def get_synthetic_inputs(self, input_name, nclass):
"""Generating synthetic data matching real data shape and type."""
inputs = tf.random_uniform(
self.get_input_shapes('train')[0], dtype=self.data_type)
inputs = contrib_framework.local_variable(inputs, name=input_name)
boxes = tf.random_uniform(
[self.batch_size, ssd_constants.NUM_SSD_BOXES, 4], dtype=tf.float32)
classes = tf.random_uniform(
[self.batch_size, ssd_constants.NUM_SSD_BOXES, 1], dtype=tf.float32)
nboxes = tf.random_uniform(
[self.batch_size], minval=1, maxval=10, dtype=tf.float32)
return (inputs, boxes, classes, nboxes)
def get_synthetic_inputs(self, input_name, nclass):
# Synthetic input should be within [0, 255].
image_shape, label_shape = self.get_input_shapes('train')
inputs = tf.truncated_normal(image_shape,
dtype=self.data_type,
mean=127,
stddev=60,
name=self.model_name +
'_synthetic_inputs')
inputs = contrib_framework.local_variable(inputs, name=input_name)
labels = tf.random_uniform(label_shape,
minval=0,
maxval=nclass - 1,
dtype=tf.int32,
name=self.model_name + '_synthetic_labels')
return (inputs, labels)
def _streaming_sum(scalar_tensor): """Create a sum metric and update op.""" sum_metric = framework.local_variable(constant_op.constant(0.0)) sum_update = sum_metric.assign_add(scalar_tensor) return sum_metric, sum_update
def _streaming_sum(scalar_tensor):
"""Create a sum metric and update op."""
sum_metric = framework.local_variable(constant_op.constant(0.0))
sum_update = sum_metric.assign_add(scalar_tensor)
return sum_metric, sum_update