Converted Resnet50 vicregl0p75

brainscore_vision/models/resnet50-vicregl0p75/__init__.py

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+from brainscore_vision import model_registry
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+from .model import get_model, get_layers
+
+model_registry['resnet50-vicregl0p75'] = lambda: ModelCommitment(identifier='resnet50-vicregl0p75', activations_model=get_model('resnet50-vicregl0p75'), layers=get_layers('resnet50-vicregl0p75'))

brainscore_vision/models/resnet50-vicregl0p75/model.py

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+from brainscore_vision.model_helpers.check_submission import check_models
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+from torchvision.models import resnet50
+from brainscore_vision.model_helpers.s3 import load_weight_file
+import functools
+import torch
+import os
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+
+def get_model(name):
+    """
+    This method fetches an instance of a base model. The instance has to be callable and return a xarray object,
+    containing activations. There exist standard wrapper implementations for common libraries, like pytorch and
+    keras. Checkout the examples folder, to see more. For custom implementations check out the implementation of the
+    wrappers.
+    :param name: the name of the model to fetch
+    :return: the model instance
+    """
+    assert name == 'resnet50-vicregl0p75'
+    #model = torchvision.models.resnet50(pretrained=True)
+    model = resnet50(pretrained=False).eval()
+    weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+                                   relative_path="resnet50-vicregl0p75/resnet50_alpha0p75.pth",
+                                   version_id="KRPVOjtEZwh2M9RmEtYeNLqQYNCF8HGi",
+                                   sha1="37a5e0d06050260d73ffbeb3ad163974e4361435")
+    ckpt= torch.load(weights_path, map_location=torch.device('cpu'))
+    ckpt['fc.weight'] = model.fc.weight
+    ckpt['fc.bias'] = model.fc.bias
+    model.load_state_dict(ckpt)
+    preprocessing = functools.partial(load_preprocess_images, image_size=224)
+    wrapper = PytorchWrapper(identifier='resnet50-vicregl0p75', model=model, preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'resnet50-vicregl0p75'
+    """
+    This method returns a list of string layer names to consider per model. The benchmarks maps brain regions to
+    layers and uses this list as a set of possible layers. The lists doesn't have to contain all layers, the less the
+    faster the benchmark process works. Additionally the given layers have to produce an activations vector of at least
+    size 25! The layer names are delivered back to the model instance and have to be resolved in there. For a pytorch
+    model, the layer name are for instance dot concatenated per module, e.g. "features.2".
+    :param name: the name of the model, to return the layers for
+    :return: a list of strings containing all layers, that should be considered as brain area.
+    """
+    return ['conv1', 'layer1.0', 'layer1.1', 'layer1.2', 
+        'layer2.0', 'layer2.1', 'layer2.2', 'layer2.3',
+        'layer3.0', 'layer3.1', 'layer3.2', 'layer3.3',
+         'layer3.4', 'layer3.5', 'layer4.0', 'layer4.1', 'layer4.2','avgpool']
+
+
+def get_bibtex(model_identifier):
+    """
+    A method returning the bibtex reference of the requested model as a string.
+    """
+    return '''@article{DBLP:journals/corr/HeZRS15,
+  author       = {Kaiming He and
+                  Xiangyu Zhang and
+                  Shaoqing Ren and
+                  Jian Sun},
+  title        = {Deep Residual Learning for Image Recognition},
+  journal      = {CoRR},
+  volume       = {abs/1512.03385},
+  year         = {2015},
+  url          = {http://arxiv.org/abs/1512.03385},
+  eprinttype    = {arXiv},
+  eprint       = {1512.03385},
+  timestamp    = {Wed, 25 Jan 2023 11:01:16 +0100},
+  biburl       = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}'''
+
+
+if __name__ == '__main__':
+    # Use this method to ensure the correctness of the BaseModel implementations.
+    # It executes a mock run of brain-score benchmarks.
+    check_models.check_base_models(__name__)

brainscore_vision/models/resnet50-vicregl0p75/requirements.txt

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+torchvision
+torch

brainscore_vision/models/resnet50-vicregl0p75/test.py

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+import brainscore_vision
+import pytest
+
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('resnet50-vicregl0p75')
+    assert model.identifier == 'resnet50-vicregl0p75'