Bump torchvision from 0.13.0+cpu to 0.14.0

[dependabot[bot]]

Bumps torchvision from 0.13.0+cpu to 0.14.0.

Release notes

Sourced from torchvision's releases.

TorchVision 0.14, including new model registration API, new models, weights, augmentations, and more

Highlights

[BETA] New Model Registration API

Following up on the multi-weight support API that was released on the previous version, we have added a new model registration API to help users retrieve models and weights. There are now 4 new methods under the torchvision.models module: get_model, get_model_weights, get_weight, and list_models. Here are examples of how we can use them:

import torchvision
from torchvision.models import get_model, get_model_weights, list_models
max_params = 5000000
tiny_models = []
for model_name in list_models(module=torchvision.models):
weights_enum = get_model_weights(model_name)
if len([w for w in weights_enum if w.meta["num_params"] <= max_params]) > 0:
tiny_models.append(model_name)
print(tiny_models)
['mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mobilenet_v2', ...]
model = get_model(tiny_models[0], weights="DEFAULT")
print(sum(x.numel() for x in model.state_dict().values()))
2239188

As of now, this API is still beta and there might be changes in the future in order to improve its usability based on your feedback.

New Architecture and Model Variants

Classification Models

We’ve added the Swin Transformer V2 architecture along with pre-trained weights for its tiny/small/base variants. In addition, we have added support for the MaxViT transformer. Here is an example on how to use the models:

import torch
from torchvision.models import *
image = torch.rand(1, 3, 224, 224)
model = swin_v2_t(weights="DEFAULT").eval()
model = maxvit_t(weights="DEFAULT").eval()
prediction = model(image)
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[pcaversaccio]

@dependabot recreate

[dependabot[bot]]

Superseded by #107.