def main():
args = parse_args()
logger = log.get_logger(args.log)
args.logger = logger
logger.info('*' * 80)
logger.info('the args are the below')
logger.info('*' * 80)
for x in args.__dict__:
logger.info(x + ',' + str(args.__dict__[x]))
logger.info(cfg.config[args.dataset])
logger.info('*' * 80)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if not os.path.exists(args.param_dir):
os.mkdir(args.param_dir)
torch.manual_seed(long(time.time()))
model = bdcn.BDCN(pretrain=args.pretrain, logger=logger)
if args.complete_pretrain:
model.load_state_dict(torch.load(args.complete_pretrain))
logger.info(model)
train(model, args)
def main():
args = parse_args()
# Choose the GPUs
os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
logger = log.get_logger(args.log)
args.logger = logger
logger.info('*' * 80)
logger.info('the args are the below')
logger.info('*' * 80)
for x in args.__dict__:
logger.info(x + ',' + str(args.__dict__[x]))
logger.info('*' * 80)
if not os.path.exists(args.param_dir):
os.mkdir(args.param_dir)
torch.manual_seed(int(time.time()))
model = bdcn.BDCN(pretrain=args.pretrain, logger=logger)
if args.complete_pretrain:
model.load_state_dict(torch.load(args.complete_pretrain))
train(model, args)
def main():
import time
print(time.localtime())
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
model = bdcn.BDCN()
model.load_state_dict(torch.load('%s' % (args.model)))
test(model, args)
def main():
import time
print time.localtime()
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
model = bdcn.BDCN(rate=args.rate)
model.load_state_dict(torch.load('%s' % (args.model)))
# print model.fuse.weight.data, model.fuse.bias.data
print model.fuse.weight.data
test(model, args)
def main():
import time
print (time.localtime())
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
device = torch.device('cpu' if torch.cuda.device_count() == 0 else 'cuda')
model = bdcn.BDCN().to(device)
model.load_state_dict(torch.load('%s' % (args.model), map_location=device))
test(model, args, running_on=device)
def main():
import time
print(time.localtime())
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
model = bdcn.BDCN()
model.load_state_dict(
torch.load('%s' % (params_dir[args.train_dataset]),
map_location=torch.device('cpu')))
test(model, args)
def setup(opts):
model = bdcn.BDCN()
if torch.cuda.is_available():
model.load_state_dict(torch.load(opts["checkpoint"]))
model.cuda()
else:
model.load_state_dict(
torch.load(opts["checkpoint"], map_location='cpu'))
model.eval()
return model
def main():
import time
print(time.localtime())
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
device = torch.device('cpu' if torch.cuda.device_count() == 0 else 'cuda')
# model = bdcn.BDCN()
model = bdcn.BDCN().to(device)
model_dir = os.path.join('params', args.train_data, args.model)
model.load_state_dict(torch.load('%s' % (model_dir), map_location=device))
print("====== Checkpoint> ", model_dir, "==========")
test(model, args, running_on=device)
def main():
inputDir = ['images']
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
logging.info('Loading model...')
model = bdcn.BDCN()
logging.info('Loading state...')
model.load_state_dict(torch.load('%s' % (args.model)))
logging.info('Start image processing...')
for inputDir in inputDir:
args.inputDir = inputDir
args.cuda = True
forwardAll(model, args)
def main():
args = parse_args()
# Choose the GPUs
os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
model = bdcn.BDCN()
if torch.cuda.is_available():
map_location = lambda storage, loc: storage.cuda()
else:
map_location = 'cpu'
model.load_state_dict(
torch.load('%s' % (args.model), map_location=map_location))
test(model, args)
def execute(gpu, cuda, trained_model, res_dir, data_root, test_lst):
# Import model and use gpu
os.environ['CUDA_VISIBLE_DEVICES'] = gpu
model = bdcn.BDCN()
##### Using GPU ####
if cuda:
model.load_state_dict(torch.load('%s' % (trained_model)))
else:
#### Using CPU ####
model.load_state_dict(
torch.load('%s' % (trained_model),
map_location=torch.device('cpu')))
# Call test function and detect edges
test(model, cuda, res_dir, data_root, test_lst)
def main():
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
logging.info('Loading model...')
model = bdcn.BDCN()
logging.info('Loading state...')
model.load_state_dict(torch.load('%s' % (args.model)))
logging.info('Start image processing...')
