def main(_):
params = exp_factory.get_exp_config(FLAGS.experiment)
for config_file in FLAGS.config_file or []:
params = hyperparams.override_params_dict(params,
config_file,
is_strict=True)
if FLAGS.params_override:
params = hyperparams.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params.validate()
params.lock()
export_saved_model_lib.export_inference_graph(
input_type=FLAGS.input_type,
batch_size=FLAGS.batch_size,
input_image_size=[int(x) for x in FLAGS.input_image_size.split(',')],
params=params,
checkpoint_path=FLAGS.checkpoint_path,
export_dir=FLAGS.export_dir,
export_module=basnet.BASNetModule(
params=params,
batch_size=FLAGS.batch_size,
input_image_size=[
int(x) for x in FLAGS.input_image_size.split(',')
]),
export_checkpoint_subdir='checkpoint',
export_saved_model_subdir='saved_model')
def _export_model_with_log_model_flops_and_params(self, params):
export_saved_model_lib.export_inference_graph(
input_type='image_tensor',
batch_size=1,
input_image_size=[64, 64],
params=params,
checkpoint_path=os.path.join(self.tempdir, 'unused-ckpt'),
export_dir=self.tempdir,
log_model_flops_and_params=True)
def test_retinanet_task(self, unused_export):
tempdir = self.create_tempdir()
params = configs.retinanet.retinanet_resnetfpn_coco()
print(params.task.model.backbone)
params.task.model.backbone.resnet.model_id = 18
params.task.model.num_classes = 2
params.task.model.max_level = 6
export_saved_model_lib.export_inference_graph(
input_type='image_tensor',
batch_size=1,
input_image_size=[64, 64],
params=params,
checkpoint_path=os.path.join(tempdir, 'unused-ckpt'),
export_dir=tempdir,
log_model_flops_and_params=True)
self.assertTrue(
tf.io.gfile.exists(os.path.join(tempdir, 'model_params.txt')))
self.assertTrue(
tf.io.gfile.exists(os.path.join(tempdir, 'model_flops.txt')))
def main(_):
params = exp_factory.get_exp_config(FLAGS.experiment)
for config_file in FLAGS.config_file or []:
params = hyperparams.override_params_dict(params,
config_file,
is_strict=True)
if FLAGS.params_override:
params = hyperparams.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params.validate()
params.lock()
input_image_size = [int(x) for x in FLAGS.input_image_size.split(',')]
input_specs = tf.keras.layers.InputSpec(
shape=[FLAGS.batch_size, *input_image_size, 3])
model = factory.build_panoptic_maskrcnn(input_specs=input_specs,
model_config=params.task.model)
export_module = panoptic_segmentation.PanopticSegmentationModule(
params=params,
model=model,
batch_size=FLAGS.batch_size,
input_image_size=[int(x) for x in FLAGS.input_image_size.split(',')],
num_channels=3)
export_saved_model_lib.export_inference_graph(
input_type=FLAGS.input_type,
batch_size=FLAGS.batch_size,
input_image_size=input_image_size,
params=params,
checkpoint_path=FLAGS.checkpoint_path,
export_dir=FLAGS.export_dir,
export_module=export_module,
export_checkpoint_subdir='checkpoint',
export_saved_model_subdir='saved_model')
def main(_):
flags.mark_flag_as_required('export_dir')
flags.mark_flag_as_required('checkpoint_path')
params = exp_factory.get_exp_config(FLAGS.experiment)
for config_file in FLAGS.config_file or []:
params = hyperparams.override_params_dict(params,
config_file,
is_strict=True)
if FLAGS.params_override:
params = hyperparams.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params.validate()
params.lock()
input_image_size = FLAGS.input_image_size
export_module = semantic_segmentation_3d.SegmentationModule(
params=params,
batch_size=1,
input_image_size=input_image_size,
num_channels=FLAGS.num_channels)
export_saved_model_lib.export_inference_graph(
input_type=FLAGS.input_type,
batch_size=FLAGS.batch_size,
input_image_size=input_image_size,
params=params,
checkpoint_path=FLAGS.checkpoint_path,
export_dir=FLAGS.export_dir,
num_channels=FLAGS.num_channels,
export_module=export_module,
export_checkpoint_subdir='checkpoint',
export_saved_model_subdir='saved_model')
