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# ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
"""
Benchmark inference speed of Deformable DETR.
"""
import os
import time
import argparse
import torch
from main import get_args_parser as get_main_args_parser
from models import build_model
from datasets import build_dataset
from util.misc import nested_tensor_from_tensor_list
def get_benckmark_arg_parser():
parser = argparse.ArgumentParser('Benchmark inference speed of Deformable DETR.')
parser.add_argument('--num_iters', type=int, default=300, help='total iters to benchmark speed')
parser.add_argument('--warm_iters', type=int, default=5, help='ignore first several iters that are very slow')
parser.add_argument('--batch_size', type=int, default=1, help='batch size in inference')
parser.add_argument('--resume', type=str, help='load the pre-trained checkpoint')
return parser
@torch.no_grad()
def measure_average_inference_time(model, inputs, num_iters=100, warm_iters=5):
ts = []
for iter_ in range(num_iters):
torch.cuda.synchronize()
t_ = time.perf_counter()
model(inputs)
torch.cuda.synchronize()
t = time.perf_counter() - t_
if iter_ >= warm_iters:
ts.append(t)
print(ts)
return sum(ts) / len(ts)
def benchmark():
args, _ = get_benckmark_arg_parser().parse_known_args()
main_args = get_main_args_parser().parse_args(_)
assert args.warm_iters < args.num_iters and args.num_iters > 0 and args.warm_iters >= 0
assert args.batch_size > 0
assert args.resume is None or os.path.exists(args.resume)
dataset = build_dataset('val', main_args)
model, _, _ = build_model(main_args)
model.cuda()
model.eval()
if args.resume is not None:
ckpt = torch.load(args.resume, map_location=lambda storage, loc: storage)
model.load_state_dict(ckpt['model'])
inputs = nested_tensor_from_tensor_list([dataset.__getitem__(0)[0].cuda() for _ in range(args.batch_size)])
t = measure_average_inference_time(model, inputs, args.num_iters, args.warm_iters)
return 1.0 / t * args.batch_size
if __name__ == '__main__':
fps = benchmark()
print(f'Inference Speed: {fps:.1f} FPS')
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