config = yaml.load(f)
out_dir, logger = init_logging(opt.log_dir)
logger.info(opt)
logger.info(yaml.dump(config))
if opt.mode == "train":
batch_size = config["batch_size"]
img_shape = 2 * [config["spatial_size"]] + [3]
data_shape = [batch_size] + img_shape
init_shape = [config["init_batches"] * batch_size] + img_shape
box_factor = config["box_factor"]
data_index = config["data_index"]
batches = get_batches(data_shape,
data_index,
train=True,
box_factor=box_factor)
init_batches = get_batches(init_shape,
data_index,
train=True,
box_factor=box_factor)
valid_batches = get_batches(data_shape,
data_index,
train=False,
box_factor=box_factor)
logger.info("Number of training samples: {}".format(batches.n))
logger.info("Number of validation samples: {}".format(valid_batches.n))
model = Model(config, out_dir, logger)
if opt.checkpoint is not None:
model.restore_graph(opt.checkpoint)
model_path = '/Volumes/Qin-Warehouse/Warehouse-Data/Variational-U-Net/log/2019-02-06T18-10-49/checkpoints/model.ckpt-100000'
config_path = 'deepfashion_local.yaml'
with open(config_path) as f:
config = yaml.load(f)
batch_size = config["batch_size"]
img_shape = 2 * [config["spatial_size"]] + [3]
data_index = config["data_index"]
box_factor = config["box_factor"]
data_shape = [batch_size] + img_shape
init_shape = [config["init_batches"] * batch_size] + img_shape
testing_batches = get_batches(data_shape,
data_index,
train=False,
box_factor=box_factor,
shuffle=False)
parser = argparse.ArgumentParser()
parser.add_argument("--likelihood_loss", choices=['l1', 'vgg_perception'])
parser.set_defaults(retrain=False)
opt = parser.parse_args()
model = Model(config, out_dir, logger, opt.likelihood_loss)
print('restoring the graph ... ')
model.restore_graph(model_path)
def restore_launch(mission_type, bch_limit=None):
print('mission type: ', mission_type)
