def main(argv):
if not len(argv) >= 2:
print('Usage: ./trainer.py <config_file, e.g., configs/bottle_0_t5> '
'[model_dir (/tmp/model)]')
exit(0)
config_file = argv[1]
if len(argv) > 2:
model_dir = argv[2]
else:
model_dir = '/tmp/model'
fname = os.path.join(utils.KEYPOSE_PATH, config_file + '.yaml')
with open(fname, 'r') as f:
params, _, _ = utils.get_params(param_file=f)
dset_dir = os.path.join(utils.KEYPOSE_PATH, params.dset_dir)
# Configuration has the dset directory, now get more info from there.
with open(fname, 'r') as f:
params, _, _ = utils.get_params(
param_file=f,
cam_file=os.path.join(os.path.join(dset_dir, 'data_params.pbtxt')))
params.model_dir = model_dir
params.dset_dir = dset_dir
print('Parameters to train and eval:\n', params.make_dict())
train_and_eval(
params,
model_fn=est.est_model_fn,
input_fn=inp.create_input_fn,
save_checkpoints_secs=600,
eval_throttle_secs=600,
eval_steps=1000,
)
def main(argv):
if not len(argv) >= 3:
print('Usage: ./predict.py <model_dir> <image_dir> [show|<object>]')
exit(0)
model_dir = argv[1]
image_dir = argv[2]
mesh_file = None
if len(argv) > 3:
mesh_file = argv[3]
fname = os.path.join(model_dir, 'params.yaml')
with open(fname, 'r') as f:
params, camera_model, camera_image = utils.get_params(
param_file=f,
cam_file=os.path.join(model_dir, 'data_params.pbtxt'),
cam_image_file=os.path.join(image_dir, 'data_params.pbtxt'))
predict(model_dir,
image_dir,
params,
camera_model,
camera_image,
mesh_file=mesh_file)