for j in range(7, 0, -1):
if i < j:
append_img = numpy.ones(
img_shape[0] * img_shape[1] *
img_shape[2]).reshape(img_shape[:1] + (1, ) + img_shape[1:])
else:
append_img = numpy.zeros(
img_shape[0] * img_shape[1] *
img_shape[2]).reshape(img_shape[:1] + (1, ) + img_shape[1:])
lossmask = numpy.hstack([lossmask, append_img])
lossmask = numpy.broadcast_to(lossmask, (batch_size, ) +
lossmask.shape).astype(numpy.float32)
lossmask_list.append(lossmask)
model = network.CNNAE3D512()
optimizer = chainer.optimizers.Adam(alpha=0.0002, beta1=0.5)
optimizer.setup(model)
xp = cuda.cupy if gpu > -1 else numpy
if gpu > -1:
cuda.check_cuda_available()
cuda.get_device(gpu).use()
model.to_gpu()
N = len(dir_path_list)
for epoch in range(maximum_epoch):
print epoch
loss_gen_sum = 0.
perm = numpy.random.permutation(N)
for i in range(N):
help='File path of a downexposure model.',
default='./models/downexposure_model.pth')
parser.add_argument('-um',
help='File path of a upexposure model.',
default='./models/upexposure_model.pth')
args = parser.parse_args()
f_path = args.i
model_path_list = [args.dm, args.um]
base_outdir_path = args.o
gpu_list = []
if args.gpu != '-1':
for gpu_num in (args.gpu).split(','):
gpu_list.append(int(gpu_num))
model_list = [network.CNNAE3D512().cuda(), network.CNNAE3D512().cuda()]
print "load_weight"
for i in range(2):
state_dict = torch.load(model_path_list[i])
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:]
# print name
new_state_dict[name] = v
model_list[i].load_state_dict(new_state_dict)
# model_list[i].load_state_dict(torch.load(model_path_list[i]))
def estimate_images(input_img, model):
model.train_dropout = False
#input image RGB to BRG
def __init__(self):
parser = argparse.ArgumentParser(description='')
parser.add_argument('-i',
help='Directory path of training data.',
default='./training_samples')
parser.add_argument('-o',
help='Saved path Wof an output model file.',
default='./models/downexposure_model/')
parser.add_argument(
'-l',
help='Learning type. (0:downexposure, 1:upexposure)',
default='0')
parser.add_argument('-gpu', help='GPU device specifier.', default='1')
args = parser.parse_args()
#cuda check
if torch.cuda.is_available():
self.use_cuda = True
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
self.use_cuda = False
torch.set_default_tensor_type('torch.FloatTensor')
self.lr = 0.0002
# self.smoothing = config.smoothing
# self.max_resl = config.max_resl
# self.trns_tick = config.trns_tick
self.TICK = 1000
self.globalIter = 0
self.globalTick = time.clock()
self.kimgs = 0
self.stack = 0
# self.renew_everything()
# self.use_tb = config.use_tb
# if self.use_tb:
# self.tb = tensorboard.tf_recorder()
# gpu = int(args.gpu)
#learning_type: 0: downexposure, 1: upexposure
self.is_upexposure_trained = int(args.l)
#saved path of an output model file - default: models/downexposures
self.out_path = args.o
self.dir_path_list = glob.glob(args.i + '/*')
# print(self.dir_path_list)
# self.dir_path_list = self.dir_path_list[:1]
# print(self.dir_path_list)
batch_size = 1
self.maximum_epoch = 200
self.predicted_window_len = 8
self.lossmask_list = list()
img_shape = (3, 512, 512)
for i in range(self.predicted_window_len):
lossmask = np.ones(img_shape[0] * img_shape[1] *
img_shape[2]).reshape(img_shape[:1] + (1, ) +
img_shape[1:])
for j in range(7, 0, -1):
if i < j:
append_img = np.ones(img_shape[0] * img_shape[1] *
img_shape[2]).reshape(img_shape[:1] +
(1, ) +
img_shape[1:])
else:
append_img = np.zeros(img_shape[0] * img_shape[1] *
img_shape[2]).reshape(img_shape[:1] +
(1, ) +
img_shape[1:])
lossmask = np.hstack([lossmask, append_img])
lossmask = np.broadcast_to(lossmask, (batch_size, ) +
lossmask.shape).astype(np.float32)
self.lossmask_list.append(lossmask)
self.model = network.CNNAE3D512()
print('network structure')
print(self.model)
self.mse = torch.nn.MSELoss()
if torch.cuda.is_available():
self.mse = self.mse.cuda()
self.model = self.model.cuda()
torch.cuda.manual_seed((int)(time.time()))
gpus = []
for i in range(2):
gpus.append(i)
self.model = torch.nn.DataParallel(self.model, device_ids=gpus)
self.TARGET_WIDTH = 512
self.TARGET_HEIGHT = 512
self.IMG_WIDTH = 1920
self.IMG_HEIGHT = 1080