def create_bbox_plotter(self):
self.bbox_plotter = TextRecBBOXPlotter(
self.load_image(self.lines[0][0]),
os.path.join(self.args.model_dir, 'eval_bboxes'),
self.target_shape,
self.metrics,
visualization_anchors=[["localization_net", "vis_anchor"], ["recognition_net", "vis_anchor"]],
render_extracted_rois=False,
invoke_before_training=True,
render_intermediate_bboxes=self.args.render_all_bboxes,
)
self.lines = self.lines[:self.args.num_rois]
net, 'model_{.updater.iteration}.npz'), (args.snapshot_interval,
'iteration'))
# bbox plotter test
if not args.test_image:
test_image = validation_dataset.get_example(0)[0]
else:
test_image = train_dataset.load_image(args.test_image)
bbox_plotter = (TextRecBBOXPlotter(
test_image,
os.path.join(log_dir, 'boxes'),
target_shape,
metrics,
send_bboxes=args.send_bboxes,
upstream_port=args.port,
visualization_anchors=[["localization_net", "vis_anchor"],
["recognition_net", "vis_anchor"]],
render_extracted_rois=False,
invoke_before_training=True,
render_intermediate_bboxes=args.render_all_bboxes,
), (10, 'iteration'))
trainer = get_trainer(
net,
updater,
log_dir,
fields_to_print,
epochs=args.epochs,
snapshot_interval=args.snapshot_interval,
print_interval=args.log_interval,