PyPI 官网下载 | cvargparse-0.1.6.tar.gz

# Wrapper for `argparse` Some sample code (with some pseudo DL framework): ```python from cvargparse import GPUParser, ArgFactory, Arg from dlframework import Model, LRSchedule, Updater, Iterator, to_gpu, load_data def main(args): data = load_data(args.data, args.labels) model = Model(args.model_weights) # we can select multiple GPUs. use the first GPU for the initial model creation GPU = args.gpu[0] if GPU >= 0: to_gpu(model, GPU) lr_schedule = LRSchedule(args.lr, args.lr_shift, args.lr_decrease_rate, args.lr_target) updater = Updater(model, lr_schedule, decay=args.decay) it = Iterator(data, args.batch_size) for epoch in range(args.epochs): for batch in it: updater.train(model, batch) parser = GPUParser(ArgFactory([ Arg("data", type=str), Arg("labels", type=str), Arg("model_weights", type=str), ])\ .epochs()\ .batch_size()\ .learning_rate(lr=1e-3)\ .weight_decay(5e-3)\ .seed()\ .debug()) parser.init_logger() main(parser.parse_args()) ``` This script can be called as following: ```bash python script.py path/to/data path/to/labels path/to/model \ --gpu 0 1 \ -lr 0.001 -lrs 30 -lrd 0.1 -lrt 1e-7 \ --batch_size 32 \ --epochs 90 \ --loglevel DEBUG \ --logfile path/to/logs ``` ## Main Features ### Argument Factory * pre-defined frequently used arguments * each factory method return the factory itself, hence one can chain the factory calls * some factory methods support default value definition ```python from cvargparse import GPUParser, ArgFactory, Arg factory = ArgFactory([ Arg("data", type=str), Arg("labels", type=str), Arg("model_weights", type=str), ]) facotry.epochs() facotry.batch_size() factory.weight_decay(5e-3) factory.learning_rate(lr=1e-3) factory.debug().seed() parser = GPUParser(factory) args = parser.parse_args() ``` ### Argument Choices * case insensitive * deafult choice definition * automatic argument generation * pythonic way of argument-to-value access ```python import logging from cvargparse.utils import BaseChoiceType from dlframework.models import VGG19, ResNet, InceptionV3 from dlframework.optimizers import Adam, RMSProp, MomentumSGD class ModelTypes(BaseChoiceType): Default = ResNet Resnet = ResNet VGG = VGG19 Inception = InceptionV3 class OptimizerTypes(BaseChoiceType): Default = Adam adam = Adam rms = RMSProp sgd = MomentumSGD def main(args): model_type = ModelType.get(args.model_type) logging.info("Creating \"{}\" model".format(model_type.name)) model_cls = model_type.value model = model_cls(args.model_weights) opt_type = OptimizerType.get(args.model_type) logging.info("Using \"{}\" optimizer".format(opt_type.name)) opt_cls = opt_type.value opt = opt_cls(args.learning_rate, model) # further training / optimization code factory = ArgFactory([ Arg("data", type=str), Arg("labels", type=str), Arg("model_weights", type=str), ModelTypes.as_arg(name="model_type", short_name="mt", help_text="Model type selection"), OptimizerTypes.as_arg(name="optimizer", short_name="opt", help_text="Optimizer selection"), ]) facotry.epochs() facotry.batch_size() factory.weight_decay(5e-3) factory.learning_rate(lr=1e-3) factory.debug().seed() parser = GPUParser(factory) parser.init_logger() main(parser.parse_args()) ``` ```bash python script.py path/to/data path/to/labels path/to/model \ --model_type resnet --optimizer adam ... ```