Update yaml configs
This commit is contained in:
D-X-Y
@@ -3,7 +3,7 @@
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#####################################################
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# python exps/basic/xmain.py --save_dir outputs/x #
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#####################################################
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import sys, time, torch, random, argparse
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import os, sys, time, torch, random, argparse
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from copy import deepcopy
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from pathlib import Path
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@@ -12,24 +12,38 @@ print("LIB-DIR: {:}".format(lib_dir))
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from xautodl.xmisc import nested_call_by_yaml
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from xautodl import xmisc
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def main(args):
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train_data = nested_call_by_yaml(args.train_data_config, args.data_path)
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valid_data = nested_call_by_yaml(args.valid_data_config, args.data_path)
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train_data = xmisc.nested_call_by_yaml(args.train_data_config, args.data_path)
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valid_data = xmisc.nested_call_by_yaml(args.valid_data_config, args.data_path)
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logger = xmisc.Logger(args.save_dir, prefix="seed-{:}-".format(args.rand_seed))
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import pdb
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pdb.set_trace()
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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train_data, valid_data, xshape, class_num = get_datasets(
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args.dataset, args.data_path, args.cutout_length
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logger.log("Create the logger: {:}".format(logger))
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logger.log("Arguments : -------------------------------")
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for name, value in args._get_kwargs():
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logger.log("{:16} : {:}".format(name, value))
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logger.log("Python Version : {:}".format(sys.version.replace("\n", " ")))
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logger.log("PyTorch Version : {:}".format(torch.__version__))
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logger.log("cuDNN Version : {:}".format(torch.backends.cudnn.version()))
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logger.log("CUDA available : {:}".format(torch.cuda.is_available()))
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logger.log("CUDA GPU numbers : {:}".format(torch.cuda.device_count()))
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logger.log(
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"CUDA_VISIBLE_DEVICES : {:}".format(
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os.environ["CUDA_VISIBLE_DEVICES"]
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if "CUDA_VISIBLE_DEVICES" in os.environ
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else "None"
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)
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)
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logger.log("The training data is:\n{:}".format(train_data))
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logger.log("The validation data is:\n{:}".format(valid_data))
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model = xmisc.nested_call_by_yaml(args.model_config)
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logger.log("The model is:\n{:}".format(model))
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logger.log("The model size is {:.4f} M".format(xmisc.count_parameters(model)))
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=args.batch_size,
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@@ -44,100 +58,25 @@ def main(args):
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num_workers=args.workers,
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pin_memory=True,
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)
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# get configures
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model_config = load_config(args.model_config, {"class_num": class_num}, logger)
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optim_config = load_config(args.optim_config, {"class_num": class_num}, logger)
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if args.model_source == "normal":
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base_model = obtain_model(model_config)
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elif args.model_source == "nas":
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base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
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elif args.model_source == "autodl-searched":
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base_model = obtain_model(model_config, args.extra_model_path)
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elif args.model_source in ("x", "xmodel"):
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base_model = obtain_xmodel(model_config)
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else:
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raise ValueError("invalid model-source : {:}".format(args.model_source))
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flop, param = get_model_infos(base_model, xshape)
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logger.log("model ====>>>>:\n{:}".format(base_model))
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logger.log("model information : {:}".format(base_model.get_message()))
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logger.log("-" * 50)
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logger.log(
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"Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
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param, flop, flop / 1e3
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)
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logger.log("The training loader: {:}".format(train_loader))
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logger.log("The validation loader: {:}".format(valid_loader))
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optimizer = xmisc.nested_call_by_yaml(
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args.optim_config,
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model.parameters(),
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lr=args.lr,
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weight_decay=args.weight_decay,
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)
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logger.log("-" * 50)
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logger.log("train_data : {:}".format(train_data))
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logger.log("valid_data : {:}".format(valid_data))
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optimizer, scheduler, criterion = get_optim_scheduler(
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base_model.parameters(), optim_config
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)
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logger.log("optimizer : {:}".format(optimizer))
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logger.log("scheduler : {:}".format(scheduler))
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logger.log("criterion : {:}".format(criterion))
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loss = xmisc.nested_call_by_yaml(args.loss_config)
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last_info, model_base_path, model_best_path = (
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logger.path("info"),
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logger.path("model"),
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logger.path("best"),
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)
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network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()
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logger.log("The optimizer is:\n{:}".format(optimizer))
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logger.log("The loss is {:}".format(loss))
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start".format(last_info)
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)
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last_infox = torch.load(last_info)
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start_epoch = last_infox["epoch"] + 1
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last_checkpoint_path = last_infox["last_checkpoint"]
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if not last_checkpoint_path.exists():
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logger.log(
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"Does not find {:}, try another path".format(last_checkpoint_path)
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)
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last_checkpoint_path = (
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last_info.parent
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/ last_checkpoint_path.parent.name
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/ last_checkpoint_path.name
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)
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checkpoint = torch.load(last_checkpoint_path)
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
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last_info, start_epoch
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)
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)
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elif args.resume is not None:
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assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
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args.resume
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)
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checkpoint = torch.load(args.resume)
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start_epoch = checkpoint["epoch"] + 1
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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logger.log(
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"=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
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args.resume, start_epoch
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)
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)
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elif args.init_model is not None:
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assert Path(
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args.init_model
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).exists(), "Can not find the initialization file : {:}".format(args.init_model)
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checkpoint = torch.load(args.init_model)
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base_model.load_state_dict(checkpoint["base-model"])
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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logger.log("=> initialize the model from {:}".format(args.init_model))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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model, loss = torch.nn.DataParallel(model).cuda(), loss.cuda()
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import pdb
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pdb.set_trace()
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train_func, valid_func = get_procedures(args.procedure)
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@@ -284,7 +223,7 @@ def main(args):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Train a model with a loss function.",
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description="Train a classification model with a loss function.",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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@@ -293,27 +232,21 @@ if __name__ == "__main__":
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parser.add_argument("--resume", type=str, help="Resume path.")
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parser.add_argument("--init_model", type=str, help="The initialization model path.")
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parser.add_argument("--model_config", type=str, help="The path to the model config")
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parser.add_argument("--optim_config", type=str, help="The optimizer config file.")
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parser.add_argument("--loss_config", type=str, help="The loss config file.")
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parser.add_argument(
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"--optim_config", type=str, help="The path to the optimizer config"
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"--train_data_config", type=str, help="The training dataset config path."
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)
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parser.add_argument(
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"--train_data_config", type=str, help="The dataset config path."
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)
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parser.add_argument(
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"--valid_data_config", type=str, help="The dataset config path."
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)
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parser.add_argument(
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"--data_path", type=str, help="The path to the dataset."
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"--valid_data_config", type=str, help="The validation dataset config path."
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)
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parser.add_argument("--data_path", type=str, help="The path to the dataset.")
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parser.add_argument("--algorithm", type=str, help="The algorithm.")
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# Optimization options
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parser.add_argument("--lr", type=float, help="The learning rate")
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parser.add_argument("--weight_decay", type=float, help="The weight decay")
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parser.add_argument("--batch_size", type=int, default=2, help="The batch size.")
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parser.add_argument(
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"--workers",
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type=int,
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default=8,
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help="number of data loading workers (default: 8)",
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)
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parser.add_argument("--workers", type=int, default=4, help="The number of workers")
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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