chenxuanhong
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding:utf-8 -*-
|
|
#############################################################
|
|
# File: speed_test.py
|
|
# Created Date: Thursday February 10th 2022
|
|
# Author: Chen Xuanhong
|
|
# Email: chenxuanhongzju@outlook.com
|
|
# Last Modified: Tuesday, 15th February 2022 12:54:56 pm
|
|
# Modified By: Chen Xuanhong
|
|
# Copyright (c) 2022 Shanghai Jiao Tong University
|
|
#############################################################
|
|
import os
|
|
import time
|
|
|
|
import torch
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
script = "Generator_modulation_depthwise_config"
|
|
class_name = "Generator"
|
|
arcface_ckpt= "arcface_ckpt/arcface_checkpoint.tar"
|
|
model_config={
|
|
"id_dim": 512,
|
|
"g_kernel_size": 3,
|
|
"in_channel":16,
|
|
"res_num": 9
|
|
}
|
|
|
|
os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
|
|
print("GPU used : ", os.environ['CUDA_VISIBLE_DEVICES'])
|
|
|
|
gscript_name = "components." + script
|
|
|
|
|
|
package = __import__(gscript_name, fromlist=True)
|
|
gen_class= getattr(package, class_name)
|
|
gen = gen_class(**model_config)
|
|
model = gen.cuda().eval().requires_grad_(False)
|
|
arcface1 = torch.load(arcface_ckpt, map_location=torch.device("cpu"))
|
|
arcface = arcface1['model'].module
|
|
arcface = arcface.cuda()
|
|
arcface.eval().requires_grad_(False)
|
|
|
|
|
|
id_img = torch.rand((4,3,112,112)).cuda()
|
|
id_latent = torch.rand((4,512)).cuda()
|
|
# cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]
|
|
|
|
attr = torch.rand((4,3,512,512)).cuda()
|
|
|
|
import datetime
|
|
start_time = time.time()
|
|
for i in range(100):
|
|
with torch.no_grad():
|
|
|
|
id_latent = arcface(id_img)
|
|
|
|
results = model(attr, id_latent)
|
|
elapsed = time.time() - start_time
|
|
elapsed = str(datetime.timedelta(seconds=elapsed))
|
|
information="Elapsed [{}]".format(elapsed)
|
|
print(information) |