def ExtractFeatures(self, input_image, input_scales,
input_global_scales_ind):
extracted_features = export_model_utils.ExtractGlobalFeatures(
input_image,
input_scales,
input_global_scales_ind,
lambda x: self._model(x, training=False),
multi_scale_pool_type=self._multi_scale_pool_type,
normalize_global_descriptor=self._normalize_global_descriptor,
normalization_function=_NormalizeImages())
named_output_tensors = {}
named_output_tensors['global_descriptors'] = tf.identity(
extracted_features, name='global_descriptors')
return named_output_tensors
def ExtractFeatures(self, input_image, input_scales,
input_global_scales_ind):
extracted_features = export_model_utils.ExtractGlobalFeatures(
input_image,
input_scales,
input_global_scales_ind,
lambda x: self._model.backbone.build_call(x, training=False),
multi_scale_pool_type=self._multi_scale_pool_type,
normalize_global_descriptor=self._normalize_global_descriptor)
print("inside ExtractFeatures called")
named_output_tensors = {}
if self._multi_scale_pool_type == 'None':
named_output_tensors['global_descriptors'] = tf.identity(
extracted_features, name='global_descriptors')
else:
named_output_tensors['global_descriptor'] = tf.identity(
extracted_features, name='global_descriptor')
return named_output_tensors
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
export_path = FLAGS.export_path
if os.path.exists(export_path):
raise ValueError('Export_path already exists.')
with tf.Graph().as_default() as g, tf.compat.v1.Session(graph=g) as sess:
# Setup the model for extraction.
model = delf_model.Delf(block3_strides=False, name='DELF')
# Initial forward pass to build model.
images = tf.zeros((1, 321, 321, 3), dtype=tf.float32)
model(images)
# Setup the multiscale extraction.
input_image = tf.compat.v1.placeholder(tf.uint8,
shape=(None, None, 3),
name='input_image')
if FLAGS.input_scales_list is None:
input_scales = tf.compat.v1.placeholder(tf.float32,
shape=[None],
name='input_scales')
else:
input_scales = tf.constant(
[float(s) for s in FLAGS.input_scales_list],
dtype=tf.float32,
shape=[len(FLAGS.input_scales_list)],
name='input_scales')
extracted_features = export_model_utils.ExtractGlobalFeatures(
input_image,
input_scales,
lambda x: model.backbone(x, training=False),
multi_scale_pool_type=FLAGS.multi_scale_pool_type,
normalize_global_descriptor=FLAGS.normalize_global_descriptor)
# Load the weights.
checkpoint_path = FLAGS.ckpt_path
model.load_weights(checkpoint_path)
print('Checkpoint loaded from ', checkpoint_path)
named_input_tensors = {'input_image': input_image}
if FLAGS.input_scales_list is None:
named_input_tensors['input_scales'] = input_scales
# Outputs to the exported model.
named_output_tensors = {}
if FLAGS.multi_scale_pool_type == 'None':
named_output_tensors['global_descriptors'] = tf.identity(
extracted_features, name='global_descriptors')
else:
named_output_tensors['global_descriptor'] = tf.identity(
extracted_features, name='global_descriptor')
# Export the model.
signature_def = (
tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs=_build_tensor_info(named_input_tensors),
outputs=_build_tensor_info(named_output_tensors)))
print('Exporting trained model to:', export_path)
builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(
export_path)
init_op = None
builder.add_meta_graph_and_variables(
sess, [tf.compat.v1.saved_model.tag_constants.SERVING],
signature_def_map={
tf.compat.v1.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature_def
},
main_op=init_op)
builder.save()