Register adversarially trained backbones in timm

[mzweilin]

📝 Description

This PR registers 48 model weights from adversarial training in timm. Users can specify adversarially trained backbones with names like resnet18.adv_l2_0.1 and wide_resnet50_2.adv_linf_1. We remove the support to the __AT__ token in the backbone string to conform with the timm format.

Here're the all available weights:

model_names = ["resnet18", "resnet50", "wide_resnet50_2"]
l2_epsilons = [0, 0.01, 0.03, 0.05, 0.1, 0.25, 0.5, 1, 3, 5]
linf_epsilons = [0, 0.5, 1, 2, 4, 8]

The model weights are derived from https://huggingface.co/madrylab/robust-imagenet-models

Here is a simple test to verify that the model weights are loaded:

import torch
from anomalib.models.components.feature_extractors.timm import TimmFeatureExtractor

weights = torch.hub.load_state_dict_from_url("https://huggingface.co/mzweilin/robust-imagenet-models/resolve/main/wide_resnet50_2_l2_eps5.pth")
backbone = "wide_resnet50_2.adv_l2_5"

model =  TimmFeatureExtractor(backbone=backbone, layers=[], pre_trained=True)
assert torch.all(weights["conv1.weight"] == model.feature_extractor.conv1.weight)

✨ Changes

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