def evaluate(self, test=True):
""" Evaluate network on the validation set. """
if test is True:
key = 'TEST'
data_directory = flags['data_directory'] + 'Test/'
else: # valid
key = 'VALID'
data_directory = flags['data_directory'] + 'Valid/'
print('Detecting images in %s set' % key)
data_info = (self.num_images[key], flags['num_classes'],
flags['classes'])
tf_inputs = (self.x['EVAL'], self.im_dims['EVAL'])
tf_outputs = (self.roi_proposal_net['EVAL'].get_rois(),
self.fast_rcnn_net['EVAL'].get_cls_prob(),
self.fast_rcnn_net['EVAL'].get_bbox_refinement())
class_metrics = test_net(self.sess,
data_directory,
data_info,
tf_inputs,
tf_outputs,
vis=self.flags['vis'])
self.record_eval_metrics(class_metrics, key)
def test(self):
""" Evaluate network on the test set. """
data_info = (self.num_images['TEST'], flags['num_classes'], flags['classes'])
tf_inputs = (self.x['TEST'], self.im_dims['TEST'])
tf_outputs = (self.roi_proposal_net['TEST'].get_rois(),
self.fast_rcnn_net['TEST'].get_cls_prob(),
self.fast_rcnn_net['TEST'].get_bbox_refinement())
class_metrics = test_net(self.sess, flags['data_directory'], data_info, tf_inputs, tf_outputs)
print(class_metrics)
def evaluate(self, test=True):
""" Evaluate network on the validation set. """
key = 'TEST' if test is True else 'VALID'
print('Detecting images in %s set' % key)
tf_inputs = (self.x['EVAL'], self.im_dims['EVAL'])
tf_outputs = (self.roi_proposal_net['EVAL'].get_rois(),
self.fast_rcnn_net['EVAL'].get_cls_prob(),
self.fast_rcnn_net['EVAL'].get_bbox_refinement())
class_metrics = test_net(flags['data_directory'], self.names[key], self.sess, tf_inputs, tf_outputs, key=key, thresh=0.5, vis=self.flags['vis'])
self.record_eval_metrics(class_metrics, key)
def evaluate(self, test=True):
""" Evaluate network on the validation set. """
key = 'TEST' if test is True else 'VALID'
print('Detecting images in %s set' % key)
tf_inputs = (self.x['EVAL'], self.im_dims['EVAL'])
tf_outputs = (self.roi_proposal_net['EVAL'].get_rois(),
self.fast_rcnn_net['EVAL'].get_cls_prob(),
self.fast_rcnn_net['EVAL'].get_bbox_refinement())
class_metrics = test_net(flags['data_directory'], self.names[key], self.sess, tf_inputs, tf_outputs, key=key, thresh=0.5, vis=self.flags['vis'])
self.record_eval_metrics(class_metrics, key)
def evaluate(self, sess, img, raw_output_up, preds_summary, test=True):
""" Evaluate network on the validation set. """
key = 'TEST' if test is True else 'VALID'
tf_inputs = (img)
tf_outputs = (self.roi_proposal_net['EVAL'].get_rois(),
self.fast_rcnn_net['EVAL'].get_bbox_refinement(),
raw_output_up, preds_summary)
class_metrics = test_net(self.flags['data_directory'],
self.names,
sess,
tf_inputs,
tf_outputs,
key=key,
thresh=0.5,
vis=True)