Models

This module defines mappings from strings to models. This was necessary to isolate the model being run in memsave_torch.util.estimate and having a separate torch runtime for every single run. Otherwise, CUDA does not clear memory unless absolutely required, even on calling the torch.cuda.empty_cache() function, which makes memory measurements very difficult.

Utility file defining the various models, add more in the conv_model_fns dict.

experiments.util.models.prefix_in_pairs(prefix: str, it: List[str]) → List[str]

Prefixes the given prefix after each entry of the list it.

Example

>>> models = ['resnet101', 'convnext']
>>> prefix_in_pairs('memsave_', models)
['resnet101', 'memsave_resnet101', 'convnext', 'memsave_convnext']

Parameters:

• prefix (str) – Prefix to be added

• it (List[str]) – Description

Returns:

The output iterable with items prefixed in pairs

Return type:

List[str]

experiments.util.models.conv_model_fns

All Models defined to be used as strings in experiments.paper_demo script

Model Name	Model Function
deepmodel	experiments.util.models._conv_model1()
deeprelumodel	experiments.util.models._convrelu_model1()
deeprelupoolmodel	experiments.util.models._convrelupool_model1()
alexnet	torchvision.models.alexnet()
convnext_base	torchvision.models.convnext_base()
resnet101	torchvision.models.resnet101()
vgg16	torchvision.models.vgg16()
resnet18	torchvision.models.resnet18()
fasterrcnn_resnet50_fpn_v2	torchvision.models.detection.fasterrcnn_resnet50_fpn_v2()
retinanet_resnet50_fpn_v2	torchvision.models.detection.retinanet_resnet50_fpn_v2()
ssdlite320_mobilenet_v3_large	torchvision.models.detection.ssdlite320_mobilenet_v3_large()
deeplabv3_resnet101	torchvision.models.segmentation.deeplabv3_resnet101()
fcn_resnet101	torchvision.models.segmentation.fcn_resnet101()
efficientnet_v2_l	torchvision.models.efficientnet_v2_l()
mobilenet_v3_large	torchvision.models.mobilenet_v3_large()
resnext101_64x4d	torchvision.models.resnext101_64x4d()