Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,802 Bytes
b56342d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
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 torch.nn.functional as F
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_pretrained.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/RWBI/test/testA/'
out_path = os.path.join('results', args.job_name, 'images_rwbi')
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 = 1000
# 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()
factor = 8
h, w = img_tensor.shape[2], img_tensor.shape[3]
H, W = ((h + factor) // factor) * factor, ((w + factor) // factor) * factor
padh = H - h if h % factor != 0 else 0
padw = W - w if w % factor != 0 else 0
img_tensor = F.pad(img_tensor, (0, padw, 0, padh), 'reflect')
H, W = img_tensor.shape[2], img_tensor.shape[3]
result_image, decomp1, decomp2 = model(img_tensor)
if warm_up == 20:
break
for file in os.listdir(blur_path):
if not os.path.isdir(out_path):
os.mkdir(out_path)
img = cv2.imread(blur_path + '/' + file)
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()
factor = 8
h, w = img_tensor.shape[2], img_tensor.shape[3]
H, W = ((h + factor) // factor) * factor, ((w + factor) // factor) * factor
padh = H - h if h % factor != 0 else 0
padw = W - w if w % factor != 0 else 0
img_tensor = F.pad(img_tensor, (0, padw, 0, padh), 'reflect')
H, W = img_tensor.shape[2], img_tensor.shape[3]
#with torch.autocast(device_type='cuda', dtype=torch.float16):
start = time.time()
result_image, decomp1, decomp2 = model(img_tensor)
stop = time.time()
result_image = result_image[:, :, :h, :w]
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 + '/' + file
torchvision.utils.save_image(result_image, out_file_name) |