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#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
A script to benchmark builtin models.
Note: this script has an extra dependency of psutil.
"""
import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import DistributedDataParallel
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import LazyConfig, get_cfg, instantiate
from detectron2.data import (
DatasetFromList,
build_detection_test_loader,
build_detection_train_loader,
)
from detectron2.data.benchmark import DataLoaderBenchmark
from detectron2.engine import AMPTrainer, SimpleTrainer, default_argument_parser, hooks, launch
from detectron2.modeling import build_model
from detectron2.solver import build_optimizer
from detectron2.utils import comm
from detectron2.utils.collect_env import collect_env_info
from detectron2.utils.events import CommonMetricPrinter
from detectron2.utils.logger import setup_logger
logger = logging.getLogger("detectron2")
def setup(args):
if args.config_file.endswith(".yaml"):
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.SOLVER.BASE_LR = 0.001 # Avoid NaNs. Not useful in this script anyway.
cfg.merge_from_list(args.opts)
cfg.freeze()
else:
cfg = LazyConfig.load(args.config_file)
cfg = LazyConfig.apply_overrides(cfg, args.opts)
setup_logger(distributed_rank=comm.get_rank())
return cfg
def create_data_benchmark(cfg, args):
if args.config_file.endswith(".py"):
dl_cfg = cfg.dataloader.train
dl_cfg._target_ = DataLoaderBenchmark
return instantiate(dl_cfg)
else:
kwargs = build_detection_train_loader.from_config(cfg)
kwargs.pop("aspect_ratio_grouping", None)
kwargs["_target_"] = DataLoaderBenchmark
return instantiate(kwargs)
def RAM_msg():
vram = psutil.virtual_memory()
return "RAM Usage: {:.2f}/{:.2f} GB".format(
(vram.total - vram.available) / 1024 ** 3, vram.total / 1024 ** 3
)
def benchmark_data(args):
cfg = setup(args)
logger.info("After spawning " + RAM_msg())
benchmark = create_data_benchmark(cfg, args)
benchmark.benchmark_distributed(250, 10)
# test for a few more rounds
for k in range(10):
logger.info(f"Iteration {k} " + RAM_msg())
benchmark.benchmark_distributed(250, 1)
def benchmark_data_advanced(args):
# benchmark dataloader with more details to help analyze performance bottleneck
cfg = setup(args)
benchmark = create_data_benchmark(cfg, args)
if comm.get_rank() == 0:
benchmark.benchmark_dataset(100)
benchmark.benchmark_mapper(100)
benchmark.benchmark_workers(100, warmup=10)
benchmark.benchmark_IPC(100, warmup=10)
if comm.get_world_size() > 1:
benchmark.benchmark_distributed(100)
logger.info("Rerun ...")
benchmark.benchmark_distributed(100)
def benchmark_train(args):
cfg = setup(args)
model = build_model(cfg)
logger.info("Model:\n{}".format(model))
if comm.get_world_size() > 1:
model = DistributedDataParallel(
model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
)
optimizer = build_optimizer(cfg, model)
checkpointer = DetectionCheckpointer(model, optimizer=optimizer)
checkpointer.load(cfg.MODEL.WEIGHTS)
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 2
data_loader = build_detection_train_loader(cfg)
dummy_data = list(itertools.islice(data_loader, 100))
def f():
data = DatasetFromList(dummy_data, copy=False, serialize=False)
while True:
yield from data
max_iter = 400
trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(model, f(), optimizer)
trainer.register_hooks(
[
hooks.IterationTimer(),
hooks.PeriodicWriter([CommonMetricPrinter(max_iter)]),
hooks.TorchProfiler(
lambda trainer: trainer.iter == max_iter - 1, cfg.OUTPUT_DIR, save_tensorboard=True
),
]
)
trainer.train(1, max_iter)
@torch.no_grad()
def benchmark_eval(args):
cfg = setup(args)
if args.config_file.endswith(".yaml"):
model = build_model(cfg)
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0
data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
else:
model = instantiate(cfg.model)
model.to(cfg.train.device)
DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
cfg.dataloader.num_workers = 0
data_loader = instantiate(cfg.dataloader.test)
model.eval()
logger.info("Model:\n{}".format(model))
dummy_data = DatasetFromList(list(itertools.islice(data_loader, 100)), copy=False)
def f():
while True:
yield from dummy_data
for k in range(5): # warmup
model(dummy_data[k])
max_iter = 300
timer = Timer()
with tqdm.tqdm(total=max_iter) as pbar:
for idx, d in enumerate(f()):
if idx == max_iter:
break
model(d)
pbar.update()
logger.info("{} iters in {} seconds.".format(max_iter, timer.seconds()))
if __name__ == "__main__":
parser = default_argument_parser()
parser.add_argument("--task", choices=["train", "eval", "data", "data_advanced"], required=True)
args = parser.parse_args()
assert not args.eval_only
logger.info("Environment info:\n" + collect_env_info())
if "data" in args.task:
print("Initial " + RAM_msg())
if args.task == "data":
f = benchmark_data
if args.task == "data_advanced":
f = benchmark_data_advanced
elif args.task == "train":
"""
Note: training speed may not be representative.
The training cost of a R-CNN model varies with the content of the data
and the quality of the model.
"""
f = benchmark_train
elif args.task == "eval":
f = benchmark_eval
# only benchmark single-GPU inference.
assert args.num_gpus == 1 and args.num_machines == 1
launch(f, args.num_gpus, args.num_machines, args.machine_rank, args.dist_url, args=(args,))
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