument('--num_workers', default=4, type=int,
help='Number of workers for training data loader')
parser.add_argument('--num_eval_workers', default=4, type=int,
help='Number of workers for evaluation data loader')
parser.add_argument('--pin_mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
parser.set_defaults(pin_mem=True)
# Distributed training
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_on_itp', action='store_true')
parser.add_argument('--dist_url', default='env://',
help='Url used to set up distributed training')
# Experiments
parser.add_argument('--debug', action='store_true')
parser.add_argument('--run_viz', action='store_true',
help='Specify to run only the visualization/inference given a trained model.')
parser.add_argument('--max_n_viz_obj', default=64, type=int,
help='Max number of objects to visualize during training.')
# Data
parser.add_argument('--train_epoch_len_multiplier', default=32, type=int,
help='# examples per training epoch is # objects * train_epoch_len_multiplier')
parser.add_argument('--eval_epoch_len_multiplier', default=1, type=int,
help='# examples per eval epoch is # objects * eval_epoch_len_multiplier')
# CO3D
parser.add_argument('--co3d_path', type=str, default='co3d_data',
help='Path to CO3D v2 data.')
parser.add_argument('--holdout_categories', action='store_true',
help='If true, hold out 10 categories and train on only the remaining 41 categories.')
parser.add_argument('--co3d_world_size', default=3.0, type=float,
help='The world space we consider is \in [-co3d_world_size, co3d_world_size] in each dimension.')
# Hypersim
parser.add_argument('--use_hypersim', action='store_true',
help='If true, use hypersim, else, co3d.')
parser.add_argument('--hypersim_path', default="hypersim_data", type=str,
help="Path to Hypersim data.")
# Data aug
parser.add_argument('--random_scale_delta', default=0.2, type=float,
help='Random scaling each example by a scaler \in [1 - random_scale_delta, 1 + random_scale_delta].')
parser.add_argument('--random_shift', default=1.0, type=float,
help='Random shifting an example in each axis by an amount \in [-random_shift, random_shift]')
parser.add_argument('--random_rotate_degree', default=180, type=int,
help='Random rotation degrees.')
# Smapling, evaluation, and coordinate system
parser.add_argument('--shrink_threshold', default=10.0, type=float,
help='Any points with distance beyond this value will be shrunk.')
parser.add_argument('--semisphere_size', default=6.0, type=float,
help='The Hypersim task predicts points in a semisphere in front of the camera.'
'This value specifies the size of the semisphere.')
parser.add_argument('--eval_granularity', default=0.1, type=float,
help='Granularity of the evaluation points.')
parser.add_argument('--viz_granularity', default=0.1, type=float,
help='Granularity of points in visaulizatoin.')
parser.add_argument('--eval_score_threshold', default=0.1, type=float,
help='Score threshold for evaluation.')
parser.add_argument('--eval_dist_threshold', default=0.1, type=float,
help='Points closer than this amount to a groud-truth is considered correct.')
parser.add_argument('--train_dist_threshold', default=0.1, type=float,
help='Points closer than this amount is considered positive in training.')
return parser
def build_loader(args, num_tasks, global_rank, is_train, dataset_type, collate_fn, dataset_maps):
'''Build data loader'''
dataset = dataset_type(args, is_train=is_train, dataset_maps=dataset_maps)
sampler_train = torch.utils.data.DistributedSampler(
dataset, num_replicas=num_tasks, rank=global_rank, shuffle=is_train
)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=args.batch_size if is_train else args.eval_batch_size,
sampler=sampler_train,
num_workers=args.num_workers if is_train else args.num_eval_workers,
pin_memory=args.pin_mem,
collate_fn=collate_fn,
)
return data_loader
def main(args):
misc.init_distributed_mode(args)
print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
print("{}".format(args).replace(', ', ',\n'))
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + misc.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
num_tasks = misc.get_world_size()
global_rank = misc.get_rank()
# define the model
model = mcc_model.get_mcc_model(
rgb_weight=args.rgb_weight,
occupancy_weight=args.occupancy_weight,
args=args,
)
model.to(device)
model_without_ddp = model
print("Model = %s" % str(model_without_ddp))
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 / 512
print("base lr: %.2e" % (args.blr))
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)
model_without_ddp = model.module
# following timm: set wd as 0 for bias and norm layers
param_groups = optim_factory.add_weight_decay(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()
misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
if args.use_hypersim:
dataset_type = HyperSimDataset
collate_fn = hypersim_collate_fn
dataset_maps = None
else:
dataset_type = CO3DV2Dataset
collate_fn = co3dv2_collate_fn
dataset_maps = get_all_dataset_maps(
args.co3d_path, args.holdout_categories,
)
dataset_viz = dataset_type(args, is_train=False, is_viz=True, dataset_maps=dataset_maps)
sampler_viz = torch.utils.data.DistributedSampler(
dataset_viz, num_replicas=num_tasks, rank=global_rank, shuffle=False
)
data_loader_viz = torch.utils.data.DataLoader(
dataset_viz, batch_size=1,
sampler=sampler_viz,
num_workers=args.num_eval_workers,
pin_memory=args.pin_mem,
collate_fn=collate_fn,
)
if args.run_viz:
run_viz(
model, data_loader_viz,
device, args=args, epoch=0,
)
exit()
data_loader_train, data_loader_val = [
build_loader(
args, num_tasks, global_rank,
is_train=is_train,
dataset_type=dataset_type, collate_fn=collate_fn, dataset_maps=dataset_maps
) for is_train in [True, False]
]
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
print(f'Epoch {epoch}:')
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
train_stats = train_one_epoch(
model, data_loader_train,
optimizer, device, epoch, loss_scaler,
args=args,
)
val_stats = {}
if (epoch % 5 == 4 or epoch + 1 == args.epochs) or args.debug:
val_stats = eval_one_epoch(
model, data_loader_val,
device, args=args,
)
if ((epoch % 10 == 9 or epoch + 1 == args.epochs) or args.debug):
run_viz(
model, data_loader_viz,
device, args=args, epoch=epoch,
)
if args.output_dir and (epoch % 10 == 9 or epoch + 1 == args.epochs):
misc.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'val_{k}': v for k, v in val_stats.items()},
'epoch': epoch,}
if args.output_dir and misc.is_main_process():
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
run_viz(
model, data_loader_viz,
device, args=args, epoch=-1,
)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if __name__ == '__main__':
args = get_args_parser()
args = args.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)
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