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train_net_codet.py
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train_net_codet.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Mingxuan Liu from https://github.com/CVMI-Lab/CoDet/blob/main/train_net.py
import logging
import os
import sys
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import time
import datetime
from fvcore.common.timer import Timer
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
from detectron2.config import get_cfg
from detectron2.data import (
MetadataCatalog,
build_detection_test_loader,
)
from detectron2.engine import default_argument_parser, default_setup, launch
from detectron2.evaluation import (
inference_on_dataset,
print_csv_format,
LVISEvaluator,
COCOEvaluator,
)
from detectron2.modeling import build_model
from detectron2.solver import build_lr_scheduler, build_optimizer
from detectron2.utils.events import (
CommonMetricPrinter,
EventStorage,
JSONWriter,
TensorboardXWriter,
)
from detectron2.data.dataset_mapper import DatasetMapper
from detectron2.data.build import build_detection_train_loader
from detectron2.utils.logger import setup_logger
from torch.cuda.amp import GradScaler
sys.path.insert(0, 'third_party/CenterNet2/')
from centernet.config import add_centernet_config
sys.path.insert(0, 'third_party/Deformable-DETR')
from codet.config import add_codet_config
from codet.data.custom_build_augmentation import build_custom_augmentation
from codet.data.custom_dataset_dataloader import build_custom_train_loader
from codet.data.custom_dataset_mapper import CustomDatasetMapper, DetrDatasetMapper
from codet.custom_solver import build_custom_optimizer
from codet.evaluation.oideval import OIDEvaluator
from codet.evaluation.custom_coco_eval import CustomCOCOEvaluator
from codet.modeling.utils import reset_cls_test
from codet.evaluation.inateval import INATEvaluator
from codet.evaluation.fsodeval import FSODEvaluator
logger = logging.getLogger("detectron2")
def do_test(cfg, model):
results = OrderedDict()
for d, dataset_name in enumerate(cfg.DATASETS.TEST):
if cfg.MODEL.RESET_CLS_TESTS:
reset_cls_test(
model,
cfg.MODEL.TEST_CLASSIFIERS[d],
cfg.MODEL.TEST_NUM_CLASSES[d])
mapper = None if cfg.INPUT.TEST_INPUT_TYPE == 'default' \
else DatasetMapper(
cfg, False, augmentations=build_custom_augmentation(cfg, False))
data_loader = build_detection_test_loader(cfg, dataset_name, mapper=mapper)
output_folder = os.path.join(
cfg.OUTPUT_DIR, "inference_{}_{}".format(dataset_name, cfg.MODEL.TEST_CLASSIFIERS[
d])) # Miu: added classifier name into output name
# cfg.OUTPUT_DIR, "inference_{}".format(dataset_name))
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
if evaluator_type == "lvis" or cfg.GEN_PSEDO_LABELS:
evaluator = LVISEvaluator(dataset_name, cfg, True, output_folder)
elif evaluator_type == 'coco':
if dataset_name == 'coco_generalized_zeroshot_val':
# Additionally plot mAP for 'seen classes' and 'unseen classes'
evaluator = CustomCOCOEvaluator(dataset_name, cfg, True, output_folder)
else:
evaluator = COCOEvaluator(dataset_name, cfg, True, output_folder)
elif evaluator_type == 'oid':
evaluator = OIDEvaluator(dataset_name, cfg, True, output_folder)
elif evaluator_type == 'inat':
evaluator = INATEvaluator(dataset_name, cfg, True, output_folder)
elif evaluator_type == 'fsod':
evaluator = FSODEvaluator(dataset_name, cfg, True, output_folder)
else:
assert 0, evaluator_type
results[dataset_name] = inference_on_dataset(
model, data_loader, evaluator)
if comm.is_main_process():
logger.info("Evaluation results for {} in csv format:".format(
dataset_name))
print_csv_format(results[dataset_name])
if len(results) == 1:
results = list(results.values())[0]
return results
def do_train(cfg, model, resume=False):
model.train()
if cfg.SOLVER.USE_CUSTOM_SOLVER:
optimizer = build_custom_optimizer(cfg, model)
else:
assert cfg.SOLVER.OPTIMIZER == 'SGD'
assert cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE != 'full_model'
assert cfg.SOLVER.BACKBONE_MULTIPLIER == 1.
