def benchmark(config):
"""Computes and stores predictions and then
evaluates them on COCO metrics and supplementary benchmarking script."""
print("Benchmarking Baseline method {}.".format(
config["detector"]["type"]))
# Create new directory for run outputs
# In what location should we put this new directory?
output_dir = config["output_dir"]
mkdir_if_missing(output_dir)
# Save config in run directory
config.save(os.path.join(output_dir, config["save_conf_name"]))
# directory of test images and segmasks
detector_type = config["detector"]["type"]
if detector_type == "euclidean" or detector_type == "region_growing":
from sd_maskrcnn.pcl.pydetect import detect
elif detector_type == "gop" or detector_type == "mcg":
from sd_maskrcnn.gop.detect import detect
else:
print("Detector type not supported")
exit()
# Create predictions and record where everything gets stored.
pred_mask_dir, pred_info_dir, gt_mask_dir = detect(
detector_type,
config["detector"][detector_type],
output_dir,
config["dataset"],
)
ap, ar = coco_benchmark(pred_mask_dir, pred_info_dir, gt_mask_dir)
if config["vis"]["predictions"]:
visualize_predictions(
output_dir,
config["dataset"],
pred_mask_dir,
pred_info_dir,
show_bbox=config["vis"]["show_bbox_pred"],
show_class=config["vis"]["show_class_pred"],
)
if config["vis"]["s_bench"]:
s_benchmark(
output_dir,
config["dataset"],
pred_mask_dir,
pred_info_dir,
gt_mask_dir,
)
print("Saved benchmarking output to {}.\n".format(output_dir))
return ap, ar
def benchmark(config):
"""Computes and stores predictions and then
evaluates them on COCO metrics and supplementary benchmarking script."""
print("Benchmarking Baseline method {}.".format(
config['detector']['type']))
# Create new directory for run outputs
# In what location should we put this new directory?
output_dir = config['output_dir']
mkdir_if_missing(output_dir)
# Save config in run directory
config.save(os.path.join(output_dir, config['save_conf_name']))
# directory of test images and segmasks
detector_type = config['detector']['type']
if detector_type == 'euclidean' or detector_type == 'region_growing':
from sd_maskrcnn.pcl.pydetect import detect
elif detector_type == 'gop' or detector_type == 'mcg':
from sd_maskrcnn.gop.detect import detect
else:
print('Detector type not supported')
exit()
# Create predictions and record where everything gets stored.
pred_mask_dir, pred_info_dir, gt_mask_dir = \
detect(detector_type, config['detector'][detector_type], output_dir, config['dataset'])
ap, ar = coco_benchmark(pred_mask_dir, pred_info_dir, gt_mask_dir)
if config['vis']['predictions']:
visualize_predictions(output_dir,
config['dataset'],
pred_mask_dir,
pred_info_dir,
show_bbox=config['vis']['show_bbox_pred'],
show_class=config['vis']['show_class_pred'])
if config['vis']['s_bench']:
s_benchmark(output_dir, config['dataset'], pred_mask_dir,
pred_info_dir, gt_mask_dir)
print("Saved benchmarking output to {}.\n".format(output_dir))
return ap, ar