# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
save_only = getattr(cfg, 'save_prediction_only', False)
eval_results(
results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset,
save_only=save_only)
if __name__ == '__main__':
parser = ArgsParser()
parser.add_argument(
"--json_eval",
action='store_true',
default=False,
help="Whether to re eval with already exists bbox.json or mask.json")
parser.add_argument(
"-f",
"--output_eval",
default=None,
type=str,
help="Evaluation file directory, default is current directory.")
FLAGS = parser.parse_args()
main()
vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
vdl_mAP_step += 1
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
train_loader.reset()
if __name__ == '__main__':
parser = ArgsParser()
parser.add_argument("-r",
"--resume_checkpoint",
default=None,
type=str,
help="Checkpoint path for resuming training.")
parser.add_argument("--fp16",
action='store_true',
default=False,
help="Enable mixed precision training.")
parser.add_argument("--loss_scale",
default=8.,
type=float,
help="Mixed precision training loss scale.")
parser.add_argument("--eval",
action='store_true',
def parse_args():
parser = ArgsParser()
parser.add_argument(
"--infer_dir",
type=str,
default=None,
help="Directory for images to perform inference on.")
parser.add_argument(
"--infer_img",
type=str,
default=None,
help="Image path, has higher priority over --infer_dir")
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory for storing the output visualization files.")
parser.add_argument(
"--draw_threshold",
type=float,
default=0.5,
help="Threshold to reserve the result for visualization.")
parser.add_argument(
"--slim_config",
default=None,
type=str,
help="Configuration file of slim method.")
parser.add_argument(
"--use_vdl",
type=bool,
default=False,
help="Whether to record the data to VisualDL.")
parser.add_argument(
'--vdl_log_dir',
type=str,
default="vdl_log_dir/image",
help='VisualDL logging directory for image.')
parser.add_argument(
"--save_txt",
type=bool,
default=False,
help="Whether to save inference result in txt.")
args = parser.parse_args()
return args
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
save_checkpoint(exe, eval_prog,
os.path.join(save_dir, "best_model"),
train_prog)
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
train_loader.reset()
if __name__ == '__main__':
enable_static_mode()
parser = ArgsParser()
parser.add_argument("--loss_scale",
default=8.,
type=float,
help="Mixed precision training loss scale.")
parser.add_argument("--eval",
action='store_true',
default=False,
help="Whether to perform evaluation in train")
parser.add_argument(
"--output_eval",
default=None,
type=str,
help="Evaluation directory, default is current directory.")
parser.add_argument(
"--not_quant_pattern",
inputs_def = cfg['TestReader']['inputs_def']
inputs_def['use_dataloader'] = False
feed_vars, _ = model.build_inputs(**inputs_def)
# postprocess not need in exclude_nms, exclude NMS in exclude_nms mode
test_fetches = model.test(feed_vars, exclude_nms=FLAGS.exclude_nms)
infer_prog = infer_prog.clone(True)
check_py_func(infer_prog)
exe.run(startup_prog)
checkpoint.load_params(exe, infer_prog, cfg.weights)
dump_infer_config(FLAGS, cfg)
save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog)
if __name__ == '__main__':
parser = ArgsParser()
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory for storing the output model files.")
parser.add_argument(
"--exclude_nms",
action='store_true',
default=False,
help="Whether prune NMS for benchmark")
FLAGS = parser.parse_args()
main()
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
save_name = get_save_image_name(FLAGS.output_dir, image_path)
logger.info(
"Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)
except (StopIteration, fluid.core.EOFException):
loader.reset()
if __name__ == '__main__':
enable_static_mode()
parser = ArgsParser()
parser.add_argument("--infer_dir",
type=str,
default=None,
help="Directory for images to perform inference on.")
parser.add_argument(
"--infer_img",
type=str,
default=None,
help="Image path, has higher priority over --infer_dir")
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory for storing the output visualization files.")
parser.add_argument(
