"image/object/encoding/bbox",
"image/object/encoding/bbox/class",
"image/object/encoding/bbox/score"
],
padded_shapes={
"image": model.hyperparams["tf_image_shape"],
"image/object/bbox": [None, 4],
"image/object/bbox/label": [None],
"image/object/encoding/bbox": [None, 4],
"image/object/encoding/bbox/class": [None],
"image/object/encoding/bbox/score": [None]
},
name="data_provider")
# get batch
batch = data_provider.get(arguments.dataset_split)
# build model
model.build_from_scratch(
weight_decay=5e-5,
training=False,
inference_device=arguments.inference_device,
optimization_device=arguments.optimization_device)
# initialize variables
uninitialized_variables = tfu_get_uninitialized_variables(session)
if len(uninitialized_variables) > 0:
session.run(tf.variables_initializer(uninitialized_variables))
# restore model
saver = tf.train.Saver()
