Select module classes and functions using yaml, without any if-statements.
Project description
easy_module_attribute_getter
Installation
pip install easy_module_attribute_getter
The Problem: unmaintainable if-statements and switches
It's common to specify script parameters in yaml config files. For example:
models:
modelA:
densenet121:
pretrained: True
memory_efficient: True
modelB:
resnext50_32x4d:
pretrained: True
losses:
lossA:
CrossEntropyLoss:
lossB:
L1Loss:
Usually, the config file is loaded and then various if-statements or switches are used to instantiate objects etc:
if args.models["modelA"] == "densenet121":
modelA = torchvision.models.densenet121(pretrained = args.pretrained)
elif args.models["modelA"] == "googlenet":
modelA = torchvision.models.googlenet(pretrained = args.pretrained)
elif args.models["modelA"] == "resnet50":
modelA = torchvision.models.resnet50(pretrained = args.pretrained)
elif args.models["modelA"] == "inception_v3":
modelA = torchvision.models.inception_v3(pretrained = args.pretrained)
...
if args.losses["lossA"] == "CrossEntropyLoss":
lossA = torch.nn.CrossEntropyLoss()
elif args.losses["lossA"] == "L1Loss":
lossA = torch.nn.L1Loss()
...
The Solution
Use this package, and get rid of all those annoying if-statements and switches:
from easy_module_attribute_getter import YamlReader, PytorchGetter
yaml_reader = YamlReader()
args, _, _ = yaml_reader.load_yamls(['example.yaml'])
pytorch_getter = PytorchGetter()
models = pytorch_getter.get_multiple("model", args.models)
losses = pytorch_getter.get_multiple("loss", args.losses)
"models" and "losses" are dictionaries that map from strings to the desired objects.
Override complex config options via the command line:
The example yaml file contains 'models' which maps to a nested dictionary. This key can optionally be overridden at the command line, using the standard python notation for nested dictionaries. In this example, instead of loading densenet121 and resnext50, as specified in the config file, the program will instead load googlenet and resnet18.
python example.py --models {modelA: {googlenet: {pretrained: True}}, modelB: {resnet18: {pretrained: True}}}
Easily register your own modules into an existing getter.
from pytorch_metric_learning import losses, miners, samplers
pytorch_getter = PytorchGetter()
pytorch_getter.register('loss', losses)
pytorch_getter.register('miner', miners)
pytorch_getter.register('sampler', samplers)
metric_loss = pytorch_getter.get('loss', class_name='ProxyNCALoss', return_uninitialized=True)
kl_div_loss = pytorch_getter.get('loss', class_name='KLDivLoss', return_uninitialized=True)
In the above example, the 'loss' key already exists, so the 'losses' module will be appended to the existing module.
Load multiple yaml files into one args object
Provide a list of filepaths:
args, _, _ = yaml_reader.load_yamls(['models.yaml', 'optimizers.yaml', 'transforms.yaml'])
Or provide a root path and a dictionary mapping subfolder names to the bare filename
root_path = "/where/your/yaml/subfolders/are/"
subfolder_to_name_dict = {"models": "default", "optimizers": "special_trial", "transforms": "blah"}
args, _, _ = yaml_reader.load_yamls(root_path=root_path, subfolder_to_name_dict=subfolder_to_name_dict)
Pytorch-specific features
Transforms
Specify transforms in your config file:
transforms:
train:
Resize:
size: 256
RandomResizedCrop:
scale: 0.16 1
ratio: 0.75 1.33
size: 227
RandomHorizontalFlip:
p: 0.5
eval:
Resize:
size: 256
CenterCrop:
size: 227
Then load a composed transform in your script:
transforms = {}
for k, v in args.transforms.items():
transforms[k] = pytorch_getter.get_composed_img_transform(v, mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
Optimizers, schedulers, and gradient clippers
Optionally specify the scheduler and gradient clipping norm, within the optimizer parameters.
optimizers:
modelA:
Adam:
lr: 0.00001
weight_decay: 0.00005
scheduler:
StepLR:
step_size: 2
gamma: 0.95
clip_grad_norm: 1
modelB:
RMSprop:
lr: 0.00001
weight_decay: 0.00005
Create the optimizers:
optimizers = {}
schedulers = {}
grad_clippers = {}
for k, v in models.items():
optimizers[k], schedulers[k], grad_clippers[k] = pytorch_getter.get_optimizer(v, yaml_dict=args.optimizers[k])
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