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import argparse
import sys
import time
import warnings
sys.path.append('./')
warnings.filterwarnings("ignore")
import torch
import torch.backends.cudnn as cudnn
import utils.config as config
from model import build_segmenter
def get_parser():
parser = argparse.ArgumentParser(
description='Pytorch Referring Expression Segmentation')
parser.add_argument('--config',
default='path to xxx.yaml',
type=str,
help='config file')
parser.add_argument('--opts',
default=None,
nargs=argparse.REMAINDER,
help='override some settings in the config.')
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def main():
# init arguments
args = get_parser()
torch.cuda.set_device(0)
# create model
model, _ = build_segmenter(args)
model = model.cuda()
model.eval()
# set cudnn state
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
# init dummy tensor
image = torch.randn(1, 3, 416, 416).cuda()
text = torch.randint(4096, size=(1, args.word_len)).long().cuda()
# init time & memory
avg_time = 0
avg_mem = 0
# record initial gpu memory
mem = torch.cuda.max_memory_allocated()
with torch.no_grad():
for i in range(500):
start_time = time.time()
_ = model(image, text)
torch.cuda.synchronize()
if (i+1) >= 100:
avg_time += (time.time() - start_time)
avg_mem += (torch.cuda.max_memory_allocated() - mem) / 1.073742e9
params = count_parameters(model) * 1e-6
print('#########################################')
print("Average Parameters : {:.2f} M".format(params))
print("Average FPS: {:.2f}".format(400/avg_time))
print("Average GPU Memory: {:.2f} GB".format(avg_mem/400))
print('#########################################')
if __name__ == '__main__':
main()
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