optimizer = build_optimizer(cfg, model)
scheduler = build_lr_scheduler(cfg, optimizer)
checkpointer = DetectionCheckpointer(
model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler
)
start_iter = checkpointer.resume_or_load(
cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
if not resume:
start_iter = 0
max_iter = cfg.SOLVER.MAX_ITER if cfg.SOLVER.TRAIN_ITER < 0 else cfg.SOLVER.TRAIN_ITER
periodic_checkpointer = PeriodicCheckpointer(
checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter
)
writers = (
[
CommonMetricPrinter(max_iter),
JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
TensorboardXWriter(cfg.OUTPUT_DIR),
]
if comm.is_main_process()
else []
)
use_custom_mapper = cfg.WITH_IMAGE_LABELS
MapperClass = CustomDatasetMapper if use_custom_mapper else DatasetMapper
mapper = MapperClass(cfg, True) if cfg.INPUT.CUSTOM_AUG == '' else \
DetrDatasetMapper(cfg, True) if cfg.INPUT.CUSTOM_AUG == 'DETR' else \
MapperClass(cfg, True, augmentations=build_custom_augmentation(cfg, True))
if cfg.DATALOADER.SAMPLER_TRAIN in ['TrainingSampler', 'RepeatFactorTrainingSampler']:
data_loader = build_detection_train_loader(cfg, mapper=mapper)
else:
data_loader = build_custom_train_loader(cfg, mapper=mapper)
if cfg.FP16:
scaler = GradScaler()
logger.info("Starting training from iteration {}".format(start_iter))
with EventStorage(start_iter) as storage:
step_timer = Timer()
data_timer = Timer()
start_time = time.perf_counter()
for data, iteration in zip(data_loader, range(start_iter, max_iter)):
data_time = data_timer.seconds()
storage.put_scalars(data_time=data_time)
step_timer.reset()
iteration = iteration + 1
storage.step()
loss_dict = model(data)
losses = sum(
loss for k, loss in loss_dict.items())
assert torch.isfinite(losses).all(), loss_dict
loss_dict_reduced = {k: v.item() \
for k, v in comm.reduce_dict(loss_dict).items()}
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
if comm.is_main_process():
storage.put_scalars(
total_loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
if cfg.FP16:
scaler.scale(losses).backward()
scaler.step(optimizer)
scaler.update()
else:
losses.backward()
optimizer.step()
storage.put_scalar(
"lr", optimizer.param_groups[0]["lr"], smoothing_hint=False)
step_time = step_timer.seconds()
storage.put_scalars(time=step_time)
data_timer.reset()
scheduler.step()
if (cfg.TEST.EVAL_PERIOD > 0
and iteration % cfg.TEST.EVAL_PERIOD == 0
and iteration != max_iter):
do_test(cfg, model)
comm.synchronize()
if iteration - start_iter > 5 and \
(iteration % 20 == 0 or iteration == max_iter):
for writer in writers:
writer.write()
periodic_checkpointer.step(iteration)
total_time = time.perf_counter() - start_time
logger.info(
"Total training time: {}".format(
str(datetime.timedelta(seconds=int(total_time)))))
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_centernet_config(cfg)
add_codet_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
if '/auto' in cfg.OUTPUT_DIR:
file_name = os.path.basename(args.config_file)[:-5]
cfg.OUTPUT_DIR = cfg.OUTPUT_DIR.replace('/auto', '/{}'.format(file_name))
logger.info('OUTPUT_DIR: {}'.format(cfg.OUTPUT_DIR))
cfg.freeze()
default_setup(cfg, args)
setup_logger(output=cfg.OUTPUT_DIR, \
distributed_rank=comm.get_rank(), name="codet")
return cfg
def main(args):
cfg = setup(args)
model = build_model(cfg)
logger.info("Model:\n{}".format(model))
if args.eval_only:
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume
)
return do_test(cfg, model)
distributed = comm.get_world_size() > 1
if distributed:
model = DistributedDataParallel(
model, device_ids=[comm.get_local_rank()], broadcast_buffers=False,
find_unused_parameters=cfg.FIND_UNUSED_PARAM
)
do_train(cfg, model, resume=args.resume)
return do_test(cfg, model)
if __name__ == "__main__":
args = default_argument_parser()
args = args.parse_args()
if args.num_machines == 1:
args.dist_url = 'tcp://127.0.0.1:{}'.format(
torch.randint(11111, 60000, (1,))[0].item())
else:
if args.dist_url == 'host':
args.dist_url = 'tcp://{}:12345'.format(
os.environ['SLURM_JOB_NODELIST'])
elif not args.dist_url.startswith('tcp'):
tmp = os.popen(
'echo $(scontrol show job {} | grep BatchHost)'.format(
args.dist_url)
).read()
tmp = tmp[tmp.find('=') + 1: -1]
args.dist_url = 'tcp://{}:12345'.format(tmp)
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)