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test_cbam_dwt123.py
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test_cbam_dwt123.py
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import sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import cv2
import os
import argparse
import glob
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from utils import *
from networks.generator_cbam123 import BRN, print_network
import time
parser = argparse.ArgumentParser(description="AID_DWT_Test")
parser.add_argument("--logdir", type=str, default='./logs/DWT/Rain200H_cbam_100/')
parser.add_argument("--data_path", type=str, default='/media/ubuntu/Seagate/RainData/Rain200H/test/small/rain/')
parser.add_argument("--save_path", type=str, default='./results/DWT/Rain200H_CBAM_100/')
parser.add_argument('--save_path_r', type=str, default='./results/DWT/Rain200H_CBAM_100/streak/')
parser.add_argument("--use_GPU", type=bool, default=True, help='use GPU or not')
parser.add_argument("--gpu_id", type=str, default='1', help='GPU id')
parser.add_argument("--inter_iter", type=int, default=8, help='number of inter_iteration')
opt = parser.parse_args()
if opt.use_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
def main():
if not os.path.isdir(opt.save_path):
os.makedirs(opt.save_path)
if not os.path.isdir(opt.save_path_r):
os.makedirs(opt.save_path_r)
# Build model
print('Loading model ...\n')
model = BRN(opt.inter_iter, opt.use_GPU)
model = nn.DataParallel(model)
# print_network(model)
if opt.use_GPU:
model = model.cuda()
state_dict = torch.load(os.path.join(opt.logdir, 'net_epoch95.pth'))
model.load_state_dict(state_dict)
# from collections import OrderedDict
# new_state_dict = OrderedDict()
# for k, v in state_dict.items():
# name = k[7:] # remove `module.`
# new_state_dict[name] = v
# model.load_state_dict(new_state_dict)
# # TODO: Beta-Variable
# beta = torch.cuda.FloatTensor([1.0])
# beta_Var = Variable(beta, requires_grad=True)
# model.load_state_dict(torch.load(os.path.join(opt.logdir, 'net_latest.pth')))
model.eval()
# load data info
print('Loading data info ...\n')
# process data
time_test = 0
count = 0
for img_name in os.listdir(opt.data_path):
if is_image(img_name):
img_path = os.path.join(opt.data_path, img_name)
fname = os.path.basename(img_name)
fname = os.path.splitext(fname)[0]
# rain_name= fname + 'streak.png'
# image
Img = cv2.imread(img_path)
h, w, c = Img.shape
b, g, r = cv2.split(Img)
Img = cv2.merge([r, g, b])
Img = normalize(np.float32(Img))
Img = np.expand_dims(Img.transpose(2, 0, 1), 0)
ISource = torch.Tensor(Img)
INoisy = ISource
if opt.use_GPU:
ISource, INoisy = Variable(ISource.cuda()), Variable(INoisy.cuda())
else:
ISource, INoisy = Variable(ISource), Variable(INoisy)
with torch.no_grad(): # this can save much memory
torch.cuda.synchronize()
start_time = time.time()
out, out_list, rain, rain_list, = model(INoisy)
out = torch.clamp(out, 0., 1.)
rain = torch.clamp(rain, 0., 1.)
torch.cuda.synchronize()
end_time = time.time()
dur_time = end_time - start_time
print(img_name)
print(dur_time)
time_test += dur_time
# print('out_list: ', len(out_list))
# TODO: for output image layer
# for i in range(len(out_list)):
# temp_out = out_list[i]
# temp_out = torch.clamp(temp_out, 0., 1.)
# temp_out = np.uint8(255 * temp_out.data.cpu().numpy().squeeze())
# temp_out = temp_out.transpose(1, 2, 0)
# b, g, r = cv2.split(temp_out)
# temp_out = cv2.merge([r, g, b])
# save_path = os.path.join(opt.save_path, ('temp_out_%d.png' % i))
# cv2.imwrite(save_path, temp_out)
# TODO: for output rain layer
# for j in range(len(rain_list)):
# temp_rain = rain_list[j]
# temp_rain = torch.clamp(temp_rain, 0., 1.)
# temp_rain = np.uint8(255 * temp_rain.data.cpu().numpy().squeeze())
# temp_rain = temp_rain.transpose(1, 2, 0)
# b, g, r = cv2.split(temp_rain)
# temp_rain = cv2.merge([r, g, b])
# save_path = os.path.join(opt.save_path, ('temp_rain_%d.png' % j))
# cv2.imwrite(save_path, temp_rain)
if opt.use_GPU:
save_out = np.uint8(255 * out.data.cpu().numpy().squeeze())
save_rain = np.uint8(255 * rain.data.cpu().numpy().squeeze())
# save_rain = np.uint8(255 * rain)
else:
save_out = np.uint8(255 * out.data.cpu().numpy().squeeze())
save_rain = np.uint8(255 * rain.data.cpu().numpy().squeeze())
# save_rain = np.uint8(255 * rain)
save_out = save_out.transpose(1, 2, 0)
save_rain = save_rain.transpose(1, 2, 0)
b, g, r = cv2.split(save_out)
save_out = cv2.merge([r, g, b])
b, g, r = cv2.split(save_rain)
save_rain = cv2.merge([r, g, b])
save_path = opt.save_path
save_path_r = opt.save_path_r
cv2.imwrite(os.path.join(save_path, img_name), save_out)
cv2.imwrite(os.path.join(save_path_r, img_name), save_rain)
count = count + 1
print('Avg. time:', time_test / count)
if __name__ == "__main__":
main()