inputDirs = [
'/run/user/1000/gvfs/smb-share:server=192.168.0.253,share=data/Master/datasets/bsr_bsds500/BSR/BSDS500/data/images/test_png/'
#'/run/user/1000/gvfs/smb-share:server=192.168.0.253,share=data/Master/datasets/test_dataset/hdr_fusion/flicker_synthetic/flicker_1'
]
#baseDir = '/run/user/1000/gvfs/smb-share:server=192.168.0.253,share=data/Master/datasets/'
#inputDirs = [
#args.inputDir
#os.path.join(baseDir, 'rgbd_dataset_freiburg1_desk/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg1_desk2/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg1_plant/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg1_room/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg1_rpy/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg1_xyz/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg2_desk/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg2_xyz/rgb/'),
#os.path.join(baseDir, 'rgbd_dataset_freiburg3_long_office_household/rgb/'),
#]
# baseDir = '/run/user/1000/gvfs/smb-share:server=192.168.0.253,share=data/Master/datasets/test_dataset'
# inputDirs = [
# os.path.join(baseDir, 'hdr_fusion', 'flicker_synthetic', 'flicker_1'),
# os.path.join(baseDir, 'hdr_fusion', 'smooth_synthetic', 'flicker_2'),
# os.path.join(baseDir, 'nyu_depth_v2', 'basements', 'basement_001c'),
# os.path.join(baseDir, 'nyu_depth_v2', 'cafe', 'cafe_0001c'),
# os.path.join(baseDir, 'nyu_depth_v2', 'classrooms', 'classroom_0014'),
# os.path.join(baseDir, 'tum', 'rgbd_dataset_freiburg1_desk', 'rgb'),
# os.path.join(baseDir, 'tum', 'rgbd_dataset_freiburg1_xyz', 'rgb'),
# os.path.join(baseDir, 'tum', 'rgbd_dataset_freiburg2_xyz', 'rgb'),
# ]
for inputDir in inputDirs:
args.inputDir = inputDir
args.cuda = True
forwardAll(model, args)
def main():
args = parse_args()
logger = log.get_logger(args.log)
args.logger = logger
logger.info('*' * 80)
logger.info('the args are the below')
logger.info('*' * 80)
for x in args.__dict__:
logger.info(x + ',' + str(args.__dict__[x]))
logger.info(cfg.config[args.dataset])
logger.info('*' * 80)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if not os.path.exists(args.param_dir):
os.mkdir(args.param_dir)
torch.manual_seed(long(time.time()))
model = bdcn.BDCN(pretrain=args.pretrain, logger=logger)
pytorch_total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('TOTAL TRAINABLE PARAMS: ' + str(pytorch_total_params))
if args.complete_pretrain:
model.load_state_dict(torch.load(args.complete_pretrain))
logger.info(model)
train(model, args)
def main():
args = parse_args()
logger = log.get_logger(args.log)
args.logger = logger
print('*' * 80)
print('the args are the below')
logger.info('*' * 80)
for x in args.__dict__:
print(x + ',' + str(args.__dict__[x]))
print(cfg.config[args.dataset])
print('*' * 80)
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if not os.path.exists(args.param_dir):
os.mkdir(args.param_dir)
torch.manual_seed(int(time.time()))
device = torch.device('cpu' if torch.cuda.device_count() == 0 else 'cuda')
model = bdcn.BDCN(pretrain=args.pretrain, logger=logger).to(device)
if args.complete_pretrain:
model.load_state_dict(
torch.load(args.complete_pretrain, map_location=device))
logger.info(model)
train(model, args, device=device)
# print(os.path.join(test_root, nm))
# data = cv2.resize(data, (data.shape[1]/2, data.shape[0]/2), interpolation=cv2.INTER_LINEAR)
data = np.array(imagen, np.float32)
data -= mean_bgr
data = data.transpose((2, 0, 1))
data = torch.from_numpy(data).float().unsqueeze(0)
data = Variable(data)
out = model(data)
out = [torch.sigmoid(x).cpu().data.numpy()[0, 0] for x in out]
img = out[-1]
img = 255 * img
#print(img)
print('Overall Time use: ', time.time() - start_time)
#a = np.reshape(out,weigh,heigh)
#print(type(img))
cv2.imshow("out", img)
cv2.waitKey()
if __name__ == '__main__':
model = bdcn.BDCN()
model.load_state_dict(
torch.load("bdcn_pretrained_on_bsds500.pth",
map_location=torch.device('cpu')))
model.eval()
#print(model)
test(model)
