add resnet18_random to models (#1512)

Co-authored-by: Jenkins <[email protected]>

brainscore_vision/models/resnet18_random/__init__.py

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+from brainscore_vision import model_registry
+from .model import get_model
+
+# Register the model with the identifier 'resnet18_random'
+model_registry['resnet18_random'] = lambda: get_model('resnet18_random')

brainscore_vision/models/resnet18_random/model.py

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+import torch
+from torchvision.models import resnet18
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+import functools
+
+# Define preprocessing (resize to 224x224 as required by ResNet)
+preprocessing = functools.partial(load_preprocess_images, image_size=224)
+
+# Define ResNet18 with random weights
+def get_model(name):
+    assert name == 'resnet18_random'
+    # Load ResNet18 without pre-trained weights
+    model = resnet18(pretrained=False)
+    # Wrap the model with Brain-Score's PytorchWrapper
+    activations_model = PytorchWrapper(identifier='resnet18_random', model=model, preprocessing=preprocessing)
+    return ModelCommitment(
+        identifier='resnet18_random',
+        activations_model=activations_model,
+        # Specify layers for evaluation
+        layers=['layer1', 'layer2', 'layer3', 'layer4', 'avgpool']
+    )
+
+# Specify layers to test
+def get_layers(name):
+    assert name == 'resnet18_random'
+    return ['layer1', 'layer2', 'layer3', 'layer4', 'avgpool']
+
+# Optional: Provide a BibTeX reference for the model
+def get_bibtex(model_identifier):
+    return """
+    @misc{resnet18_test_consistency,
+    title={ResNet18 with Random Weights},
+    author={Clear Glue},
+    year={2024},
+    }
+    """
+
+if __name__ == '__main__':
+    from brainscore_vision.model_helpers.check_submission import check_models
+    check_models.check_base_models(__name__)

brainscore_vision/models/resnet18_random/requirements.txt

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+torch
+torchvision

brainscore_vision/models/resnet18_random/test.py

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+import pytest
+import brainscore_vision
+
+@pytest.mark.travis_slow
+def test_resnet18_random():
+    model = brainscore_vision.load_model('resnet18_random')
+    assert model.identifier == 'resnet18_random'
+
+
+
+# AssertionError: No registrations found for resnet18_random
+# ⚡ master ~/vision python -m brainscore_vision score --model_identifier='resnet50_tutorial' --benchmark_identifier='MajajHong2015public.IT-pls'