def package(self, rbf_file, sw_dir, doc_dir, package_name):
image_dir = os.path.join(utils.get_work_dir(), "image_0")
if not os.path.exists(image_dir):
os.makedirs(image_dir)
gbs_name = os.path.splitext(os.path.basename(
self.afu_desc_file))[0] + GBS_EXT
gbs_path = os.path.join(image_dir, gbs_name)
self.create_gbs(rbf_file, gbs_path)
shutil.copyfile(
self.afu_desc_file,
os.path.join(image_dir, os.path.basename(self.afu_desc_file)))
package_dir = os.path.join(utils.get_work_dir(), "package")
shutil.make_archive(os.path.join(package_dir, "image_0"),
ARCHIVE_FORMAT, image_dir)
if sw_dir:
shutil.make_archive(os.path.join(package_dir, "sw"),
ARCHIVE_FORMAT, sw_dir)
if doc_dir:
shutil.make_archive(os.path.join(package_dir, "docs"),
ARCHIVE_FORMAT, doc_dir)
shutil.make_archive(package_name, ARCHIVE_FORMAT, package_dir)
shutil.rmtree(utils.get_work_dir())
def package(self, rbf_file, sw_dir, doc_dir, package_name):
image_dir = os.path.join(utils.get_work_dir(), "image_0")
if not os.path.exists(image_dir):
os.makedirs(image_dir)
gbs_name = os.path.splitext(
os.path.basename(
self.afu_desc_file))[0] + GBS_EXT
gbs_path = os.path.join(image_dir, gbs_name)
self.create_gbs(rbf_file, gbs_path)
shutil.copyfile(
self.afu_desc_file, os.path.join(
image_dir, os.path.basename(
self.afu_desc_file)))
package_dir = os.path.join(utils.get_work_dir(), "package")
shutil.make_archive(
os.path.join(
package_dir,
"image_0"),
ARCHIVE_FORMAT,
image_dir)
if sw_dir:
shutil.make_archive(
os.path.join(
package_dir,
"sw"),
ARCHIVE_FORMAT,
sw_dir)
if doc_dir:
shutil.make_archive(
os.path.join(
package_dir,
"docs"),
ARCHIVE_FORMAT,
doc_dir)
shutil.make_archive(package_name, ARCHIVE_FORMAT, package_dir)
shutil.rmtree(utils.get_work_dir())
"scheduler_state": scheduler.state_dict(),
"best_macro_OA_now": best_macro_OA_now,
'best_macro_OA_iter_now':best_macro_OA_iter_now,
}
save_path = os.path.join(writer.file_writer.get_logdir(), "{}_{}_last_model.pkl".format(cfg.model.arch, cfg.data.dataloader))
torch.save(state, save_path)
if __name__ == "__main__":
cfg = args.get_argparser('configs/psr_siamdiff_polar_c3.yml')
del cfg.test
torch.backends.cudnn.benchmark = True
# generate work dir
run_id = osp.join(r'runs', cfg.model.arch + '_' + cfg.train.loss.name + '_' + cfg.train.optimizer.name)
run_id = utils.get_work_dir(run_id)
writer = SummaryWriter(log_dir=run_id)
config_fig = types.dict2fig(cfg.to_flatten_dict())
# plt.savefig(r'./tmp/ff.png')
writer.add_figure('config', config_fig, close=True)
# writer.add_hparams(types.flatten_dict_summarWriter(cfg), {'a': 'b'})
writer.flush()
# logger
logger = get_logger(run_id)
# print('-'*100)
logger.info(f'RUNDIR: {run_id}')
logger.info(f'using config file: {cfg.config_file}')
shutil.copy(cfg.config_file, run_id)
}
save_path = os.path.join(
writer.file_writer.get_logdir(),
"{}_{}_last_model.pkl".format(cfg['model']['arch'],
cfg['data']['dataloader']))
torch.save(state, save_path)
if __name__ == "__main__":
cfg = args.get_argparser(r'configs/tile.yml')
del cfg.test
torch.backends.cudnn.benchmark = True
# generate work dir
run_id = utils.get_work_dir(
osp.join(
r'runs', cfg.model.arch + '_' + cfg.train.loss.name + '_' +
cfg.train.optimizer.name + '_' + cfg.train.lr.name))
writer = SummaryWriter(log_dir=run_id)
config_fig = types.dict2fig(cfg.to_flatten_dict())
# plt.savefig(r'./tmp/ff.png')
writer.add_figure('config', config_fig, close=True)
# writer.add_hparams(types.flatten_dict_summarWriter(cfg), {'a': 'b'})
writer.flush()
# logger
logger = get_logger(run_id)
# print('RUNDIR: {}'.format(logdir))
logger.info(f'RUNDIR: {run_id}')
shutil.copy(cfg.config_file, run_id)
train(cfg, writer, logger)
# score_val,_ = running_metrics_val.get_scores()
acc = score['Acc']
# acc_train = score_train['Acc']
# acc_val = score_val['Acc']
logger.info(f'acc : {acc}\tOA:{acc.mean()}')
micro_OA = score['Overall_Acc']
miou = score['Mean_IoU']
logger.info(f'overall acc: {micro_OA}, mean iou: {miou}')
# logger.info(f'acc of train set: {acc_train} \nacc of val set: {acc_val}')
if __name__ == '__main__':
cfg = args.get_argparser('configs/psr_siamdiff_pauli.yml')
del cfg.train
torch.backends.cudnn.benchmark = True
run_id = utils.get_work_dir(
osp.join(cfg.test.out_path,
osp.split(osp.split(cfg.test.pth)[0])[1]))
# writer = SummaryWriter(log_dir=run_id)
# config_fig = types.dict2fig(cfg.to_flatten_dict())
# writer.add_figure('config', config_fig, close=True)
# writer.flush()
shutil.copy(cfg.config_file, run_id)
# logger
logger = get_logger(run_id)
logger.info(f'RUN DIR: {run_id}')
test(cfg, logger, run_id)
logger.info(f'RUN DIR: {run_id}')