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on
Zero
Running
on
Zero
File size: 2,831 Bytes
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from __future__ import print_function
import numpy as np
import torch
import cv2
import yaml
import os
from torch.autograd import Variable
from models.networks import get_generator
import torchvision
import time
import argparse
def get_args():
parser = argparse.ArgumentParser('Test an image')
parser.add_argument('--job_name', default='xyscannet',
type=str, help='current job s name')
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
with open(os.path.join('config/', args.job_name, 'config_stage2.yaml')) as cfg: # change the yaml file to config_pretrained if ablation
#with open(os.path.join('config/', args.job_name, 'config_stage2.yaml')) as cfg: # change the yaml file to config_pretrained if ablation
config = yaml.safe_load(cfg)
blur_path = '/scratch/user/hanzhou1996/datasets/deblur/HIDE/test/testA/'
out_path = os.path.join('results', args.job_name, 'images_hide')
weights_path = os.path.join('results', args.job_name, 'models', 'best_XYScanNet_stage2.pth') # change the model name to test different phases: final/best
if not os.path.isdir(out_path):
os.mkdir(out_path)
model = get_generator(config['model'])
model.load_state_dict(torch.load(weights_path))
model = model.cuda()
test_time = 0
iteration = 0
total_image_number = 2025
# warm up
warm_up = 0
print('Hardware warm-up')
for img_name in os.listdir(blur_path):
warm_up += 1
img = cv2.imread(blur_path + '/' + img_name)
img_tensor = torch.from_numpy(np.transpose(img / 255, (2, 0, 1)).astype('float32')) - 0.5
with torch.no_grad():
img_tensor = Variable(img_tensor.unsqueeze(0)).cuda()
result_image, decomp1, decomp2 = model(img_tensor)
#result_image = model(img_tensor)
if warm_up == 20:
break
break
for img_name in os.listdir(blur_path):
img = cv2.imread(blur_path + '/' + img_name)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_tensor = torch.from_numpy(np.transpose(img / 255, (2, 0, 1)).astype('float32')) - 0.5
with torch.no_grad():
iteration += 1
img_tensor = Variable(img_tensor.unsqueeze(0)).cuda()
start = time.time()
result_image, decomp1, decomp2 = model(img_tensor)
#result_image = model(img_tensor)
stop = time.time()
print('Image:{}/{}, CNN Runtime:{:.4f}'.format(iteration, total_image_number, (stop - start)))
test_time += stop - start
print('Average Runtime:{:.4f}'.format(test_time / float(iteration)))
result_image = result_image + 0.5
out_file_name = out_path + '/' + img_name
torchvision.utils.save_image(result_image, out_file_name) |