def _setup_net(self, saved_vars_dict):
model_output = basic_net(self.cnn_net, self.data_batches,
self.config.num_classes, False)
self.output = tf.reshape(model_output, (-1, ), self.OUTPUT_NODE_NAME)
assign_ops = assign_vars(self._vars(), saved_vars_dict)
self.initializer = tf.group(*assign_ops,
name=self.INITIALIZER_NODE_NAME)
def _setup_net(self, saved_vars_dict):
self.length_output, self.numbers_output = nsr_net(
self.cnn_net, self.data_batches, self.config.max_number_length,
self.is_training)
self.output = stack_output(self.max_number_length,
self.length_output,
self.numbers_output,
name='output')
assign_ops = assign_vars(self._vars(), saved_vars_dict)
self.initializer = tf.group(*assign_ops, name='initializer')
def _setup_net(self, saved_vars_dict):
with self.cnn_net.variable_scope([self.data_batches]) as vs:
collection_name = self.cnn_net.end_points_collection_name(vs)
net_out, _ = self.cnn_net.cnn_layers(self.data_batches,
vs,
collection_name,
is_training=self.is_training)
self.output = tf.reshape(
net_out,
[H * W * (self.config.num_classes + 5) * self.max_number_length],
name=YOLOToExportModel.OUTPUT_NODE_NAME)
_, self.output_boxes, self.output_classes, self.output_classes_probs = \
build_export_output(net_out, H, W, self.max_number_length, self.config.num_classes, self.config.threshold)
self.output_boxes = tf.identity(self.output_boxes, name='output_boxes')
self.output_classes = tf.identity(self.output_classes,
'output_classes')
self.output_classes_probs = tf.identity(self.output_classes_probs,
'output_classes_probs')
assign_ops = assign_vars(self._vars(), saved_vars_dict)
self.initializer = tf.group(*assign_ops, name='initializer')